@@ -252,7 +251,7 @@ Note:
* Download the examples via clone the source code.
```bash
- git clone -b v2.2 https://github.com/Microsoft/nni.git
+ git clone -b v2.3 https://github.com/Microsoft/nni.git
```
* Run the MNIST example.
@@ -299,10 +298,7 @@ You can use these commands to get more information about the experiment
* Open the `Web UI url` in your browser, you can view detailed information of the experiment and all the submitted trial jobs as shown below. [Here](https://nni.readthedocs.io/en/stable/Tutorial/WebUI.html) are more Web UI pages.
-
-
-
-
+
## **Releases and Contributing**
NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by [filling an issue](https://github.com/microsoft/nni/issues/new/choose).
@@ -320,6 +316,7 @@ We appreciate all contributions and thank all the contributors!
## **Feedback**
* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
+* Open or participate in a [discussion](https://github.com/microsoft/nni/discussions).
* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
Join IM discussion groups:
diff --git a/dependencies/required.txt b/dependencies/required.txt
index 493ccc95f4..917b808377 100644
--- a/dependencies/required.txt
+++ b/dependencies/required.txt
@@ -3,7 +3,7 @@ hyperopt == 0.1.2
json_tricks
netifaces
psutil
-ruamel.yaml
+pyyaml
requests
responses
schema
@@ -19,3 +19,5 @@ numpy < 1.20 ; sys_platform != "win32" and python_version < "3.7"
numpy ; sys.platform != "win32" and python_version >= "3.7"
scipy < 1.6 ; python_version < "3.7"
scipy ; python_version >= "3.7"
+pandas < 1.2 ; python_version < "3.7"
+matplotlib < 3.4 ; python_version < "3.7"
diff --git a/dependencies/required_extra.txt b/dependencies/required_extra.txt
index 65940c9194..9bf0cac29a 100644
--- a/dependencies/required_extra.txt
+++ b/dependencies/required_extra.txt
@@ -11,3 +11,6 @@ statsmodels==0.12.0
# PPOTuner
gym
+
+# DNGO
+pybnn
diff --git a/docs/en_US/CommunitySharings/AutoCompletion.rst b/docs/en_US/CommunitySharings/AutoCompletion.rst
index 8664aa733f..f9b97a1efb 100644
--- a/docs/en_US/CommunitySharings/AutoCompletion.rst
+++ b/docs/en_US/CommunitySharings/AutoCompletion.rst
@@ -25,7 +25,7 @@ Step 1. Download ``bash-completion``
cd ~
wget https://raw.githubusercontent.com/microsoft/nni/{nni-version}/tools/bash-completion
-Here, {nni-version} should by replaced by the version of NNI, e.g., ``master``, ``v2.2``. You can also check the latest ``bash-completion`` script :githublink:`here `.
+Here, {nni-version} should by replaced by the version of NNI, e.g., ``master``, ``v2.3``. You can also check the latest ``bash-completion`` script :githublink:`here `.
.. cannot find :githublink:`here `.
diff --git a/docs/en_US/Compression/AutoCompression.rst b/docs/en_US/Compression/AutoCompression.rst
new file mode 100644
index 0000000000..3df1e2e6c0
--- /dev/null
+++ b/docs/en_US/Compression/AutoCompression.rst
@@ -0,0 +1,119 @@
+Auto Compression with NNI Experiment
+====================================
+
+If you want to compress your model, but don't know what compression algorithm to choose, or don't know what sparsity is suitable for your model, or just want to try more possibilities, auto compression may help you.
+Users can choose different compression algorithms and define the algorithms' search space, then auto compression will launch an NNI experiment and try different compression algorithms with varying sparsity automatically.
+Of course, in addition to the sparsity rate, users can also introduce other related parameters into the search space.
+If you don't know what is search space or how to write search space, `this <./Tutorial/SearchSpaceSpec.rst>`__ is for your reference.
+Auto compression using experience is similar to the NNI experiment in python.
+The main differences are as follows:
+
+* Use a generator to help generate search space object.
+* Need to provide the model to be compressed, and the model should have already been pre-trained.
+* No need to set ``trial_command``, additional need to set ``auto_compress_module`` as ``AutoCompressionExperiment`` input.
+
+Generate search space
+---------------------
+
+Due to the extensive use of nested search space, we recommend a using generator to configure search space.
+The following is an example. Using ``add_config()`` add subconfig, then ``dumps()`` search space dict.
+
+.. code-block:: python
+
+ from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionSearchSpaceGenerator
+
+ generator = AutoCompressionSearchSpaceGenerator()
+ generator.add_config('level', [
+ {
+ "sparsity": {
+ "_type": "uniform",
+ "_value": [0.01, 0.99]
+ },
+ 'op_types': ['default']
+ }
+ ])
+ generator.add_config('qat', [
+ {
+ 'quant_types': ['weight', 'output'],
+ 'quant_bits': {
+ 'weight': 8,
+ 'output': 8
+ },
+ 'op_types': ['Conv2d', 'Linear']
+ }])
+
+ search_space = generator.dumps()
+
+Now we support the following pruners and quantizers:
+
+.. code-block:: python
+
+ PRUNER_DICT = {
+ 'level': LevelPruner,
+ 'slim': SlimPruner,
+ 'l1': L1FilterPruner,
+ 'l2': L2FilterPruner,
+ 'fpgm': FPGMPruner,
+ 'taylorfo': TaylorFOWeightFilterPruner,
+ 'apoz': ActivationAPoZRankFilterPruner,
+ 'mean_activation': ActivationMeanRankFilterPruner
+ }
+
+ QUANTIZER_DICT = {
+ 'naive': NaiveQuantizer,
+ 'qat': QAT_Quantizer,
+ 'dorefa': DoReFaQuantizer,
+ 'bnn': BNNQuantizer
+ }
+
+Provide user model for compression
+----------------------------------
+
+Users need to inherit ``AbstractAutoCompressionModule`` and override the abstract class function.
+
+.. code-block:: python
+
+ from nni.algorithms.compression.pytorch.auto_compress import AbstractAutoCompressionModule
+
+ class AutoCompressionModule(AbstractAutoCompressionModule):
+ @classmethod
+ def model(cls) -> nn.Module:
+ ...
+ return _model
+
+ @classmethod
+ def evaluator(cls) -> Callable[[nn.Module], float]:
+ ...
+ return _evaluator
+
+Users need to implement at least ``model()`` and ``evaluator()``.
+If you use iterative pruner, you need to additional implement ``optimizer_factory()``, ``criterion()`` and ``sparsifying_trainer()``.
+If you want to finetune the model after compression, you need to implement ``optimizer_factory()``, ``criterion()``, ``post_compress_finetuning_trainer()`` and ``post_compress_finetuning_epochs()``.
+The ``optimizer_factory()`` should return a factory function, the input is an iterable variable, i.e. your ``model.parameters()``, and the output is an optimizer instance.
+The two kinds of ``trainer()`` should return a trainer with input ``model, optimizer, criterion, current_epoch``.
+The full abstract interface refers to :githublink:`interface.py `.
+An example of ``AutoCompressionModule`` implementation refers to :githublink:`auto_compress_module.py `.
+
+Launch NNI experiment
+---------------------
+
+Similar to launch from python, the difference is no need to set ``trial_command`` and put the user-provided ``AutoCompressionModule`` as ``AutoCompressionExperiment`` input.
+
+.. code-block:: python
+
+ from pathlib import Path
+ from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionExperiment
+
+ from auto_compress_module import AutoCompressionModule
+
+ experiment = AutoCompressionExperiment(AutoCompressionModule, 'local')
+ experiment.config.experiment_name = 'auto compression torch example'
+ experiment.config.trial_concurrency = 1
+ experiment.config.max_trial_number = 10
+ experiment.config.search_space = search_space
+ experiment.config.trial_code_directory = Path(__file__).parent
+ experiment.config.tuner.name = 'TPE'
+ experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+ experiment.config.training_service.use_active_gpu = True
+
+ experiment.run(8088)
diff --git a/docs/en_US/Compression/AutoPruningUsingTuners.rst b/docs/en_US/Compression/AutoPruningUsingTuners.rst
deleted file mode 100644
index 85d67ba4c5..0000000000
--- a/docs/en_US/Compression/AutoPruningUsingTuners.rst
+++ /dev/null
@@ -1,75 +0,0 @@
-Automatic Model Pruning using NNI Tuners
-========================================
-
-It's convenient to implement auto model pruning with NNI compression and NNI tuners
-
-First, model compression with NNI
----------------------------------
-
-You can easily compress a model with NNI compression. Take pruning for example, you can prune a pretrained model with L2FilterPruner like this
-
-.. code-block:: python
-
- from nni.algorithms.compression.pytorch.pruning import L2FilterPruner
- config_list = [{ 'sparsity': 0.5, 'op_types': ['Conv2d'] }]
- pruner = L2FilterPruner(model, config_list)
- pruner.compress()
-
-The 'Conv2d' op_type stands for the module types defined in :githublink:`default_layers.py ` for pytorch.
-
-Therefore ``{ 'sparsity': 0.5, 'op_types': ['Conv2d'] }``\ means that **all layers with specified op_types will be compressed with the same 0.5 sparsity**. When ``pruner.compress()`` called, the model is compressed with masks and after that you can normally fine tune this model and **pruned weights won't be updated** which have been masked.
-
-Then, make this automatic
--------------------------
-
-The previous example manually chose L2FilterPruner and pruned with a specified sparsity. Different sparsity and different pruners may have different effects on different models. This process can be done with NNI tuners.
-
-Firstly, modify our codes for few lines
-
-.. code-block:: python
-
- import nni
- from nni.algorithms.compression.pytorch.pruning import *
-
- params = nni.get_parameters()
- sparsity = params['sparsity']
- pruner_name = params['pruner']
- model_name = params['model']
-
- model, pruner = get_model_pruner(model_name, pruner_name, sparsity)
- pruner.compress()
-
- train(model) # your code for fine-tuning the model
- acc = test(model) # test the fine-tuned model
- nni.report_final_results(acc)
-
-Then, define a ``config`` file in YAML to automatically tuning model, pruning algorithm and sparsity.
-
-.. code-block:: yaml
-
- searchSpace:
- sparsity:
- _type: choice
- _value: [0.25, 0.5, 0.75]
- pruner:
- _type: choice
- _value: ['slim', 'l2filter', 'fpgm', 'apoz']
- model:
- _type: choice
- _value: ['vgg16', 'vgg19']
- trainingService:
- platform: local
- trialCodeDirectory: .
- trialCommand: python3 basic_pruners_torch.py --nni
- trialConcurrency: 1
- trialGpuNumber: 0
- tuner:
- name: GridSearch
-
-The full example can be found :githublink:`here `
-
-Finally, start the searching via
-
-.. code-block:: bash
-
- nnictl create -c config.yml
diff --git a/docs/en_US/Compression/DependencyAware.rst b/docs/en_US/Compression/DependencyAware.rst
index d68a2b22c0..05daafe7f2 100644
--- a/docs/en_US/Compression/DependencyAware.rst
+++ b/docs/en_US/Compression/DependencyAware.rst
@@ -54,11 +54,11 @@ To enable the dependency-aware mode for ``L1FilterPruner``\ :
# for FPGMPruner
# pruner = FPGMPruner(model, config_list, dependency_aware=True, dummy_input=dummy_input)
# for ActivationAPoZRankFilterPruner
- # pruner = ActivationAPoZRankFilterPruner(model, config_list, statistics_batch_num=1, , dependency_aware=True, dummy_input=dummy_input)
+ # pruner = ActivationAPoZRankFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1, dependency_aware=True, dummy_input=dummy_input)
# for ActivationMeanRankFilterPruner
- # pruner = ActivationMeanRankFilterPruner(model, config_list, statistics_batch_num=1, dependency_aware=True, dummy_input=dummy_input)
+ # pruner = ActivationMeanRankFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1, dependency_aware=True, dummy_input=dummy_input)
# for TaylorFOWeightFilterPruner
- # pruner = TaylorFOWeightFilterPruner(model, config_list, statistics_batch_num=1, dependency_aware=True, dummy_input=dummy_input)
+ # pruner = TaylorFOWeightFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1, dependency_aware=True, dummy_input=dummy_input)
pruner.compress()
diff --git a/docs/en_US/Compression/Framework.rst b/docs/en_US/Compression/Framework.rst
index fa46b60230..453163b5ef 100644
--- a/docs/en_US/Compression/Framework.rst
+++ b/docs/en_US/Compression/Framework.rst
@@ -29,8 +29,7 @@ Compressor is the base class for pruner and quntizer, it provides a unified inte
'op_types': ['Conv2d', 'Linear'],
}]
- optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
- pruner = LevelPruner(model, configure_list, optimizer)
+ pruner = LevelPruner(model, configure_list)
model = pruner.compress()
# model is ready for pruning, now start finetune the model,
@@ -103,7 +102,8 @@ Users can also remove this collector like this:
Pruner
------
-A pruner receives ``model``\ , ``config_list`` and ``optimizer`` as arguments. It prunes the model per the ``config_list`` during training loop by adding a hook on ``optimizer.step()``.
+A pruner receives ``model`` , ``config_list`` as arguments.
+Some pruners like ``TaylorFOWeightFilter Pruner`` prune the model per the ``config_list`` during training loop by adding a hook on ``optimizer.step()``.
Pruner class is a subclass of Compressor, so it contains everything in the Compressor class and some additional components only for pruning, it contains:
diff --git a/docs/en_US/Compression/Pruner.rst b/docs/en_US/Compression/Pruner.rst
index eb9c32c875..56259742fc 100644
--- a/docs/en_US/Compression/Pruner.rst
+++ b/docs/en_US/Compression/Pruner.rst
@@ -5,6 +5,7 @@ We provide several pruning algorithms that support fine-grained weight pruning a
**One-shot Pruning**
+
* `Level Pruner <#level-pruner>`__ ((fine-grained pruning))
* `Slim Pruner <#slim-pruner>`__
* `FPGM Pruner <#fpgm-pruner>`__
@@ -25,6 +26,7 @@ We provide several pruning algorithms that support fine-grained weight pruning a
* `ADMM Pruner <#admm-pruner>`__
**Others**
+
* `Lottery Ticket Hypothesis <#lottery-ticket-hypothesis>`__
Level Pruner
@@ -71,7 +73,7 @@ PyTorch code
from nni.algorithms.compression.pytorch.pruning import SlimPruner
config_list = [{ 'sparsity': 0.8, 'op_types': ['BatchNorm2d'] }]
- pruner = SlimPruner(model, config_list)
+ pruner = SlimPruner(model, config_list, optimizer, trainer, criterion)
pruner.compress()
User configuration for Slim Pruner
@@ -269,7 +271,7 @@ PyTorch code
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
- pruner = ActivationAPoZRankFilterPruner(model, config_list, statistics_batch_num=1)
+ pruner = ActivationAPoZRankFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1)
pruner.compress()
Note: ActivationAPoZRankFilterPruner is used to prune convolutional layers within deep neural networks, therefore the ``op_types`` field supports only convolutional layers.
@@ -304,7 +306,7 @@ PyTorch code
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
- pruner = ActivationMeanRankFilterPruner(model, config_list, statistics_batch_num=1)
+ pruner = ActivationMeanRankFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1)
pruner.compress()
Note: ActivationMeanRankFilterPruner is used to prune convolutional layers within deep neural networks, therefore the ``op_types`` field supports only convolutional layers.
@@ -344,7 +346,7 @@ PyTorch code
'sparsity': 0.5,
'op_types': ['Conv2d']
}]
- pruner = TaylorFOWeightFilterPruner(model, config_list, statistics_batch_num=1)
+ pruner = TaylorFOWeightFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1)
pruner.compress()
User configuration for TaylorFOWeightFilter Pruner
@@ -389,7 +391,7 @@ PyTorch code
# optimizer.step(), so an optimizer is required to prune the model.
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
- pruner = AGPPruner(model, config_list, optimizer, pruning_algorithm='level')
+ pruner = AGPPruner(model, config_list, optimizer, trainer, criterion, pruning_algorithm='level')
pruner.compress()
AGP pruner uses ``LevelPruner`` algorithms to prune the weight by default, however you can set ``pruning_algorithm`` parameter to other values to use other pruning algorithms:
@@ -404,14 +406,6 @@ AGP pruner uses ``LevelPruner`` algorithms to prune the weight by default, howev
* ``apoz``\ : ActivationAPoZRankFilterPruner
* ``mean_activation``\ : ActivationMeanRankFilterPruner
-You should add code below to update epoch number when you finish one epoch in your training code.
-
-PyTorch code
-
-.. code-block:: python
-
- pruner.update_epoch(epoch)
-
User configuration for AGP Pruner
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -620,7 +614,7 @@ PyTorch code
'op_types': ['Conv2d'],
'op_names': ['conv2']
}]
- pruner = ADMMPruner(model, config_list, trainer=trainer, num_iterations=30, epochs=5)
+ pruner = ADMMPruner(model, config_list, trainer, num_iterations=30, epochs_per_iteration=5)
pruner.compress()
You can view :githublink:`example ` for more information.
diff --git a/docs/en_US/Compression/QuantizationSpeedup.rst b/docs/en_US/Compression/QuantizationSpeedup.rst
index 862278c6d2..b371e2e358 100644
--- a/docs/en_US/Compression/QuantizationSpeedup.rst
+++ b/docs/en_US/Compression/QuantizationSpeedup.rst
@@ -39,6 +39,7 @@ After getting mixed precision engine, users can do inference with input data.
Note
+* Recommend using "cpu"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not "cpu"(host), this tool will transpose it to "cpu" which may increases unnecessary overhead.
* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).
* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.
diff --git a/docs/en_US/Compression/Quantizer.rst b/docs/en_US/Compression/Quantizer.rst
index 2af973e4f2..073d0cd961 100644
--- a/docs/en_US/Compression/Quantizer.rst
+++ b/docs/en_US/Compression/Quantizer.rst
@@ -162,7 +162,7 @@ PyTorch code
config_list = [{
'quant_types': ['weight'],
'quant_bits': 8,
- 'op_types': 'default'
+ 'op_types': ['default']
}]
quantizer = DoReFaQuantizer(model, config_list)
quantizer.compress()
diff --git a/docs/en_US/Compression/QuickStart.rst b/docs/en_US/Compression/QuickStart.rst
index 1113e5a753..0e4b33b692 100644
--- a/docs/en_US/Compression/QuickStart.rst
+++ b/docs/en_US/Compression/QuickStart.rst
@@ -31,17 +31,16 @@ The specification of configuration can be found `here <./Tutorial.rst#specify-th
Step2. Choose a pruner and compress the model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-First instantiate the chosen pruner with your model and configuration as arguments, then invoke ``compress()`` to compress your model. Note that, some algorithms may check gradients for compressing, so we also define an optimizer and pass it to the pruner.
+First instantiate the chosen pruner with your model and configuration as arguments, then invoke ``compress()`` to compress your model. Note that, some algorithms may check gradients for compressing, so we may also define an optimizer and pass it to the pruner.
.. code-block:: python
from nni.algorithms.compression.pytorch.pruning import LevelPruner
- optimizer_finetune = torch.optim.SGD(model.parameters(), lr=0.01)
- pruner = LevelPruner(model, config_list, optimizer_finetune)
+ pruner = LevelPruner(model, config_list)
model = pruner.compress()
-Then, you can train your model using traditional training approach (e.g., SGD), pruning is applied transparently during the training. Some pruners (e.g., L1FilterPruner, FPGMPruner) prune once at the beginning, the following training can be seen as fine-tune. Some pruners (e.g., AGPPruner) prune your model iteratively, the masks are adjusted epoch by epoch during training.
+Some pruners (e.g., L1FilterPruner, FPGMPruner) prune once, some pruners (e.g., AGPPruner) prune your model iteratively, the masks are adjusted epoch by epoch during training.
Note that, ``pruner.compress`` simply adds masks on model weights, it does not include fine-tuning logic. If users want to fine tune the compressed model, they need to write the fine tune logic by themselves after ``pruner.compress``.
diff --git a/docs/en_US/Compression/advanced.rst b/docs/en_US/Compression/advanced.rst
index 67764d1cd2..e0df64214f 100644
--- a/docs/en_US/Compression/advanced.rst
+++ b/docs/en_US/Compression/advanced.rst
@@ -6,4 +6,4 @@ Advanced Usage
Framework <./Framework>
Customize a new algorithm <./CustomizeCompressor>
- Automatic Model Compression <./AutoPruningUsingTuners>
+ Automatic Model Compression (Beta) <./AutoCompression>
diff --git a/docs/en_US/Compression/pruning.rst b/docs/en_US/Compression/pruning.rst
index d2b4b065af..023b0feeb5 100644
--- a/docs/en_US/Compression/pruning.rst
+++ b/docs/en_US/Compression/pruning.rst
@@ -23,4 +23,3 @@ For details, please refer to the following tutorials:
Pruners
Dependency Aware Mode
Model Speedup
- Automatic Model Pruning with NNI Tuners
diff --git a/docs/en_US/NAS/Advanced.rst b/docs/en_US/NAS/Advanced.rst
deleted file mode 100644
index 96dc07596c..0000000000
--- a/docs/en_US/NAS/Advanced.rst
+++ /dev/null
@@ -1,136 +0,0 @@
-Customize a NAS Algorithm
-=========================
-
-Extend the Ability of One-Shot Trainers
----------------------------------------
-
-Users might want to do multiple things if they are using the trainers on real tasks, for example, distributed training, half-precision training, logging periodically, writing tensorboard, dumping checkpoints and so on. As mentioned previously, some trainers do have support for some of the items listed above; others might not. Generally, there are two recommended ways to add anything you want to an existing trainer: inherit an existing trainer and override, or copy an existing trainer and modify.
-
-Either way, you are walking into the scope of implementing a new trainer. Basically, implementing a one-shot trainer is no different from any traditional deep learning trainer, except that a new concept called mutator will reveal itself. So that the implementation will be different in at least two places:
-
-
-* Initialization
-
-.. code-block:: python
-
- model = Model()
- mutator = MyMutator(model)
-
-
-* Training
-
-.. code-block:: python
-
- for _ in range(epochs):
- for x, y in data_loader:
- mutator.reset() # reset all the choices in model
- out = model(x) # like traditional model
- loss = criterion(out, y)
- loss.backward()
- # no difference below
-
-To demonstrate what mutators are for, we need to know how one-shot NAS normally works. Usually, one-shot NAS "co-optimize model weights and architecture weights". It repeatedly: sample an architecture or combination of several architectures from the supernet, train the chosen architectures like traditional deep learning model, update the trained parameters to the supernet, and use the metrics or loss as some signal to guide the architecture sampler. The mutator, is the architecture sampler here, often defined to be another deep-learning model. Therefore, you can treat it as any model, by defining parameters in it and optimizing it with optimizers. One mutator is initialized with exactly one model. Once a mutator is binded to a model, it cannot be rebinded to another model.
-
-``mutator.reset()`` is the core step. That's where all the choices in the model are finalized. The reset result will be always effective, until the next reset flushes the data. After the reset, the model can be seen as a traditional model to do forward-pass and backward-pass.
-
-Finally, mutators provide a method called ``mutator.export()`` that export a dict with architectures to the model. Note that currently this dict this a mapping from keys of mutables to tensors of selection. So in order to dump to json, users need to convert the tensors explicitly into python list.
-
-Meanwhile, NNI provides some useful tools so that users can implement trainers more easily. See `Trainers <./NasReference.rst>`__ for details.
-
-Implement New Mutators
-----------------------
-
-To start with, here is the pseudo-code that demonstrates what happens on ``mutator.reset()`` and ``mutator.export()``.
-
-.. code-block:: python
-
- def reset(self):
- self.apply_on_model(self.sample_search())
-
-.. code-block:: python
-
- def export(self):
- return self.sample_final()
-
-On reset, a new architecture is sampled with ``sample_search()`` and applied on the model. Then the model is trained for one or more steps in search phase. On export, a new architecture is sampled with ``sample_final()`` and **do nothing to the model**. This is either for checkpoint or exporting the final architecture.
-
-The requirements of return values of ``sample_search()`` and ``sample_final()`` are the same: a mapping from mutable keys to tensors. The tensor can be either a BoolTensor (true for selected, false for negative), or a FloatTensor which applies weight on each candidate. The selected branches will then be computed (in ``LayerChoice``\ , modules will be called; in ``InputChoice``\ , it's just tensors themselves), and reduce with the reduction operation specified in the choices. For most algorithms only worrying about the former part, here is an example of your mutator implementation.
-
-.. code-block:: python
-
- class RandomMutator(Mutator):
- def __init__(self, model):
- super().__init__(model) # don't forget to call super
- # do something else
-
- def sample_search(self):
- result = dict()
- for mutable in self.mutables: # this is all the mutable modules in user model
- # mutables share the same key will be de-duplicated
- if isinstance(mutable, LayerChoice):
- # decided that this mutable should choose `gen_index`
- gen_index = np.random.randint(mutable.length)
- result[mutable.key] = torch.tensor([i == gen_index for i in range(mutable.length)],
- dtype=torch.bool)
- elif isinstance(mutable, InputChoice):
- if mutable.n_chosen is None: # n_chosen is None, then choose any number
- result[mutable.key] = torch.randint(high=2, size=(mutable.n_candidates,)).view(-1).bool()
- # else do something else
- return result
-
- def sample_final(self):
- return self.sample_search() # use the same logic here. you can do something different
-
-The complete example of random mutator can be found :githublink:`here `.
-
-For advanced usages, e.g., users want to manipulate the way modules in ``LayerChoice`` are executed, they can inherit ``BaseMutator``\ , and overwrite ``on_forward_layer_choice`` and ``on_forward_input_choice``\ , which are the callback implementation of ``LayerChoice`` and ``InputChoice`` respectively. Users can still use property ``mutables`` to get all ``LayerChoice`` and ``InputChoice`` in the model code. For details, please refer to :githublink:`reference ` here to learn more.
-
-.. tip::
- A useful application of random mutator is for debugging. Use
-
- .. code-block:: python
-
- mutator = RandomMutator(model)
- mutator.reset()
-
- will immediately set one possible candidate in the search space as the active one.
-
-Implemented a Distributed NAS Tuner
------------------------------------
-
-Before learning how to write a distributed NAS tuner, users should first learn how to write a general tuner. read `Customize Tuner <../Tuner/CustomizeTuner.rst>`__ for tutorials.
-
-When users call "\ `nnictl ss_gen <../Tutorial/Nnictl.rst>`__\ " to generate search space file, a search space file like this will be generated:
-
-.. code-block:: json
-
- {
- "key_name": {
- "_type": "layer_choice",
- "_value": ["op1_repr", "op2_repr", "op3_repr"]
- },
- "key_name": {
- "_type": "input_choice",
- "_value": {
- "candidates": ["in1_key", "in2_key", "in3_key"],
- "n_chosen": 1
- }
- }
- }
-
-This is the exact search space tuners will receive in ``update_search_space``. It's then tuners' responsibility to interpret the search space and generate new candidates in ``generate_parameters``. A valid "parameters" will be in the following format:
-
-.. code-block:: json
-
- {
- "key_name": {
- "_value": "op1_repr",
- "_idx": 0
- },
- "key_name": {
- "_value": ["in2_key"],
- "_idex": [1]
- }
- }
-
-Send it through ``generate_parameters``\ , and the tuner would look like any HPO tuner. Refer to `SPOS <./SPOS.rst>`__ example code for an example.
diff --git a/docs/en_US/NAS/retiarii/ApiReference.rst b/docs/en_US/NAS/ApiReference.rst
similarity index 91%
rename from docs/en_US/NAS/retiarii/ApiReference.rst
rename to docs/en_US/NAS/ApiReference.rst
index 9d8cd03059..359fc5c3aa 100644
--- a/docs/en_US/NAS/retiarii/ApiReference.rst
+++ b/docs/en_US/NAS/ApiReference.rst
@@ -75,8 +75,8 @@ Oneshot Trainers
.. autoclass:: nni.retiarii.oneshot.pytorch.SinglePathTrainer
:members:
-Strategies
-----------
+Exploration Strategies
+----------------------
.. autoclass:: nni.retiarii.strategy.Random
:members:
@@ -90,6 +90,9 @@ Strategies
.. autoclass:: nni.retiarii.strategy.TPEStrategy
:members:
+.. autoclass:: nni.retiarii.strategy.PolicyBasedRL
+ :members:
+
Retiarii Experiments
--------------------
@@ -98,3 +101,8 @@ Retiarii Experiments
.. autoclass:: nni.retiarii.experiment.pytorch.RetiariiExeConfig
:members:
+
+Utilities
+---------
+
+.. autofunction:: nni.retiarii.serialize
\ No newline at end of file
diff --git a/docs/en_US/NAS/Benchmarks.rst b/docs/en_US/NAS/Benchmarks.rst
index 4749b46543..79df006db6 100644
--- a/docs/en_US/NAS/Benchmarks.rst
+++ b/docs/en_US/NAS/Benchmarks.rst
@@ -9,7 +9,7 @@ NAS Benchmarks
Introduction
------------
-To imporve the reproducibility of NAS algorithms as well as reducing computing resource requirements, researchers proposed a series of NAS benchmarks such as `NAS-Bench-101 `__\ , `NAS-Bench-201 `__\ , `NDS `__\ , etc. NNI provides a query interface for users to acquire these benchmarks. Within just a few lines of code, researcher are able to evaluate their NAS algorithms easily and fairly by utilizing these benchmarks.
+To improve the reproducibility of NAS algorithms as well as reducing computing resource requirements, researchers proposed a series of NAS benchmarks such as `NAS-Bench-101 `__\ , `NAS-Bench-201 `__\ , `NDS `__\ , etc. NNI provides a query interface for users to acquire these benchmarks. Within just a few lines of code, researcher are able to evaluate their NAS algorithms easily and fairly by utilizing these benchmarks.
Prerequisites
-------------
@@ -32,7 +32,7 @@ To avoid storage and legality issues, we do not provide any prepared databases.
git clone -b ${NNI_VERSION} https://github.com/microsoft/nni
cd nni/examples/nas/benchmarks
- Replace ``${NNI_VERSION}`` with a released version name or branch name, e.g., ``v2.2``.
+ Replace ``${NNI_VERSION}`` with a released version name or branch name, e.g., ``v2.3``.
#.
Install dependencies via ``pip3 install -r xxx.requirements.txt``. ``xxx`` can be ``nasbench101``\ , ``nasbench201`` or ``nds``.
diff --git a/docs/en_US/NAS/CDARTS.rst b/docs/en_US/NAS/CDARTS.rst
deleted file mode 100644
index 1504a56fa6..0000000000
--- a/docs/en_US/NAS/CDARTS.rst
+++ /dev/null
@@ -1,72 +0,0 @@
-CDARTS
-======
-
-Introduction
-------------
-
-`CDARTS `__ builds a cyclic feedback mechanism between the search and evaluation networks. First, the search network generates an initial topology for evaluation, so that the weights of the evaluation network can be optimized. Second, the architecture topology in the search network is further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks, and thus enables the evolution of the topology to fit the final evaluation network.
-
-In implementation of ``CdartsTrainer``\ , it first instantiates two models and two mutators (one for each). The first model is the so-called "search network", which is mutated with a ``RegularizedDartsMutator`` -- a mutator with subtle differences with ``DartsMutator``. The second model is the "evaluation network", which is mutated with a discrete mutator that leverages the previous search network mutator, to sample a single path each time. Trainers train models and mutators alternatively. Users can refer to `paper `__ if they are interested in more details on these trainers and mutators.
-
-Reproduction Results
---------------------
-
-This is CDARTS based on the NNI platform, which currently supports CIFAR10 search and retrain. ImageNet search and retrain should also be supported, and we provide corresponding interfaces. Our reproduced results on NNI are slightly lower than the paper, but much higher than the original DARTS. Here we show the results of three independent experiments on CIFAR10.
-
-.. list-table::
- :header-rows: 1
- :widths: auto
-
- * - Runs
- - Paper
- - NNI
- * - 1
- - 97.52
- - 97.44
- * - 2
- - 97.53
- - 97.48
- * - 3
- - 97.58
- - 97.56
-
-
-Examples
---------
-
-`Example code `__
-
-.. code-block:: bash
-
- # In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
- git clone https://github.com/Microsoft/nni.git
-
- # install apex for distributed training.
- git clone https://github.com/NVIDIA/apex
- cd apex
- python setup.py install --cpp_ext --cuda_ext
-
- # search the best architecture
- cd examples/nas/legacy/cdarts
- bash run_search_cifar.sh
-
- # train the best architecture.
- bash run_retrain_cifar.sh
-
-Reference
----------
-
-PyTorch
-^^^^^^^
-
-.. autoclass:: nni.algorithms.nas.pytorch.cdarts.CdartsTrainer
- :members:
-
-.. autoclass:: nni.algorithms.nas.pytorch.cdarts.RegularizedDartsMutator
- :members:
-
-.. autoclass:: nni.algorithms.nas.pytorch.cdarts.DartsDiscreteMutator
- :members:
-
-.. autoclass:: nni.algorithms.nas.pytorch.cdarts.RegularizedMutatorParallel
- :members:
diff --git a/docs/en_US/NAS/ClassicNas.rst b/docs/en_US/NAS/ClassicNas.rst
deleted file mode 100644
index cec54d910f..0000000000
--- a/docs/en_US/NAS/ClassicNas.rst
+++ /dev/null
@@ -1,59 +0,0 @@
-.. role:: raw-html(raw)
- :format: html
-
-
-Classic NAS Algorithms
-======================
-
-In classic NAS algorithms, each architecture is trained as a trial and the NAS algorithm acts as a tuner. Thus, this training mode naturally fits within the NNI hyper-parameter tuning framework, where Tuner generates new architecture for the next trial and trials run in the training service.
-
-Quick Start
------------
-
-The following example shows how to use classic NAS algorithms. You can see it is quite similar to NNI hyper-parameter tuning.
-
-.. code-block:: python
-
- model = Net()
-
- # get the chosen architecture from tuner and apply it on model
- get_and_apply_next_architecture(model)
- train(model) # your code for training the model
- acc = test(model) # test the trained model
- nni.report_final_result(acc) # report the performance of the chosen architecture
-
-First, instantiate the model. Search space has been defined in this model through ``LayerChoice`` and ``InputChoice``. After that, user should invoke ``get_and_apply_next_architecture(model)`` to settle down to a specific architecture. This function receives the architecture from tuner (i.e., the classic NAS algorithm) and applies the architecture to ``model``. At this point, ``model`` becomes a specific architecture rather than a search space. Then users are free to train this model just like training a normal PyTorch model. After get the accuracy of this model, users should invoke ``nni.report_final_result(acc)`` to report the result to the tuner.
-
-At this point, trial code is ready. Then, we can prepare an NNI experiment, i.e., search space file and experiment config file. Different from NNI hyper-parameter tuning, search space file is automatically generated from the trial code by running the command (the detailed usage of this command can be found `here <../Tutorial/Nnictl.rst>`__\ ):
-
-``nnictl ss_gen --trial_command="the command for running your trial code"``
-
-A file named ``nni_auto_gen_search_space.json`` is generated by this command. Then put the path of the generated search space in the field ``searchSpacePath`` of the experiment config file. The other fields of the config file can be filled by referring `this tutorial <../Tutorial/QuickStart.rst>`__.
-
-Currently, we only support :githublink:`PPO Tuner ` for classic NAS. More classic NAS algorithms will be supported soon.
-
-The complete examples can be found :githublink:`here ` for PyTorch and :githublink:`here ` for TensorFlow.
-
-Standalone mode for easy debugging
-----------------------------------
-
-We support a standalone mode for easy debugging, where you can directly run the trial command without launching an NNI experiment. This is for checking whether your trial code can correctly run. The first candidate(s) are chosen for ``LayerChoice`` and ``InputChoice`` in this standalone mode.
-
-:raw-html:``
-
-Regularized Evolution Tuner
----------------------------
-
-This is a tuner geared for NNI’s Neural Architecture Search (NAS) interface. It uses the `evolution algorithm `__.
-
-The tuner first randomly initializes the number of ``population`` models and evaluates them. After that, every time to produce a new architecture, the tuner randomly chooses the number of ``sample`` architectures from ``population``\ , then mutates the best model in ``sample``\ , the parent model, to produce the child model. The mutation includes the hidden mutation and the op mutation. The hidden state mutation consists of replacing a hidden state with another hidden state from within the cell, subject to the constraint that no loops are formed. The op mutation behaves like the hidden state mutation as far as replacing one op with another op from the op set. Note that keeping the child model the same as its parent is not allowed. After evaluating the child model, it is added to the tail of the ``population``\ , then pops the front one.
-
-Note that **trial concurrency should be less than the population of the model**\ , otherwise NO_MORE_TRIAL exception will be raised.
-
-The whole procedure is summarized by the pseudocode below.
-
-
-.. image:: ../../img/EvoNasTuner.png
- :target: ../../img/EvoNasTuner.png
- :alt:
-
diff --git a/docs/en_US/NAS/Cream.rst b/docs/en_US/NAS/Cream.rst
deleted file mode 100644
index 4aa9c9cf41..0000000000
--- a/docs/en_US/NAS/Cream.rst
+++ /dev/null
@@ -1,156 +0,0 @@
-Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search
-=======================================================================================
-
-* `Paper `__
-* `Models-Google Drive `__
-* `Models-Baidu Disk (PWD: wqw6) `__
-* `BibTex `__
-
-In this work, we present a simple yet effective architecture distillation method. The central idea is that subnetworks can learn collaboratively and teach each other throughout the training process, aiming to boost the convergence of individual models. We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training. Distilling knowledge from the prioritized paths is able to boost the training of subnetworks. Since the prioritized paths are changed on the fly depending on their performance and complexity, the final obtained paths are the cream of the crop. The discovered architectures achieve superior performance compared to the recent `MobileNetV3 `__ and `EfficientNet `__ families under aligned settings.
-
-.. image:: https://raw.githubusercontent.com/microsoft/Cream/main/demo/intro.jpg
-
-Reproduced Results
-------------------
-
-Top-1 Accuracy on ImageNet. The top-1 accuracy of Cream search algorithm surpasses MobileNetV3 and EfficientNet-B0/B1 on ImageNet.
-The training with 16 Gpus is a little bit superior than 8 Gpus, as below.
-
-.. list-table::
- :header-rows: 1
- :widths: auto
-
- * - Model (M Flops)
- - 8Gpus
- - 16Gpus
- * - 14M
- - 53.7
- - 53.8
- * - 43M
- - 65.8
- - 66.5
- * - 114M
- - 72.1
- - 72.8
- * - 287M
- - 76.7
- - 77.6
- * - 481M
- - 78.9
- - 79.2
- * - 604M
- - 79.4
- - 80.0
-
-
-
-.. image:: ../../img/cream_flops100.jpg
- :scale: 50%
-
-.. image:: ../../img/cream_flops600.jpg
- :scale: 50%
-
-Examples
---------
-
-`Example code `__
-
-Please run the following scripts in the example folder.
-
-Data Preparation
-----------------
-
-You need to first download the `ImageNet-2012 `__ to the folder ``./data/imagenet`` and move the validation set to the subfolder ``./data/imagenet/val``. To move the validation set, you cloud use `the following script `__ .
-
-Put the imagenet data in ``./data``. It should be like following:
-
-.. code-block:: bash
-
- ./data/imagenet/train
- ./data/imagenet/val
- ...
-
-Quick Start
------------
-
-1. Search
-^^^^^^^^^
-
-First, build environments for searching.
-
-.. code-block:: bash
-
- pip install -r ./requirements
-
- git clone https://github.com/NVIDIA/apex.git
- cd apex
- python setup.py install --cpp_ext --cuda_ext
-
-To search for an architecture, you need to configure the parameters ``FLOPS_MINIMUM`` and ``FLOPS_MAXIMUM`` to specify the desired model flops, such as [0,600]MB flops. You can specify the flops interval by changing these two parameters in ``./configs/train.yaml``
-
-.. code-block:: bash
-
- FLOPS_MINIMUM: 0 # Minimum Flops of Architecture
- FLOPS_MAXIMUM: 600 # Maximum Flops of Architecture
-
-For example, if you expect to search an architecture with model flops <= 200M, please set the ``FLOPS_MINIMUM`` and ``FLOPS_MAXIMUM`` to be ``0`` and ``200``.
-
-After you specify the flops of the architectures you would like to search, you can search an architecture now by running:
-
-.. code-block:: bash
-
- python -m torch.distributed.launch --nproc_per_node=8 ./train.py --cfg ./configs/train.yaml
-
-The searched architectures need to be retrained and obtain the final model. The final model is saved in ``.pth.tar`` format. Retraining code will be released soon.
-
-2. Retrain
-^^^^^^^^^^^
-
-To train searched architectures, you need to configure the parameter ``MODEL_SELECTION`` to specify the model Flops. To specify which model to train, you should add ``MODEL_SELECTION`` in ``./configs/retrain.yaml``. You can select one from [14,43,112,287,481,604], which stands for different Flops(MB).
-
-.. code-block:: bash
-
- MODEL_SELECTION: 43 # Retrain 43m model
- MODEL_SELECTION: 481 # Retrain 481m model
- ......
-
-To train random architectures, you need specify ``MODEL_SELECTION`` to ``-1`` and configure the parameter ``INPUT_ARCH``\ :
-
-.. code-block:: bash
-
- MODEL_SELECTION: -1 # Train random architectures
- INPUT_ARCH: [[0], [3], [3, 3], [3, 1, 3], [3, 3, 3, 3], [3, 3, 3], [0]] # Random Architectures
- ......
-
-After adding ``MODEL_SELECTION`` in ``./configs/retrain.yaml``\ , you need to use the following command to train the model.
-
-.. code-block:: bash
-
- python -m torch.distributed.launch --nproc_per_node=8 ./retrain.py --cfg ./configs/retrain.yaml
-
-3. Test
-^^^^^^^^^
-
-To test our trained of models, you need to use ``MODEL_SELECTION`` in ``./configs/test.yaml`` to specify which model to test.
-
-.. code-block:: bash
-
- MODEL_SELECTION: 43 # test 43m model
- MODEL_SELECTION: 481 # test 470m model
- ......
-
-After specifying the flops of the model, you need to write the path to the resume model in ``./test.sh``.
-
-.. code-block:: bash
-
- RESUME_PATH: './43.pth.tar'
- RESUME_PATH: './481.pth.tar'
- ......
-
-We provide 14M/43M/114M/287M/481M/604M pretrained models in `google drive `__ or `[Models-Baidu Disk (password: wqw6)] `__ .
-
-After downloading the pretrained models and adding ``MODEL_SELECTION`` and ``RESUME_PATH`` in './configs/test.yaml', you need to use the following command to test the model.
-
-.. code-block:: bash
-
- python -m torch.distributed.launch --nproc_per_node=8 ./test.py --cfg ./configs/test.yaml
diff --git a/docs/en_US/NAS/DARTS.rst b/docs/en_US/NAS/DARTS.rst
index fde97fe105..20dfef7ba4 100644
--- a/docs/en_US/NAS/DARTS.rst
+++ b/docs/en_US/NAS/DARTS.rst
@@ -56,11 +56,8 @@ Reference
PyTorch
^^^^^^^
-.. autoclass:: nni.algorithms.nas.pytorch.darts.DartsTrainer
- :members:
-
-.. autoclass:: nni.algorithms.nas.pytorch.darts.DartsMutator
- :members:
+.. autoclass:: nni.retiarii.oneshot.pytorch.DartsTrainer
+ :noindex:
Limitations
-----------
diff --git a/docs/en_US/NAS/ENAS.rst b/docs/en_US/NAS/ENAS.rst
index 29048dd35a..60415e5450 100644
--- a/docs/en_US/NAS/ENAS.rst
+++ b/docs/en_US/NAS/ENAS.rst
@@ -39,8 +39,5 @@ Reference
PyTorch
^^^^^^^
-.. autoclass:: nni.algorithms.nas.pytorch.enas.EnasTrainer
- :members:
-
-.. autoclass:: nni.algorithms.nas.pytorch.enas.EnasMutator
- :members:
+.. autoclass:: nni.retiarii.oneshot.pytorch.EnasTrainer
+ :noindex:
diff --git a/docs/en_US/NAS/ExecutionEngines.rst b/docs/en_US/NAS/ExecutionEngines.rst
new file mode 100644
index 0000000000..5be6525c78
--- /dev/null
+++ b/docs/en_US/NAS/ExecutionEngines.rst
@@ -0,0 +1,59 @@
+Execution Engines
+=================
+
+Execution engine is for running Retiarii Experiment. NNI supports three execution engines, users can choose a speicific engine according to the type of their model mutation definition and their requirements for cross-model optimizations.
+
+* **Pure-python execution engine** is the default engine, it supports the model space expressed by `inline mutation API <./MutationPrimitives.rst>`__.
+
+* **Graph-based execution engine** supports the use of `inline mutation APIs <./MutationPrimitives.rst>`__ and model spaces represented by `mutators <./Mutators.rst>`__. It requires the user's model to be parsed by `TorchScript `__.
+
+* **CGO execution engine** has the same requirements and capabilities as the **Graph-based execution engine**. But further enables cross-model optimizations, which makes model space exploration faster.
+
+Pure-python Execution Engine
+----------------------------
+
+Pure-python Execution Engine is the default engine, we recommend users to keep using this execution engine, if they are new to NNI NAS. Pure-python execution engine plays magic within the scope of inline mutation APIs, while does not touch the rest of user model. Thus, it has minimal requirement on user model.
+
+One steps are needed to use this engine now.
+
+1. Add ``@nni.retiarii.model_wrapper`` decorator outside the whole PyTorch model.
+
+.. note:: You should always use ``super().__init__()`` instead of ``super(MyNetwork, self).__init__()`` in the PyTorch model, because the latter one has issues with model wrapper.
+
+Graph-based Execution Engine
+----------------------------
+
+For graph-based execution engine, it converts user-defined model to a graph representation (called graph IR) using `TorchScript `__, each instantiated module in the model is converted to a subgraph. Then mutations are applied to the graph to generate new graphs. Each new graph is then converted back to PyTorch code and executed on the user specified training service.
+
+Users may find ``@basic_unit`` helpful in some cases. ``@basic_unit`` here means the module will not be converted to a subgraph, instead, it is converted to a single graph node as a basic unit.
+
+``@basic_unit`` is usually used in the following cases:
+
+* When users want to tune initialization parameters of a module using ``ValueChoice``, then decorate the module with ``@basic_unit``. For example, ``self.conv = MyConv(kernel_size=nn.ValueChoice([1, 3, 5]))``, here ``MyConv`` should be decorated.
+
+* When a module cannot be successfully parsed to a subgraph, decorate the module with ``@basic_unit``. The parse failure could be due to complex control flow. Currently Retiarii does not support adhoc loop, if there is adhoc loop in a module's forward, this class should be decorated as serializable module. For example, the following ``MyModule`` should be decorated.
+
+ .. code-block:: python
+
+ @basic_unit
+ class MyModule(nn.Module):
+ def __init__(self):
+ ...
+ def forward(self, x):
+ for i in range(10): # <- adhoc loop
+ ...
+
+* Some inline mutation APIs require their handled module to be decorated with ``@basic_unit``. For example, user-defined module that is provided to ``LayerChoice`` as a candidate op should be decorated.
+
+Three steps are need to use graph-based execution engine.
+
+1. Remove ``@nni.retiarii.model_wrapper`` if there is any in your model.
+2. Add ``config.execution_engine = 'base'`` to ``RetiariiExeConfig``. The default value of ``execution_engine`` is 'py', which means pure-python execution engine.
+3. Add ``@basic_unit`` when necessary following the above guidelines.
+
+For exporting top models, graph-based execution engine supports exporting source code for top models by running ``exp.export_top_models(formatter='code')``.
+
+CGO Execution Engine
+--------------------
+
+CGO execution engine does cross-model optimizations based on the graph-based execution engine. This execution engine will be `released in v2.4 `__.
diff --git a/docs/en_US/NAS/ExplorationStrategies.rst b/docs/en_US/NAS/ExplorationStrategies.rst
new file mode 100644
index 0000000000..e312832010
--- /dev/null
+++ b/docs/en_US/NAS/ExplorationStrategies.rst
@@ -0,0 +1,35 @@
+Exploration Strategies for Multi-trial NAS
+==========================================
+
+Usage of Exploration Strategy
+-----------------------------
+
+To use an exploration strategy, users simply instantiate an exploration strategy and pass the instantiated object to ``RetiariiExperiment``. Below is a simple example.
+
+.. code-block:: python
+
+ import nni.retiarii.strategy as strategy
+
+ exploration_strategy = strategy.Random(dedup=True) # dedup=False if deduplication is not wanted
+
+Supported Exploration Strategies
+--------------------------------
+
+NNI provides the following exploration strategies for multi-trial NAS. Users could also `customize new exploration strategies <./WriteStrategy.rst>`__.
+
+.. list-table::
+ :header-rows: 1
+ :widths: auto
+
+ * - Name
+ - Brief Introduction of Algorithm
+ * - `Random Strategy <./ApiReference.rst#nni.retiarii.strategy.Random>`__
+ - Randomly sampling new model(s) from user defined model space. (``nni.retiarii.strategy.Random``)
+ * - `Grid Search <./ApiReference.rst#nni.retiarii.strategy.GridSearch>`__
+ - Sampling new model(s) from user defined model space using grid search algorithm. (``nni.retiarii.strategy.GridSearch``)
+ * - `Regularized Evolution <./ApiReference.rst#nni.retiarii.strategy.RegularizedEvolution>`__
+ - Generating new model(s) from generated models using `regularized evolution algorithm `__ . (``nni.retiarii.strategy.RegularizedEvolution``)
+ * - `TPE Strategy <./ApiReference.rst#nni.retiarii.strategy.TPEStrategy>`__
+ - Sampling new model(s) from user defined model space using `TPE algorithm `__ . (``nni.retiarii.strategy.TPEStrategy``)
+ * - `RL Strategy <./ApiReference.rst#nni.retiarii.strategy.PolicyBasedRL>`__
+ - It uses `PPO algorithm `__ to sample new model(s) from user defined model space. (``nni.retiarii.strategy.PolicyBasedRL``)
\ No newline at end of file
diff --git a/docs/en_US/NAS/FBNet.rst b/docs/en_US/NAS/FBNet.rst
index 1fb18f4165..8e92b1cd4b 100644
--- a/docs/en_US/NAS/FBNet.rst
+++ b/docs/en_US/NAS/FBNet.rst
@@ -1,6 +1,8 @@
FBNet
======
+.. note:: This one-shot NAS is still implemented under NNI NAS 1.0, and will `be migrated to Retiarii framework in v2.4 `__.
+
For the mobile application of facial landmark, based on the basic architecture of PFLD model, we have applied the FBNet (Block-wise DNAS) to design an concise model with the trade-off between latency and accuracy. References are listed as below:
@@ -148,4 +150,4 @@ The checkpoints of pre-trained supernet and subnet are offered as below:
* `Supernet `__
* `Subnet `__
-* `ONNX model `__
+* `ONNX model `__
\ No newline at end of file
diff --git a/docs/en_US/NAS/ModelEvaluators.rst b/docs/en_US/NAS/ModelEvaluators.rst
new file mode 100644
index 0000000000..5e241004a6
--- /dev/null
+++ b/docs/en_US/NAS/ModelEvaluators.rst
@@ -0,0 +1,43 @@
+Model Evaluators
+================
+
+A model evaluator is for training and validating each generated model.
+
+Usage of Model Evaluator
+------------------------
+
+In multi-trial NAS, a sampled model should be able to be executed on a remote machine or a training platform (e.g., AzureML, OpenPAI). Thus, both the model and its model evaluator should be correctly serialized. To make NNI correctly serialize model evaluator, users should apply ``serialize`` on some of their functions and objects.
+
+.. _serializer:
+
+`serialize <./ApiReference.rst#utilities>`__ enables re-instantiation of model evaluator in another process or machine. It is implemented by recording the initialization parameters of user instantiated evaluator.
+
+The evaluator related APIs provided by Retiarii have already supported serialization, for example ``pl.Classification``, ``pl.DataLoader``, no need to apply ``serialize`` on them. In the following case users should use ``serialize`` API manually.
+
+If the initialization parameters of the evaluator APIs (e.g., ``pl.Classification``, ``pl.DataLoader``) are not primitive types (e.g., ``int``, ``string``), they should be applied with ``serialize``. If those parameters' initialization parameters are not primitive types, ``serialize`` should also be applied. In a word, ``serialize`` should be applied recursively if necessary.
+
+Below is an example, ``transforms.Compose``, ``transforms.Normalize``, and ``MNIST`` are serialized manually using ``serialize``. ``serialize`` takes a class ``cls`` as its first argument, its following arguments are the arguments for initializing this class. ``pl.Classification`` is not applied ``serialize`` because it is already serializable as an API provided by NNI.
+
+.. code-block:: python
+
+ import nni.retiarii.evaluator.pytorch.lightning as pl
+ from nni.retiarii import serialize
+ from torchvision import transforms
+
+ transform = serialize(transforms.Compose, [serialize(transforms.ToTensor()), serialize(transforms.Normalize, (0.1307,), (0.3081,))])
+ train_dataset = serialize(MNIST, root='data/mnist', train=True, download=True, transform=transform)
+ test_dataset = serialize(MNIST, root='data/mnist', train=False, download=True, transform=transform)
+ evaluator = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
+ val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
+ max_epochs=10)
+
+Supported Model Evaluators
+--------------------------
+
+NNI provides some commonly used model evaluators for users' convenience. If these model evaluators do not meet users' requirement, they can customize new model evaluators following the tutorial `here <./WriteTrainer.rst>`__.
+
+.. autoclass:: nni.retiarii.evaluator.pytorch.lightning.Classification
+ :noindex:
+
+.. autoclass:: nni.retiarii.evaluator.pytorch.lightning.Regression
+ :noindex:
diff --git a/docs/en_US/NAS/MutationPrimitives.rst b/docs/en_US/NAS/MutationPrimitives.rst
new file mode 100644
index 0000000000..77a1be8606
--- /dev/null
+++ b/docs/en_US/NAS/MutationPrimitives.rst
@@ -0,0 +1,41 @@
+Mutation Primitives
+===================
+
+To make users easily express a model space within their PyTorch/TensorFlow model, NNI provides some inline mutation APIs as shown below.
+
+* `nn.LayerChoice <./ApiReference.rst#nni.retiarii.nn.pytorch.LayerChoice>`__. It allows users to put several candidate operations (e.g., PyTorch modules), one of them is chosen in each explored model.
+
+ .. code-block:: python
+
+ # import nni.retiarii.nn.pytorch as nn
+ # declared in `__init__` method
+ self.layer = nn.LayerChoice([
+ ops.PoolBN('max', channels, 3, stride, 1),
+ ops.SepConv(channels, channels, 3, stride, 1),
+ nn.Identity()
+ ]))
+ # invoked in `forward` method
+ out = self.layer(x)
+
+* `nn.InputChoice <./ApiReference.rst#nni.retiarii.nn.pytorch.InputChoice>`__. It is mainly for choosing (or trying) different connections. It takes several tensors and chooses ``n_chosen`` tensors from them.
+
+ .. code-block:: python
+
+ # import nni.retiarii.nn.pytorch as nn
+ # declared in `__init__` method
+ self.input_switch = nn.InputChoice(n_chosen=1)
+ # invoked in `forward` method, choose one from the three
+ out = self.input_switch([tensor1, tensor2, tensor3])
+
+* `nn.ValueChoice <./ApiReference.rst#nni.retiarii.nn.pytorch.ValueChoice>`__. It is for choosing one value from some candidate values. It can only be used as input argument of basic units, that is, modules in ``nni.retiarii.nn.pytorch`` and user-defined modules decorated with ``@basic_unit``.
+
+ .. code-block:: python
+
+ # import nni.retiarii.nn.pytorch as nn
+ # used in `__init__` method
+ self.conv = nn.Conv2d(XX, XX, kernel_size=nn.ValueChoice([1, 3, 5])
+ self.op = MyOp(nn.ValueChoice([0, 1]), nn.ValueChoice([-1, 1]))
+
+* `nn.Repeat <./ApiReference.rst#nni.retiarii.nn.pytorch.Repeat>`__. Repeat a block by a variable number of times.
+
+* `nn.Cell <./ApiReference.rst#nni.retiarii.nn.pytorch.Cell>`__. `This cell structure is popularly used in NAS literature `__. Specifically, the cell consists of multiple "nodes". Each node is a sum of multiple operators. Each operator is chosen from user specified candidates, and takes one input from previous nodes and predecessors. Predecessor means the input of cell. The output of cell is the concatenation of some of the nodes in the cell (currently all the nodes).
\ No newline at end of file
diff --git a/docs/en_US/NAS/Mutators.rst b/docs/en_US/NAS/Mutators.rst
new file mode 100644
index 0000000000..3e02f89d46
--- /dev/null
+++ b/docs/en_US/NAS/Mutators.rst
@@ -0,0 +1,64 @@
+Express Mutations with Mutators
+===============================
+
+Besides the inline mutation APIs demonstrated `here <./MutationPrimitives.rst>`__, NNI provides a more general approach to express a model space, i.e., *Mutator*, to cover more complex model spaces. Those inline mutation APIs are also implemented with mutator in the underlying system, which can be seen as a special case of model mutation.
+
+.. note:: Mutator and inline mutation APIs cannot be used together.
+
+A mutator is a piece of logic to express how to mutate a given model. Users are free to write their own mutators. Then a model space is expressed with a base model and a list of mutators. A model in the model space is sampled by applying the mutators on the base model one after another. An example is shown below.
+
+.. code-block:: python
+
+ applied_mutators = []
+ applied_mutators.append(BlockMutator('mutable_0'))
+ applied_mutators.append(BlockMutator('mutable_1'))
+
+``BlockMutator`` is defined by users to express how to mutate the base model.
+
+Write a mutator
+---------------
+
+User-defined mutator should inherit ``Mutator`` class, and implement mutation logic in the member function ``mutate``.
+
+.. code-block:: python
+
+ from nni.retiarii import Mutator
+ class BlockMutator(Mutator):
+ def __init__(self, target: str, candidates: List):
+ super(BlockMutator, self).__init__()
+ self.target = target
+ self.candidate_op_list = candidates
+
+ def mutate(self, model):
+ nodes = model.get_nodes_by_label(self.target)
+ for node in nodes:
+ chosen_op = self.choice(self.candidate_op_list)
+ node.update_operation(chosen_op.type, chosen_op.params)
+
+The input of ``mutate`` is graph IR (Intermediate Representation) of the base model (please refer to `here <./ApiReference.rst>`__ for the format and APIs of the IR), users can mutate the graph using the graph's member functions (e.g., ``get_nodes_by_label``, ``update_operation``). The mutation operations can be combined with the API ``self.choice``, in order to express a set of possible mutations. In the above example, the node's operation can be changed to any operation from ``candidate_op_list``.
+
+Use placehoder to make mutation easier: ``nn.Placeholder``. If you want to mutate a subgraph or node of your model, you can define a placeholder in this model to represent the subgraph or node. Then, use mutator to mutate this placeholder to make it real modules.
+
+.. code-block:: python
+
+ ph = nn.Placeholder(
+ label='mutable_0',
+ kernel_size_options=[1, 3, 5],
+ n_layer_options=[1, 2, 3, 4],
+ exp_ratio=exp_ratio,
+ stride=stride
+ )
+
+``label`` is used by mutator to identify this placeholder. The other parameters are the information that is required by mutator. They can be accessed from ``node.operation.parameters`` as a dict, it could include any information that users want to put to pass it to user defined mutator. The complete example code can be found in :githublink:`Mnasnet base model `.
+
+Starting an experiment is almost the same as using inline mutation APIs. The only difference is that the applied mutators should be passed to ``RetiariiExperiment``. Below is a simple example.
+
+.. code-block:: python
+
+ exp = RetiariiExperiment(base_model, trainer, applied_mutators, simple_strategy)
+ exp_config = RetiariiExeConfig('local')
+ exp_config.experiment_name = 'mnasnet_search'
+ exp_config.trial_concurrency = 2
+ exp_config.max_trial_number = 10
+ exp_config.training_service.use_active_gpu = False
+ exp.run(exp_config, 8081)
diff --git a/docs/en_US/NAS/NasGuide.rst b/docs/en_US/NAS/NasGuide.rst
deleted file mode 100644
index 45475c686a..0000000000
--- a/docs/en_US/NAS/NasGuide.rst
+++ /dev/null
@@ -1,88 +0,0 @@
-One-shot NAS algorithms
-=======================
-
-Besides `classic NAS algorithms <./ClassicNas.rst>`__\ , users also apply more advanced one-shot NAS algorithms to find better models from a search space. There are lots of related works about one-shot NAS algorithms, such as `SMASH `__\ , `ENAS `__\ , `DARTS `__\ , `FBNet `__\ , `ProxylessNAS `__\ , `SPOS `__\ , `Single-Path NAS `__\ , `Understanding One-shot `__ and `GDAS `__. One-shot NAS algorithms usually build a supernet containing every candidate in the search space as its subnetwork, and in each step, a subnetwork or combination of several subnetworks is trained.
-
-Currently, several one-shot NAS methods are supported on NNI. For example, ``DartsTrainer``\ , which uses SGD to train architecture weights and model weights iteratively, and ``ENASTrainer``\ , which `uses a controller to train the model `__. New and more efficient NAS trainers keep emerging in research community and some will be implemented in future releases of NNI.
-
-Search with One-shot NAS Algorithms
------------------------------------
-
-Each one-shot NAS algorithm implements a trainer, for which users can find usage details in the description of each algorithm. Here is a simple example, demonstrating how users can use ``EnasTrainer``.
-
-.. code-block:: python
-
- # this is exactly same as traditional model training
- model = Net()
- dataset_train = CIFAR10(root="./data", train=True, download=True, transform=train_transform)
- dataset_valid = CIFAR10(root="./data", train=False, download=True, transform=valid_transform)
- criterion = nn.CrossEntropyLoss()
- optimizer = torch.optim.SGD(model.parameters(), 0.05, momentum=0.9, weight_decay=1.0E-4)
-
- # use NAS here
- def top1_accuracy(output, target):
- # this is the function that computes the reward, as required by ENAS algorithm
- batch_size = target.size(0)
- _, predicted = torch.max(output.data, 1)
- return (predicted == target).sum().item() / batch_size
-
- def metrics_fn(output, target):
- # metrics function receives output and target and computes a dict of metrics
- return {"acc1": top1_accuracy(output, target)}
-
- from nni.algorithms.nas.pytorch import enas
- trainer = enas.EnasTrainer(model,
- loss=criterion,
- metrics=metrics_fn,
- reward_function=top1_accuracy,
- optimizer=optimizer,
- batch_size=128
- num_epochs=10, # 10 epochs
- dataset_train=dataset_train,
- dataset_valid=dataset_valid,
- log_frequency=10) # print log every 10 steps
- trainer.train() # training
- trainer.export(file="model_dir/final_architecture.json") # export the final architecture to file
-
-``model`` is the one with `user defined search space <./WriteSearchSpace.rst>`__. Then users should prepare training data and model evaluation metrics. To search from the defined search space, a one-shot algorithm is instantiated, called trainer (e.g., EnasTrainer). The trainer exposes a few arguments that you can customize. For example, the loss function, the metrics function, the optimizer, and the datasets. These should satisfy most usage requirements and we do our best to make sure our built-in trainers work on as many models, tasks, and datasets as possible.
-
-**Note that** when using one-shot NAS algorithms, there is no need to start an NNI experiment. Users can directly run this Python script (i.e., ``train.py``\ ) through ``python3 train.py`` without ``nnictl``. After training, users can export the best one of the found models through ``trainer.export()``.
-
-Each trainer in NNI has its targeted scenario and usage. Some trainers have the assumption that the task is a classification task; some trainers might have a different definition of "epoch" (e.g., an ENAS epoch = some child steps + some controller steps). Most trainers do not have support for distributed training: they won't wrap your model with ``DataParallel`` or ``DistributedDataParallel`` to do that. So after a few tryouts, if you want to actually use the trainers on your very customized applications, you might need to `customize your trainer <./Advanced.rst#extend-the-ability-of-one-shot-trainers>`__.
-
-Furthermore, one-shot NAS can be visualized with our NAS UI. `See more details. <./Visualization.rst>`__
-
-Retrain with Exported Architecture
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-After the search phase, it's time to train the found architecture. Unlike many open-source NAS algorithms who write a whole new model specifically for retraining. We found that the search model and retraining model are usually very similar, and therefore you can construct your final model with the exact same model code. For example
-
-.. code-block:: python
-
- model = Net()
- apply_fixed_architecture(model, "model_dir/final_architecture.json")
-
-The JSON is simply a mapping from mutable keys to choices. Choices can be expressed in:
-
-
-* A string: select the candidate with corresponding name.
-* A number: select the candidate with corresponding index.
-* A list of string: select the candidates with corresponding names.
-* A list of number: select the candidates with corresponding indices.
-* A list of boolean values: a multi-hot array.
-
-For example,
-
-.. code-block:: json
-
- {
- "LayerChoice1": "conv5x5",
- "LayerChoice2": 6,
- "InputChoice3": ["layer1", "layer3"],
- "InputChoice4": [1, 2],
- "InputChoice5": [false, true, false, false, true]
- }
-
-After applying, the model is then fixed and ready for final training. The model works as a single model, and unused parameters and modules are pruned.
-
-Also, refer to `DARTS <./DARTS.rst>`__ for code exemplifying retraining.
diff --git a/docs/en_US/NAS/NasReference.rst b/docs/en_US/NAS/NasReference.rst
deleted file mode 100644
index 6df2be425b..0000000000
--- a/docs/en_US/NAS/NasReference.rst
+++ /dev/null
@@ -1,99 +0,0 @@
-NAS Reference
-=============
-
-.. contents::
-
-Mutables
---------
-
-.. autoclass:: nni.nas.pytorch.mutables.Mutable
- :members:
-
-.. autoclass:: nni.nas.pytorch.mutables.LayerChoice
- :members:
-
-.. autoclass:: nni.nas.pytorch.mutables.InputChoice
- :members:
-
-.. autoclass:: nni.nas.pytorch.mutables.MutableScope
- :members:
-
-Utilities
-^^^^^^^^^
-
-.. autofunction:: nni.nas.pytorch.utils.global_mutable_counting
-
-Mutators
---------
-
-.. autoclass:: nni.nas.pytorch.base_mutator.BaseMutator
- :members:
-
-.. autoclass:: nni.nas.pytorch.mutator.Mutator
- :members:
-
-Random Mutator
-^^^^^^^^^^^^^^
-
-.. autoclass:: nni.algorithms.nas.pytorch.random.RandomMutator
- :members:
-
-Utilities
-^^^^^^^^^
-
-.. autoclass:: nni.nas.pytorch.utils.StructuredMutableTreeNode
- :members:
-
-Trainers
---------
-
-Trainer
-^^^^^^^
-
-.. autoclass:: nni.nas.pytorch.base_trainer.BaseTrainer
- :members:
-
-.. autoclass:: nni.nas.pytorch.trainer.Trainer
- :members:
-
-Retrain
-^^^^^^^
-
-.. autofunction:: nni.nas.pytorch.fixed.apply_fixed_architecture
-
-.. autoclass:: nni.nas.pytorch.fixed.FixedArchitecture
- :members:
-
-Distributed NAS
-^^^^^^^^^^^^^^^
-
-.. autofunction:: nni.algorithms.nas.pytorch.classic_nas.get_and_apply_next_architecture
-
-.. autoclass:: nni.algorithms.nas.pytorch.classic_nas.mutator.ClassicMutator
- :members:
-
-Callbacks
-^^^^^^^^^
-
-.. autoclass:: nni.nas.pytorch.callbacks.Callback
- :members:
-
-.. autoclass:: nni.nas.pytorch.callbacks.LRSchedulerCallback
- :members:
-
-.. autoclass:: nni.nas.pytorch.callbacks.ArchitectureCheckpoint
- :members:
-
-.. autoclass:: nni.nas.pytorch.callbacks.ModelCheckpoint
- :members:
-
-Utilities
-^^^^^^^^^
-
-.. autoclass:: nni.nas.pytorch.utils.AverageMeterGroup
- :members:
-
-.. autoclass:: nni.nas.pytorch.utils.AverageMeter
- :members:
-
-.. autofunction:: nni.nas.pytorch.utils.to_device
diff --git a/docs/en_US/NAS/OneshotTrainer.rst b/docs/en_US/NAS/OneshotTrainer.rst
new file mode 100644
index 0000000000..76baa36b92
--- /dev/null
+++ b/docs/en_US/NAS/OneshotTrainer.rst
@@ -0,0 +1,37 @@
+One-shot NAS
+============
+
+Before reading this tutorial, we highly recommend you to first go through the tutorial of how to `define a model space <./QuickStart.rst#define-your-model-space>`__.
+
+Model Search with One-shot Trainer
+----------------------------------
+
+With a defined model space, users can explore the space in two ways. One is using strategy and single-arch evaluator as demonstrated `here <./QuickStart.rst#explore-the-defined-model-space>`__. The other is using one-shot trainer, which consumes much less computational resource compared to the first one. In this tutorial we focus on this one-shot approach. The principle of one-shot approach is combining all the models in a model space into one big model (usually called super-model or super-graph). It takes charge of both search, training and testing, by training and evaluating this big model.
+
+We list the supported one-shot trainers here:
+
+* DARTS trainer
+* ENAS trainer
+* ProxylessNAS trainer
+* Single-path (random) trainer
+
+See `API reference <./ApiReference.rst>`__ for detailed usages. Here, we show an example to use DARTS trainer manually.
+
+.. code-block:: python
+
+ from nni.retiarii.oneshot.pytorch import DartsTrainer
+ trainer = DartsTrainer(
+ model=model,
+ loss=criterion,
+ metrics=lambda output, target: accuracy(output, target, topk=(1,)),
+ optimizer=optim,
+ num_epochs=args.epochs,
+ dataset=dataset_train,
+ batch_size=args.batch_size,
+ log_frequency=args.log_frequency,
+ unrolled=args.unrolled
+ )
+ trainer.fit()
+ final_architecture = trainer.export()
+
+**Format of the exported architecture.** TBD.
diff --git a/docs/en_US/NAS/Overview.rst b/docs/en_US/NAS/Overview.rst
index a8d20a2e7d..3b177231a6 100644
--- a/docs/en_US/NAS/Overview.rst
+++ b/docs/en_US/NAS/Overview.rst
@@ -1,116 +1,82 @@
-Neural Architecture Search (NAS) on NNI
+Retiarii for Neural Architecture Search
=======================================
+.. Note:: NNI's latest NAS supports are all based on Retiarii Framework, users who are still on `early version using NNI NAS v1.0 `__ shall migrate your work to Retiarii as soon as possible.
+
.. contents::
+Motivation
+----------
+
+Automatic neural architecture search is playing an increasingly important role in finding better models. Recent research has proven the feasibility of automatic NAS and has led to models that beat many manually designed and tuned models. Representative works include `NASNet `__\ , `ENAS `__\ , `DARTS `__\ , `Network Morphism `__\ , and `Evolution `__. In addition, new innovations continue to emerge.
+
+However, it is pretty hard to use existing NAS work to help develop common DNN models. Therefore, we designed `Retiarii `__, a novel NAS/HPO framework, and implemented it in NNI. It helps users easily construct a model space (or search space, tuning space), and utilize existing NAS algorithms. The framework also facilitates NAS innovation and is used to design new NAS algorithms.
+
Overview
--------
-Automatic neural architecture search is taking an increasingly important role in finding better models. Recent research has proved the feasibility of automatic NAS and has lead to models that beat many manually designed and tuned models. Some representative works are `NASNet `__\ , `ENAS `__\ , `DARTS `__\ , `Network Morphism `__\ , and `Evolution `__. Further, new innovations keep emerging.
-
-However, it takes a great effort to implement NAS algorithms, and it's hard to reuse the code base of existing algorithms for new ones. To facilitate NAS innovations (e.g., the design and implementation of new NAS models, the comparison of different NAS models side-by-side, etc.), an easy-to-use and flexible programming interface is crucial.
+There are three key characteristics of the Retiarii framework:
-With this motivation, our ambition is to provide a unified architecture in NNI, accelerate innovations on NAS, and apply state-of-the-art algorithms to real-world problems faster.
+* Simple APIs are provided for defining model search space within PyTorch/TensorFlow model.
+* SOTA NAS algorithms are built-in to be used for exploring model search space.
+* System-level optimizations are implemented for speeding up the exploration.
-With the unified interface, there are two different modes for architecture search. `One <#supported-one-shot-nas-algorithms>`__ is the so-called one-shot NAS, where a super-net is built based on a search space and one-shot training is used to generate a good-performing child model. `The other <#supported-classic-nas-algorithms>`__ is the traditional search-based approach, where each child model within the search space runs as an independent trial. We call it classic NAS.
+There are two types of model space exploration approach: **Multi-trial NAS** and **One-shot NAS**. Mutli-trial NAS trains each sampled model in the model space independently, while One-shot NAS samples the model from a super model. After constructing the model space, users can use either exploration appraoch to explore the model space.
-NNI also provides dedicated `visualization tool <#nas-visualization>`__ for users to check the status of the neural architecture search process.
-Supported Classic NAS Algorithms
---------------------------------
+Multi-trial NAS
+---------------
-The procedure of classic NAS algorithms is similar to hyper-parameter tuning, users use ``nnictl`` to start experiments and each model runs as a trial. The difference is that search space file is automatically generated from user model (with search space in it) by running ``nnictl ss_gen``. The following table listed supported tuning algorihtms for classic NAS mode. More algorihtms will be supported in future release.
+Multi-trial NAS means each sampled model from model space is trained independently. A typical multi-trial NAS is `NASNet `__. The algorithm to sample models from model space is called exploration strategy. NNI has supported the following exploration strategies for multi-trial NAS.
.. list-table::
:header-rows: 1
:widths: auto
- * - Name
+ * - Exploration Strategy Name
- Brief Introduction of Algorithm
- * - :githublink:`Random Search `
- - Randomly pick a model from search space
- * - `PPO Tuner <../Tuner/BuiltinTuner.rst#PPO-Tuner>`__
- - PPO Tuner is a Reinforcement Learning tuner based on PPO algorithm. `Reference Paper `__
+ * - Random Strategy
+ - Randomly sampling new model(s) from user defined model space. (``nni.retiarii.strategy.Random``)
+ * - Grid Search
+ - Sampling new model(s) from user defined model space using grid search algorithm. (``nni.retiarii.strategy.GridSearch``)
+ * - Regularized Evolution
+ - Generating new model(s) from generated models using `regularized evolution algorithm `__ . (``nni.retiarii.strategy.RegularizedEvolution``)
+ * - TPE Strategy
+ - Sampling new model(s) from user defined model space using `TPE algorithm `__ . (``nni.retiarii.strategy.TPEStrategy``)
+ * - RL Strategy
+ - It uses `PPO algorithm `__ to sample new model(s) from user defined model space. (``nni.retiarii.strategy.PolicyBasedRL``)
-Please refer to `here `__ for the usage of classic NAS algorithms.
+Please refer to `here <./multi_trial_nas.rst>`__ for detailed usage of multi-trial NAS.
-Supported One-shot NAS Algorithms
----------------------------------
+One-shot NAS
+------------
-NNI currently supports the one-shot NAS algorithms listed below and is adding more. Users can reproduce an algorithm or use it on their own dataset. We also encourage users to implement other algorithms with `NNI API <#use-nni-api>`__\ , to benefit more people.
+One-shot NAS means building model space into a super-model, training the super-model with weight sharing, and then sampling models from the super-model to find the best one. `DARTS `__ is a typical one-shot NAS.
+Below is the supported one-shot NAS algorithms. More one-shot NAS will be supported soon.
.. list-table::
:header-rows: 1
:widths: auto
- * - Name
+ * - One-shot Algorithm Name
- Brief Introduction of Algorithm
* - `ENAS `__
- `Efficient Neural Architecture Search via Parameter Sharing `__. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. It uses parameter sharing between child models to achieve fast speed and excellent performance.
* - `DARTS `__
- `DARTS: Differentiable Architecture Search `__ introduces a novel algorithm for differentiable network architecture search on bilevel optimization.
- * - `P-DARTS `__
- - `Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation `__ is based on DARTS. It introduces an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure.
* - `SPOS `__
- `Single Path One-Shot Neural Architecture Search with Uniform Sampling `__ constructs a simplified supernet trained with a uniform path sampling method and applies an evolutionary algorithm to efficiently search for the best-performing architectures.
- * - `CDARTS `__
- - `Cyclic Differentiable Architecture Search `__ builds a cyclic feedback mechanism between the search and evaluation networks. It introduces a cyclic differentiable architecture search framework which integrates the two networks into a unified architecture.
* - `ProxylessNAS `__
- `ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware `__. It removes proxy, directly learns the architectures for large-scale target tasks and target hardware platforms.
- * - `FBNet `__
- - `FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search `__. It is a block-wise differentiable neural network architecture search method with the hardware-aware constraint.
- * - `TextNAS `__
- - `TextNAS: A Neural Architecture Search Space tailored for Text Representation `__. It is a neural architecture search algorithm tailored for text representation.
- * - `Cream `__
- - `Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search `__. It is a new NAS algorithm distilling prioritized paths in search space, without using evolutionary algorithms. Achieving competitive performance on ImageNet, especially for small models (e.g. <200 M FLOPs).
-
-
-One-shot algorithms run **standalone without nnictl**. NNI supports both PyTorch and Tensorflow 2.X.
-
-Here are some common dependencies to run the examples. PyTorch needs to be above 1.2 to use ``BoolTensor``.
-
-
-* tensorboard
-* PyTorch 1.2+
-* git
-
-Please refer to `here `__ for the usage of one-shot NAS algorithms.
-
-One-shot NAS can be visualized with our visualization tool. Learn more details `here <./Visualization.rst>`__.
-Search Space Zoo
-----------------
-
-NNI provides some predefined search space which can be easily reused. By stacking the extracted cells, user can quickly reproduce those NAS models.
-
-Search Space Zoo contains the following NAS cells:
-
-
-* `DartsCell <./SearchSpaceZoo.rst#DartsCell>`__
-* `ENAS micro <./SearchSpaceZoo.rst#ENASMicroLayer>`__
-* `ENAS macro <./SearchSpaceZoo.rst#ENASMacroLayer>`__
-* `NAS Bench 201 <./SearchSpaceZoo.rst#nas-bench-201>`__
-
-Using NNI API to Write Your Search Space
-----------------------------------------
-
-The programming interface of designing and searching a model is often demanded in two scenarios.
-
-
-#. When designing a neural network, there may be multiple operation choices on a layer, sub-model, or connection, and it's undetermined which one or combination performs best. So, it needs an easy way to express the candidate layers or sub-models.
-#. When applying NAS on a neural network, it needs a unified way to express the search space of architectures, so that it doesn't need to update trial code for different search algorithms.
-
-For using NNI NAS, we suggest users to first go through `the tutorial of NAS API for building search space <./WriteSearchSpace.rst>`__.
-
-NAS Visualization
------------------
-
-To help users track the process and status of how the model is searched under specified search space, we developed a visualization tool. It visualizes search space as a super-net and shows importance of subnets and layers/operations, as well as how the importance changes along with the search process. Please refer to `the document of NAS visualization <./Visualization.rst>`__ for how to use it.
+Please refer to `here `__ for detailed usage of one-shot NAS algorithms.
Reference and Feedback
----------------------
-
-* To `report a bug `__ for this feature in GitHub;
-* To `file a feature or improvement request `__ for this feature in GitHub.
+* `Quick Start <./QuickStart.rst>`__ ;
+* `Construct Your Model Space <./construct_space.rst>`__ ;
+* `Retiarii: A Deep Learning Exploratory-Training Framework `__ ;
+* To `report a bug `__ for this feature in GitHub ;
+* To `file a feature or improvement request `__ for this feature in GitHub .
diff --git a/docs/en_US/NAS/PDARTS.rst b/docs/en_US/NAS/PDARTS.rst
deleted file mode 100644
index 4884ce33b7..0000000000
--- a/docs/en_US/NAS/PDARTS.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-P-DARTS
-=======
-
-Examples
---------
-
-:githublink:`Example code `
-
-.. code-block:: bash
-
- # In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
- git clone https://github.com/Microsoft/nni.git
-
- # search the best architecture
- cd examples/nas/legacy/pdarts
- python3 search.py
-
- # train the best architecture, it's the same progress as darts.
- cd ../darts
- python3 retrain.py --arc-checkpoint ../pdarts/checkpoints/epoch_2.json
diff --git a/docs/en_US/NAS/Proxylessnas.rst b/docs/en_US/NAS/Proxylessnas.rst
index 2a1c8980d1..c6771ec302 100644
--- a/docs/en_US/NAS/Proxylessnas.rst
+++ b/docs/en_US/NAS/Proxylessnas.rst
@@ -9,7 +9,7 @@ The paper `ProxylessNAS: Direct Neural Architecture Search on Target Task and Ha
Usage
-----
-To use ProxylessNAS training/searching approach, users need to specify search space in their model using `NNI NAS interface `__\ , e.g., ``LayerChoice``\ , ``InputChoice``. After defining and instantiating the model, the following work can be leaved to ProxylessNasTrainer by instantiating the trainer and passing the model to it.
+To use ProxylessNAS training/searching approach, users need to specify search space in their model using `NNI NAS interface <./MutationPrimitives.rst>`__\ , e.g., ``LayerChoice``\ , ``InputChoice``. After defining and instantiating the model, the following work can be leaved to ProxylessNasTrainer by instantiating the trainer and passing the model to it.
.. code-block:: python
diff --git a/docs/en_US/NAS/retiarii/Tutorial.rst b/docs/en_US/NAS/QuickStart.rst
similarity index 64%
rename from docs/en_US/NAS/retiarii/Tutorial.rst
rename to docs/en_US/NAS/QuickStart.rst
index 3c1af8e838..2aa8492036 100644
--- a/docs/en_US/NAS/retiarii/Tutorial.rst
+++ b/docs/en_US/NAS/QuickStart.rst
@@ -1,24 +1,17 @@
-Neural Architecture Search with Retiarii (Alpha)
-================================================
+Quick Start of Retiarii on NNI
+==============================
-*This is a pre-release, its interfaces may subject to minor changes. The roadmap of this feature is: experimental in V2.0 -> alpha version in V2.1 -> beta version in V2.2 -> official release in V2.3. Feel free to give us your comments and suggestions.*
-
-`Retiarii `__ is a new framework to support neural architecture search and hyper-parameter tuning. It allows users to express various search space with high flexibility, to reuse many SOTA search algorithms, and to leverage system level optimizations to speed up the search process. This framework provides the following new user experiences.
-
-* Search space can be expressed directly in user model code. A tuning space can be expressed during defining a model.
-* Neural architecture candidates and hyper-parameter candidates are more friendly supported in an experiment.
-* The experiment can be launched directly from python code.
-
-.. Note:: `Our previous NAS framework <../Overview.rst>`__ is still supported for now, but will be migrated to Retiarii framework in V2.3.
.. contents::
-There are mainly two crucial components for a neural architecture search task, namely,
+In this quick start tutorial, we use multi-trial NAS as an example to show how to construct and explore a model space. There are mainly three crucial components for a neural architecture search task, namely,
* Model search space that defines the set of models to explore.
* A proper strategy as the method to explore this search space.
* A model evaluator that reports the performance of a given model.
+One-shot NAS tutorial can be found `here <./OneshotTrainer.rst>`__.
+
.. note:: Currently, PyTorch is the only supported framework by Retiarii, and we have only tested with **PyTorch 1.6 and 1.7**. This documentation assumes PyTorch context but it should also apply to other frameworks, that is in our future plan.
Define your Model Space
@@ -37,8 +30,8 @@ Below is a very simple example of defining a base model, it is almost the same a
import torch.nn.functional as F
import nni.retiarii.nn.pytorch as nn
+ from nni.retiarii import model_wrapper
- @basic_unit
class BasicBlock(nn.Module):
def __init__(self, const):
self.const = const
@@ -53,6 +46,7 @@ Below is a very simple example of defining a base model, it is almost the same a
def forward(self, x):
return self.pool(self.conv(x))
+ @model_wrapper # this decorator should be put on the out most PyTorch module
class Model(nn.Module):
def __init__(self):
super().__init__()
@@ -61,10 +55,6 @@ Below is a very simple example of defining a base model, it is almost the same a
def forward(self, x):
return F.relu(self.convpool(self.mymodule(x)))
-The above example also shows how to use ``@basic_unit``. ``@basic_unit`` is decorated on a user-defined module to tell Retiarii that there will be no mutation within this module, Retiarii can treat it as a basic unit (i.e., as a blackbox). It is useful when (1) users want to mutate the initialization parameters of this module, or (2) Retiarii fails to parse this module due to complex control flow (e.g., ``for``, ``while``). More detailed description of ``@basic_unit`` can be found `here <./Advanced.rst>`__.
-
-Users can refer to :githublink:`Darts base model ` and :githublink:`Mnasnet base model ` for more complicated examples.
-
Define Model Mutations
^^^^^^^^^^^^^^^^^^^^^^
@@ -72,7 +62,7 @@ A base model is only one concrete model not a model space. We provide APIs and p
We provide some APIs as shown below for users to easily express possible mutations after defining a base model. The APIs can be used just like PyTorch module. This approach is also called inline mutations.
-* ``nn.LayerChoice``. It allows users to put several candidate operations (e.g., PyTorch modules), one of them is chosen in each explored model. Note that if the candidate is a user-defined module, it should be decorated as a `basic unit <./Advanced.rst>`__ with ``@basic_unit``. In the following example, ``ops.PoolBN`` and ``ops.SepConv`` should be decorated.
+* ``nn.LayerChoice``. It allows users to put several candidate operations (e.g., PyTorch modules), one of them is chosen in each explored model.
.. code-block:: python
@@ -114,27 +104,21 @@ All the APIs have an optional argument called ``label``, mutations with the same
nn.Linear(nn.ValueChoice([32, 64, 128], label='hidden_dim'), 3)
)
-Detailed API description and usage can be found `here <./ApiReference.rst>`__\. Example of using these APIs can be found in :githublink:`Darts base model `. We are actively enriching the set of inline mutations, to make it easier to express a new search space.
-
-If the inline mutation APIs are not enough for your scenario, you can refer to `defining model space using mutators <./Advanced.rst#express-mutations-with-mutators>`__ to write more complex model spaces.
+Detailed API description and usage can be found `here <./ApiReference.rst>`__\. Example of using these APIs can be found in :githublink:`Darts base model `. We are actively enriching the set of inline mutation APIs, to make it easier to express a new search space. Please refer to `here <./construct_space.rst>`__ for more tutorials about how to express complex model spaces.
Explore the Defined Model Space
-------------------------------
There are basically two exploration approaches: (1) search by evaluating each sampled model independently and (2) one-shot weight-sharing based search. We demonstrate the first approach below in this tutorial. Users can refer to `here <./OneshotTrainer.rst>`__ for the second approach.
-Users can choose a proper search strategy to explore the model space, and use a chosen or user-defined model evaluator to evaluate the performance of each sampled model.
+Users can choose a proper exploration strategy to explore the model space, and use a chosen or user-defined model evaluator to evaluate the performance of each sampled model.
-Choose a search strategy
+Pick a search strategy
^^^^^^^^^^^^^^^^^^^^^^^^
-Retiarii currently supports the following search strategies:
-
-* Grid search: enumerate all the possible models defined in the space.
-* Random: randomly pick the models from search space.
-* Regularized evolution: a genetic algorithm that explores the space based on inheritance and mutation.
+Retiarii supports many `exploration strategies <./ExplorationStrategies.rst>`__.
-Choose (i.e., instantiate) a search strategy is very easy. An example is as follows,
+Simply choosing (i.e., instantiate) an exploration strategy as below.
.. code-block:: python
@@ -142,14 +126,10 @@ Choose (i.e., instantiate) a search strategy is very easy. An example is as foll
search_strategy = strategy.Random(dedup=True) # dedup=False if deduplication is not wanted
-Detailed descriptions and usages of available strategies can be found `here <./ApiReference.rst>`__ .
-
-Choose or write a model evaluator
+Pick or write a model evaluator
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-In the NAS process, the search strategy repeatedly generates new models. A model evaluator is for training and validating each generated model. The obtained performance of a generated model is collected and sent to search strategy for generating better models.
-
-The model evaluator should correctly identify the use case of the model and the optimization goal. For example, on a classification task, an dataset is needed, the loss function could be cross entropy and the optimized metric could be accuracy. On a regression task, the optimized metric could be mean-squared-error.
+In the NAS process, the exploration strategy repeatedly generates new models. A model evaluator is for training and validating each generated model. The obtained performance of a generated model is collected and sent to the exploration strategy for generating better models.
In the context of PyTorch, Retiarii has provided two built-in model evaluators, designed for simple use cases: classification and regression. These two evaluators are built upon the awesome library PyTorch-Lightning.
@@ -172,7 +152,7 @@ As the model evaluator is running in another process (possibly in some remote ma
Detailed descriptions and usages of model evaluators can be found `here <./ApiReference.rst>`__ .
-If the built-in model evaluators do not meet your requirement, or you already wrote the training code and just want to use it, you can follow `the guide to write a new evaluator <./WriteTrainer.rst>`__ .
+If the built-in model evaluators do not meet your requirement, or you already wrote the training code and just want to use it, you can follow `the guide to write a new model evaluator <./WriteTrainer.rst>`__ .
.. note:: In case you want to run the model evaluator locally for debug purpose, you can directly run the evaluator via ``evaluator._execute(Net)`` (note that it has to be ``Net``, not ``Net()``). However, this API is currently internal and subject to change.
@@ -195,11 +175,19 @@ After all the above are prepared, it is time to start an experiment to do the mo
exp_config.training_service.use_active_gpu = False
exp.run(exp_config, 8081)
-The complete code of a simple MNIST example can be found :githublink:`here `.
-
-**Local Debug Mode**: When running an experiment, it is easy to get some trivial errors in trial code, such as shape mismatch, undefined variable. To quickly fix these kinds of errors, we provide local debug mode which locally applies mutators once and runs only that generated model. To use local debug mode, users can simply invoke the API `debug_mutated_model(base_model, trainer, applied_mutators)`.
+The complete code of a simple MNIST example can be found :githublink:`here `. Users can also run Retiarii Experiment on `different training services <../training_services.rst>`__ besides ``local`` training service.
Visualize the Experiment
------------------------
-Users can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment. For example, open ``localhost::8081`` in your browser, 8081 is the port that you set in ``exp.run``. Please refer to `here <../../Tutorial/WebUI.rst>`__ for details.
\ No newline at end of file
+Users can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment. For example, open ``localhost::8081`` in your browser, 8081 is the port that you set in ``exp.run``. Please refer to `here <../../Tutorial/WebUI.rst>`__ for details.
+
+Export Top Models
+-----------------
+
+Users can export top models after the exploration is done using ``export_top_models``.
+
+.. code-block:: python
+
+ for model_code in exp.export_top_models(formatter='dict'):
+ print(model_code)
diff --git a/docs/en_US/NAS/SPOS.rst b/docs/en_US/NAS/SPOS.rst
index 7dd040ff91..9c08eba469 100644
--- a/docs/en_US/NAS/SPOS.rst
+++ b/docs/en_US/NAS/SPOS.rst
@@ -98,14 +98,8 @@ Reference
PyTorch
^^^^^^^
-.. autoclass:: nni.algorithms.nas.pytorch.spos.SPOSEvolution
- :members:
-
-.. autoclass:: nni.algorithms.nas.pytorch.spos.SPOSSupernetTrainer
- :members:
-
-.. autoclass:: nni.algorithms.nas.pytorch.spos.SPOSSupernetTrainingMutator
- :members:
+.. autoclass:: nni.retiarii.oneshot.pytorch.SinglePathTrainer
+ :noindex:
Known Limitations
-----------------
diff --git a/docs/en_US/NAS/SearchSpaceZoo.rst b/docs/en_US/NAS/SearchSpaceZoo.rst
deleted file mode 100644
index 6117e71d81..0000000000
--- a/docs/en_US/NAS/SearchSpaceZoo.rst
+++ /dev/null
@@ -1,281 +0,0 @@
-.. role:: raw-html(raw)
- :format: html
-
-
-Search Space Zoo
-================
-
-DartsCell
----------
-
-DartsCell is extracted from :githublink:`CNN model `. A DartsCell is a directed acyclic graph containing an ordered sequence of N nodes and each node stands for a latent representation (e.g. feature map in a convolutional network). Directed edges from Node 1 to Node 2 are associated with some operations that transform Node 1 and the result is stored on Node 2. The `Candidate operators <#predefined-operations-darts>`__ between nodes is predefined and unchangeable. One edge represents an operation that chosen from the predefined ones to be applied to the starting node of the edge. One cell contains two input nodes, a single output node, and other ``n_node`` nodes. The input nodes are defined as the cell outputs in the previous two layers. The output of the cell is obtained by applying a reduction operation (e.g. concatenation) to all the intermediate nodes. To make the search space continuous, the categorical choice of a particular operation is relaxed to a softmax over all possible operations. By adjusting the weight of softmax on every node, the operation with the highest probability is chosen to be part of the final structure. A CNN model can be formed by stacking several cells together, which builds a search space. Note that, in DARTS paper all cells in the model share the same structure.
-
-One structure in the Darts search space is shown below. Note that, NNI merges the last one of the four intermediate nodes and the output node.
-
-
-.. image:: ../../img/NAS_Darts_cell.svg
- :target: ../../img/NAS_Darts_cell.svg
- :alt:
-
-
-The predefined operators are shown `here <#predefined-operations-darts>`__.
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.DartsCell
- :members:
-
-Example code
-^^^^^^^^^^^^
-
-:githublink:`example code `
-
-.. code-block:: bash
-
- git clone https://github.com/Microsoft/nni.git
- cd nni/examples/nas/search_space_zoo
- # search the best structure
- python3 darts_example.py
-
-:raw-html:``
-
-Candidate operators
-^^^^^^^^^^^^^^^^^^^
-
-All supported operators for Darts are listed below.
-
-
-*
- MaxPool / AvgPool
-
-
- * MaxPool: Call ``torch.nn.MaxPool2d``. This operation applies a 2D max pooling over all input channels. Its parameters ``kernel_size=3`` and ``padding=1`` are fixed. The pooling result will pass through a BatchNorm2d then return as the result.
- *
- AvgPool: Call ``torch.nn.AvgPool2d``. This operation applies a 2D average pooling over all input channels. Its parameters ``kernel_size=3`` and ``padding=1`` are fixed. The pooling result will pass through a BatchNorm2d then return as the result.
-
- MaxPool / AvgPool with ``kernel_size=3`` and ``padding=1`` followed by BatchNorm2d
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.darts_ops.PoolBN
-
-*
- SkipConnect
-
- There is no operation between two nodes. Call ``torch.nn.Identity`` to forward what it gets to the output.
-
-*
- Zero operation
-
- There is no connection between two nodes.
-
-*
- DilConv3x3 / DilConv5x5
-
- :raw-html:``\ DilConv3x3: (Dilated) depthwise separable Conv. It's a 3x3 depthwise convolution with ``C_in`` groups, followed by a 1x1 pointwise convolution. It reduces the amount of parameters. Input is first passed through relu, then DilConv and finally batchNorm2d. **Note that the operation is not Dilated Convolution, but we follow the convention in NAS papers to name it DilConv.** 3x3 DilConv has parameters ``kernel_size=3``\ , ``padding=1`` and 5x5 DilConv has parameters ``kernel_size=5``\ , ``padding=4``.
-
- .. autoclass:: nni.nas.pytorch.search_space_zoo.darts_ops.DilConv
-
-*
- SepConv3x3 / SepConv5x5
-
- Composed of two DilConvs with fixed ``kernel_size=3``\ , ``padding=1`` or ``kernel_size=5``\ , ``padding=2`` sequentially.
-
- .. autoclass:: nni.nas.pytorch.search_space_zoo.darts_ops.SepConv
-
-ENASMicroLayer
---------------
-
-This layer is extracted from the model designed :githublink:`here `. A model contains several blocks that share the same architecture. A block is made up of some normal layers and reduction layers, ``ENASMicroLayer`` is a unified implementation of the two types of layers. The only difference between the two layers is that reduction layers apply all operations with ``stride=2``.
-
-ENAS Micro employs a DAG with N nodes in one cell, where the nodes represent local computations, and the edges represent the flow of information between the N nodes. One cell contains two input nodes and a single output node. The following nodes choose two previous nodes as input and apply two operations from `predefined ones <#predefined-operations-enas>`__ then add them as the output of this node. For example, Node 4 chooses Node 1 and Node 3 as inputs then applies ``MaxPool`` and ``AvgPool`` on the inputs respectively, then adds and sums them as the output of Node 4. Nodes that are not served as input for any other node are viewed as the output of the layer. If there are multiple output nodes, the model will calculate the average of these nodes as the layer output.
-
-The ENAS micro search space is shown below.
-
-
-.. image:: ../../img/NAS_ENAS_micro.svg
- :target: ../../img/NAS_ENAS_micro.svg
- :alt:
-
-
-The predefined operators can be seen `here <#predefined-operations-enas>`__.
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.ENASMicroLayer
- :members:
-
-The Reduction Layer is made up of two Conv operations followed by BatchNorm, each of them will output ``C_out//2`` channels and concat them in channels as the output. The Convolution has ``kernel_size=1`` and ``stride=2``\ , and they perform alternate sampling on the input to reduce the resolution without loss of information. This layer is wrapped in ``ENASMicroLayer``.
-
-Example code
-^^^^^^^^^^^^
-
-:githublink:`example code `
-
-.. code-block:: bash
-
- git clone https://github.com/Microsoft/nni.git
- cd nni/examples/nas/search_space_zoo
- # search the best cell structure
- python3 enas_micro_example.py
-
-:raw-html:``
-
-Candidate operators
-^^^^^^^^^^^^^^^^^^^
-
-All supported operators for ENAS micro search are listed below.
-
-
-*
- MaxPool / AvgPool
-
-
- * MaxPool: Call ``torch.nn.MaxPool2d``. This operation applies a 2D max pooling over all input channels followed by BatchNorm2d. Its parameters are fixed to ``kernel_size=3``\ , ``stride=1`` and ``padding=1``.
- * AvgPool: Call ``torch.nn.AvgPool2d``. This operation applies a 2D average pooling over all input channels followed by BatchNorm2d. Its parameters are fixed to ``kernel_size=3``\ , ``stride=1`` and ``padding=1``.
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.enas_ops.Pool
-
-*
- SepConv
-
-
- * SepConvBN3x3: ReLU followed by a `DilConv <#DilConv>`__ and BatchNorm. Convolution parameters are ``kernel_size=3``\ , ``stride=1`` and ``padding=1``.
- *
- SepConvBN5x5: Do the same operation as the previous one but it has different kernel sizes and paddings, which is set to 5 and 2 respectively.
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.enas_ops.SepConvBN
-
-*
- SkipConnect
-
- Call ``torch.nn.Identity`` to connect directly to the next cell.
-
-ENASMacroLayer
---------------
-
-In Macro search, the controller makes two decisions for each layer: i) the `operation <#macro-operations>`__ to perform on the result of the previous layer, ii) which the previous layer to connect to for SkipConnects. ENAS uses a controller to design the whole model architecture instead of one of its components. The output of operations is going to concat with the tensor of the chosen layer for SkipConnect. NNI provides `predefined operators <#macro-operations>`__ for macro search, which are listed in `Candidate operators <#macro-operations>`__.
-
-Part of one structure in the ENAS macro search space is shown below.
-
-
-.. image:: ../../img/NAS_ENAS_macro.svg
- :target: ../../img/NAS_ENAS_macro.svg
- :alt:
-
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.ENASMacroLayer
- :members:
-
-To describe the whole search space, NNI provides a model, which is built by stacking the layers.
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.ENASMacroGeneralModel
- :members:
-
-Example code
-^^^^^^^^^^^^
-
-:githublink:`example code `
-
-.. code-block:: bash
-
- git clone https://github.com/Microsoft/nni.git
- cd nni/examples/nas/search_space_zoo
- # search the best cell structure
- python3 enas_macro_example.py
-
-:raw-html:``
-
-Candidate operators
-^^^^^^^^^^^^^^^^^^^
-
-All supported operators for ENAS macro search are listed below.
-
-
-*
- ConvBranch
-
- All input first passes into a StdConv, which is made up of a 1x1Conv followed by BatchNorm2d and ReLU. Then the intermediate result goes through one of the operations listed below. The final result is calculated through a BatchNorm2d and ReLU as post-procedure.
-
-
- * Separable Conv3x3: If ``separable=True``\ , the cell will use `SepConv <#DilConv>`__ instead of normal Conv operation. SepConv's ``kernel_size=3``\ , ``stride=1`` and ``padding=1``.
- * Separable Conv5x5: SepConv's ``kernel_size=5``\ , ``stride=1`` and ``padding=2``.
- * Normal Conv3x3: If ``separable=False``\ , the cell will use a normal Conv operations with ``kernel_size=3``\ , ``stride=1`` and ``padding=1``.
- *
- Normal Conv5x5: Conv's ``kernel_size=5``\ , ``stride=1`` and ``padding=2``.
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.enas_ops.ConvBranch
-
-*
- PoolBranch
-
- All input first passes into a StdConv, which is made up of a 1x1Conv followed by BatchNorm2d and ReLU. Then the intermediate goes through pooling operation followed by BatchNorm.
-
-
- * AvgPool: Call ``torch.nn.AvgPool2d``. This operation applies a 2D average pooling over all input channels. Its parameters are fixed to ``kernel_size=3``\ , ``stride=1`` and ``padding=1``.
- *
- MaxPool: Call ``torch.nn.MaxPool2d``. This operation applies a 2D max pooling over all input channels. Its parameters are fixed to ``kernel_size=3``\ , ``stride=1`` and ``padding=1``.
-
-.. autoclass:: nni.nas.pytorch.search_space_zoo.enas_ops.PoolBranch
-
-NAS-Bench-201
--------------
-
-NAS Bench 201 defines a unified search space, which is algorithm agnostic. The predefined skeleton consists of a stack of cells that share the same architecture. Every cell contains four nodes and a DAG is formed by connecting edges among them, where the node represents the sum of feature maps and the edge stands for an operation transforming a tensor from the source node to the target node. The predefined candidate operators can be found in `Candidate operators <#nas-bench-201-reference>`__.
-
-The search space of NAS Bench 201 is shown below.
-
-
-.. image:: ../../img/NAS_Bench_201.svg
- :target: ../../img/NAS_Bench_201.svg
- :alt:
-
-
-.. autoclass:: nni.nas.pytorch.nasbench201.NASBench201Cell
- :members:
-
-Example code
-^^^^^^^^^^^^
-
-:githublink:`example code `
-
-.. code-block:: bash
-
- # for structure searching
- git clone https://github.com/Microsoft/nni.git
- cd nni/examples/nas/search_space_zoo
- python3 nas_bench_201.py
-
-:raw-html:``
-
-Candidate operators
-^^^^^^^^^^^^^^^^^^^
-
-All supported operators for NAS Bench 201 are listed below.
-
-
-*
- AvgPool
-
- If the number of input channels is not equal to the number of output channels, the input will first pass through a ``ReLUConvBN`` layer with ``kernel_size=1``\ , ``stride=1``\ , ``padding=0``\ , and ``dilation=0``.
- Call ``torch.nn.AvgPool2d``. This operation applies a 2D average pooling over all input channels followed by BatchNorm2d. Its parameters are fixed to ``kernel_size=3`` and ``padding=1``.
-
-.. autoclass:: nni.nas.pytorch.nasbench201.nasbench201_ops.Pooling
- :members:
-
-*
- Conv
-
-
- * Conv1x1: Consist of a sequence of ReLU, ``nn.Cinv2d`` and BatchNorm. The Conv operation's parameter is fixed to ``kernal_size=1``\ , ``padding=0``\ , and ``dilation=1``.
- * Conv3x3: Consist of a sequence of ReLU, ``nn.Cinv2d`` and BatchNorm. The Conv operation's parameter is fixed to ``kernal_size=3``\ , ``padding=1``\ , and ``dilation=1``.
-
-.. autoclass:: nni.nas.pytorch.nasbench201.nasbench201_ops.ReLUConvBN
- :members:
-
-*
- SkipConnect
-
- Call ``torch.nn.Identity`` to connect directly to the next cell.
-
-*
- Zeroize
-
- Generate zero tensors indicating there is no connection from the source node to the target node.
-
-.. autoclass:: nni.nas.pytorch.nasbench201.nasbench201_ops.Zero
- :members:
diff --git a/docs/en_US/NAS/TextNAS.rst b/docs/en_US/NAS/TextNAS.rst
deleted file mode 100644
index 341ddbbf62..0000000000
--- a/docs/en_US/NAS/TextNAS.rst
+++ /dev/null
@@ -1,94 +0,0 @@
-TextNAS
-=======
-
-Introduction
-------------
-
-This is the implementation of the TextNAS algorithm proposed in the paper `TextNAS: A Neural Architecture Search Space tailored for Text Representation `__. TextNAS is a neural architecture search algorithm tailored for text representation, more specifically, TextNAS is based on a novel search space consists of operators widely adopted to solve various NLP tasks, and TextNAS also supports multi-path ensemble within a single network to balance the width and depth of the architecture.
-
-The search space of TextNAS contains:
-
-.. code-block:: bash
-
- * 1-D convolutional operator with filter size 1, 3, 5, 7
- * recurrent operator (bi-directional GRU)
- * self-attention operator
- * pooling operator (max/average)
-
-
-Following the ENAS algorithm, TextNAS also utilizes parameter sharing to accelerate the search speed and adopts a reinforcement-learning controller for the architecture sampling and generation. Please refer to the paper for more details of TextNAS.
-
-Preparation
------------
-
-Prepare the word vectors and SST dataset, and organize them in data directory as shown below:
-
-.. code-block:: bash
-
- textnas
- ├── data
- │ ├── sst
- │ │ └── trees
- │ │ ├── dev.txt
- │ │ ├── test.txt
- │ │ └── train.txt
- │ └── glove.840B.300d.txt
- ├── dataloader.py
- ├── model.py
- ├── ops.py
- ├── README.md
- ├── search.py
- └── utils.py
-
-The following link might be helpful for finding and downloading the corresponding dataset:
-
-
-* `GloVe: Global Vectors for Word Representation `__
-
- * `glove.840B.300d.txt `__
-
-* `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank `__
-
- * `trainDevTestTrees_PTB.zip `__
-
-Examples
---------
-
-Search Space
-^^^^^^^^^^^^
-
-:githublink:`Example code `
-
-.. code-block:: bash
-
- # In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
- git clone https://github.com/Microsoft/nni.git
-
- # search the best architecture
- cd examples/nas/legacy/textnas
-
- # view more options for search
- python3 search.py -h
-
-After each search epoch, 10 sampled architectures will be tested directly. Their performances are expected to be 40% - 42% after 10 epochs.
-
-By default, 20 sampled architectures will be exported into ``checkpoints`` directory for next step.
-
-retrain
-^^^^^^^
-
-.. code-block:: bash
-
- # In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
- git clone https://github.com/Microsoft/nni.git
-
- # search the best architecture
- cd examples/nas/legacy/textnas
-
- # default to retrain on sst-2
- sh run_retrain.sh
-
-Reference
----------
-
-TextNAS directly uses EnasTrainer, please refer to `ENAS <./ENAS.rst>`__ for the trainer APIs.
diff --git a/docs/en_US/NAS/Visualization.rst b/docs/en_US/NAS/Visualization.rst
deleted file mode 100644
index 9588b2cc41..0000000000
--- a/docs/en_US/NAS/Visualization.rst
+++ /dev/null
@@ -1,86 +0,0 @@
-NAS Visualization (Experimental)
-================================
-
-Built-in Trainers Support
--------------------------
-
-Currently, only ENAS and DARTS support visualization. Examples of `ENAS <./ENAS.rst>`__ and `DARTS <./DARTS.rst>`__ has demonstrated how to enable visualization in your code, namely, adding this before ``trainer.train()``\ :
-
-.. code-block:: python
-
- trainer.enable_visualization()
-
-This will create a directory ``logs/`` in your working folder, in which you will find two files ``graph.json`` and ``log``.
-
-You don't have to wait until your program finishes to launch NAS UI, but it's important that these two files have been already created. Launch NAS UI with
-
-.. code-block:: bash
-
- nnictl webui nas --logdir logs/ --port
-
-Visualize a Customized Trainer
-------------------------------
-
-If you are interested in how to customize a trainer, please read this `doc <./Advanced.rst#extend-the-ability-of-one-shot-trainers>`__.
-
-You should do two modifications to an existing trainer to enable visualization:
-
-
-#. Export your graph before training, with
-
-.. code-block:: python
-
- vis_graph = self.mutator.graph(inputs)
- # `inputs` is a dummy input to your model. For example, torch.randn((1, 3, 32, 32)).cuda()
- # If your model has multiple inputs, it should be a tuple.
- with open("/path/to/your/logdir/graph.json", "w") as f:
- json.dump(vis_graph, f)
-
-
-#. Logging the choices you've made. You can do it once per epoch, once per mini-batch or whatever frequency you'd like.
-
-.. code-block:: python
-
- def __init__(self):
- # ...
- self.status_writer = open("/path/to/your/logdir/log", "w") # create a writer
-
- def train(self):
- # ...
- print(json.dumps(self.mutator.status()), file=self.status_writer, flush=True) # dump a record of status
-
-If you are implementing your customized trainer inheriting ``Trainer``. We have provided ``enable_visualization()`` and ``_write_graph_status()`` for easy-to-use purposes. All you need to do is calling ``trainer.enable_visualization()`` before start, and ``trainer._write_graph_status()`` each time you want to do the logging. But remember both of these APIs are experimental and subject to change in future.
-
-Last but not least, invode NAS UI with
-
-.. code-block:: bash
-
- nnictl webui nas --logdir /path/to/your/logdir
-
-NAS UI Preview
---------------
-
-
-.. image:: ../../img/nasui-1.png
- :target: ../../img/nasui-1.png
- :alt:
-
-
-
-.. image:: ../../img/nasui-2.png
- :target: ../../img/nasui-2.png
- :alt:
-
-
-Limitations
------------
-
-
-* NAS visualization only works with PyTorch >=1.4. We've tested it on PyTorch 1.3.1 and it doesn't work.
-* We rely on PyTorch support for tensorboard for graph export, which relies on ``torch.jit``. It will not work if your model doesn't support ``jit``.
-* There are known performance issues when loading a moderate-size graph with many op choices (like DARTS search space).
-
-Feedback
---------
-
-NAS UI is currently experimental. We welcome your feedback. `Here `__ we have listed all the to-do items of NAS UI in the future. Feel free to comment (or `submit a new issue `__\ ) if you have other suggestions.
diff --git a/docs/en_US/NAS/retiarii/OneshotTrainer.rst b/docs/en_US/NAS/WriteOneshot.rst
similarity index 61%
rename from docs/en_US/NAS/retiarii/OneshotTrainer.rst
rename to docs/en_US/NAS/WriteOneshot.rst
index ab6ef33d82..c190099f3e 100644
--- a/docs/en_US/NAS/retiarii/OneshotTrainer.rst
+++ b/docs/en_US/NAS/WriteOneshot.rst
@@ -1,45 +1,5 @@
-One-shot Experiments on Retiarii
-================================
-
-Before reading this tutorial, we highly recommend you to first go through the tutorial of how to `define a model space <./Tutorial.rst#define-your-model-space>`__.
-
-Model Search with One-shot Trainer
-----------------------------------
-
-With a defined model space, users can explore the space in two ways. One is using strategy and single-arch evaluator as demonstrated `here <./Tutorial.rst#explore-the-defined-model-space>`__. The other is using one-shot trainer, which consumes much less computational resource compared to the first one. In this tutorial we focus on this one-shot approach. The principle of one-shot approach is combining all the models in a model space into one big model (usually called super-model or super-graph). It takes charge of both search, training and testing, by training and evaluating this big model.
-
-We list the supported one-shot trainers here:
-
-* DARTS trainer
-* ENAS trainer
-* ProxylessNAS trainer
-* Single-path (random) trainer
-
-See `API reference <./ApiReference.rst>`__ for detailed usages. Here, we show an example to use DARTS trainer manually.
-
-.. code-block:: python
-
- from nni.retiarii.oneshot.pytorch import DartsTrainer
- trainer = DartsTrainer(
- model=model,
- loss=criterion,
- metrics=lambda output, target: accuracy(output, target, topk=(1,)),
- optimizer=optim,
- num_epochs=args.epochs,
- dataset=dataset_train,
- batch_size=args.batch_size,
- log_frequency=args.log_frequency,
- unrolled=args.unrolled
- )
- trainer.fit()
- final_architecture = trainer.export()
-
-**Format of the exported architecture.** TBD.
-
-One-shot experiment can be visualized with NAS UI, please refer to `here <../Visualization.rst>`__ for the usage guidance. Note that NAS visualization is under intensive development.
-
Customize a New One-shot Trainer
---------------------------------
+================================
One-shot trainers should inherit ``nni.retiarii.oneshot.BaseOneShotTrainer``, and need to implement ``fit()`` (used to conduct the fitting and searching process) and ``export()`` method (used to return the searched best architecture).
diff --git a/docs/en_US/NAS/WriteSearchSpace.rst b/docs/en_US/NAS/WriteSearchSpace.rst
deleted file mode 100644
index 6d47d6d803..0000000000
--- a/docs/en_US/NAS/WriteSearchSpace.rst
+++ /dev/null
@@ -1,67 +0,0 @@
-Write A Search Space
-====================
-
-Genrally, a search space describes candiate architectures from which users want to find the best one. Different search algorithms, no matter classic NAS or one-shot NAS, can be applied on the search space. NNI provides APIs to unified the expression of neural architecture search space.
-
-A search space can be built on a base model. This is also a common practice when a user wants to apply NAS on an existing model. Take `MNIST on PyTorch `__ as an example. Note that NNI provides the same APIs for expressing search space on PyTorch and TensorFlow.
-
-.. code-block:: python
-
- from nni.nas.pytorch import mutables
-
- class Net(nn.Module):
- def __init__(self):
- super(Net, self).__init__()
- self.conv1 = mutables.LayerChoice([
- nn.Conv2d(1, 32, 3, 1),
- nn.Conv2d(1, 32, 5, 3)
- ]) # try 3x3 kernel and 5x5 kernel
- self.conv2 = nn.Conv2d(32, 64, 3, 1)
- self.dropout1 = nn.Dropout2d(0.25)
- self.dropout2 = nn.Dropout2d(0.5)
- self.fc1 = nn.Linear(9216, 128)
- self.fc2 = nn.Linear(128, 10)
-
- def forward(self, x):
- x = self.conv1(x)
- x = F.relu(x)
- # ... same as original ...
- return output
-
-The example above adds an option of choosing conv5x5 at conv1. The modification is as simple as declaring a ``LayerChoice`` with the original conv3x3 and a new conv5x5 as its parameter. That's it! You don't have to modify the forward function in any way. You can imagine conv1 as any other module without NAS.
-
-So how about the possibilities of connections? This can be done using ``InputChoice``. To allow for a skip connection on the MNIST example, we add another layer called conv3. In the following example, a possible connection from conv2 is added to the output of conv3.
-
-.. code-block:: python
-
- from nni.nas.pytorch import mutables
-
- class Net(nn.Module):
- def __init__(self):
- # ... same ...
- self.conv2 = nn.Conv2d(32, 64, 3, 1)
- self.conv3 = nn.Conv2d(64, 64, 1, 1)
- # declaring that there is exactly one candidate to choose from
- # search strategy will choose one or None
- self.skipcon = mutables.InputChoice(n_candidates=1)
- # ... same ...
-
- def forward(self, x):
- x = self.conv1(x)
- x = F.relu(x)
- x = self.conv2(x)
- x0 = self.skipcon([x]) # choose one or none from [x]
- x = self.conv3(x)
- if x0 is not None: # skipconnection is open
- x += x0
- x = F.max_pool2d(x, 2)
- # ... same ...
- return output
-
-Input choice can be thought of as a callable module that receives a list of tensors and outputs the concatenation/sum/mean of some of them (sum by default), or ``None`` if none is selected. Like layer choices, input choices should be initialized in ``__init__`` and called in ``forward``. This is to allow search algorithms to identify these choices and do necessary preparations.
-
-``LayerChoice`` and ``InputChoice`` are both **mutables**. Mutable means "changeable". As opposed to traditional deep learning layers/modules which have fixed operation types once defined, models with mutable are essentially a series of possible models.
-
-Users can specify a **key** for each mutable. By default, NNI will assign one for you that is globally unique, but in case users want to share choices (for example, there are two ``LayerChoice``\ s with the same candidate operations and you want them to have the same choice, i.e., if first one chooses the i-th op, the second one also chooses the i-th op), they can give them the same key. The key marks the identity for this choice and will be used in the dumped checkpoint. So if you want to increase the readability of your exported architecture, manually assigning keys to each mutable would be a good idea. For advanced usage on mutables (e.g., ``LayerChoice`` and ``InputChoice``\ ), see `Mutables <./NasReference.rst>`__.
-
-With search space defined, the next step is searching for the best model from it. Please refer to `classic NAS algorithms <./ClassicNas.rst>`__ and `one-shot NAS algorithms <./NasGuide.rst>`__ for how to search from your defined search space.
diff --git a/docs/en_US/NAS/retiarii/WriteStrategy.rst b/docs/en_US/NAS/WriteStrategy.rst
similarity index 69%
rename from docs/en_US/NAS/retiarii/WriteStrategy.rst
rename to docs/en_US/NAS/WriteStrategy.rst
index 6f354b3b85..e324385f67 100644
--- a/docs/en_US/NAS/retiarii/WriteStrategy.rst
+++ b/docs/en_US/NAS/WriteStrategy.rst
@@ -1,7 +1,7 @@
-Customize A New Strategy
-========================
+Customize Exploration Strategy
+==============================
-To write a new strategy, you should inherit the base strategy class ``BaseStrategy``, then implement the member function ``run``. This member function takes ``base_model`` and ``applied_mutators`` as its input arguments. It can simply apply the user specified mutators in ``applied_mutators`` onto ``base_model`` to generate a new model. When a mutator is applied, it should be bound with a sampler (e.g., ``RandomSampler``). Every sampler implements the ``choice`` function which chooses value(s) from candidate values. The ``choice`` functions invoked in mutators are executed with the sampler.
+If users want to innovate a new exploration strategy, they can easily customize a new one following the interface provided by NNI. Specifically, users should inherit the base strategy class ``BaseStrategy``, then implement the member function ``run``. This member function takes ``base_model`` and ``applied_mutators`` as its input arguments. It can simply apply the user specified mutators in ``applied_mutators`` onto ``base_model`` to generate a new model. When a mutator is applied, it should be bound with a sampler (e.g., ``RandomSampler``). Every sampler implements the ``choice`` function which chooses value(s) from candidate values. The ``choice`` functions invoked in mutators are executed with the sampler.
Below is a very simple random strategy, which makes the choices completely random.
diff --git a/docs/en_US/NAS/retiarii/WriteTrainer.rst b/docs/en_US/NAS/WriteTrainer.rst
similarity index 100%
rename from docs/en_US/NAS/retiarii/WriteTrainer.rst
rename to docs/en_US/NAS/WriteTrainer.rst
diff --git a/docs/en_US/NAS/construct_space.rst b/docs/en_US/NAS/construct_space.rst
new file mode 100644
index 0000000000..b32489d4a7
--- /dev/null
+++ b/docs/en_US/NAS/construct_space.rst
@@ -0,0 +1,11 @@
+#####################
+Construct Model Space
+#####################
+
+NNI provides powerful APIs for users to easily express model space (or search space). First, users can use mutation primitives (e.g., ValueChoice, LayerChoice) to inline a space in their model. Second, NNI provides simple interface for users to customize new mutators for expressing more complicated model spaces. In most cases, the mutation primitives are enough to express users' model spaces.
+
+.. toctree::
+ :maxdepth: 1
+
+ Mutation Primitives
+ Customize Mutators
\ No newline at end of file
diff --git a/docs/en_US/NAS/multi_trial_nas.rst b/docs/en_US/NAS/multi_trial_nas.rst
new file mode 100644
index 0000000000..9e77216fb8
--- /dev/null
+++ b/docs/en_US/NAS/multi_trial_nas.rst
@@ -0,0 +1,13 @@
+Multi-trial NAS
+===============
+
+In multi-trial NAS, users need model evaluator to evaluate the performance of each sampled model, and need an exploration strategy to sample models from a defined model space. Here, users could use NNI provided model evaluators or write their own model evalutor. They can simply choose a exploration strategy. Advanced users can also customize new exploration strategy. For a simple example about how to run a multi-trial NAS experiment, please refer to `Quick Start <./QuickStart.rst>`__.
+
+.. toctree::
+ :maxdepth: 1
+
+ Model Evaluators
+ Customize Model Evaluator
+ Exploration Strategies
+ Customize Exploration Strategies
+ Execution Engines
diff --git a/docs/en_US/NAS/one_shot_nas.rst b/docs/en_US/NAS/one_shot_nas.rst
index c0efeb282c..e2ea4c8619 100644
--- a/docs/en_US/NAS/one_shot_nas.rst
+++ b/docs/en_US/NAS/one_shot_nas.rst
@@ -1,19 +1,16 @@
-One-shot NAS Algorithms
-=======================
+One-shot NAS
+============
-One-shot NAS algorithms leverage weight sharing among models in neural architecture search space to train a supernet, and use this supernet to guide the selection of better models. This type of algorihtms greatly reduces computational resource compared to independently training each model from scratch (which we call "Classic NAS"). NNI has supported many popular One-shot NAS algorithms as following.
+One-shot NAS algorithms leverage weight sharing among models in neural architecture search space to train a supernet, and use this supernet to guide the selection of better models. This type of algorihtms greatly reduces computational resource compared to independently training each model from scratch (which we call "Multi-trial NAS"). NNI has supported many popular One-shot NAS algorithms as following.
.. toctree::
:maxdepth: 1
- Quick Start
+ Run One-shot NAS
ENAS
DARTS
- P-DARTS
SPOS
- CDARTS
ProxylessNAS
FBNet
- TextNAS
- Cream
+ Customize one-shot NAS
diff --git a/docs/en_US/NAS/retiarii/Advanced.rst b/docs/en_US/NAS/retiarii/Advanced.rst
deleted file mode 100644
index 12568b8ab6..0000000000
--- a/docs/en_US/NAS/retiarii/Advanced.rst
+++ /dev/null
@@ -1,113 +0,0 @@
-Advanced Tutorial
-=================
-
-Pure-python execution engine (experimental)
--------------------------------------------
-
-If you are experiencing issues with TorchScript, or the generated model code by Retiarii, there is another execution engine called Pure-python execution engine which doesn't need the code-graph conversion. This should generally not affect models and strategies in most cases, but customized mutation might not be supported.
-
-This will come as the default execution engine in future version of Retiarii.
-
-Two steps are needed to enable this engine now.
-
-1. Add ``@nni.retiarii.model_wrapper`` decorator outside the whole PyTorch model.
-2. Add ``config.execution_engine = 'py'`` to ``RetiariiExeConfig``.
-
-``@basic_unit`` and ``serializer``
-----------------------------------
-
-.. _serializer:
-
-``@basic_unit`` and ``serialize`` can be viewed as some kind of serializer. They are designed for making the whole model (including training) serializable to be executed on another process or machine.
-
-**@basic_unit** annotates that a module is a basic unit, i.e, no need to understand the details of this module. The effect is that it prevents Retiarii to parse this module. To understand this, we first briefly explain how Retiarii works: it converts user-defined model to a graph representation (called graph IR) using `TorchScript `__, each instantiated module in the model is converted to a subgraph. Then mutations are applied to the graph to generate new graphs. Each new graph is then converted back to PyTorch code and executed. ``@basic_unit`` here means the module will not be converted to a subgraph, instead, it is converted to a single graph node as a basic unit. That is, the module will not be unfolded anymore. When the module is not unfolded, mutations on initialization parameters of this module becomes easier.
-
-``@basic_unit`` is usually used in the following cases:
-
-* When users want to tune initialization parameters of a module using ``ValueChoice``, then decorate the module with ``@basic_unit``. For example, ``self.conv = MyConv(kernel_size=nn.ValueChoice([1, 3, 5]))``, here ``MyConv`` should be decorated.
-
-* When a module cannot be successfully parsed to a subgraph, decorate the module with ``@basic_unit``. The parse failure could be due to complex control flow. Currently Retiarii does not support adhoc loop, if there is adhoc loop in a module's forward, this class should be decorated as serializable module. For example, the following ``MyModule`` should be decorated.
-
- .. code-block:: python
-
- @basic_unit
- class MyModule(nn.Module):
- def __init__(self):
- ...
- def forward(self, x):
- for i in range(10): # <- adhoc loop
- ...
-
-* Some inline mutation APIs require their handled module to be decorated with ``@basic_unit``. For example, user-defined module that is provided to ``LayerChoice`` as a candidate op should be decorated.
-
-**serialize** is mainly used for serializing model training logic. It enables re-instantiation of model evaluator in another process or machine. Re-instantiation is necessary because most of time model and evaluator should be sent to training services. ``serialize`` is implemented by recording the initialization parameters of user instantiated evaluator.
-
-The evaluator related APIs provided by Retiarii have already supported serialization, for example ``pl.Classification``, ``pl.DataLoader``, no need to apply ``serialize`` on them. In the following case users should use ``serialize`` API manually.
-
-If the initialization parameters of the evaluator APIs (e.g., ``pl.Classification``, ``pl.DataLoader``) are not primitive types (e.g., ``int``, ``string``), they should be applied with ``serialize``. If those parameters' initialization parameters are not primitive types, ``serialize`` should also be applied. In a word, ``serialize`` should be applied recursively if necessary.
-
-
-Express Mutations with Mutators
--------------------------------
-
-Besides inline mutations which have been demonstrated `here <./Tutorial.rst>`__, Retiarii provides a more general approach to express a model space: *Mutator*. Inline mutations APIs are also implemented with mutator, which can be seen as a special case of model mutation.
-
-.. note:: Mutator and inline mutation APIs cannot be used together.
-
-A mutator is a piece of logic to express how to mutate a given model. Users are free to write their own mutators. Then a model space is expressed with a base model and a list of mutators. A model in the model space is sampled by applying the mutators on the base model one after another. An example is shown below.
-
-.. code-block:: python
-
- applied_mutators = []
- applied_mutators.append(BlockMutator('mutable_0'))
- applied_mutators.append(BlockMutator('mutable_1'))
-
-``BlockMutator`` is defined by users to express how to mutate the base model.
-
-Write a mutator
-^^^^^^^^^^^^^^^
-
-User-defined mutator should inherit ``Mutator`` class, and implement mutation logic in the member function ``mutate``.
-
-.. code-block:: python
-
- from nni.retiarii import Mutator
- class BlockMutator(Mutator):
- def __init__(self, target: str, candidates: List):
- super(BlockMutator, self).__init__()
- self.target = target
- self.candidate_op_list = candidates
-
- def mutate(self, model):
- nodes = model.get_nodes_by_label(self.target)
- for node in nodes:
- chosen_op = self.choice(self.candidate_op_list)
- node.update_operation(chosen_op.type, chosen_op.params)
-
-The input of ``mutate`` is graph IR (Intermediate Representation) of the base model (please refer to `here <./ApiReference.rst>`__ for the format and APIs of the IR), users can mutate the graph using the graph's member functions (e.g., ``get_nodes_by_label``, ``update_operation``). The mutation operations can be combined with the API ``self.choice``, in order to express a set of possible mutations. In the above example, the node's operation can be changed to any operation from ``candidate_op_list``.
-
-Use placehoder to make mutation easier: ``nn.Placeholder``. If you want to mutate a subgraph or node of your model, you can define a placeholder in this model to represent the subgraph or node. Then, use mutator to mutate this placeholder to make it real modules.
-
-.. code-block:: python
-
- ph = nn.Placeholder(
- label='mutable_0',
- kernel_size_options=[1, 3, 5],
- n_layer_options=[1, 2, 3, 4],
- exp_ratio=exp_ratio,
- stride=stride
- )
-
-``label`` is used by mutator to identify this placeholder. The other parameters are the information that are required by mutator. They can be accessed from ``node.operation.parameters`` as a dict, it could include any information that users want to put to pass it to user defined mutator. The complete example code can be found in :githublink:`Mnasnet base model `.
-
-Starting an experiment is almost the same as using inline mutation APIs. The only difference is that the applied mutators should be passed to ``RetiariiExperiment``. Below is a simple example.
-
-.. code-block:: python
-
- exp = RetiariiExperiment(base_model, trainer, applied_mutators, simple_strategy)
- exp_config = RetiariiExeConfig('local')
- exp_config.experiment_name = 'mnasnet_search'
- exp_config.trial_concurrency = 2
- exp_config.max_trial_number = 10
- exp_config.training_service.use_active_gpu = False
- exp.run(exp_config, 8081)
diff --git a/docs/en_US/NAS/retiarii/retiarii_index.rst b/docs/en_US/NAS/retiarii/retiarii_index.rst
deleted file mode 100644
index 73ea9c9aa0..0000000000
--- a/docs/en_US/NAS/retiarii/retiarii_index.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-#################
-Retiarii Overview
-#################
-
-`Retiarii `__ is a deep learning framework that supports the exploratory training on a neural network model space, rather than on a single neural network model.
-
-Exploratory training with Retiarii allows user to express various search space for **Neural Architecture Search** and **Hyper-Parameter Tuning** with high flexibility.
-
-As previous NAS and HPO supports, the new framework continued the ability for allowing user to reuse SOTA search algorithms, and to leverage system level optimizations to speed up the search process.
-
-Follow the instructions below to start your journey with Retiarii.
-
-.. toctree::
- :maxdepth: 2
-
- Quick Start
- Write a Model Evaluator
- One-shot NAS
- Advanced Tutorial
- Customize a New Strategy
- Retiarii APIs
diff --git a/docs/en_US/Release.rst b/docs/en_US/Release.rst
index 506ff84007..edbc045a35 100644
--- a/docs/en_US/Release.rst
+++ b/docs/en_US/Release.rst
@@ -5,6 +5,94 @@
Change Log
==========
+Release 2.3 - 6/15/2021
+-----------------------
+
+Major Updates
+^^^^^^^^^^^^^
+
+Neural Architecture Search
+""""""""""""""""""""""""""
+
+* Retiarii Framework (NNI NAS 2.0) Beta Release with new features:
+
+ * Support new high-level APIs: ``Repeat`` and ``Cell`` (#3481)
+ * Support pure-python execution engine (#3605)
+ * Support policy-based RL strategy (#3650)
+ * Support nested ModuleList (#3652)
+ * Improve documentation (#3785)
+
+ **Note**: there are more exciting features of Retiarii planned in the future releases, please refer to `Retiarii Roadmap `__ for more information.
+
+* Add new NAS algorithm: Blockwise DNAS FBNet (#3532, thanks the external contributor @alibaba-yiwuyao)
+
+Model Compression
+"""""""""""""""""
+
+* Support Auto Compression Framework (#3631)
+* Support slim pruner in Tensorflow (#3614)
+* Support LSQ quantizer (#3503, thanks the external contributor @chenbohua3)
+* Improve APIs for iterative pruners (#3507 #3688)
+
+Training service & Rest
+"""""""""""""""""""""""
+
+* Support 3rd-party training service (#3662 #3726)
+* Support setting prefix URL (#3625 #3674 #3672 #3643)
+* Improve NNI manager logging (#3624)
+* Remove outdated TensorBoard code on nnictl (#3613)
+
+Hyper-Parameter Optimization
+""""""""""""""""""""""""""""
+
+* Add new tuner: DNGO (#3479 #3707)
+* Add benchmark for tuners (#3644 #3720 #3689)
+
+WebUI
+"""""
+
+* Improve search parameters on trial detail page (#3651 #3723 #3715)
+* Make selected trials consistent after auto-refresh in detail table (#3597)
+* Add trial stdout button on local mode (#3653 #3690)
+
+Examples & Documentation
+""""""""""""""""""""""""
+
+* Convert all trial examples' from config v1 to config v2 (#3721 #3733 #3711 #3600)
+* Add new jupyter notebook examples (#3599 #3700)
+
+Dev Excellent
+"""""""""""""
+
+* Upgrade dependencies in Dockerfile (#3713 #3722)
+* Substitute PyYAML for ``ruamel.yaml`` (#3702)
+* Add pipelines for AML and hybrid training service and experiment config V2 (#3477 #3648)
+* Add pipeline badge in README (#3589)
+* Update issue bug report template (#3501)
+
+
+Bug Fixes & Minor Updates
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* Fix syntax error on Windows (#3634)
+* Fix a logging related bug (#3705)
+* Fix a bug in GPU indices (#3721)
+* Fix a bug in FrameworkController (#3730)
+* Fix a bug in ``export_data_url format`` (#3665)
+* Report version check failure as a warning (#3654)
+* Fix bugs and lints in nnictl (#3712)
+* Fix bug of ``optimize_mode`` on WebUI (#3731)
+* Fix bug of ``useActiveGpu`` in AML v2 config (#3655)
+* Fix bug of ``experiment_working_directory`` in Retiarii config (#3607)
+* Fix a bug in mask conflict (#3629, thanks the external contributor @Davidxswang)
+* Fix a bug in model speedup shape inference (#3588, thanks the external contributor @Davidxswang)
+* Fix a bug in multithread on Windows (#3604, thanks the external contributor @Ivanfangsc)
+* Delete redundant code in training service (#3526, thanks the external contributor @maxsuren)
+* Fix typo in DoReFa compression doc (#3693, thanks the external contributor @Erfandarzi)
+* Update docstring in model compression (#3647, thanks the external contributor @ichejun)
+* Fix a bug when using Kubernetes container (#3719, thanks the external contributor @rmfan)
+
+
Release 2.2 - 4/26/2021
-----------------------
diff --git a/docs/en_US/ResearchPublications.rst b/docs/en_US/ResearchPublications.rst
index bad00be3cc..1ca12930e0 100644
--- a/docs/en_US/ResearchPublications.rst
+++ b/docs/en_US/ResearchPublications.rst
@@ -95,11 +95,14 @@ New Algorithms
.. code-block:: bibtex
- @article{gao2020opevo,
- title={OpEvo: An Evolutionary Method for Tensor Operator Optimization},
- author={Gao, Xiaotian and Wei, Cui and Zhang, Lintao and Yang, Mao},
- journal={arXiv preprint arXiv:2006.05664},
- year={2020}
+ @article{Gao2021opevo,
+ title={OpEvo: An Evolutionary Method for Tensor Operator Optimization},
+ volume={35},
+ url={https://ojs.aaai.org/index.php/AAAI/article/view/17462},
+ number={14},
+ journal={Proceedings of the AAAI Conference on Artificial Intelligence},
+ author={Gao, Xiaotian and Cui, Wei and Zhang, Lintao and Yang, Mao},
+ year={2021}, month={May}, pages={12320-12327}
}
Measurement and Understanding
diff --git a/docs/en_US/TrainingService/AMLMode.rst b/docs/en_US/TrainingService/AMLMode.rst
index aa6452917e..1f799a682d 100644
--- a/docs/en_US/TrainingService/AMLMode.rst
+++ b/docs/en_US/TrainingService/AMLMode.rst
@@ -124,7 +124,7 @@ Run the following commands to start the example experiment:
nnictl create --config config_aml.yml
-Replace ``${NNI_VERSION}`` with a released version name or branch name, e.g., ``v2.2``.
+Replace ``${NNI_VERSION}`` with a released version name or branch name, e.g., ``v2.3``.
Monitor your code in the cloud by using the studio
--------------------------------------------------
diff --git a/docs/en_US/TrainingService/Overview.rst b/docs/en_US/TrainingService/Overview.rst
index 058276227d..5f0727e8dd 100644
--- a/docs/en_US/TrainingService/Overview.rst
+++ b/docs/en_US/TrainingService/Overview.rst
@@ -68,3 +68,13 @@ Step 2. **Submit the first trial.** To initiate a trial, usually (in non-reuse m
.. Warning:: The working directory of trial command has exactly the same content as ``codeDir``, but can have different paths (even on different machines) Local mode is the only training service that shares one ``codeDir`` across all trials. Other training services copies a ``codeDir`` from the shared copy prepared in step 1 and each trial has an independent working directory. We strongly advise users not to rely on the shared behavior in local mode, as it will make your experiments difficult to scale to other training services.
Step 3. **Collect metrics.** NNI then monitors the status of trial, updates the status (e.g., from ``WAITING`` to ``RUNNING``\ , ``RUNNING`` to ``SUCCEEDED``\ ) recorded, and also collects the metrics. Currently, most training services are implemented in an "active" way, i.e., training service will call the RESTful API on NNI manager to update the metrics. Note that this usually requires the machine that runs NNI manager to be at least accessible to the worker node.
+
+
+Training Service Under Reuse Mode
+---------------------------------
+
+When reuse mode is enabled, a cluster, such as a remote machine or a computer instance on AML, will launch a long-running environment, so that NNI will submit trials to these environments iteratively, which saves the time to create new jobs. For instance, using OpenPAI training platform under reuse mode can avoid the overhead of pulling docker images, creating containers, and downloading data repeatedly.
+
+In the reuse mode, user needs to make sure each trial can run independently in the same job (e.g., avoid loading checkpoints from previous trials).
+
+.. note:: Currently, only `Local <./LocalMode.rst>`__, `Remote <./RemoteMachineMode.rst>`__, `OpenPAI <./PaiMode.rst>`__ and `AML <./AMLMode.rst>`__ training services support resue mode. For Remote and OpenPAI training platforms, you can enable reuse mode according to `here <../reference/experiment_config.rst>`__ manually. AML is implemented under reuse mode, so the default mode is reuse mode, no need to manually enable.
diff --git a/docs/en_US/TrialExample/SquadEvolutionExamples.rst b/docs/en_US/TrialExample/SquadEvolutionExamples.rst
index c9dd77e260..869ade893e 100644
--- a/docs/en_US/TrialExample/SquadEvolutionExamples.rst
+++ b/docs/en_US/TrialExample/SquadEvolutionExamples.rst
@@ -120,7 +120,7 @@ Modify ``nni/examples/trials/ga_squad/config_pai.yml``\ , here is the default co
#Your nni_manager ip
nniManagerIp: 10.10.10.10
tuner:
- codeDir: https://github.com/Microsoft/nni/tree/v2.2/examples/tuners/ga_customer_tuner
+ codeDir: https://github.com/Microsoft/nni/tree/v2.3/examples/tuners/ga_customer_tuner
classFileName: customer_tuner.py
className: CustomerTuner
classArgs:
diff --git a/docs/en_US/Tuner/BuiltinTuner.rst b/docs/en_US/Tuner/BuiltinTuner.rst
index 768913b373..37a48245ef 100644
--- a/docs/en_US/Tuner/BuiltinTuner.rst
+++ b/docs/en_US/Tuner/BuiltinTuner.rst
@@ -41,11 +41,10 @@ Currently, we support the following algorithms:
- BOHB is a follow-up work to Hyperband. It targets the weakness of Hyperband that new configurations are generated randomly without leveraging finished trials. For the name BOHB, HB means Hyperband, BO means Bayesian Optimization. BOHB leverages finished trials by building multiple TPE models, a proportion of new configurations are generated through these models. `Reference Paper `__
* - `GP Tuner <#GPTuner>`__
- Gaussian Process Tuner is a sequential model-based optimization (SMBO) approach with Gaussian Process as the surrogate. `Reference Paper `__\ , `Github Repo `__
- * - `PPO Tuner <#PPOTuner>`__
- - PPO Tuner is a Reinforcement Learning tuner based on PPO algorithm. `Reference Paper `__
* - `PBT Tuner <#PBTTuner>`__
- PBT Tuner is a simple asynchronous optimization algorithm which effectively utilizes a fixed computational budget to jointly optimize a population of models and their hyperparameters to maximize performance. `Reference Paper `__
-
+ * - `DNGO Tuner <#DNGOTuner>`__
+ - Use of neural networks as an alternative to GPs to model distributions over functions in bayesian optimization.
Usage of Built-in Tuners
------------------------
@@ -498,69 +497,67 @@ As a strategy in a Sequential Model-based Global Optimization (SMBO) algorithm,
selection_num_warm_up: 100000
selection_num_starting_points: 250
-:raw-html:``
+:raw-html:``
-PPO Tuner
+PBT Tuner
^^^^^^^^^
..
- Built-in Tuner Name: **PPOTuner**
-
+ Built-in Tuner Name: **PBTTuner**
-Note that the only acceptable types within the search space are ``layer_choice`` and ``input_choice``. For ``input_choice``\ , ``n_chosen`` can only be 0, 1, or [0, 1]. Note, the search space file for NAS is usually automatically generated through the command `nnictl ss_gen <../Tutorial/Nnictl.rst>`__.
**Suggested scenario**
-PPOTuner is a Reinforcement Learning tuner based on the PPO algorithm. PPOTuner can be used when using the NNI NAS interface to do neural architecture search. In general, the Reinforcement Learning algorithm needs more computing resources, though the PPO algorithm is relatively more efficient than others. It's recommended to use this tuner when you have a large amount of computional resources available. You could try it on a very simple task, such as the :githublink:`mnist-nas ` example. `See details <./PPOTuner.rst>`__
+Population Based Training (PBT) bridges and extends parallel search methods and sequential optimization methods. It requires relatively small computation resource, by inheriting weights from currently good-performing ones to explore better ones periodically. With PBTTuner, users finally get a trained model, rather than a configuration that could reproduce the trained model by training the model from scratch. This is because model weights are inherited periodically through the whole search process. PBT can also be seen as a training approach. If you don't need to get a specific configuration, but just expect a good model, PBTTuner is a good choice. `See details <./PBTTuner.rst>`__
-**classArgs Requirements:**
+**classArgs requirements:**
-* **optimize_mode** (*'maximize' or 'minimize'*\ ) - If 'maximize', the tuner will try to maximize metrics. If 'minimize', the tuner will try to minimize metrics.
-* **trials_per_update** (*int, optional, default = 20*\ ) - The number of trials to be used for one update. It must be divisible by minibatch_size. ``trials_per_update`` is recommended to be an exact multiple of ``trialConcurrency`` for better concurrency of trials.
-* **epochs_per_update** (*int, optional, default = 4*\ ) - The number of epochs for one update.
-* **minibatch_size** (*int, optional, default = 4*\ ) - Mini-batch size (i.e., number of trials for a mini-batch) for the update. Note that trials_per_update must be divisible by minibatch_size.
-* **ent_coef** (*float, optional, default = 0.0*\ ) - Policy entropy coefficient in the optimization objective.
-* **lr** (*float, optional, default = 3e-4*\ ) - Learning rate of the model (lstm network); constant.
-* **vf_coef** (*float, optional, default = 0.5*\ ) - Value function loss coefficient in the optimization objective.
-* **max_grad_norm** (*float, optional, default = 0.5*\ ) - Gradient norm clipping coefficient.
-* **gamma** (*float, optional, default = 0.99*\ ) - Discounting factor.
-* **lam** (*float, optional, default = 0.95*\ ) - Advantage estimation discounting factor (lambda in the paper).
-* **cliprange** (*float, optional, default = 0.2*\ ) - Cliprange in the PPO algorithm, constant.
+* **optimize_mode** (*'maximize' or 'minimize'*\ ) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
+* **all_checkpoint_dir** (*str, optional, default = None*\ ) - Directory for trials to load and save checkpoint, if not specified, the directory would be "~/nni/checkpoint/\ :raw-html:``\ ". Note that if the experiment is not local mode, users should provide a path in a shared storage which can be accessed by all the trials.
+* **population_size** (*int, optional, default = 10*\ ) - Number of trials in a population. Each step has this number of trials. In our implementation, one step is running each trial by specific training epochs set by users.
+* **factors** (*tuple, optional, default = (1.2, 0.8)*\ ) - Factors for perturbation of hyperparameters.
+* **fraction** (*float, optional, default = 0.2*\ ) - Fraction for selecting bottom and top trials.
-**Example Configuration:**
+**Usage example**
.. code-block:: yaml
# config.yml
tuner:
- builtinTunerName: PPOTuner
+ builtinTunerName: PBTTuner
classArgs:
optimize_mode: maximize
-:raw-html:``
+Note that, to use this tuner, your trial code should be modified accordingly, please refer to `the document of PBTTuner <./PBTTuner.rst>`__ for details.
-PBT Tuner
-^^^^^^^^^
+:raw-html:``
+
+DNGO Tuner
+^^^^^^^^^^
..
- Built-in Tuner Name: **PBTTuner**
+ Built-in Tuner Name: **DNGOTuner**
+
+DNGO advisor requires `pybnn`, which can be installed with the following command.
+
+.. code-block:: bash
+ pip install nni[DNGO]
**Suggested scenario**
-Population Based Training (PBT) bridges and extends parallel search methods and sequential optimization methods. It requires relatively small computation resource, by inheriting weights from currently good-performing ones to explore better ones periodically. With PBTTuner, users finally get a trained model, rather than a configuration that could reproduce the trained model by training the model from scratch. This is because model weights are inherited periodically through the whole search process. PBT can also be seen as a training approach. If you don't need to get a specific configuration, but just expect a good model, PBTTuner is a good choice. `See details <./PBTTuner.rst>`__
+Applicable to large scale hyperparameter optimization. Bayesian optimization that rapidly finds competitive models on benchmark object recognition tasks using convolutional networks, and image caption generation using neural language models.
**classArgs requirements:**
* **optimize_mode** (*'maximize' or 'minimize'*\ ) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
-* **all_checkpoint_dir** (*str, optional, default = None*\ ) - Directory for trials to load and save checkpoint, if not specified, the directory would be "~/nni/checkpoint/\ :raw-html:``\ ". Note that if the experiment is not local mode, users should provide a path in a shared storage which can be accessed by all the trials.
-* **population_size** (*int, optional, default = 10*\ ) - Number of trials in a population. Each step has this number of trials. In our implementation, one step is running each trial by specific training epochs set by users.
-* **factors** (*tuple, optional, default = (1.2, 0.8)*\ ) - Factors for perturbation of hyperparameters.
-* **fraction** (*float, optional, default = 0.2*\ ) - Fraction for selecting bottom and top trials.
+* **sample_size** (*int, default = 1000*) - Number of samples to select in each iteration. The best one will be picked from the samples as the next trial.
+* **trials_per_update** (*int, default = 20*) - Number of trials to collect before updating the model.
+* **num_epochs_per_training** (*int, default = 500*) - Number of epochs to train DNGO model.
**Usage example**
@@ -568,12 +565,10 @@ Population Based Training (PBT) bridges and extends parallel search methods and
# config.yml
tuner:
- builtinTunerName: PBTTuner
+ builtinTunerName: DNGOTuner
classArgs:
optimize_mode: maximize
-Note that, to use this tuner, your trial code should be modified accordingly, please refer to `the document of PBTTuner <./PBTTuner.rst>`__ for details.
-
**Reference and Feedback**
------------------------------
diff --git a/docs/en_US/Tuner/PPOTuner.rst b/docs/en_US/Tuner/PPOTuner.rst
deleted file mode 100644
index ce769e1d3b..0000000000
--- a/docs/en_US/Tuner/PPOTuner.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-PPO Tuner on NNI
-================
-
-PPOTuner
---------
-
-This is a tuner geared for NNI's Neural Architecture Search (NAS) interface. It uses the `ppo algorithm `__. The implementation inherits the main logic of the ppo2 OpenAI implementation `here `__ and is adapted for the NAS scenario.
-
-We had successfully tuned the mnist-nas example and has the following result:
-
-.. Note:: we are refactoring this example to the latest NAS interface, will publish the example codes after the refactor.
-
-.. image:: ../../img/ppo_mnist.png
- :target: ../../img/ppo_mnist.png
- :alt:
-
-
-We also tune :githublink:`the macro search space for image classification in the enas paper ` (with a limited epoch number for each trial, i.e., 8 epochs), which is implemented using the NAS interface and tuned with PPOTuner. Here is Figure 7 from the `enas paper `__ to show what the search space looks like
-
-
-.. image:: ../../img/enas_search_space.png
- :target: ../../img/enas_search_space.png
- :alt:
-
-
-The figure above was the chosen architecture. Each square is a layer whose operation was chosen from 6 options. Each dashed line is a skip connection, each square layer can choose 0 or 1 skip connections, getting the output from a previous layer. **Note that**\ , in original macro search space, each square layer could choose any number of skip connections, while in our implementation, it is only allowed to choose 0 or 1.
-
-The results are shown in figure below (see the experimenal config :githublink:`here `\ :
-
-
-.. image:: ../../img/ppo_cifar10.png
- :target: ../../img/ppo_cifar10.png
- :alt:
-
diff --git a/docs/en_US/Tutorial/Contributing.rst b/docs/en_US/Tutorial/Contributing.rst
index bf8cc0a31f..43486a2be9 100644
--- a/docs/en_US/Tutorial/Contributing.rst
+++ b/docs/en_US/Tutorial/Contributing.rst
@@ -71,4 +71,4 @@ Our documentation is built with :githublink:`sphinx `.
* It's an image link which needs to be formatted with embedded html grammar, please use global URL like ``https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png``, which can be automatically generated by dragging picture onto `Github Issue `__ Box.
- * It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at ``https://github.com/Microsoft/nni/tree/v2.2/`` (:githublink:`mnist.py ` for example).
+ * It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at ``https://github.com/Microsoft/nni/tree/v2.3/`` (:githublink:`mnist.py ` for example).
diff --git a/docs/en_US/Tutorial/ExperimentConfig.rst b/docs/en_US/Tutorial/ExperimentConfig.rst
index 3bb2449076..0f32e3606c 100644
--- a/docs/en_US/Tutorial/ExperimentConfig.rst
+++ b/docs/en_US/Tutorial/ExperimentConfig.rst
@@ -1,5 +1,7 @@
-Experiment Config Reference
-===========================
+Experiment Config Reference (legacy)
+====================================
+
+This is the previous version (V1) of experiment configuration specification. It is still supported for now, but we recommend users to use `the new version of experiment configuration (V2) <../reference/experiment_config.rst>`_.
A config file is needed when creating an experiment. The path of the config file is provided to ``nnictl``.
The config file is in YAML format.
diff --git a/docs/en_US/Tutorial/HowToUseSharedStorage.rst b/docs/en_US/Tutorial/HowToUseSharedStorage.rst
index 18ab42f4f0..7a19746e95 100644
--- a/docs/en_US/Tutorial/HowToUseSharedStorage.rst
+++ b/docs/en_US/Tutorial/HowToUseSharedStorage.rst
@@ -7,7 +7,7 @@ All the information generated by the experiment will be stored under ``/nni`` fo
All the output produced by the trial will be located under ``/nni/{EXPERIMENT_ID}/trials/{TRIAL_ID}/nnioutput`` folder in your shared storage.
This saves you from finding for experiment-related information in various places.
Remember that your trial working directory is ``/nni/{EXPERIMENT_ID}/trials/{TRIAL_ID}``, so if you upload your data in this shared storage, you can open it like a local file in your trial code without downloading it.
-And we will develop more practical features in the future based on shared storage.
+And we will develop more practical features in the future based on shared storage. The config reference can be found `here <../reference/experiment_config.html#sharedstorageconfig>`_.
.. note::
Shared storage is currently in the experimental stage. We suggest use AzureBlob under Ubuntu/CentOS/RHEL, and NFS under Ubuntu/CentOS/RHEL/Fedora/Debian for remote.
diff --git a/docs/en_US/Tutorial/InstallationLinux.rst b/docs/en_US/Tutorial/InstallationLinux.rst
index 9db4b731bf..74e186e218 100644
--- a/docs/en_US/Tutorial/InstallationLinux.rst
+++ b/docs/en_US/Tutorial/InstallationLinux.rst
@@ -24,7 +24,7 @@ Install NNI through source code
.. code-block:: bash
- git clone -b v2.2 https://github.com/Microsoft/nni.git
+ git clone -b v2.3 https://github.com/Microsoft/nni.git
cd nni
python3 -m pip install --upgrade pip setuptools
python3 setup.py develop
@@ -37,7 +37,7 @@ If you want to perform a persist install instead, we recommend to build your own
.. code-block:: bash
- git clone -b v2.2 https://github.com/Microsoft/nni.git
+ git clone -b v2.3 https://github.com/Microsoft/nni.git
cd nni
export NNI_RELEASE=2.0
python3 -m pip install --upgrade pip setuptools wheel
@@ -59,7 +59,7 @@ Verify installation
.. code-block:: bash
- git clone -b v2.2 https://github.com/Microsoft/nni.git
+ git clone -b v2.3 https://github.com/Microsoft/nni.git
*
Run the MNIST example.
diff --git a/docs/en_US/Tutorial/InstallationWin.rst b/docs/en_US/Tutorial/InstallationWin.rst
index c2fb9bd763..189d9fc5db 100644
--- a/docs/en_US/Tutorial/InstallationWin.rst
+++ b/docs/en_US/Tutorial/InstallationWin.rst
@@ -40,7 +40,7 @@ If you want to contribute to NNI, refer to `setup development environment `, to avoid too much pressure on network, we limit the number of files to 2000 and total size to 300MB. If your codeDir contains too many files, you can choose which files and subfolders should be excluded by adding a ``.nniignore`` file that works like a ``.gitignore`` file. For more details on how to write this file, see the `git documentation `__.
*Example:* :githublink:`config.yml ` and :githublink:`.nniignore `
diff --git a/docs/en_US/Tutorial/SearchSpaceSpec.rst b/docs/en_US/Tutorial/SearchSpaceSpec.rst
index 0512e4d394..27ce2c31d3 100644
--- a/docs/en_US/Tutorial/SearchSpaceSpec.rst
+++ b/docs/en_US/Tutorial/SearchSpaceSpec.rst
@@ -243,13 +243,25 @@ Search Space Types Supported by Each Tuner
-
-
-
+ * - DNGO Tuner
+ - :raw-html:`✓`
+ -
+ - :raw-html:`✓`
+ - :raw-html:`✓`
+ - :raw-html:`✓`
+ - :raw-html:`✓`
+ - :raw-html:`✓`
+ -
+ -
+ -
+ -
Known Limitations:
*
- GP Tuner and Metis Tuner support only **numerical values** in search space (\ ``choice`` type values can be no-numerical with other tuners, e.g. string values). Both GP Tuner and Metis Tuner use Gaussian Process Regressor(GPR). GPR make predictions based on a kernel function and the 'distance' between different points, it's hard to get the true distance between no-numerical values.
+ GP Tuner, Metis Tuner and DNGO tuner support only **numerical values** in search space (\ ``choice`` type values can be no-numerical with other tuners, e.g. string values). Both GP Tuner and Metis Tuner use Gaussian Process Regressor(GPR). GPR make predictions based on a kernel function and the 'distance' between different points, it's hard to get the true distance between no-numerical values.
*
Note that for nested search space:
diff --git a/docs/en_US/Tutorial/WebUI.rst b/docs/en_US/Tutorial/WebUI.rst
index c978cf0133..3cfbe3e485 100644
--- a/docs/en_US/Tutorial/WebUI.rst
+++ b/docs/en_US/Tutorial/WebUI.rst
@@ -207,6 +207,43 @@ Click the tab ``Trials Detail`` to see the status of all trials. Specifically:
+* Support searching for a specific trial by its id, status, Trial No. and trial parameters.
+
+1. Trial id:
+
+.. image:: ../../img/webui-img/detail/searchId.png
+ :target: ../../img/webui-img/detail/searchId.png
+ :alt: searchTrialId
+
+
+2. Trial No.:
+
+.. image:: ../../img/webui-img/detail/searchNo.png
+ :target: ../../img/webui-img/detail/searchNo.png
+ :alt: searchTrialNo.
+
+
+3. Trial status:
+
+.. image:: ../../img/webui-img/detail/searchStatus.png
+ :target: ../../img/webui-img/detail/searchStatus.png
+ :alt: searchStatus
+
+4. Trial parameters:
+
+(1) parameters whose type is choice:
+
+.. image:: ../../img/webui-img/detail/searchParameterChoice.png
+ :target: ../../img/webui-img/detail/searchParameterChoice.png
+ :alt: searchParameterChoice
+
+(2) parameters whose type is not choice:
+
+.. image:: ../../img/webui-img/detail/searchParameterRange.png
+ :target: ../../img/webui-img/detail/searchParameterRange.png
+ :alt: searchParameterRange
+
+
* The button named ``Add column`` can select which column to show on the table. If you run an experiment whose final result is a dict, you can see other keys in the table. You can choose the column ``Intermediate count`` to watch the trial's progress.
@@ -229,14 +266,7 @@ Click the tab ``Trials Detail`` to see the status of all trials. Specifically:
:alt: compareTrialsGraph
-
-* Support to search for a specific trial by it's id, status, Trial No. and parameters.
-
-
-.. image:: ../../img/webui-img/search-trial.png
- :target: ../../img/webui-img/search-trial.png
- :alt: searchTrial
-
+* ``Tensorboard`` please refer `doc `__.
* You can use the button named ``Copy as python`` to copy the trial's parameters.
@@ -248,15 +278,22 @@ Click the tab ``Trials Detail`` to see the status of all trials. Specifically:
-* If you run on the OpenPAI or Kubeflow platform, you can also see the nfs log.
+* You could see trial logs on the tab of ``Log``. There are three buttons ``View trial log``, ``View trial error`` and ``View trial stdout`` on local mode. If you run on the OpenPAI or Kubeflow platform, you could see trial stdout and nfs log.
+
+1. local mode:
+
+.. image:: ../../img/webui-img/detail/log-local.png
+ :target: ../../img/webui-img/detail/log-local.png
+ :alt: logOnLocal
+
+2. OpenPAI, Kubeflow and other mode:
.. image:: ../../img/webui-img/detail-pai.png
:target: ../../img/webui-img/detail-pai.png
:alt: detailPai
-
* Intermediate Result Graph: you can see the default metric in this graph by clicking the intermediate button.
@@ -273,3 +310,16 @@ Click the tab ``Trials Detail`` to see the status of all trials. Specifically:
:target: ../../img/webui-img/kill-running.png
:alt: killTrial
+
+
+* Customized trial: you can change this trial parameters and then submit it to the experiment. If you want to rerun a failed trial you could submit the same parameters to the experiment.
+
+.. image:: ../../img/webui-img/detail/customizedTrialButton.png
+ :target: ../../img/webui-img/detail/customizedTrialButton.png
+ :alt: customizedTrialButton
+
+
+
+.. image:: ../../img/webui-img/detail/customizedTrial.png
+ :target: ../../img/webui-img/detail/customizedTrial.png
+ :alt: customizedTrial
diff --git a/docs/en_US/Tutorial/python_api_start.ipynb b/docs/en_US/Tutorial/python_api_start.ipynb
index 22ca8a4fb1..95ceb6d99b 100644
--- a/docs/en_US/Tutorial/python_api_start.ipynb
+++ b/docs/en_US/Tutorial/python_api_start.ipynb
@@ -79,7 +79,7 @@
"output_type": "stream",
"text": [
"[2021-03-05 12:12:19] Creating experiment, Experiment ID: wdt0le3v\n",
- "[2021-03-05 12:12:19] Statring web server...\n",
+ "[2021-03-05 12:12:19] Starting web server...\n",
"[2021-03-05 12:12:20] Setting up...\n",
"[2021-03-05 12:12:20] Web UI URLs: http://127.0.0.1:8080 http://10.0.1.5:8080 http://172.17.0.1:8080\n"
]
diff --git a/docs/en_US/_templates/index.html b/docs/en_US/_templates/index.html
index 35ccb70e79..503b487631 100644
--- a/docs/en_US/_templates/index.html
+++ b/docs/en_US/_templates/index.html
@@ -5,7 +5,7 @@
{% block document %}
NNI supports and is tested on Ubuntu >= 16.04, macOS >= 10.14.1,
- and Windows 10 >= 1809. Simply run the following `pip install`
- in an environment that has `python 64-bit >= 3.6`.
+ and Windows 10 >= 1809. Simply run the following pip install
+ in an environment that has python 64-bit >= 3.6.
Open the Web UI url in your browser, you can view detail information of the experiment and
all the submitted trial jobs as shown below. Here are more Web UI
pages.
-
-
-
-
+
diff --git a/docs/en_US/builtin_tuner.rst b/docs/en_US/builtin_tuner.rst
index 1e6fd36a73..6282beca58 100644
--- a/docs/en_US/builtin_tuner.rst
+++ b/docs/en_US/builtin_tuner.rst
@@ -20,5 +20,4 @@ Tuner receives metrics from `Trial` to evaluate the performance of a specific pa
Network Morphism
Hyperband
BOHB
- PPO Tuner
PBT Tuner
diff --git a/docs/en_US/conf.py b/docs/en_US/conf.py
index 794f97fd1a..6df286bde0 100644
--- a/docs/en_US/conf.py
+++ b/docs/en_US/conf.py
@@ -27,7 +27,7 @@
# The short X.Y version
version = ''
# The full version, including alpha/beta/rc tags
-release = 'v2.2'
+release = 'v2.3'
# -- General configuration ---------------------------------------------------
diff --git a/docs/en_US/hpo_benchmark.rst b/docs/en_US/hpo_benchmark.rst
index a82baab79f..8e58674620 100644
--- a/docs/en_US/hpo_benchmark.rst
+++ b/docs/en_US/hpo_benchmark.rst
@@ -14,6 +14,8 @@ Terminology
* **tuner**\ : a tuner or advisor defined in the hpo folder, or a custom tuner provided by the user.
* **architecture**\ : an architecture is a specific method for solving the tasks, along with a set of hyperparameters to optimize (i.e., the search space). In our implementation, the architecture calls tuner multiple times to obtain possible hyperparameter configurations, and produces the final prediction for a task. See ``./nni/extensions/NNI/architectures`` for examples.
+Note: currently, the only architecture supported is random forest. The architecture implementation and search space definition can be found in ``./nni/extensions/NNI/architectures/run_random_forest.py``. The tasks in benchmarks "nnivalid" and "nnismall" are suitable to solve with random forests.
+
Setup
^^^^^
@@ -32,11 +34,11 @@ Run predefined benchmarks on existing tuners
This script runs the benchmark 'nnivalid', which consists of a regression task, a binary classification task, and a multi-class classification task. After the script finishes, you can find a summary of the results in the folder results_[time]/reports/. To run on other predefined benchmarks, change the ``benchmark`` variable in ``runbenchmark_nni.sh``. Some benchmarks are defined in ``/examples/trials/benchmarking/automlbenchmark/nni/benchmarks``\ , and others are defined in ``/examples/trials/benchmarking/automlbenchmark/automlbenchmark/resources/benchmarks/``. One example of larger benchmarks is "nnismall", which consists of 8 regression tasks, 8 binary classification tasks, and 8 multi-class classification tasks.
-By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details.
+By default, the script runs the benchmark on all embedded tuners in NNI. If provided a list of tuners in [tuner-names], it only runs the tuners in the list. Currently, the following tuner names are supported: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "DNGOTuner", "Hyperband", "BOHB". It is also possible to evaluate custom tuners. See the next sections for details.
By default, the script runs the specified tuners against the specified benchmark one by one. To run all the experiments simultaneously in the background, set the "serialize" flag to false in ``runbenchmark_nni.sh``.
-Note: the SMAC tuner and the BOHB advisor has to be manually installed before any experiments can be run on it. Please refer to `this page `_ for more details on installing SMAC and BOHB.
+Note: the SMAC tuner, DNGO tuner, and the BOHB advisor has to be manually installed before any experiments can be run on it. Please refer to `this page `_ for more details on installing SMAC and BOHB.
Run customized benchmarks on existing tuners
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -60,15 +62,15 @@ To use custom tuners, first make sure that the tuner inherits from ``nni.tuner.T
A Benchmark Example
^^^^^^^^^^^^^^^^^^^
-As an example, we ran the "nnismall" benchmark on the following 8 tuners: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "DngoTuner". (The DngoTuner is not available as a built-in tuner at the time this article is written.) As some of the tasks contains a considerable amount of training data, it took about 2 days to run the whole benchmark on one tuner using a single CPU core. For a more detailed description of the tasks, please check ``/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt``.
+As an example, we ran the "nnismall" benchmark on the following 8 tuners: "TPE", "Random", "Anneal", "Evolution", "SMAC", "GPTuner", "MetisTuner", "DNGOTuner". As some of the tasks contains a considerable amount of training data, it took about 2 days to run the whole benchmark on one tuner using a single CPU core. For a more detailed description of the tasks, please check ``/examples/trials/benchmarking/automlbenchmark/nni/benchmarks/nnismall_description.txt``. For binary and multi-class classification tasks, the metric "auc" and "logloss" were used for evaluation, while for regression, "r2" and "rmse" were used.
-After the script finishes, the final scores of each tuner is summarized in the file ``results[time]/reports/performances.txt``. Since the file is large, we only show the following screenshot and summarize other important statistics instead.
+After the script finishes, the final scores of each tuner are summarized in the file ``results[time]/reports/performances.txt``. Since the file is large, we only show the following screenshot and summarize other important statistics instead.
.. image:: ../img/hpo_benchmark/performances.png
:target: ../img/hpo_benchmark/performances.png
:alt:
-When the results are parsed, the tuners are ranked based on their final performance. ``results[time]/reports/rankings.txt`` presents a ranking of the tuners for each metric (logloss, rmse, auc), and the rankings of tuners for each metric (another view of the same data).
+In addition, when the results are parsed, the tuners are ranked based on their final performance. ``results[time]/reports/rankings.txt`` presents the average ranking of the tuners for each metric (logloss, rmse, auc). Here we present the data in the first three tables. Also, for every tuner, their performance for each type of metric is summarized (another view of the same data). We present this statistics in the fourth table.
Average rankings for metric rmse:
@@ -195,6 +197,7 @@ Besides these reports, our script also generates two graphs for each fold of eac
:target: ../img/hpo_benchmark/car_fold1_2.jpg
:alt:
+For example, the previous two graphs are generated for fold 1 of the task "car". In the first graph, we can observe that most tuners find a relatively good solution within 40 trials. In this experiment, among all tuners, the DNGOTuner converges fastest to the best solution (within 10 trials). Its score improved three times in the entire experiment. In the second graph, we observe that most tuners have their score flucturate between 0.8 and 1 throughout the experiment duration. However, it seems that the Anneal tuner (green line) is more unstable (having more fluctuations) while the GPTuner has a more stable pattern. Regardless, although this pattern can to some extent be interpreted as a tuner's position on the explore-exploit tradeoff, it cannot be used for a comprehensive evaluation of a tuner's effectiveness.
.. image:: ../img/hpo_benchmark/christine_fold0_1.jpg
:target: ../img/hpo_benchmark/christine_fold0_1.jpg
diff --git a/docs/en_US/nas.rst b/docs/en_US/nas.rst
index 24adf34ef2..ed8edfce56 100644
--- a/docs/en_US/nas.rst
+++ b/docs/en_US/nas.rst
@@ -1,6 +1,6 @@
-##########################
-Neural Architecture Search
-##########################
+#############################################
+Retiarii for Neural Architecture Search (NAS)
+#############################################
Automatic neural architecture search is taking an increasingly important role on finding better models.
Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually tuned models.
@@ -10,20 +10,24 @@ However, it takes great efforts to implement NAS algorithms, and it is hard to r
To facilitate NAS innovations (e.g., design and implement new NAS models, compare different NAS models side-by-side),
an easy-to-use and flexible programming interface is crucial.
-Therefore, we provide a unified interface for NAS,
-to accelerate innovations on NAS, and apply state-of-art algorithms on real world problems faster.
-For details, please refer to the following tutorials:
+Thus, we design `Retiarii `__. It is a deep learning framework that supports the exploratory training on a neural network model space, rather than on a single neural network model.
+Exploratory training with Retiarii allows user to express various search spaces for *Neural Architecture Search* and *Hyper-Parameter Tuning* with high flexibility.
+
+Some frequently used terminologies in this document:
+
+* *Model search space*: it means a set of models from which the best model is explored/searched. Sometimes we use *search space* or *model space* in short.
+* *Exploration strategy*: the algorithm that is used to explore a model search space.
+* *Model evaluator*: it is used to train a model and evaluate the model's performance.
+
+Follow the instructions below to start your journey with Retiarii.
.. toctree::
:maxdepth: 2
Overview
- Write A Search Space
- Classic NAS
+ Quick Start
+ Construct Model Space
+ Multi-trial NAS
One-shot NAS
- Retiarii NAS (Alpha)
- Customize a NAS Algorithm
- NAS Visualization
- Search Space Zoo
NAS Benchmarks
- API Reference
+ NAS API References
diff --git a/docs/en_US/reference.rst b/docs/en_US/reference.rst
index 640bf1d387..70d410ed2c 100644
--- a/docs/en_US/reference.rst
+++ b/docs/en_US/reference.rst
@@ -5,12 +5,12 @@ References
:maxdepth: 2
nnictl Commands
- Experiment Configuration
- Experiment Configuration V2
+ Experiment Configuration
+ Experiment Configuration (legacy)
Search Space
NNI Annotation
SDK API References
Supported Framework Library
- Launch from python
+ Launch from Python
Shared Storage
Tensorboard
diff --git a/docs/en_US/reference/experiment_config.rst b/docs/en_US/reference/experiment_config.rst
index f3779bd1aa..44b0d57c33 100644
--- a/docs/en_US/reference/experiment_config.rst
+++ b/docs/en_US/reference/experiment_config.rst
@@ -2,25 +2,28 @@
Experiment Config Reference
===========================
-Notes
-=====
+A config file is needed when creating an experiment. This document describes the rules to write a config file and provides some examples.
-1. This document list field names is ``camelCase``.
- They need to be converted to ``snake_case`` for Python library ``nni.experiment``.
+.. Note::
-2. In this document type of fields are formatted as `Python type hint `__.
- Therefore JSON objects are called `dict` and arrays are called `list`.
+ 1. This document lists field names with ``camelCase``. If users use these fields in the pythonic way with NNI Python APIs (e.g., ``nni.experiment``), the field names should be converted to ``snake_case``.
-.. _path:
+ 2. In this document, the type of fields are formatted as `Python type hint `_. Therefore JSON objects are called `dict` and arrays are called `list`.
-3. Some fields take a path to file or directory.
- Unless otherwise noted, both absolute path and relative path are supported, and ``~`` will be expanded to home directory.
+ .. _path:
- - When written in YAML file, relative paths are relative to the directory containing that file.
- - When assigned in Python code, relative paths are relative to current working directory.
- - All relative paths are converted to absolute when loading YAML file into Python class, and when saving Python class to YAML file.
+ 3. Some fields take a path to a file or directory. Unless otherwise noted, both absolute path and relative path are supported, and ``~`` will be expanded to the home directory.
-4. Setting a field to ``None`` or ``null`` is equivalent to not setting the field.
+ - When written in the YAML file, relative paths are relative to the directory containing that file.
+ - When assigned in Python code, relative paths are relative to the current working directory.
+ - All relative paths are converted to absolute when loading YAML file into Python class, and when saving Python class to YAML file.
+
+ 4. Setting a field to ``None`` or ``null`` is equivalent to not setting the field.
+
+.. contents:: Contents
+ :local:
+ :depth: 3
+
Examples
========
@@ -105,7 +108,7 @@ ExperimentConfig
experimentName
--------------
-Mnemonic name of the experiment. This will be shown in web UI and nnictl.
+Mnemonic name of the experiment, which will be shown in WebUI and nnictl.
type: ``Optional[str]``
@@ -113,11 +116,11 @@ type: ``Optional[str]``
searchSpaceFile
---------------
-Path_ to a JSON file containing the search space.
+Path_ to the JSON file containing the search space.
type: ``Optional[str]``
-Search space format is determined by tuner. Common format for built-in tuners is documeted `here <../Tutorial/SearchSpaceSpec.rst>`__.
+Search space format is determined by tuner. The common format for built-in tuners is documented `here <../Tutorial/SearchSpaceSpec.rst>`__.
Mutually exclusive to `searchSpace`_.
@@ -145,6 +148,8 @@ type: ``str``
The command will be executed in bash on Linux and macOS, and in PowerShell on Windows.
+Note that using ``python3`` on Linux and macOS, and using ``python`` on Windows.
+
trialCodeDirectory
------------------
@@ -155,8 +160,8 @@ type: ``str``
default: ``"."``
-All files in this directory will be sent to training machine, unless there is a ``.nniignore`` file.
-(See nniignore section of `quick start guide <../Tutorial/QuickStart.rst>`__ for details.)
+All files in this directory will be sent to the training machine, unless in the ``.nniignore`` file.
+(See :ref:`nniignore ` for details.)
trialConcurrency
@@ -176,11 +181,11 @@ Number of GPUs used by each trial.
type: ``Optional[int]``
-This field might have slightly different meaning for various training services,
+This field might have slightly different meanings for various training services,
especially when set to ``0`` or ``None``.
-See training service's document for details.
+See `training service's document <../training_services.rst>`__ for details.
-In local mode, setting the field to zero will prevent trials from accessing GPU (by empty ``CUDA_VISIBLE_DEVICES``).
+In local mode, setting the field to ``0`` will prevent trials from accessing GPU (by empty ``CUDA_VISIBLE_DEVICES``).
And when set to ``None``, trials will be created and scheduled as if they did not use GPU,
but they can still use all GPU resources if they want.
@@ -196,7 +201,7 @@ format: ``number + s|m|h|d``
examples: ``"10m"``, ``"0.5h"``
-When time runs out, the experiment will stop creating trials but continue to serve web UI.
+When time runs out, the experiment will stop creating trials but continue to serve WebUI.
maxTrialNumber
@@ -206,19 +211,19 @@ Limit the number of trials to create if specified.
type: ``Optional[int]``
-When the budget runs out, the experiment will stop creating trials but continue to serve web UI.
+When the budget runs out, the experiment will stop creating trials but continue to serve WebUI.
nniManagerIp
------------
-IP of current machine, used by training machines to access NNI manager. Not used in local mode.
+IP of the current machine, used by training machines to access NNI manager. Not used in local mode.
type: ``Optional[str]``
If not specified, IPv4 address of ``eth0`` will be used.
-Must be set on Windows and systems using predictable network interface name, except for local mode.
+Except for the local mode, it is highly recommended to set this field manually.
useAnnotation
@@ -242,19 +247,19 @@ type: ``bool``
default: ``False``
-When enabled, logging will be more verbose and some internal validation will be loosen.
+When enabled, logging will be more verbose and some internal validation will be loosened.
logLevel
--------
-Set log level of whole system.
+Set log level of the whole system.
type: ``Optional[str]``
values: ``"trace"``, ``"debug"``, ``"info"``, ``"warning"``, ``"error"``, ``"fatal"``
-Defaults to "info" or "debug", depending on `debug`_ option.
+Defaults to "info" or "debug", depending on `debug`_ option. When debug mode is enabled, Loglevel is set to "debug", otherwise, Loglevel is set to "info".
Most modules of NNI will be affected by this value, including NNI manager, tuner, training service, etc.
@@ -266,13 +271,13 @@ For Python modules, "trace" acts as logging level 0 and "fatal" acts as ``loggin
experimentWorkingDirectory
--------------------------
-Specify the `directory `_ to place log, checkpoint, metadata, and other run-time stuff.
+Specify the :ref:`directory ` to place log, checkpoint, metadata, and other run-time stuff.
type: ``Optional[str]``
By default uses ``~/nni-experiments``.
-NNI will create a subdirectory named by experiment ID, so it is safe to use same directory for multiple experiments.
+NNI will create a subdirectory named by experiment ID, so it is safe to use the same directory for multiple experiments.
tunerGpuIndices
@@ -280,53 +285,67 @@ tunerGpuIndices
Limit the GPUs visible to tuner, assessor, and advisor.
-type: ``Optional[list[int] | str]``
+type: ``Optional[list[int] | str | int]``
This will be the ``CUDA_VISIBLE_DEVICES`` environment variable of tuner process.
-Because tuner, assessor, and advisor run in same process, this option will affect them all.
+Because tuner, assessor, and advisor run in the same process, this option will affect them all.
tuner
-----
-Specify the tuner.
+Specify the tuner.
type: Optional `AlgorithmConfig`_
+The built-in tuners can be found `here <../builtin_tuner.rst>`__ and you can follow `this tutorial <../Tuner/CustomizeTuner.rst>`__ to customize a new tuner.
+
assessor
--------
-Specify the assessor.
+Specify the assessor.
type: Optional `AlgorithmConfig`_
+The built-in assessors can be found `here <../builtin_assessor.rst>`__ and you can follow `this tutorial <../Assessor/CustomizeAssessor.rst>`__ to customize a new assessor.
+
advisor
-------
-Specify the advisor.
+Specify the advisor.
type: Optional `AlgorithmConfig`_
+NNI provides two built-in advisors: `BOHB <../Tuner/BohbAdvisor.rst>`__ and `Hyperband <../Tuner/HyperbandAdvisor.rst>`__, and you can follow `this tutorial <../Tuner/CustomizeAdvisor.rst>`__ to customize a new advisor.
+
trainingService
---------------
-Specify `training service <../TrainingService/Overview.rst>`__.
+Specify the `training service <../TrainingService/Overview.rst>`__.
type: `TrainingServiceConfig`_
+sharedStorage
+-------------
+
+Configure the shared storage, detailed usage can be found `here <../Tutorial/HowToUseSharedStorage.rst>`__.
+
+type: Optional `SharedStorageConfig`_
+
+
AlgorithmConfig
^^^^^^^^^^^^^^^
``AlgorithmConfig`` describes a tuner / assessor / advisor algorithm.
-For custom algorithms, there are two ways to describe them:
+For customized algorithms, there are two ways to describe them:
- 1. `Register the algorithm <../Tuner/InstallCustomizedTuner.rst>`__ to use it like built-in. (preferred)
+ 1. `Register the algorithm <../Tutorial/InstallCustomizedAlgos.rst>`__ to use it like built-in. (preferred)
2. Specify code directory and class name directly.
@@ -334,17 +353,17 @@ For custom algorithms, there are two ways to describe them:
name
----
-Name of built-in or registered algorithm.
+Name of the built-in or registered algorithm.
-type: ``str`` for built-in and registered algorithm, ``None`` for other custom algorithm
+type: ``str`` for the built-in and registered algorithm, ``None`` for other customized algorithms.
className
---------
-Qualified class name of not registered custom algorithm.
+Qualified class name of not registered customized algorithm.
-type: ``None`` for built-in and registered algorithm, ``str`` for other custom algorithm
+type: ``None`` for the built-in and registered algorithm, ``str`` for other customized algorithms.
example: ``"my_tuner.MyTuner"``
@@ -352,9 +371,9 @@ example: ``"my_tuner.MyTuner"``
codeDirectory
-------------
-`Path`_ to directory containing the custom algorithm class.
+`Path`_ to the directory containing the customized algorithm class.
-type: ``None`` for built-in and registered algorithm, ``str`` for other custom algorithm
+type: ``None`` for the built-in and registered algorithm, ``str`` for other customized algorithms.
classArgs
@@ -370,41 +389,51 @@ See algorithm's document for supported value.
TrainingServiceConfig
^^^^^^^^^^^^^^^^^^^^^
-One of following:
+One of the following:
- `LocalConfig`_
- `RemoteConfig`_
-- `OpenpaiConfig `_
+- :ref:`OpenpaiConfig `
- `AmlConfig`_
+- `HybridConfig`_
-For other training services, we suggest to use `v1 config schema <../Tutorial/ExperimentConfig.rst>`_ for now.
+For `Kubeflow <../TrainingService/KubeflowMode.rst>`_, `FrameworkController <../TrainingService/FrameworkControllerMode.rst>`_, and `AdaptDL <../TrainingService/AdaptDLMode.rst>`_ training platforms, it is suggested to use `v1 config schema <../Tutorial/ExperimentConfig.rst>`_ for now.
LocalConfig
-^^^^^^^^^^^
+-----------
-Detailed `here <../TrainingService/LocalMode.rst>`__.
+Detailed usage can be found `here <../TrainingService/LocalMode.rst>`__.
platform
---------
+""""""""
Constant string ``"local"``.
useActiveGpu
-------------
+""""""""""""
Specify whether NNI should submit trials to GPUs occupied by other tasks.
type: ``Optional[bool]``
-Must be set when `trialGpuNumber` greater than zero.
+Must be set when `trialGpuNumber`_ greater than zero.
-If your are using desktop system with GUI, set this to ``True``.
+Following processes can make GPU "active":
+
+ - non-NNI CUDA programs
+ - graphical desktop
+ - trials submitted by other NNI instances, if you have more than one NNI experiments running at same time
+ - other users' CUDA programs, if you are using a shared server
+
+If you are using a graphical OS like Windows 10 or Ubuntu desktop, set this field to ``True``, otherwise, the GUI will prevent NNI from launching any trial.
+
+When you create multiple NNI experiments and ``useActiveGpu`` is set to ``True``, they will submit multiple trials to the same GPU(s) simultaneously.
maxTrialNumberPerGpu
----------------------
+""""""""""""""""""""
Specify how many trials can share one GPU.
@@ -414,11 +443,11 @@ default: ``1``
gpuIndices
-----------
+""""""""""
Limit the GPUs visible to trial processes.
-type: ``Optional[list[int] | str]``
+type: ``Optional[list[int] | str | int]``
If `trialGpuNumber`_ is less than the length of this value, only a subset will be visible to each trial.
@@ -426,18 +455,18 @@ This will be used as ``CUDA_VISIBLE_DEVICES`` environment variable.
RemoteConfig
-^^^^^^^^^^^^
+------------
-Detailed `here <../TrainingService/RemoteMachineMode.rst>`__.
+Detailed usage can be found `here <../TrainingService/RemoteMachineMode.rst>`__.
platform
---------
+""""""""
Constant string ``"remote"``.
machineList
------------
+"""""""""""
List of training machines.
@@ -445,18 +474,18 @@ type: list of `RemoteMachineConfig`_
reuseMode
----------
+"""""""""
-Enable reuse `mode <../Tutorial/ExperimentConfig.rst#reuse>`__.
+Enable `reuse mode <../TrainingService/Overview.rst#training-service-under-reuse-mode>`__.
type: ``bool``
RemoteMachineConfig
-^^^^^^^^^^^^^^^^^^^
+"""""""""""""""""""
host
-----
+****
IP or hostname (domain name) of the machine.
@@ -464,7 +493,7 @@ type: ``str``
port
-----
+****
SSH service port.
@@ -474,7 +503,7 @@ default: ``22``
user
-----
+****
Login user name.
@@ -482,7 +511,7 @@ type: ``str``
password
---------
+********
Login password.
@@ -492,7 +521,7 @@ If not specified, `sshKeyFile`_ will be used instead.
sshKeyFile
-----------
+**********
`Path`_ to sshKeyFile (identity file).
@@ -502,7 +531,7 @@ Only used when `password`_ is not specified.
sshPassphrase
--------------
+*************
Passphrase of SSH identity file.
@@ -510,7 +539,7 @@ type: ``Optional[str]``
useActiveGpu
-------------
+************
Specify whether NNI should submit trials to GPUs occupied by other tasks.
@@ -518,9 +547,22 @@ type: ``bool``
default: ``False``
+Must be set when `trialGpuNumber`_ greater than zero.
+
+Following processes can make GPU "active":
+
+ - non-NNI CUDA programs
+ - graphical desktop
+ - trials submitted by other NNI instances, if you have more than one NNI experiments running at same time
+ - other users' CUDA programs, if you are using a shared server
+
+If your remote machine is a graphical OS like Ubuntu desktop, set this field to ``True``, otherwise, the GUI will prevent NNI from launching any trial.
+
+When you create multiple NNI experiments and ``useActiveGpu`` is set to ``True``, they will submit multiple trials to the same GPU(s) simultaneously.
+
maxTrialNumberPerGpu
---------------------
+********************
Specify how many trials can share one GPU.
@@ -530,11 +572,11 @@ default: ``1``
gpuIndices
-----------
+**********
Limit the GPUs visible to trial processes.
-type: ``Optional[list[int] | str]``
+type: ``Optional[list[int] | str | int]``
If `trialGpuNumber`_ is less than the length of this value, only a subset will be visible to each trial.
@@ -542,47 +584,51 @@ This will be used as ``CUDA_VISIBLE_DEVICES`` environment variable.
pythonPath
--------------------
+**********
-Specify a python environment, this path will insert at the front of PATH. Here are some examples:
- - (linux) pythonPath: /opt/python3.7/bin
- - (windows) pythonPath: C:/Python37
-
-Notice: If you are working on anaconda，there are some difference. You have to add "../script" and "../Library/bin" to this and separated by ";" on windows, example as below:
- - (linux anaconda) pythonPath: /home/yourname/anaconda3/envs/myenv/bin/
- - (windows anaconda) pythonPath: C:/Users/yourname/.conda/envs/myenv;C:/Users/yourname/.conda/envs/myenv/Scripts;C:/Users/yourname/.conda/envs/myenv/Library/bin
+Specify a Python environment.
type: ``Optional[str]``
+This path will be inserted at the front of PATH. Here are some examples:
+
+ - (linux) pythonPath: ``/opt/python3.7/bin``
+ - (windows) pythonPath: ``C:/Python37``
+
+If you are working on Anaconda, there is some difference. On Windows, you also have to add ``../script`` and ``../Library/bin`` separated by ``;``. Examples are as below:
+
+ - (linux anaconda) pythonPath: ``/home/yourname/anaconda3/envs/myenv/bin/``
+ - (windows anaconda) pythonPath: ``C:/Users/yourname/.conda/envs/myenv;C:/Users/yourname/.conda/envs/myenv/Scripts;C:/Users/yourname/.conda/envs/myenv/Library/bin``
+
This is useful if preparing steps vary for different machines.
.. _openpai-class:
OpenpaiConfig
-^^^^^^^^^^^^^
+-------------
-Detailed `here <../TrainingService/PaiMode.rst>`__.
+Detailed usage can be found `here <../TrainingService/PaiMode.rst>`__.
platform
---------
+""""""""
Constant string ``"openpai"``.
host
-----
+""""
Hostname of OpenPAI service.
type: ``str``
-This may includes ``https://`` or ``http://`` prefix.
+This may include ``https://`` or ``http://`` prefix.
HTTPS will be used by default.
username
---------
+""""""""
OpenPAI user name.
@@ -590,7 +636,7 @@ type: ``str``
token
------
+"""""
OpenPAI user token.
@@ -599,8 +645,36 @@ type: ``str``
This can be found in your OpenPAI user settings page.
+trialCpuNumber
+""""""""""""""
+
+Specify the CPU number of each trial to be used in OpenPAI container.
+
+type: ``int``
+
+
+trialMemorySize
+"""""""""""""""
+
+Specify the memory size of each trial to be used in OpenPAI container.
+
+type: ``str``
+
+format: ``number + tb|gb|mb|kb``
+
+examples: ``"8gb"``, ``"8192mb"``
+
+
+storageConfigName
+"""""""""""""""""
+
+Specify the storage name used in OpenPAI.
+
+type: ``str``
+
+
dockerImage
------------
+"""""""""""
Name and tag of docker image to run the trials.
@@ -609,16 +683,16 @@ type: ``str``
default: ``"msranni/nni:latest"``
-nniManagerStorageMountPoint
----------------------------
+localStorageMountPoint
+""""""""""""""""""""""
-`Mount point `_ of storage service (typically NFS) on current machine.
+:ref:`Mount point ` of storage service (typically NFS) on the local machine.
type: ``str``
containerStorageMountPoint
---------------------------
+""""""""""""""""""""""""""
Mount point of storage service (typically NFS) in docker container.
@@ -628,9 +702,9 @@ This must be an absolute path.
reuseMode
----------
+"""""""""
-Enable reuse `mode <../Tutorial/ExperimentConfig.rst#reuse>`__.
+Enable `reuse mode <../TrainingService/Overview.rst#training-service-under-reuse-mode>`__.
type: ``bool``
@@ -638,7 +712,7 @@ default: ``False``
openpaiConfig
--------------
+"""""""""""""
Embedded OpenPAI config file.
@@ -646,29 +720,29 @@ type: ``Optional[JSON]``
openpaiConfigFile
------------------
+"""""""""""""""""
`Path`_ to OpenPAI config file.
type: ``Optional[str]``
-An example can be found `here `__
+An example can be found `here `__.
AmlConfig
-^^^^^^^^^
+---------
-Detailed `here <../TrainingService/AMLMode.rst>`__.
+Detailed usage can be found `here <../TrainingService/AMLMode.rst>`__.
platform
---------
+""""""""
Constant string ``"aml"``.
dockerImage
------------
+"""""""""""
Name and tag of docker image to run the trials.
@@ -678,7 +752,7 @@ default: ``"msranni/nni:latest"``
subscriptionId
---------------
+""""""""""""""
Azure subscription ID.
@@ -686,7 +760,7 @@ type: ``str``
resourceGroup
--------------
+"""""""""""""
Azure resource group name.
@@ -694,7 +768,7 @@ type: ``str``
workspaceName
--------------
+"""""""""""""
Azure workspace name.
@@ -702,8 +776,157 @@ type: ``str``
computeTarget
--------------
+"""""""""""""
AML compute cluster name.
type: ``str``
+
+
+HybridConfig
+------------
+
+Currently only support `LocalConfig`_, `RemoteConfig`_, :ref:`OpenpaiConfig ` and `AmlConfig`_ . Detailed usage can be found `here <../TrainingService/HybridMode.rst>`__.
+
+type: list of `TrainingServiceConfig`_
+
+
+SharedStorageConfig
+^^^^^^^^^^^^^^^^^^^
+
+Detailed usage can be found `here <../Tutorial/HowToUseSharedStorage.rst>`__.
+
+
+nfsConfig
+---------
+
+storageType
+"""""""""""
+
+Constant string ``"NFS"``.
+
+
+localMountPoint
+"""""""""""""""
+
+The path that the storage has been or will be mounted in the local machine.
+
+type: ``str``
+
+If the path does not exist, it will be created automatically. Recommended to use an absolute path, i.e. ``/tmp/nni-shared-storage``.
+
+
+remoteMountPoint
+""""""""""""""""
+
+The path that the storage will be mounted in the remote achine.
+
+type: ``str``
+
+If the path does not exist, it will be created automatically. Recommended to use a relative path. i.e. ``./nni-shared-storage``.
+
+
+localMounted
+""""""""""""
+
+Specify the object and status to mount the shared storage.
+
+type: ``str``
+
+values: ``"usermount"``, ``"nnimount"``, ``"nomount"``
+
+``usermount`` means the user has already mounted this storage on localMountPoint. ``nnimount`` means NNI will try to mount this storage on localMountPoint. ``nomount`` means storage will not mount in the local machine, will support partial storages in the future.
+
+
+nfsServer
+"""""""""
+
+NFS server host.
+
+type: ``str``
+
+
+exportedDirectory
+"""""""""""""""""
+
+Exported directory of NFS server, detailed `here `_.
+
+type: ``str``
+
+
+azureBlobConfig
+---------------
+
+storageType
+"""""""""""
+
+Constant string ``"AzureBlob"``.
+
+
+localMountPoint
+"""""""""""""""
+
+The path that the storage has been or will be mounted in the local machine.
+
+type: ``str``
+
+If the path does not exist, it will be created automatically. Recommended to use an absolute path, i.e. ``/tmp/nni-shared-storage``.
+
+
+remoteMountPoint
+""""""""""""""""
+
+The path that the storage will be mounted in the remote achine.
+
+type: ``str``
+
+If the path does not exist, it will be created automatically. Recommended to use a relative path. i.e. ``./nni-shared-storage``.
+
+Note that the directory must be empty when using AzureBlob.
+
+
+localMounted
+""""""""""""
+
+Specify the object and status to mount the shared storage.
+
+type: ``str``
+
+values: ``"usermount"``, ``"nnimount"``, ``"nomount"``
+
+``usermount`` means the user has already mounted this storage on localMountPoint. ``nnimount`` means NNI will try to mount this storage on localMountPoint. ``nomount`` means storage will not mount in the local machine, will support partial storages in the future.
+
+
+storageAccountName
+""""""""""""""""""
+
+Azure storage account name.
+
+type: ``str``
+
+
+storageAccountKey
+"""""""""""""""""
+
+Azure storage account key.
+
+type: ``Optional[str]``
+
+When not set storageAccountKey, should use ``az login`` with Azure CLI at first and set `resourceGroupName`_.
+
+
+resourceGroupName
+"""""""""""""""""
+
+Resource group that AzureBlob container belongs to.
+
+type: ``Optional[str]``
+
+Required if ``storageAccountKey`` not set.
+
+containerName
+"""""""""""""
+
+AzureBlob container name.
+
+type: ``str``
diff --git a/docs/en_US/sdk_reference.rst b/docs/en_US/sdk_reference.rst
index c615f86ab8..1e13a1ff1f 100644
--- a/docs/en_US/sdk_reference.rst
+++ b/docs/en_US/sdk_reference.rst
@@ -7,5 +7,5 @@ Python API Reference
:maxdepth: 1
Auto Tune
- NAS
+ NAS
Compression
\ No newline at end of file
diff --git a/docs/img/webui-img/compare.png b/docs/img/webui-img/compare.png
index 944e3be0ea..7df344331b 100644
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index 99b4a663ee..d41301b467 100644
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diff --git a/docs/img/webui-img/detail/customizedTrial.png b/docs/img/webui-img/detail/customizedTrial.png
new file mode 100644
index 0000000000..0457faef02
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diff --git a/docs/img/webui-img/detail/customizedTrialButton.png b/docs/img/webui-img/detail/customizedTrialButton.png
new file mode 100644
index 0000000000..c348b83c17
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diff --git a/docs/img/webui-img/detail/log-local.png b/docs/img/webui-img/detail/log-local.png
new file mode 100644
index 0000000000..b1a57772fd
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diff --git a/docs/img/webui-img/detail/searchId.png b/docs/img/webui-img/detail/searchId.png
new file mode 100644
index 0000000000..3eea960f72
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diff --git a/docs/img/webui-img/detail/searchNo.png b/docs/img/webui-img/detail/searchNo.png
new file mode 100644
index 0000000000..04515f0213
Binary files /dev/null and b/docs/img/webui-img/detail/searchNo.png differ
diff --git a/docs/img/webui-img/detail/searchParameterChoice.png b/docs/img/webui-img/detail/searchParameterChoice.png
new file mode 100644
index 0000000000..2f95a0ef29
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diff --git a/docs/img/webui-img/detail/searchParameterRange.png b/docs/img/webui-img/detail/searchParameterRange.png
new file mode 100644
index 0000000000..465e11c802
Binary files /dev/null and b/docs/img/webui-img/detail/searchParameterRange.png differ
diff --git a/docs/img/webui-img/detail/searchStatus.png b/docs/img/webui-img/detail/searchStatus.png
new file mode 100644
index 0000000000..635a326d95
Binary files /dev/null and b/docs/img/webui-img/detail/searchStatus.png differ
diff --git a/docs/img/webui-img/select-trial.png b/docs/img/webui-img/select-trial.png
index 1635220581..30e89afa4f 100644
Binary files a/docs/img/webui-img/select-trial.png and b/docs/img/webui-img/select-trial.png differ
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 47c66e982d..32acd7824c 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -20,7 +20,7 @@ websockets
filelock
prettytable
psutil
-ruamel.yaml
+pyyaml
ipython
gym
tianshou
diff --git a/docs/static/css/custom.css b/docs/static/css/custom.css
index e1a667bf0a..fa4b9bb293 100644
--- a/docs/static/css/custom.css
+++ b/docs/static/css/custom.css
@@ -91,7 +91,7 @@ td.framework{
}
.ui-img img{
- height: 230px;
+ height: 350px;
}
.wy-side-nav-search>div.version{
diff --git a/docs/static/img/webui.gif b/docs/static/img/webui.gif
new file mode 100644
index 0000000000..47d6042724
Binary files /dev/null and b/docs/static/img/webui.gif differ
diff --git a/examples/experiment_config.yml b/examples/experiment_config.yml
index 2294bb3206..125544f3ac 100644
--- a/examples/experiment_config.yml
+++ b/examples/experiment_config.yml
@@ -9,13 +9,13 @@ searchSpacePath:
#choice: true, false
useAnnotation:
tuner:
- #choice: TPE, Random, Anneal, Evolution
+ #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
builtinTunerName:
classArgs:
#choice: maximize, minimize
optimize_mode:
assessor:
- #choice: Medianstop
+ #choice: Medianstop, Curvefitting
builtinAssessorName:
classArgs:
#choice: maximize, minimize
@@ -29,4 +29,4 @@ machineList:
- ip:
port:
username:
- passwd:
\ No newline at end of file
+ passwd:
diff --git a/examples/model_compress/auto_compress/torch/auto_compress_module.py b/examples/model_compress/auto_compress/torch/auto_compress_module.py
new file mode 100644
index 0000000000..a70d6e470f
--- /dev/null
+++ b/examples/model_compress/auto_compress/torch/auto_compress_module.py
@@ -0,0 +1,129 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from typing import Callable, Optional, Iterable
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torchvision import datasets, transforms
+
+from nni.algorithms.compression.pytorch.auto_compress import AbstractAutoCompressionModule
+
+torch.manual_seed(1)
+
+class LeNet(nn.Module):
+ def __init__(self):
+ super(LeNet, self).__init__()
+ self.conv1 = nn.Conv2d(1, 32, 3, 1)
+ self.conv2 = nn.Conv2d(32, 64, 3, 1)
+ self.dropout1 = nn.Dropout2d(0.25)
+ self.dropout2 = nn.Dropout2d(0.5)
+ self.fc1 = nn.Linear(9216, 128)
+ self.fc2 = nn.Linear(128, 10)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = F.relu(x)
+ x = self.conv2(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 2)
+ x = self.dropout1(x)
+ x = torch.flatten(x, 1)
+ x = self.fc1(x)
+ x = F.relu(x)
+ x = self.dropout2(x)
+ x = self.fc2(x)
+ output = F.log_softmax(x, dim=1)
+ return output
+
+_use_cuda = torch.cuda.is_available()
+
+_train_kwargs = {'batch_size': 64}
+_test_kwargs = {'batch_size': 1000}
+if _use_cuda:
+ _cuda_kwargs = {'num_workers': 1,
+ 'pin_memory': True,
+ 'shuffle': True}
+ _train_kwargs.update(_cuda_kwargs)
+ _test_kwargs.update(_cuda_kwargs)
+
+_transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize((0.1307,), (0.3081,))
+])
+
+_device = torch.device("cuda" if _use_cuda else "cpu")
+
+_train_loader = None
+_test_loader = None
+
+def _train(model, optimizer, criterion, epoch):
+ global _train_loader
+ if _train_loader is None:
+ dataset = datasets.MNIST('./data', train=True, download=True, transform=_transform)
+ _train_loader = torch.utils.data.DataLoader(dataset, **_train_kwargs)
+ model.train()
+ for data, target in _train_loader:
+ data, target = data.to(_device), target.to(_device)
+ optimizer.zero_grad()
+ output = model(data)
+ loss = criterion(output, target)
+ loss.backward()
+ optimizer.step()
+
+def _test(model):
+ global _test_loader
+ if _test_loader is None:
+ dataset = datasets.MNIST('./data', train=False, transform=_transform)
+ _test_loader = torch.utils.data.DataLoader(dataset, **_test_kwargs)
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in _test_loader:
+ data, target = data.to(_device), target.to(_device)
+ output = model(data)
+ test_loss += F.nll_loss(output, target, reduction='sum').item()
+ pred = output.argmax(dim=1, keepdim=True)
+ correct += pred.eq(target.view_as(pred)).sum().item()
+ test_loss /= len(_test_loader.dataset)
+ acc = 100 * correct / len(_test_loader.dataset)
+ print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+ test_loss, correct, len(_test_loader.dataset), acc))
+ return acc
+
+_model = LeNet().to(_device)
+_model.load_state_dict(torch.load('mnist_pretrain_lenet.pth'))
+
+class AutoCompressionModule(AbstractAutoCompressionModule):
+ @classmethod
+ def model(cls) -> nn.Module:
+ return _model
+
+ @classmethod
+ def evaluator(cls) -> Callable[[nn.Module], float]:
+ return _test
+
+ @classmethod
+ def optimizer_factory(cls) -> Optional[Callable[[Iterable], optim.Optimizer]]:
+ def _optimizer_factory(params: Iterable):
+ return torch.optim.SGD(params, lr=0.01)
+ return _optimizer_factory
+
+ @classmethod
+ def criterion(cls) -> Optional[Callable]:
+ return F.nll_loss
+
+ @classmethod
+ def sparsifying_trainer(cls, compress_algorithm_name: str) -> Optional[Callable[[nn.Module, optim.Optimizer, Callable, int], None]]:
+ return _train
+
+ @classmethod
+ def post_compress_finetuning_trainer(cls, compress_algorithm_name: str) -> Optional[Callable[[nn.Module, optim.Optimizer, Callable, int], None]]:
+ return _train
+
+ @classmethod
+ def post_compress_finetuning_epochs(cls, compress_algorithm_name: str) -> int:
+ return 2
diff --git a/examples/model_compress/auto_compress/torch/auto_compress_torch.py b/examples/model_compress/auto_compress/torch/auto_compress_torch.py
new file mode 100644
index 0000000000..5ba69ebc8e
--- /dev/null
+++ b/examples/model_compress/auto_compress/torch/auto_compress_torch.py
@@ -0,0 +1,50 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from pathlib import Path
+
+from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionExperiment, AutoCompressionSearchSpaceGenerator
+
+from auto_compress_module import AutoCompressionModule
+
+generator = AutoCompressionSearchSpaceGenerator()
+generator.add_config('level', [
+ {
+ "sparsity": {
+ "_type": "uniform",
+ "_value": [0.01, 0.99]
+ },
+ 'op_types': ['default']
+ }
+])
+generator.add_config('l1', [
+ {
+ "sparsity": {
+ "_type": "uniform",
+ "_value": [0.01, 0.99]
+ },
+ 'op_types': ['Conv2d']
+ }
+])
+generator.add_config('qat', [
+ {
+ 'quant_types': ['weight', 'output'],
+ 'quant_bits': {
+ 'weight': 8,
+ 'output': 8
+ },
+ 'op_types': ['Conv2d', 'Linear']
+ }])
+search_space = generator.dumps()
+
+experiment = AutoCompressionExperiment(AutoCompressionModule, 'local')
+experiment.config.experiment_name = 'auto compression torch example'
+experiment.config.trial_concurrency = 1
+experiment.config.max_trial_number = 10
+experiment.config.search_space = search_space
+experiment.config.trial_code_directory = Path(__file__).parent
+experiment.config.tuner.name = 'TPE'
+experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+experiment.config.training_service.use_active_gpu = True
+
+experiment.run(8088)
diff --git a/examples/model_compress/auto_compress/torch/mnist_pretrain_lenet.pth b/examples/model_compress/auto_compress/torch/mnist_pretrain_lenet.pth
new file mode 100644
index 0000000000..f5afbe63fc
Binary files /dev/null and b/examples/model_compress/auto_compress/torch/mnist_pretrain_lenet.pth differ
diff --git a/examples/model_compress/pruning/basic_pruners_torch.py b/examples/model_compress/pruning/basic_pruners_torch.py
index 51c2068aa3..1da4c6994f 100644
--- a/examples/model_compress/pruning/basic_pruners_torch.py
+++ b/examples/model_compress/pruning/basic_pruners_torch.py
@@ -230,12 +230,15 @@ def trainer(model, optimizer, criterion, epoch):
kw_args['optimizer'] = optimizer
kw_args['criterion'] = criterion
- if args.pruner in ('slim', 'mean_activation', 'apoz', 'taylorfo'):
- kw_args['sparsity_training_epochs'] = 5
+ if args.pruner in ('mean_activation', 'apoz', 'taylorfo'):
+ kw_args['sparsifying_training_batches'] = 1
+
+ if args.pruner == 'slim':
+ kw_args['sparsifying_training_epochs'] = 1
if args.pruner == 'agp':
kw_args['pruning_algorithm'] = 'l1'
- kw_args['num_iterations'] = 5
+ kw_args['num_iterations'] = 2
kw_args['epochs_per_iteration'] = 1
# Reproduced result in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS',
@@ -268,10 +271,11 @@ def trainer(model, optimizer, criterion, epoch):
if args.test_only:
test(args, model, device, criterion, test_loader)
- # Unwrap all modules to normal state
- pruner._unwrap_model()
- m_speedup = ModelSpeedup(model, dummy_input, mask_path, device)
- m_speedup.speedup_model()
+ if args.speed_up:
+ # Unwrap all modules to normal state
+ pruner._unwrap_model()
+ m_speedup = ModelSpeedup(model, dummy_input, mask_path, device)
+ m_speedup.speedup_model()
print('start finetuning...')
best_top1 = 0
@@ -332,6 +336,10 @@ def trainer(model, optimizer, criterion, epoch):
'fpgm', 'mean_activation', 'apoz', 'taylorfo'],
help='pruner to use')
+ # speed-up
+ parser.add_argument('--speed-up', action='store_true', default=False,
+ help='Whether to speed-up the pruned model')
+
# fine-tuning
parser.add_argument('--fine-tune-epochs', type=int, default=160,
help='epochs to fine tune')
diff --git a/examples/model_compress/pruning/naive_prune_tf.py b/examples/model_compress/pruning/naive_prune_tf.py
index 159ffcbe28..f224342d7e 100644
--- a/examples/model_compress/pruning/naive_prune_tf.py
+++ b/examples/model_compress/pruning/naive_prune_tf.py
@@ -10,9 +10,9 @@
import tensorflow as tf
from tensorflow.keras import Model
-from tensorflow.keras.layers import (Conv2D, Dense, Dropout, Flatten, MaxPool2D)
+from tensorflow.keras.layers import (Conv2D, Dense, Dropout, Flatten, MaxPool2D, BatchNormalization)
-from nni.algorithms.compression.tensorflow.pruning import LevelPruner
+from nni.algorithms.compression.tensorflow.pruning import LevelPruner, SlimPruner
class LeNet(Model):
"""
@@ -34,8 +34,10 @@ def __init__(self, conv_size=3, hidden_size=32, dropout_rate=0.5):
super().__init__()
self.conv1 = Conv2D(filters=32, kernel_size=conv_size, activation='relu')
self.pool1 = MaxPool2D(pool_size=2)
+ self.bn1 = BatchNormalization()
self.conv2 = Conv2D(filters=64, kernel_size=conv_size, activation='relu')
self.pool2 = MaxPool2D(pool_size=2)
+ self.bn2 = BatchNormalization()
self.flatten = Flatten()
self.fc1 = Dense(units=hidden_size, activation='relu')
self.dropout = Dropout(rate=dropout_rate)
@@ -45,8 +47,10 @@ def call(self, x):
"""Override ``Model.call`` to build LeNet-5 model."""
x = self.conv1(x)
x = self.pool1(x)
+ x = self.bn1(x)
x = self.conv2(x)
x = self.pool2(x)
+ x = self.bn2(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.dropout(x)
@@ -85,12 +89,29 @@ def main(args):
model = LeNet()
print('start training')
+
optimizer = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.9, decay=1e-4)
- model.compile(
- optimizer=optimizer,
- loss='sparse_categorical_crossentropy',
- metrics=['accuracy']
- )
+
+ if args.pruner_name == 'slim':
+ def slim_loss(y_true, y_pred):
+ loss_1 = tf.keras.losses.sparse_categorical_crossentropy(y_true=y_true, y_pred=y_pred)
+ weight_list = []
+ for layer in [model.bn1, model.bn2]:
+ weight_list.append([w for w in layer.weights if '/gamma:' in w.name][0].read_value())
+ loss_2 = 0.0001 * tf.reduce_sum([tf.reduce_sum(tf.abs(w)) for w in weight_list])
+ return loss_1 + loss_2
+ model.compile(
+ optimizer=optimizer,
+ loss=slim_loss,
+ metrics=['accuracy']
+ )
+ else:
+ model.compile(
+ optimizer=optimizer,
+ loss='sparse_categorical_crossentropy',
+ metrics=['accuracy']
+ )
+
model.fit(
train_set[0],
train_set[1],
@@ -103,13 +124,19 @@ def main(args):
optimizer_finetune = tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.9, decay=1e-4)
# create_pruner
- prune_config = [{
- 'sparsity': args.sparsity,
- 'op_types': ['default'],
- }]
-
- pruner = LevelPruner(model, prune_config)
- # pruner = create_pruner(model, args.pruner_name)
+ if args.pruner_name == 'level':
+ prune_config = [{
+ 'sparsity': args.sparsity,
+ 'op_types': ['default'],
+ }]
+ pruner = LevelPruner(model, prune_config)
+ elif args.pruner_name == 'slim':
+ prune_config = [{
+ 'sparsity': args.sparsity,
+ 'op_types': ['BatchNormalization'],
+ }]
+ pruner = SlimPruner(model, prune_config)
+
model = pruner.compress()
model.compile(
@@ -131,7 +158,7 @@ def main(args):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
- parser.add_argument('--pruner_name', type=str, default='level')
+ parser.add_argument('--pruner_name', type=str, default='level', choices=['level', 'slim'])
parser.add_argument('--batch-size', type=int, default=256)
parser.add_argument('--pretrain_epochs', type=int, default=10)
parser.add_argument('--prune_epochs', type=int, default=10)
diff --git a/examples/nas/multi-trial/mnasnet/search.py b/examples/nas/multi-trial/mnasnet/search.py
index f9f3074478..79c6cd3161 100644
--- a/examples/nas/multi-trial/mnasnet/search.py
+++ b/examples/nas/multi-trial/mnasnet/search.py
@@ -53,5 +53,6 @@
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 10
exp_config.training_service.use_active_gpu = False
+ exp_config.execution_engine = 'base'
exp.run(exp_config, 8097)
diff --git a/examples/nas/multi-trial/mnist/search.py b/examples/nas/multi-trial/mnist/search.py
index 0d9f4340fe..b3c37b04e0 100644
--- a/examples/nas/multi-trial/mnist/search.py
+++ b/examples/nas/multi-trial/mnist/search.py
@@ -4,22 +4,23 @@
import nni.retiarii.strategy as strategy
import nni.retiarii.evaluator.pytorch.lightning as pl
import torch.nn.functional as F
-from nni.retiarii import serialize
+from nni.retiarii import serialize, model_wrapper
from nni.retiarii.experiment.pytorch import RetiariiExeConfig, RetiariiExperiment, debug_mutated_model
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
-
+# comment the follwing line for graph-based execution engine
+@model_wrapper
class Net(nn.Module):
def __init__(self, hidden_size):
- super(Net, self).__init__()
+ super().__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.LayerChoice([
nn.Linear(4*4*50, hidden_size),
nn.Linear(4*4*50, hidden_size, bias=False)
- ])
+ ], label='fc1_choice')
self.fc2 = nn.Linear(hidden_size, 10)
def forward(self, x):
@@ -42,10 +43,6 @@ def forward(self, x):
val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
max_epochs=2)
- # uncomment the following two lines to debug a generated model
- #debug_mutated_model(base_model, trainer, [])
- #exit(0)
-
simple_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, trainer, [], simple_strategy)
@@ -55,8 +52,13 @@ def forward(self, x):
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 2
exp_config.training_service.use_active_gpu = False
+ export_formatter = 'dict'
+
+ # uncomment this for graph-based execution engine
+ # exp_config.execution_engine = 'base'
+ # export_formatter = 'code'
exp.run(exp_config, 8081 + random.randint(0, 100))
print('Final model:')
- for model_code in exp.export_top_models():
+ for model_code in exp.export_top_models(formatter=export_formatter):
print(model_code)
diff --git a/examples/nas/oneshot/pfld/train.py b/examples/nas/oneshot/pfld/train.py
index 183cf49529..d2530faf87 100644
--- a/examples/nas/oneshot/pfld/train.py
+++ b/examples/nas/oneshot/pfld/train.py
@@ -63,6 +63,7 @@ def main(args):
alpha=args.alpha,
beta=args.beta,
search_space=search_space,
+ start_epoch=args.start_epoch,
)
# look-up table with information of search space, flops per block, etc.
lookup_table = LookUpTable(config=nas_config, primitives=PRIMITIVES)
@@ -162,6 +163,7 @@ def parse_args():
)
parser.add_argument("--alpha", default=0.25, type=float)
parser.add_argument("--beta", default=0.6, type=float)
+ parser.add_argument("--start_epoch", default=50, type=int)
parser.add_argument("--end_epoch", default=300, type=int)
parser.add_argument(
"--snapshot", default="models", type=str, metavar="PATH"
diff --git a/examples/notebooks/Retiarii_example_multi-trial_NAS.ipynb b/examples/notebooks/Retiarii_example_multi-trial_NAS.ipynb
new file mode 100644
index 0000000000..3c44648082
--- /dev/null
+++ b/examples/notebooks/Retiarii_example_multi-trial_NAS.ipynb
@@ -0,0 +1,953 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Retiarii Example - Multi-trial NAS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This example will show Retiarii's ability to **express** and **explore** the model space for Neural Architecture Search and Hyper-Parameter Tuning in a simple way. The video demo is in [YouTube](https://youtu.be/eQUlABCO0o8) and [Bilibili](https://www.bilibili.com/video/BV14h411v7kZ/).\n",
+ "\n",
+ "Let's start the journey with Retiarii!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1: Express the Model Space"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Model space is defined by users to express a set of models that they want to explore, which contains potentially good-performing models. In Retiarii framework, a model space is defined with two parts: a base model and possible mutations on the base model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 1.1: Define the Base Model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Defining a base model is almost the same as defining a PyTorch (or TensorFlow) model. Usually, you only need to replace the code ``import torch.nn as nn`` with ``import nni.retiarii.nn.pytorch as nn`` to use NNI wrapped PyTorch modules. Below is a very simple example of defining a base model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn.functional as F\n",
+ "import nni.retiarii.nn.pytorch as nn\n",
+ "\n",
+ "class Net(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.conv1 = nn.Conv2d(3, 6, 3, padding=1)\n",
+ " self.pool = nn.MaxPool2d(2, 2)\n",
+ " self.conv2 = nn.Conv2d(6, 16, 3, padding=1)\n",
+ " self.conv3 = nn.Conv2d(16, 16, 1)\n",
+ "\n",
+ " self.bn = nn.BatchNorm2d(16)\n",
+ "\n",
+ " self.gap = nn.AdaptiveAvgPool2d(4)\n",
+ " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n",
+ " self.fc2 = nn.Linear(120, 84)\n",
+ " self.fc3 = nn.Linear(84, 10)\n",
+ "\n",
+ " def forward(self, x):\n",
+ " bs = x.size(0)\n",
+ "\n",
+ " x = self.pool(F.relu(self.conv1(x)))\n",
+ " x0 = F.relu(self.conv2(x))\n",
+ " x1 = F.relu(self.conv3(x0))\n",
+ "\n",
+ " x1 += x0\n",
+ " x = self.pool(self.bn(x1))\n",
+ "\n",
+ " x = self.gap(x).view(bs, -1)\n",
+ " x = F.relu(self.fc1(x))\n",
+ " x = F.relu(self.fc2(x))\n",
+ " x = self.fc3(x)\n",
+ " return x\n",
+ " \n",
+ "model = Net()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 1.2: Define the Model Mutations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A base model is only one concrete model, not a model space. NNI provides APIs and primitives for users to express how the base model can be mutated, i.e., a model space that includes many models. The following will use inline Mutation APIs ``LayerChoice`` to choose a layer from candidate operations and use ``InputChoice`` to try out skip connection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn.functional as F\n",
+ "import nni.retiarii.nn.pytorch as nn\n",
+ "\n",
+ "class Net(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " # self.conv1 = nn.Conv2d(3, 6, 3, padding=1)\n",
+ " self.conv1 = nn.LayerChoice([nn.Conv2d(3, 6, 3, padding=1), nn.Conv2d(3, 6, 5, padding=2)])\n",
+ " self.pool = nn.MaxPool2d(2, 2)\n",
+ " # self.conv2 = nn.Conv2d(6, 16, 3, padding=1)\n",
+ " self.conv2 = nn.LayerChoice([nn.Conv2d(6, 16, 3, padding=1), nn.Conv2d(6, 16, 5, padding=2)])\n",
+ " self.conv3 = nn.Conv2d(16, 16, 1)\n",
+ "\n",
+ " self.skipconnect = nn.InputChoice(n_candidates=2)\n",
+ " self.bn = nn.BatchNorm2d(16)\n",
+ "\n",
+ " self.gap = nn.AdaptiveAvgPool2d(4)\n",
+ " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n",
+ " self.fc2 = nn.Linear(120, 84)\n",
+ " self.fc3 = nn.Linear(84, 10)\n",
+ "\n",
+ " def forward(self, x):\n",
+ " bs = x.size(0)\n",
+ "\n",
+ " x = self.pool(F.relu(self.conv1(x)))\n",
+ " x0 = F.relu(self.conv2(x))\n",
+ " x1 = F.relu(self.conv3(x0))\n",
+ "\n",
+ " x1 = self.skipconnect([x1, x1+x0])\n",
+ " x = self.pool(self.bn(x1))\n",
+ "\n",
+ " x = self.gap(x).view(bs, -1)\n",
+ " x = F.relu(self.fc1(x))\n",
+ " x = F.relu(self.fc2(x))\n",
+ " x = self.fc3(x)\n",
+ " return x\n",
+ " \n",
+ "model = Net()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2: Explore the Model Space"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will demo ths **multi-trial** NAS method first. In the multi-trial NAS process, the search strategy repeatedly generates new models, and the model evaluator is for training and validating each generated model. The obtained performance of a generated model is collected and sent to the search strategy for generating better models. \n",
+ "\n",
+ "Users can choose a proper search strategy to explore the model space, and use a chosen or user-defined model evaluator to evaluate the performance of each sampled model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 2.1: Choose or Write a Search Strategy\n",
+ "\n",
+ "Currently, Retiarii supports many common strategies, such as Random, Regularized evolution and TPE, etc. According to the APIs of Retiarii, you can customize a new strategy easily, and there we use the TPE strategy as an example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import nni.retiarii.strategy as strategy\n",
+ "\n",
+ "simple_strategy = strategy.TPEStrategy() # choice: Random, GridSearch, RegularizedEvolution, TPEStrategy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 2.2: Choose or Write a Model Evaluator"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The model evaluator should correctly identify the use case of the model and the optimization goal. For example, on a classification task, an dataset is needed, the loss function could be cross entropy and the optimized metric could be the accuracy.\n",
+ "\n",
+ "Retiarii provides two ways for users to write a new model evaluator. In the context of PyTorch, Retiarii has provided two built-in model evaluators, designed for simple use cases: classification and regression. These two evaluators are built upon the awesome library PyTorch-Lightning. Here we take a classification task on CIFAR10 dataset as an example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:15:27] INFO (lightning/MainThread) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:15:27] INFO (lightning/MainThread) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:15:27] INFO (lightning/MainThread) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from torchvision import transforms\n",
+ "from torchvision.datasets import CIFAR10\n",
+ "from nni.retiarii import serialize\n",
+ "import nni.retiarii.evaluator.pytorch.lightning as pl\n",
+ "\n",
+ "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
+ "train_dataset = serialize(CIFAR10, root=\"./data\", train=True, download=True, transform=transform)\n",
+ "test_dataset = serialize(CIFAR10, root=\"./data\", train=False, download=True, transform=transform)\n",
+ "\n",
+ "trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=64),\n",
+ " val_dataloaders=pl.DataLoader(test_dataset, batch_size=64),\n",
+ " max_epochs=2, gpus=[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 2.3: Configure the Experiment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After all the above are prepared, it is time to configure an experiment to do the model search. The basic experiment configuration is as follows, and advanced configuration reference on [this page](https://nni.readthedocs.io/en/stable/reference/experiment_config.html).\n",
+ "\n",
+ "NNI allows users to run experiments in different training platforms to speed up the search, like Local Machine, Remote Servers, OpenPAI, Kubeflow, FrameworkController on K8S, DLWorkspace, Azure Machine Learning, AdaptDL, other cloud options, and even Hybrid mode. There we use the local mode with GPU speeding up."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nni.retiarii.experiment.pytorch import RetiariiExeConfig, RetiariiExperiment\n",
+ "\n",
+ "exp = RetiariiExperiment(model, trainer, [], simple_strategy)\n",
+ "\n",
+ "exp_config = RetiariiExeConfig('local')\n",
+ "exp_config.experiment_name = 'Retiarii example'\n",
+ "exp_config.trial_concurrency = 2\n",
+ "exp_config.max_trial_number = 10\n",
+ "exp_config.trial_gpu_number = 2\n",
+ "exp_config.max_experiment_duration = '5m'\n",
+ "exp_config.execution_engine = 'base'\n",
+ "exp_config.training_service.use_active_gpu = True"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 2.4: Run and View the Experiment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can launch the experiment now! "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:15:34] INFO (nni.experiment/MainThread) Creating experiment, Experiment ID: d9cseb3g\n",
+ "[2021-06-07 11:15:34] INFO (nni.experiment/MainThread) Connecting IPC pipe...\n",
+ "[2021-06-07 11:15:34] INFO (nni.experiment/MainThread) Statring web server...\n",
+ "[2021-06-07 11:15:35] INFO (nni.experiment/MainThread) Setting up...\n",
+ "[2021-06-07 11:15:36] INFO (nni.runtime.msg_dispatcher_base/Thread-6) Dispatcher started\n",
+ "[2021-06-07 11:15:36] INFO (nni.retiarii.experiment.pytorch/MainThread) Web UI URLs: http://127.0.0.1:8745\n",
+ "[2021-06-07 11:15:36] INFO (nni.retiarii.experiment.pytorch/MainThread) Start strategy...\n",
+ "[2021-06-07 11:15:36] INFO (nni.retiarii.strategy.tpe_strategy/MainThread) TPE strategy has been started.\n",
+ "[2021-06-07 11:15:36] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.001164 seconds\n",
+ "[2021-06-07 11:15:36] INFO (hyperopt.tpe/MainThread) TPE using 0 trials\n",
+ "[2021-06-07 11:15:36] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.001256 seconds\n",
+ "[2021-06-07 11:15:36] INFO (hyperopt.tpe/MainThread) TPE using 0 trials\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:16:31] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:16:31] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:16:31] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:16:33] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002677 seconds\n",
+ "[2021-06-07 11:16:33] INFO (hyperopt.tpe/MainThread) TPE using 1/1 trials with best loss 0.626600\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:16:36] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:16:36] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:16:36] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:16:37] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002730 seconds\n",
+ "[2021-06-07 11:16:37] INFO (hyperopt.tpe/MainThread) TPE using 1/1 trials with best loss 0.626600\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:17:26] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:17:26] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:17:26] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:17:27] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.003051 seconds\n",
+ "[2021-06-07 11:17:27] INFO (hyperopt.tpe/MainThread) TPE using 2/2 trials with best loss 0.594700\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:17:31] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:17:31] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:17:31] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:17:31] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002537 seconds\n",
+ "[2021-06-07 11:17:31] INFO (hyperopt.tpe/MainThread) TPE using 3/3 trials with best loss 0.594700\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:18:21] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:18:21] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:18:21] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:18:22] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002532 seconds\n",
+ "[2021-06-07 11:18:22] INFO (hyperopt.tpe/MainThread) TPE using 4/4 trials with best loss 0.594700\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:18:26] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:18:26] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:18:26] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:18:28] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002615 seconds\n",
+ "[2021-06-07 11:18:28] INFO (hyperopt.tpe/MainThread) TPE using 6/6 trials with best loss 0.594700\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:19:16] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:19:16] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:19:16] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:19:18] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002395 seconds\n",
+ "[2021-06-07 11:19:18] INFO (hyperopt.tpe/MainThread) TPE using 7/7 trials with best loss 0.594700\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:19:21] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:19:21] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:19:21] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:19:23] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002959 seconds\n",
+ "[2021-06-07 11:19:23] INFO (hyperopt.tpe/MainThread) TPE using 7/7 trials with best loss 0.594700\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:20:12] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:20:12] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:20:12] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:20:13] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.003336 seconds\n",
+ "[2021-06-07 11:20:13] INFO (hyperopt.tpe/MainThread) TPE using 8/8 trials with best loss 0.594700\n",
+ "Files already downloaded and verified\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:20:22] INFO (lightning/Thread-5) GPU available: True, used: True\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:20:22] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:20:22] INFO (lightning/Thread-5) LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
+ "[2021-06-07 11:20:22] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002093 seconds\n",
+ "[2021-06-07 11:20:22] INFO (hyperopt.tpe/MainThread) TPE using 9/9 trials with best loss 0.593200\n",
+ "Files already downloaded and verified\n",
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:20:26] INFO (nni.retiarii.experiment.pytorch/Thread-7) Stopping experiment, please wait...\n",
+ "[2021-06-07 11:20:26] INFO (nni.runtime.msg_dispatcher_base/Thread-6) Dispatcher exiting...\n",
+ "[2021-06-07 11:20:26] INFO (nni.retiarii.experiment.pytorch/MainThread) Strategy exit\n",
+ "[2021-06-07 11:20:26] INFO (nni.retiarii.experiment.pytorch/MainThread) Waiting for experiment to become DONE (you can ctrl+c if there is no running trial jobs)...\n",
+ "[2021-06-07 11:20:27] INFO (nni.retiarii.experiment.pytorch/Thread-7) Experiment stopped\n",
+ "[2021-06-07 11:20:29] INFO (nni.runtime.msg_dispatcher_base/Thread-6) Dispatcher terminiated\n"
+ ]
+ }
+ ],
+ "source": [
+ "exp.run(exp_config, 8745)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Besides, NNI provides WebUI to help users view the experiment results and make more advanced analysis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 2.5: Export the top Model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After searching, exporting the top model script is also very convenient."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final model:\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "\n",
+ "import nni.retiarii.nn.pytorch\n",
+ "\n",
+ "import nni\n",
+ "import torch\n",
+ "\n",
+ "\n",
+ "class _model__conv1(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.layerchoice__mutation_1_0 = torch.nn.modules.conv.Conv2d(padding=1, in_channels=3, out_channels=6, kernel_size=3)\n",
+ "\n",
+ " def forward(self, *_inputs):\n",
+ " layerchoice__mutation_1_0 = self.layerchoice__mutation_1_0(_inputs[0])\n",
+ " return layerchoice__mutation_1_0\n",
+ "\n",
+ "\n",
+ "\n",
+ "class _model__conv2(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.layerchoice__mutation_2_1 = torch.nn.modules.conv.Conv2d(padding=2, in_channels=6, out_channels=16, kernel_size=5)\n",
+ "\n",
+ " def forward(self, *_inputs):\n",
+ " layerchoice__mutation_2_1 = self.layerchoice__mutation_2_1(_inputs[0])\n",
+ " return layerchoice__mutation_2_1\n",
+ "\n",
+ "\n",
+ "\n",
+ "class _model(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.__conv1 = _model__conv1()\n",
+ " self.__pool = torch.nn.modules.pooling.MaxPool2d(kernel_size=2, stride=2)\n",
+ " self.__conv2 = _model__conv2()\n",
+ " self.__conv3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=1)\n",
+ " self.__skipconnect = nni.retiarii.nn.pytorch.ChosenInputs(chosen=[1], reduction='sum')\n",
+ " self.__bn = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16)\n",
+ " self.__gap = torch.nn.modules.pooling.AdaptiveAvgPool2d(output_size=4)\n",
+ " self.__fc1 = torch.nn.modules.linear.Linear(in_features=256, out_features=120)\n",
+ " self.__fc2 = torch.nn.modules.linear.Linear(in_features=120, out_features=84)\n",
+ " self.__fc3 = torch.nn.modules.linear.Linear(in_features=84, out_features=10)\n",
+ "\n",
+ " def forward(self, x__1):\n",
+ " __Constant1 = -1\n",
+ " __Constant2 = 1\n",
+ " __Constant4 = False\n",
+ " __Constant5 = 0\n",
+ " __conv1 = self.__conv1(x__1)\n",
+ " __aten__size6 = x__1.size(dim=__Constant5)\n",
+ " __relu9 = F.relu(__conv1, __Constant4)\n",
+ " __ListConstruct21 = [__aten__size6, __Constant1]\n",
+ " __pool = self.__pool(__relu9)\n",
+ " __conv2 = self.__conv2(__pool)\n",
+ " __relu11 = F.relu(__conv2, __Constant4)\n",
+ " __conv3 = self.__conv3(__relu11)\n",
+ " __relu13 = F.relu(__conv3, __Constant4)\n",
+ " __aten__add15 = __relu13.add(other=__relu11, alpha=__Constant2)\n",
+ " __ListConstruct16 = [__relu13, __aten__add15]\n",
+ " __skipconnect = self.__skipconnect(__ListConstruct16)\n",
+ " __bn = self.__bn(__skipconnect)\n",
+ " __pool__19 = self.__pool(__bn)\n",
+ " __gap = self.__gap(__pool__19)\n",
+ " __aten__view22 = __gap.view(size=__ListConstruct21)\n",
+ " __fc1 = self.__fc1(__aten__view22)\n",
+ " __relu24 = F.relu(__fc1, __Constant4)\n",
+ " __fc2 = self.__fc2(__relu24)\n",
+ " __relu26 = F.relu(__fc2, __Constant4)\n",
+ " __fc3 = self.__fc3(__relu26)\n",
+ " return __fc3\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Final model:')\n",
+ "for model_code in exp.export_top_models():\n",
+ " print(model_code)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/examples/notebooks/Retiarii_example_one-shot_NAS.ipynb b/examples/notebooks/Retiarii_example_one-shot_NAS.ipynb
new file mode 100644
index 0000000000..11497c9420
--- /dev/null
+++ b/examples/notebooks/Retiarii_example_one-shot_NAS.ipynb
@@ -0,0 +1,354 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Retiarii Example - One-shot NAS"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This example will show Retiarii's ability to **express** and **explore** the model space for Neural Architecture Search and Hyper-Parameter Tuning in a simple way. The video demo is in [YouTube](https://youtu.be/3nEx9GMHYEk) and [Bilibili](https://www.bilibili.com/video/BV1c54y1V7vx/).\n",
+ "\n",
+ "Let's start the journey with Retiarii!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1: Express the Model Space"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Model space is defined by users to express a set of models that they want to explore, which contains potentially good-performing models. In Retiarii framework, a model space is defined with two parts: a base model and possible mutations on the base model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 1.1: Define the Base Model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Defining a base model is almost the same as defining a PyTorch (or TensorFlow) model. Usually, you only need to replace the code ``import torch.nn as nn`` with ``import nni.retiarii.nn.pytorch as nn`` to use NNI wrapped PyTorch modules. Below is a very simple example of defining a base model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn.functional as F\n",
+ "import nni.retiarii.nn.pytorch as nn\n",
+ "\n",
+ "class Net(nn.Module):\n",
+ " def __init__(self):\n",
+ " super(Net, self).__init__()\n",
+ " self.conv1 = nn.Conv2d(3, 6, 3, padding=1)\n",
+ " self.pool = nn.MaxPool2d(2, 2)\n",
+ " self.conv2 = nn.Conv2d(6, 16, 3, padding=1)\n",
+ " self.conv3 = nn.Conv2d(16, 16, 1)\n",
+ "\n",
+ " self.bn = nn.BatchNorm2d(16)\n",
+ "\n",
+ " self.gap = nn.AdaptiveAvgPool2d(4)\n",
+ " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n",
+ " self.fc2 = nn.Linear(120, 84)\n",
+ " self.fc3 = nn.Linear(84, 10)\n",
+ "\n",
+ " def forward(self, x):\n",
+ " bs = x.size(0)\n",
+ "\n",
+ " x = self.pool(F.relu(self.conv1(x)))\n",
+ " x0 = F.relu(self.conv2(x))\n",
+ " x1 = F.relu(self.conv3(x0))\n",
+ "\n",
+ " x1 += x0\n",
+ " x = self.pool(self.bn(x1))\n",
+ "\n",
+ " x = self.gap(x).view(bs, -1)\n",
+ " x = F.relu(self.fc1(x))\n",
+ " x = F.relu(self.fc2(x))\n",
+ " x = self.fc3(x)\n",
+ " return x\n",
+ " \n",
+ "model = Net()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Step 1.2: Define the Model Mutations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A base model is only one concrete model, not a model space. NNI provides APIs and primitives for users to express how the base model can be mutated, i.e., a model space that includes many models. The following will use inline Mutation APIs ``LayerChoice`` to choose a layer from candidate operations and use ``InputChoice`` to try out skip connection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn.functional as F\n",
+ "import nni.retiarii.nn.pytorch as nn\n",
+ "\n",
+ "class Net(nn.Module):\n",
+ " def __init__(self):\n",
+ " super(Net, self).__init__()\n",
+ " # self.conv1 = nn.Conv2d(3, 6, 3, padding=1)\n",
+ " self.conv1 = nn.LayerChoice([nn.Conv2d(3, 6, 3, padding=1), nn.Conv2d(3, 6, 5, padding=2)])\n",
+ " self.pool = nn.MaxPool2d(2, 2)\n",
+ " # self.conv2 = nn.Conv2d(6, 16, 3, padding=1)\n",
+ " self.conv2 = nn.LayerChoice([nn.Conv2d(6, 16, 3, padding=1), nn.Conv2d(6, 16, 5, padding=2)])\n",
+ " self.conv3 = nn.Conv2d(16, 16, 1)\n",
+ "\n",
+ " self.skipconnect = nn.InputChoice(n_candidates=2)\n",
+ " self.bn = nn.BatchNorm2d(16)\n",
+ "\n",
+ " self.gap = nn.AdaptiveAvgPool2d(4)\n",
+ " self.fc1 = nn.Linear(16 * 4 * 4, 120)\n",
+ " self.fc2 = nn.Linear(120, 84)\n",
+ " self.fc3 = nn.Linear(84, 10)\n",
+ "\n",
+ " def forward(self, x):\n",
+ " bs = x.size(0)\n",
+ "\n",
+ " x = self.pool(F.relu(self.conv1(x)))\n",
+ " x0 = F.relu(self.conv2(x))\n",
+ " x1 = F.relu(self.conv3(x0))\n",
+ "\n",
+ " x1 = self.skipconnect([x1, x1+x0])\n",
+ " x = self.pool(self.bn(x1))\n",
+ "\n",
+ " x = self.gap(x).view(bs, -1)\n",
+ " x = F.relu(self.fc1(x))\n",
+ " x = F.relu(self.fc2(x))\n",
+ " x = self.fc3(x)\n",
+ " return x\n",
+ " \n",
+ "model = Net()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2: Explore the Model Space"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With a defined model space, users can explore the space in two ways. One is the multi-trial NAS method, which searchs by evaluating each sampled model independently. The other is using one-shot weight-sharing based search, which consumes much less computational resource compared to the first one. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this part, we focus on this **one-shot** approach. The principle of the One-shot approach is combining all the models in a model space into one big model (usually called super-model or super-graph). It takes charge of both search, training and testing, by training and evaluating this big model.\n",
+ "\n",
+ "Retiarii has supported some classic one-shot trainers, like DARTS trainer, ENAS trainer, ProxylessNAS trainer, Single-path trainer, and users can customize a new one-shot trainer according to the APIs provided by Retiarii conveniently.\n",
+ "\n",
+ "Here, we show an example to use DARTS trainer manually."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Files already downloaded and verified\n",
+ "[2021-06-07 11:12:22] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [1/391] acc1 0.093750 (0.093750) loss 2.286068 (2.286068)\n",
+ "[2021-06-07 11:12:22] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [11/391] acc1 0.093750 (0.089489) loss 2.328799 (2.309416)\n",
+ "[2021-06-07 11:12:23] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [21/391] acc1 0.093750 (0.092262) loss 2.302527 (2.309082)\n",
+ "[2021-06-07 11:12:23] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [31/391] acc1 0.109375 (0.099294) loss 2.294730 (2.304962)\n",
+ "[2021-06-07 11:12:23] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [41/391] acc1 0.203125 (0.103277) loss 2.284227 (2.302716)\n",
+ "[2021-06-07 11:12:23] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [51/391] acc1 0.078125 (0.106618) loss 2.308704 (2.300639)\n",
+ "[2021-06-07 11:12:23] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [61/391] acc1 0.203125 (0.110143) loss 2.258595 (2.298042)\n",
+ "[2021-06-07 11:12:23] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [71/391] acc1 0.078125 (0.112896) loss 2.276706 (2.294709)\n",
+ "[2021-06-07 11:12:24] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [81/391] acc1 0.078125 (0.116898) loss 2.309119 (2.292235)\n",
+ "[2021-06-07 11:12:24] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [91/391] acc1 0.093750 (0.118304) loss 2.263757 (2.289659)\n",
+ "[2021-06-07 11:12:24] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [101/391] acc1 0.109375 (0.119431) loss 2.260739 (2.287132)\n",
+ "[2021-06-07 11:12:24] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [111/391] acc1 0.109375 (0.121481) loss 2.279930 (2.284314)\n",
+ "[2021-06-07 11:12:24] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [121/391] acc1 0.046875 (0.122934) loss 2.270205 (2.281701)\n",
+ "[2021-06-07 11:12:25] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [131/391] acc1 0.156250 (0.125477) loss 2.270163 (2.278612)\n",
+ "[2021-06-07 11:12:25] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [141/391] acc1 0.171875 (0.126551) loss 2.233467 (2.276326)\n",
+ "[2021-06-07 11:12:25] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [151/391] acc1 0.109375 (0.127897) loss 2.264694 (2.274296)\n",
+ "[2021-06-07 11:12:25] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [161/391] acc1 0.250000 (0.132279) loss 2.259590 (2.271723)\n",
+ "[2021-06-07 11:12:25] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [171/391] acc1 0.093750 (0.134868) loss 2.240986 (2.269037)\n",
+ "[2021-06-07 11:12:25] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [181/391] acc1 0.218750 (0.137690) loss 2.218153 (2.266567)\n",
+ "[2021-06-07 11:12:25] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [191/391] acc1 0.078125 (0.140134) loss 2.260816 (2.264373)\n",
+ "[2021-06-07 11:12:26] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [201/391] acc1 0.156250 (0.144123) loss 2.191213 (2.261285)\n",
+ "[2021-06-07 11:12:26] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [211/391] acc1 0.125000 (0.146919) loss 2.245425 (2.258747)\n",
+ "[2021-06-07 11:12:26] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [221/391] acc1 0.218750 (0.150028) loss 2.216708 (2.255553)\n",
+ "[2021-06-07 11:12:26] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [231/391] acc1 0.250000 (0.153003) loss 2.195549 (2.252894)\n",
+ "[2021-06-07 11:12:26] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [241/391] acc1 0.234375 (0.155666) loss 2.169693 (2.249465)\n",
+ "[2021-06-07 11:12:26] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [251/391] acc1 0.218750 (0.158989) loss 2.174878 (2.246355)\n",
+ "[2021-06-07 11:12:27] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [261/391] acc1 0.312500 (0.162775) loss 2.117693 (2.243113)\n",
+ "[2021-06-07 11:12:27] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [271/391] acc1 0.265625 (0.166686) loss 2.136203 (2.239288)\n",
+ "[2021-06-07 11:12:27] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [281/391] acc1 0.234375 (0.169095) loss 2.213463 (2.236377)\n",
+ "[2021-06-07 11:12:27] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [291/391] acc1 0.218750 (0.171338) loss 2.114096 (2.232892)\n",
+ "[2021-06-07 11:12:27] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [301/391] acc1 0.203125 (0.173432) loss 2.134074 (2.229637)\n",
+ "[2021-06-07 11:12:28] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [311/391] acc1 0.265625 (0.175291) loss 2.041354 (2.225920)\n",
+ "[2021-06-07 11:12:28] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [321/391] acc1 0.250000 (0.176840) loss 2.081122 (2.222280)\n",
+ "[2021-06-07 11:12:28] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [331/391] acc1 0.140625 (0.178578) loss 2.124206 (2.219168)\n",
+ "[2021-06-07 11:12:28] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [341/391] acc1 0.250000 (0.180169) loss 2.077291 (2.215540)\n",
+ "[2021-06-07 11:12:28] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [351/391] acc1 0.250000 (0.182381) loss 2.077531 (2.211650)\n",
+ "[2021-06-07 11:12:28] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [361/391] acc1 0.312500 (0.185033) loss 2.016619 (2.207455)\n",
+ "[2021-06-07 11:12:29] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [371/391] acc1 0.250000 (0.187163) loss 2.139604 (2.202785)\n",
+ "[2021-06-07 11:12:29] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [381/391] acc1 0.281250 (0.189099) loss 2.033739 (2.198564)\n",
+ "[2021-06-07 11:12:29] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [1/2] Step [391/391] acc1 0.275000 (0.190441) loss 1.988353 (2.194509)\n",
+ "[2021-06-07 11:12:29] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [1/391] acc1 0.296875 (0.296875) loss 2.083627 (2.083627)\n",
+ "[2021-06-07 11:12:30] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [11/391] acc1 0.265625 (0.251420) loss 2.042856 (2.050898)\n",
+ "[2021-06-07 11:12:30] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [21/391] acc1 0.234375 (0.273065) loss 2.005307 (2.021047)\n",
+ "[2021-06-07 11:12:30] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [31/391] acc1 0.375000 (0.269657) loss 1.934093 (2.014375)\n",
+ "[2021-06-07 11:12:30] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [41/391] acc1 0.265625 (0.277439) loss 2.007705 (2.003260)\n",
+ "[2021-06-07 11:12:30] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [51/391] acc1 0.218750 (0.278799) loss 2.014602 (2.001039)\n",
+ "[2021-06-07 11:12:31] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [61/391] acc1 0.187500 (0.278945) loss 2.088407 (1.995837)\n",
+ "[2021-06-07 11:12:31] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [71/391] acc1 0.343750 (0.285651) loss 1.894479 (1.988130)\n",
+ "[2021-06-07 11:12:31] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [81/391] acc1 0.281250 (0.289159) loss 1.869002 (1.979012)\n",
+ "[2021-06-07 11:12:31] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [91/391] acc1 0.265625 (0.291552) loss 1.848354 (1.971483)\n",
+ "[2021-06-07 11:12:31] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [101/391] acc1 0.406250 (0.290996) loss 1.840711 (1.964297)\n",
+ "[2021-06-07 11:12:31] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [111/391] acc1 0.390625 (0.294764) loss 1.905811 (1.958954)\n",
+ "[2021-06-07 11:12:32] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [121/391] acc1 0.250000 (0.296617) loss 1.935214 (1.952315)\n",
+ "[2021-06-07 11:12:32] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [131/391] acc1 0.281250 (0.299618) loss 1.901846 (1.944634)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-07 11:12:32] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [141/391] acc1 0.312500 (0.302970) loss 1.854658 (1.939751)\n",
+ "[2021-06-07 11:12:32] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [151/391] acc1 0.218750 (0.305257) loss 1.927818 (1.934704)\n",
+ "[2021-06-07 11:12:32] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [161/391] acc1 0.343750 (0.307648) loss 1.820810 (1.927533)\n",
+ "[2021-06-07 11:12:33] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [171/391] acc1 0.312500 (0.307383) loss 1.800313 (1.924665)\n",
+ "[2021-06-07 11:12:33] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [181/391] acc1 0.484375 (0.307925) loss 1.637479 (1.920402)\n",
+ "[2021-06-07 11:12:33] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [191/391] acc1 0.359375 (0.306692) loss 1.732374 (1.917680)\n",
+ "[2021-06-07 11:12:33] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [201/391] acc1 0.406250 (0.309624) loss 1.870701 (1.911484)\n",
+ "[2021-06-07 11:12:33] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [211/391] acc1 0.328125 (0.311982) loss 1.785704 (1.905039)\n",
+ "[2021-06-07 11:12:33] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [221/391] acc1 0.265625 (0.312712) loss 1.738683 (1.901547)\n",
+ "[2021-06-07 11:12:33] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [231/391] acc1 0.359375 (0.315409) loss 1.827117 (1.894860)\n",
+ "[2021-06-07 11:12:34] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [241/391] acc1 0.375000 (0.317881) loss 1.717454 (1.888916)\n",
+ "[2021-06-07 11:12:34] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [251/391] acc1 0.328125 (0.318663) loss 1.873310 (1.886883)\n",
+ "[2021-06-07 11:12:34] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [261/391] acc1 0.390625 (0.320163) loss 1.657088 (1.881767)\n",
+ "[2021-06-07 11:12:34] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [271/391] acc1 0.421875 (0.321264) loss 1.710897 (1.877521)\n",
+ "[2021-06-07 11:12:34] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [281/391] acc1 0.421875 (0.321230) loss 1.760745 (1.875136)\n",
+ "[2021-06-07 11:12:34] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [291/391] acc1 0.375000 (0.321413) loss 1.669255 (1.872129)\n",
+ "[2021-06-07 11:12:34] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [301/391] acc1 0.328125 (0.322051) loss 1.728873 (1.868047)\n",
+ "[2021-06-07 11:12:35] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [311/391] acc1 0.375000 (0.323000) loss 1.754761 (1.864783)\n",
+ "[2021-06-07 11:12:35] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [321/391] acc1 0.437500 (0.324864) loss 1.666240 (1.859164)\n",
+ "[2021-06-07 11:12:35] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [331/391] acc1 0.421875 (0.325954) loss 1.661471 (1.856318)\n",
+ "[2021-06-07 11:12:35] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [341/391] acc1 0.328125 (0.326475) loss 1.737106 (1.853075)\n",
+ "[2021-06-07 11:12:35] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [351/391] acc1 0.343750 (0.327724) loss 1.789253 (1.849491)\n",
+ "[2021-06-07 11:12:36] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [361/391] acc1 0.250000 (0.328558) loss 1.773805 (1.846033)\n",
+ "[2021-06-07 11:12:36] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [371/391] acc1 0.312500 (0.329094) loss 1.901358 (1.844091)\n",
+ "[2021-06-07 11:12:36] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [381/391] acc1 0.250000 (0.330011) loss 1.863921 (1.841390)\n",
+ "[2021-06-07 11:12:36] INFO (nni.retiarii.oneshot.pytorch.darts/MainThread) Epoch [2/2] Step [391/391] acc1 0.325000 (0.331514) loss 1.729926 (1.837162)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from utils import accuracy\n",
+ "from torchvision import transforms\n",
+ "from torchvision.datasets import CIFAR10\n",
+ "from nni.retiarii.oneshot.pytorch import DartsTrainer\n",
+ "\n",
+ "criterion = torch.nn.CrossEntropyLoss()\n",
+ "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
+ "\n",
+ "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
+ "train_dataset = CIFAR10(root=\"./data\", train=True, download=True, transform=transform)\n",
+ "\n",
+ "trainer = DartsTrainer(\n",
+ " model=model,\n",
+ " loss=criterion,\n",
+ " metrics=lambda output, target: accuracy(output, target),\n",
+ " optimizer=optimizer,\n",
+ " num_epochs=2,\n",
+ " dataset=train_dataset,\n",
+ " batch_size=64,\n",
+ " log_frequency=10\n",
+ " )\n",
+ "\n",
+ "trainer.fit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Similarly, the optimal structure found can be exported."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final architecture: {'_mutation_1': 1, '_mutation_2': 1, '_mutation_3': [1]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Final architecture:', trainer.export())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/notebooks/tabular_data_classification_in_AML.ipynb b/examples/notebooks/tabular_data_classification_in_AML.ipynb
index c37d8aa755..5d9127994f 100644
--- a/examples/notebooks/tabular_data_classification_in_AML.ipynb
+++ b/examples/notebooks/tabular_data_classification_in_AML.ipynb
@@ -13,7 +13,7 @@
"source": [
"This simple example is to use NNI NAS 2.0(Retiarii) framework to search for the best neural architecture for tabular data classification task in Azure Machine Learning training platform.\n",
"\n",
- "The video demo is https://www.youtube.com/watch?v=PDVqBmm7Cro and https://www.bilibili.com/video/BV1oy4y1W7GF."
+ "The video demo is in [YouTube](https://www.youtube.com/watch?v=PDVqBmm7Cro) and [Bilibili](https://www.bilibili.com/video/BV1oy4y1W7GF)."
]
},
{
@@ -32,48 +32,15 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
- "import os\n",
- "import torch\n",
- "import pandas as pd\n",
- "\n",
- "from sklearn.preprocessing import LabelEncoder\n",
- "from torchvision.datasets.utils import download_url\n",
- "\n",
- "class TitanicDataset(torch.utils.data.Dataset):\n",
- " def __init__(self, root: str, train: bool = True):\n",
- " filename = 'train.csv' if train else 'eval.csv'\n",
- " if not os.path.exists(os.path.join(root, filename)):\n",
- " download_url(os.path.join(\n",
- " 'https://storage.googleapis.com/tf-datasets/titanic/', filename), root, filename)\n",
- "\n",
- " df = pd.read_csv(os.path.join(root, filename))\n",
- " object_colunmns = df.select_dtypes(include='object').columns.values\n",
- " for idx in df.columns:\n",
- " if idx in object_colunmns:\n",
- " df[idx] = LabelEncoder().fit_transform(df[idx])\n",
- " \n",
- " self.x = torch.tensor(df.iloc[:, 1:].values)\n",
- " self.y = torch.tensor(df.iloc[:, 0].values)\n",
+ "from utils import TitanicDataset\n",
+ "from nni.retiarii import serialize\n",
"\n",
- " def __len__(self):\n",
- " return len(self.y)\n",
- "\n",
- " def __getitem__(self, idx):\n",
- " return self.x[idx], self.y[idx]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "train_dataset = TitanicDataset('./data', train=True)\n",
- "test_dataset = TitanicDataset('./data', train=False)"
+ "train_dataset = serialize(TitanicDataset, root='./data', train=True)\n",
+ "test_dataset = serialize(TitanicDataset, root='./data', train=False)"
]
},
{
@@ -154,7 +121,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -223,7 +190,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -248,9 +215,38 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 09:56:10] INFO (lightning/MainThread) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 09:56:10] INFO (lightning/MainThread) TPU available: None, using: 0 TPU cores\n"
+ ]
+ }
+ ],
"source": [
"import nni.retiarii.evaluator.pytorch.lightning as pl\n",
"\n",
@@ -271,12 +267,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "After all the above are prepared, it is time to configure an experiment to do the model search. The basic experiment configuration is as follows: "
+ "After all the above are prepared, it is time to configure an experiment to do the model search. The basic experiment configuration is as follows, and advanced configuration reference on [this page](https://nni.readthedocs.io/en/stable/reference/experiment_config.html)."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -289,7 +285,7 @@
"exp_config.trial_concurrency = 2\n",
"exp_config.max_trial_number = 20\n",
"exp_config.max_experiment_duration = '2h'\n",
- "exp_config.trial_gpu_number = 1\n",
+ "exp_config.execution_engine = 'base'\n",
"exp_config.nni_manager_ip = '' # your nni_manager_ip"
]
},
@@ -297,7 +293,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Running NNI experiments on the AML(Azure Machine Learning) training service is also simple, you only need to configure the following additional fields:"
+ "Before running experiments on AML(Azure Machine Learning) training service, you need to set up corresponding environment(refer to [AML mode doc](https://nni.readthedocs.io/en/stable/TrainingService/AMLMode.html)) and configure the following additional fields:"
]
},
{
@@ -306,12 +302,22 @@
"metadata": {},
"outputs": [],
"source": [
- "exp_config.training_service.use_active_gpu = True\n",
+ "# Authenticate to your Azure subscription from the CLI.\n",
+ "# If you have finished, please skip it.\n",
+ "!az login"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"exp_config.training_service.subscription_id = '' # your subscription id\n",
"exp_config.training_service.resource_group = '' # your resource group\n",
"exp_config.training_service.workspace_name = '' # your workspace name\n",
"exp_config.training_service.compute_target = '' # your compute target\n",
- "exp_config.training_service.docker_image = '' # your docker image"
+ "exp_config.training_service.docker_image = 'msranni/nni:latest' # your docker image"
]
},
{
@@ -332,11 +338,640 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 09:56:54] INFO (nni.experiment/MainThread) Creating experiment, Experiment ID: 46den9qr\n",
+ "[2021-06-08 09:56:55] INFO (nni.experiment/MainThread) Connecting IPC pipe...\n",
+ "[2021-06-08 09:56:58] INFO (nni.experiment/MainThread) Starting web server...\n",
+ "[2021-06-08 09:57:00] INFO (nni.experiment/MainThread) Setting up...\n",
+ "[2021-06-08 09:57:05] INFO (nni.runtime.msg_dispatcher_base/Thread-8) Dispatcher started\n",
+ "[2021-06-08 09:57:05] INFO (nni.retiarii.experiment.pytorch/MainThread) Web UI URLs: http://127.0.0.1:8745\n",
+ "[2021-06-08 09:57:05] INFO (nni.retiarii.experiment.pytorch/MainThread) Start strategy...\n",
+ "[2021-06-08 09:57:05] INFO (nni.retiarii.strategy.tpe_strategy/MainThread) TPE strategy has been started.\n",
+ "[2021-06-08 09:57:05] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.001999 seconds\n",
+ "[2021-06-08 09:57:05] INFO (hyperopt.tpe/MainThread) TPE using 0 trials\n",
+ "[2021-06-08 09:57:10] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002029 seconds\n",
+ "[2021-06-08 09:57:10] INFO (hyperopt.tpe/MainThread) TPE using 0 trials\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:03:55] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:03:55] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:03:56] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.000000 seconds\n",
+ "[2021-06-08 10:03:56] INFO (hyperopt.tpe/MainThread) TPE using 1/1 trials with best loss 0.795455\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:04:46] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:04:46] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:04:46] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.000000 seconds\n",
+ "[2021-06-08 10:04:46] INFO (hyperopt.tpe/MainThread) TPE using 2/2 trials with best loss 0.795455\n",
+ "[2021-06-08 10:04:50] WARNING (nni.runtime.msg_dispatcher_base/Thread-8) assessor queue length: 20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:04:51] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:04:51] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:04:52] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.000000 seconds\n",
+ "[2021-06-08 10:04:52] INFO (hyperopt.tpe/MainThread) TPE using 3/3 trials with best loss 0.795455\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:05:46] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:05:46] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:05:48] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002999 seconds\n",
+ "[2021-06-08 10:05:48] INFO (hyperopt.tpe/MainThread) TPE using 4/4 trials with best loss 0.791667\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:05:56] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:05:56] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:05:56] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.000000 seconds\n",
+ "[2021-06-08 10:05:56] INFO (hyperopt.tpe/MainThread) TPE using 5/5 trials with best loss 0.791667\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:06:26] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:06:26] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:06:27] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.004991 seconds\n",
+ "[2021-06-08 10:06:27] INFO (hyperopt.tpe/MainThread) TPE using 6/6 trials with best loss 0.791667\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:07:06] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:07:06] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:07:07] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.006043 seconds\n",
+ "[2021-06-08 10:07:07] INFO (hyperopt.tpe/MainThread) TPE using 7/7 trials with best loss 0.784091\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:07:56] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:07:56] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:07:57] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.006004 seconds\n",
+ "[2021-06-08 10:07:57] INFO (hyperopt.tpe/MainThread) TPE using 8/8 trials with best loss 0.731061\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:08:01] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:08:01] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:08:01] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.005000 seconds\n",
+ "[2021-06-08 10:08:01] INFO (hyperopt.tpe/MainThread) TPE using 9/9 trials with best loss 0.731061\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:08:56] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:08:56] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:08:58] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.004962 seconds\n",
+ "[2021-06-08 10:08:58] INFO (hyperopt.tpe/MainThread) TPE using 10/10 trials with best loss 0.731061\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:09:01] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:09:01] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:09:03] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.003043 seconds\n",
+ "[2021-06-08 10:09:03] INFO (hyperopt.tpe/MainThread) TPE using 11/11 trials with best loss 0.731061\n",
+ "[2021-06-08 10:10:24] WARNING (nni.runtime.msg_dispatcher_base/Thread-8) assessor queue length: 20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:10:27] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:10:27] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:10:28] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002005 seconds\n",
+ "[2021-06-08 10:10:28] INFO (hyperopt.tpe/MainThread) TPE using 12/12 trials with best loss 0.731061\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:10:52] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:10:52] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:10:53] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.122046 seconds\n",
+ "[2021-06-08 10:10:53] INFO (hyperopt.tpe/MainThread) TPE using 13/13 trials with best loss 0.731061\n",
+ "[2021-06-08 10:14:52] WARNING (nni.runtime.msg_dispatcher_base/Thread-8) assessor queue length: 20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:14:52] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:14:52] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:14:53] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002038 seconds\n",
+ "[2021-06-08 10:14:53] INFO (hyperopt.tpe/MainThread) TPE using 14/14 trials with best loss 0.731061\n",
+ "[2021-06-08 10:14:56] WARNING (nni.runtime.msg_dispatcher_base/Thread-8) assessor queue length: 20\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:14:57] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:14:57] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:14:58] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.005870 seconds\n",
+ "[2021-06-08 10:14:58] INFO (hyperopt.tpe/MainThread) TPE using 15/15 trials with best loss 0.731061\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:16:07] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:16:07] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:16:08] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.004999 seconds\n",
+ "[2021-06-08 10:16:08] INFO (hyperopt.tpe/MainThread) TPE using 16/16 trials with best loss 0.712121\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:16:48] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:16:48] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:16:48] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002000 seconds\n",
+ "[2021-06-08 10:16:48] INFO (hyperopt.tpe/MainThread) TPE using 17/17 trials with best loss 0.712121\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:16:53] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:16:53] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:16:55] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002010 seconds\n",
+ "[2021-06-08 10:16:55] INFO (hyperopt.tpe/MainThread) TPE using 18/18 trials with best loss 0.712121\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:17:43] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:17:43] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:17:44] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.006001 seconds\n",
+ "[2021-06-08 10:17:44] INFO (hyperopt.tpe/MainThread) TPE using 19/19 trials with best loss 0.712121\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:18:03] INFO (lightning/Thread-5) GPU available: True, used: False\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "TPU available: None, using: 0 TPU cores\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2021-06-08 10:18:03] INFO (lightning/Thread-5) TPU available: None, using: 0 TPU cores\n",
+ "[2021-06-08 10:18:04] INFO (hyperopt.tpe/MainThread) tpe_transform took 0.002009 seconds\n",
+ "[2021-06-08 10:18:04] INFO (hyperopt.tpe/MainThread) TPE using 20/20 trials with best loss 0.712121\n",
+ "[2021-06-08 10:18:12] INFO (nni.retiarii.experiment.pytorch/Thread-9) Stopping experiment, please wait...\n",
+ "[2021-06-08 10:18:14] INFO (nni.runtime.msg_dispatcher_base/Thread-8) Dispatcher exiting...\n",
+ "[2021-06-08 10:18:14] INFO (nni.retiarii.experiment.pytorch/MainThread) Strategy exit\n",
+ "[2021-06-08 10:18:14] INFO (nni.retiarii.experiment.pytorch/MainThread) Waiting for experiment to become DONE (you can ctrl+c if there is no running trial jobs)...\n",
+ "[2021-06-08 10:18:15] INFO (nni.retiarii.experiment.pytorch/Thread-9) Experiment stopped\n",
+ "[2021-06-08 10:18:16] INFO (nni.runtime.msg_dispatcher_base/Thread-8) Dispatcher terminiated\n"
+ ]
+ }
+ ],
"source": [
- "exp.run(exp_config, 8081 + random.randint(0, 100))"
+ "exp.run(exp_config, 8745)"
]
},
{
@@ -355,9 +990,51 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final model:\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "\n",
+ "import nni.retiarii.nn.pytorch\n",
+ "\n",
+ "import torch\n",
+ "\n",
+ "\n",
+ "class _model(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.__fc1 = torch.nn.modules.linear.Linear(in_features=9, out_features=512)\n",
+ " self.__bn1 = torch.nn.modules.batchnorm.BatchNorm1d(num_features=512)\n",
+ " self.__dropout1 = torch.nn.modules.dropout.Dropout(p=0.0)\n",
+ " self.__fc2 = torch.nn.modules.linear.Linear(in_features=512, out_features=128)\n",
+ " self.__bn2 = torch.nn.modules.batchnorm.BatchNorm1d(num_features=128)\n",
+ " self.__dropout2 = torch.nn.modules.dropout.Dropout(p=0.25)\n",
+ " self.__fc3 = torch.nn.modules.linear.Linear(in_features=128, out_features=2)\n",
+ "\n",
+ " def forward(self, x__1):\n",
+ " __Constant3 = False\n",
+ " __fc1 = self.__fc1(x__1)\n",
+ " __bn1 = self.__bn1(__fc1)\n",
+ " __relu7 = F.relu(__bn1, __Constant3)\n",
+ " __dropout1 = self.__dropout1(__relu7)\n",
+ " __fc2 = self.__fc2(__dropout1)\n",
+ " __bn2 = self.__bn2(__fc2)\n",
+ " __relu11 = F.relu(__bn2, __Constant3)\n",
+ " __dropout2 = self.__dropout2(__relu11)\n",
+ " __fc3 = self.__fc3(__dropout2)\n",
+ " __sigmoid13 = F.sigmoid(__fc3)\n",
+ " return __sigmoid13\n"
+ ]
+ }
+ ],
"source": [
"print('Final model:')\n",
"for model_code in exp.export_top_models():\n",
@@ -388,9 +1065,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/utils.py b/examples/notebooks/utils.py
new file mode 100644
index 0000000000..64efb24471
--- /dev/null
+++ b/examples/notebooks/utils.py
@@ -0,0 +1,32 @@
+import os
+import torch
+import pandas as pd
+
+from sklearn.preprocessing import LabelEncoder
+from torchvision.datasets.utils import download_url
+
+class TitanicDataset(torch.utils.data.Dataset):
+ def __init__(self, root: str, train: bool = True):
+ filename = 'train.csv' if train else 'eval.csv'
+ if not os.path.exists(os.path.join(root, filename)):
+ download_url(os.path.join('https://storage.googleapis.com/tf-datasets/titanic/', filename), root, filename)
+
+ df = pd.read_csv(os.path.join(root, filename))
+ object_colunmns = df.select_dtypes(include='object').columns.values
+ for idx in df.columns:
+ if idx in object_colunmns:
+ df[idx] = LabelEncoder().fit_transform(df[idx])
+
+ self.x = df.iloc[:, 1:].values
+ self.y = df.iloc[:, 0].values
+
+ def __len__(self):
+ return len(self.y)
+
+ def __getitem__(self, idx):
+ return torch.Tensor(self.x[idx]), self.y[idx]
+
+def accuracy(output, target):
+ batch_size = target.size(0)
+ _, predicted = torch.max(output.data, 1)
+ return {"acc1": (predicted == target).sum().item() / batch_size}
\ No newline at end of file
diff --git a/examples/trials/auto-gbdt/config.yml b/examples/trials/auto-gbdt/config.yml
index 45b00a0994..38bdd5b80f 100644
--- a/examples/trials/auto-gbdt/config.yml
+++ b/examples/trials/auto-gbdt/config.yml
@@ -1,21 +1,10 @@
-authorName: default
-experimentName: example_auto-gbdt
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
trialConcurrency: 1
-maxExecDuration: 10h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: minimize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
\ No newline at end of file
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/auto-gbdt/config_metis.yml b/examples/trials/auto-gbdt/config_metis.yml
index dd78272c9c..0999d8cb52 100644
--- a/examples/trials/auto-gbdt/config_metis.yml
+++ b/examples/trials/auto-gbdt/config_metis.yml
@@ -1,21 +1,22 @@
-authorName: default
-experimentName: example_auto-gbdt-metis
+# The search space of Metis tuner is slightly different from TPE and others.
+# See Metis tuner' doc for details: https://nni.readthedocs.io/en/stable/Tuner/MetisTuner.html
+searchSpace:
+ num_leaves:
+ _type: choice
+ _value: [31, 28, 24, 20]
+ learning_rate:
+ _type: choice
+ _value: [0.01, 0.05, 0.1, 0.2]
+ bagging_freq:
+ _type: choice
+ _value: [1, 2, 4, 8, 10]
+
+trialCommand: python3 main.py
trialConcurrency: 1
-maxExecDuration: 10h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space_metis.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: MetisTuner
+ name: MetisTuner
classArgs:
- #choice: maximize, minimize
optimize_mode: minimize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
+trainingService:
+ platform: local
diff --git a/examples/trials/auto-gbdt/config_pai.yml b/examples/trials/auto-gbdt/config_pai.yml
deleted file mode 100644
index 912971a0fa..0000000000
--- a/examples/trials/auto-gbdt/config_pai.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-authorName: default
-experimentName: example_auto-gbdt
-trialConcurrency: 1
-maxExecDuration: 10h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: minimize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
\ No newline at end of file
diff --git a/examples/trials/auto-gbdt/search_space.json b/examples/trials/auto-gbdt/search_space.json
index ea09eca9e7..e55aaa6b79 100644
--- a/examples/trials/auto-gbdt/search_space.json
+++ b/examples/trials/auto-gbdt/search_space.json
@@ -1,6 +1,18 @@
{
- "num_leaves":{"_type":"randint","_value":[20, 31]},
- "learning_rate":{"_type":"choice","_value":[0.01, 0.05, 0.1, 0.2]},
- "bagging_fraction":{"_type":"uniform","_value":[0.7, 1.0]},
- "bagging_freq":{"_type":"choice","_value":[1, 2, 4, 8, 10]}
-}
\ No newline at end of file
+ "num_leaves": {
+ "_type": "randint",
+ "_value": [20, 31]
+ },
+ "learning_rate": {
+ "_type": "choice",
+ "_value": [0.01, 0.05, 0.1, 0.2]
+ },
+ "bagging_fraction": {
+ "_type": "uniform",
+ "_value": [0.7, 1.0]
+ },
+ "bagging_freq": {
+ "_type": "choice",
+ "_value": [1, 2, 4, 8, 10]
+ }
+}
diff --git a/examples/trials/auto-gbdt/search_space_metis.json b/examples/trials/auto-gbdt/search_space_metis.json
deleted file mode 100644
index 6bfbc32afa..0000000000
--- a/examples/trials/auto-gbdt/search_space_metis.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
- "num_leaves":{"_type":"choice","_value":[31, 28, 24, 20]},
- "learning_rate":{"_type":"choice","_value":[0.01, 0.05, 0.1, 0.2]},
- "bagging_freq":{"_type":"choice","_value":[1, 2, 4, 8, 10]}
-}
diff --git a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py
index 4016aae6e5..84bece36ac 100644
--- a/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py
+++ b/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/tuners.py
@@ -18,7 +18,7 @@ def get_tuner_class_dict():
config_file = str(get_config_file('registered_algorithms.yml'))
if os.path.exists(config_file):
with open(config_file, 'r') as f:
- config = yaml.load(f, Loader=yaml.SafeLoader)
+ config = yaml.safe_load(f)
else:
config = {}
ret = {}
diff --git a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml
index 5fa057ac0b..402449e557 100644
--- a/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml
+++ b/examples/trials/benchmarking/automlbenchmark/nni/frameworks.yaml
@@ -5,7 +5,7 @@ NNI:
version: 'stable'
project: https://github.com/microsoft/nni
-# type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'Hyperband', 'BOHB']
+# type in ['TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'GPTuner', 'MetisTuner', 'DNGOTuner', 'Hyperband', 'BOHB']
# arch_type in ['random_forest']
# limit_type in ['time', 'ntrials']
# limit must be an integer
@@ -66,6 +66,14 @@ MetisTuner:
limit_type: 'ntrials'
trial_limit: 10
+DNGOTuner:
+ extends: NNI
+ params:
+ tuner_type: 'DNGOTuner'
+ arch_type: 'random_forest'
+ limit_type: 'ntrials'
+ trial_limit: 10
+
Hyperband:
extends: NNI
params:
diff --git a/examples/trials/benchmarking/automlbenchmark/setup.sh b/examples/trials/benchmarking/automlbenchmark/setup.sh
index d87aea1b66..add0abb1b2 100755
--- a/examples/trials/benchmarking/automlbenchmark/setup.sh
+++ b/examples/trials/benchmarking/automlbenchmark/setup.sh
@@ -1,6 +1,3 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-
#!/bin/bash
# download automlbenchmark repository
diff --git a/examples/trials/benchmarking/hyperband/config_hyperband.yml b/examples/trials/benchmarking/hyperband/config_hyperband.yml
index a979bad44c..e79e3e0d8e 100644
--- a/examples/trials/benchmarking/hyperband/config_hyperband.yml
+++ b/examples/trials/benchmarking/hyperband/config_hyperband.yml
@@ -1,27 +1,20 @@
-authorName: default
-experimentName: example_mnist_hyperband
-trialConcurrency: 2
-maxExecDuration: 100h
-maxTrialNum: 10000
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
advisor:
- #choice: Hyperband, BOHB
- builtinAdvisorName: Hyperband
+ name: Hyperband
classArgs:
+ optimize_mode: maximize
+
#R: the maximum trial budget (could be the number of mini-batches or epochs) can be
# allocated to a trial. Each trial should use trial budget to control how long it runs.
R: 60
+
#eta: proportion of discarded trials
eta: 3
- #choice: maximize, minimize
- optimize_mode: maximize
+
#choice: serial, parallelism
exec_mode: serial
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
+
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
+trialConcurrency: 10
+trainingService:
+ platform: local
diff --git a/examples/trials/cifar10_pytorch/config.yml b/examples/trials/cifar10_pytorch/config.yml
index a44cfafa2b..b70083916e 100644
--- a/examples/trials/cifar10_pytorch/config.yml
+++ b/examples/trials/cifar10_pytorch/config.yml
@@ -1,23 +1,14 @@
-authorName: default
-experimentName: example_pytorch_cifar10
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
+trialGpuNumber: 1
trialConcurrency: 4
-maxExecDuration: 100h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 1
-localConfig:
- maxTrialNumPerGpu: 2
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
+ maxTrialNumberPerGpu: 2
+ useActiveGpu: false # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+ # Check the doc for details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu
diff --git a/examples/trials/cifar10_pytorch/config_pai.yml b/examples/trials/cifar10_pytorch/config_pai.yml
deleted file mode 100644
index 58f9bf5b51..0000000000
--- a/examples/trials/cifar10_pytorch/config_pai.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-authorName: default
-experimentName: example_pytorch_cifar10
-trialConcurrency: 1
-maxExecDuration: 100h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 1
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
diff --git a/examples/trials/cifar10_pytorch/search_space.json b/examples/trials/cifar10_pytorch/search_space.json
index 723e49b6b9..562f041183 100644
--- a/examples/trials/cifar10_pytorch/search_space.json
+++ b/examples/trials/cifar10_pytorch/search_space.json
@@ -1,5 +1,14 @@
{
- "lr":{"_type":"choice", "_value":[0.1, 0.01, 0.001, 0.0001]},
- "optimizer":{"_type":"choice", "_value":["SGD", "Adadelta", "Adagrad", "Adam", "Adamax"]},
- "model":{"_type":"choice", "_value":["vgg", "resnet18", "googlenet", "densenet121", "mobilenet", "dpn92", "senet18"]}
+ "lr": {
+ "_type": "choice",
+ "_value": [0.1, 0.01, 0.001, 0.0001]
+ },
+ "optimizer": {
+ "_type": "choice",
+ "_value": ["SGD", "Adadelta", "Adagrad", "Adam", "Adamax"]
+ },
+ "model": {
+ "_type": "choice",
+ "_value": ["vgg", "resnet18", "googlenet", "densenet121", "mobilenet", "dpn92", "senet18"]
+ }
}
diff --git a/examples/trials/efficientnet/config.yml b/examples/trials/efficientnet/config.yml
new file mode 100644
index 0000000000..0849b74477
--- /dev/null
+++ b/examples/trials/efficientnet/config.yml
@@ -0,0 +1,15 @@
+searchSpaceFile: search_net.json
+trialCodeDirectory: EfficientNet-PyTorch
+trialCommand: python3 main.py /data/imagenet -j 12 -a efficientnet --batch-size 48 --lr 0.048 --wd 1e-5 --epochs 5 --request-from-nni
+trialGpuNumber: 1
+trialConcurrency: 4
+maxTrialNumber: 100
+tuner:
+ className: tuner.FixedProductTuner
+ codeDirectory: .
+ classArgs:
+ product: 2
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
+ useActiveGpu: false # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+ # Check the doc for details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegputrial:
diff --git a/examples/trials/efficientnet/config_local.yml b/examples/trials/efficientnet/config_local.yml
deleted file mode 100644
index bbb0978be2..0000000000
--- a/examples/trials/efficientnet/config_local.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-authorName: unknown
-experimentName: example_efficient_net
-trialConcurrency: 4
-maxExecDuration: 99999d
-maxTrialNum: 100
-trainingServicePlatform: local
-searchSpacePath: search_net.json
-useAnnotation: false
-tuner:
- codeDir: .
- classFileName: tuner.py
- className: FixedProductTuner
- classArgs:
- product: 2
-trial:
- codeDir: EfficientNet-PyTorch
- command: python main.py /data/imagenet -j 12 -a efficientnet --batch-size 48 --lr 0.048 --wd 1e-5 --epochs 5 --request-from-nni
- gpuNum: 1
diff --git a/examples/trials/efficientnet/config_pai.yml b/examples/trials/efficientnet/config_pai.yml
deleted file mode 100644
index d9c4d52450..0000000000
--- a/examples/trials/efficientnet/config_pai.yml
+++ /dev/null
@@ -1,31 +0,0 @@
-authorName: unknown
-experimentName: example_efficient_net
-trialConcurrency: 8
-maxExecDuration: 48h
-maxTrialNum: 100
-trainingServicePlatform: pai
-searchSpacePath: search_net.json
-useAnnotation: false
-tuner:
- codeDir: .
- classFileName: tuner.py
- className: FixedProductTuner
- classArgs:
- product: 2
-trial:
- codeDir: EfficientNet-PyTorch
- command: sh train_imagenet.sh
- cpuNum: 4
- memoryMB: 25000
- shmMB: 25000
- gpuNum: 1
- virtualCluster: nni
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-nniManagerIp:
-paiConfig:
- userName:
- token:
- host:
diff --git a/examples/trials/efficientnet/tuner.py b/examples/trials/efficientnet/tuner.py
index 7e5bc8b60c..1917fdcf11 100644
--- a/examples/trials/efficientnet/tuner.py
+++ b/examples/trials/efficientnet/tuner.py
@@ -1,4 +1,4 @@
-from nni.gridsearch_tuner.gridsearch_tuner import GridSearchTuner
+from nni.algorithms.hpo.gridsearch_tuner import GridSearchTuner
class FixedProductTuner(GridSearchTuner):
diff --git a/examples/trials/ga_squad/config.yml b/examples/trials/ga_squad/config.yml
index e276f0633c..dcf8c5e8ed 100644
--- a/examples/trials/ga_squad/config.yml
+++ b/examples/trials/ga_squad/config.yml
@@ -1,19 +1,13 @@
-authorName: default
-experimentName: example_ga_squad
+trialCommand: python3 trial.py
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
+maxExperimentDuration: 1h
+searchSpace: {} # hard-coded in tuner
tuner:
- codeDir: ../../tuners/ga_customer_tuner
- classFileName: customer_tuner.py
- className: CustomerTuner
+ className: customer_tuner.CustomerTuner
+ codeDirectory: ../../tuners/ga_customer_tuner
classArgs:
optimize_mode: maximize
-trial:
- command: python3 trial.py
- codeDir: .
- gpuNum: 0
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/ga_squad/config_pai.yml b/examples/trials/ga_squad/config_pai.yml
deleted file mode 100644
index 756acc2e2d..0000000000
--- a/examples/trials/ga_squad/config_pai.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-authorName: default
-experimentName: example_ga_squad
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-#choice: true, false
-useAnnotation: false
-#Your nni_manager ip
-nniManagerIp: 10.10.10.10
-tuner:
- codeDir: ../../tuners/ga_customer_tuner
- classFileName: customer_tuner.py
- className: CustomerTuner
- classArgs:
- optimize_mode: maximize
-trial:
- command: chmod +x ./download.sh && ./download.sh && python3 trial.py
- codeDir: .
- gpuNum: 0
- cpuNum: 1
- memoryMB: 32869
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: /home/user/mnt
- containerNFSMountPath: /mnt/data/user
- paiStorageConfigName: confignfs-data
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
diff --git a/examples/trials/kaggle-tgs-salt/config.yml b/examples/trials/kaggle-tgs-salt/config.yml
index 1a0db8a51f..d385a3fa4d 100644
--- a/examples/trials/kaggle-tgs-salt/config.yml
+++ b/examples/trials/kaggle-tgs-salt/config.yml
@@ -1,20 +1,11 @@
-authorName: default
-experimentName: example_tgs
-trialConcurrency: 2
-maxExecDuration: 10h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-#choice: true, false
useAnnotation: true
+trialCommand: python3 train.py
+trialGpuNumber: 0
+trialConcurrency: 2
+maxTrialNumber: 10
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 train.py
- codeDir: .
- gpuNum: 1
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/mnist-advisor/config_bohb.yml b/examples/trials/mnist-advisor/config_bohb.yml
index ca20ccea43..a7502ed6b9 100644
--- a/examples/trials/mnist-advisor/config_bohb.yml
+++ b/examples/trials/mnist-advisor/config_bohb.yml
@@ -1,23 +1,18 @@
-authorName: default
-experimentName: example_mnist_bohb
+# Run following command first to install dependencies of BOHB tuner:
+# $ python3 -m pip install nni[BOHB]
+
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 10h
-maxTrialNum: 1000
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxExperimentDuration: 10h
+maxTrialNumber: 1000
advisor:
- #choice: Hyperband, BOHB
- #(BOHB should be installed through nnictl)
- builtinAdvisorName: BOHB
+ name: BOHB
classArgs:
max_budget: 27
min_budget: 1
eta: 3
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/mnist-advisor/config_hyperband.yml b/examples/trials/mnist-advisor/config_hyperband.yml
index fd06a809f5..2cf2de9b32 100644
--- a/examples/trials/mnist-advisor/config_hyperband.yml
+++ b/examples/trials/mnist-advisor/config_hyperband.yml
@@ -1,27 +1,16 @@
-authorName: default
-experimentName: example_mnist_hyperband
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
trialConcurrency: 2
-maxExecDuration: 100h
-maxTrialNum: 10000
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxExperimentDuration: 100h
+maxTrialNumber: 10000
advisor:
- #choice: Hyperband, BOHB
- builtinAdvisorName: Hyperband
+ name: Hyperband
classArgs:
- #R: the maximum trial budget (could be the number of mini-batches or epochs) can be
- # allocated to a trial. Each trial should use trial budget to control how long it runs.
- R: 100
- #eta: proportion of discarded trials
- eta: 3
- #choice: maximize, minimize
- optimize_mode: maximize
- #choice: serial, parallelism
- exec_mode: parallelism
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
+ R: 100 # the maximum trial budget (could be the number of mini-batches or epochs) can be
+ # allocated to a trial. Each trial should use trial budget to control how long it runs.
+ eta: 3 # proportion of discarded trials
+ optimize_mode: maximize # maximize or minimize
+ exec_mode: parallelism # serial or parallelism
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/mnist-advisor/config_pai.yml b/examples/trials/mnist-advisor/config_pai.yml
deleted file mode 100644
index 3c122b6fb4..0000000000
--- a/examples/trials/mnist-advisor/config_pai.yml
+++ /dev/null
@@ -1,41 +0,0 @@
-authorName: default
-experimentName: example_mnist_hyperband
-maxExecDuration: 1h
-maxTrialNum: 10000
-trialConcurrency: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-advisor:
- #choice: Hyperband, BOHB
- #(BOHB should be installed through nnictl)
- builtinAdvisorName: Hyperband
- classArgs:
- #R: the maximum trial budget
- R: 100
- #eta: proportion of discarded trials
- eta: 3
- #choice: maximize, minimize
- optimize_mode: maximize
- #choice: serial, parallelism
- exec_mode: parallelism
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: /home/user/mnt
- containerNFSMountPath: /mnt/data/user
- paiStorageConfigName: confignfs-data
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
diff --git a/examples/trials/mnist-annotation/config.yml b/examples/trials/mnist-annotation/config.yml
index b724c7b609..937ec916de 100644
--- a/examples/trials/mnist-annotation/config.yml
+++ b/examples/trials/mnist-annotation/config.yml
@@ -1,20 +1,12 @@
-authorName: default
-experimentName: example_mnist
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-#choice: true, false
useAnnotation: true
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
+trialConcurrency: 1
+maxTrialNumber: 10
+maxExperimentDuration: 1h
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/mnist-annotation/config_gpu.yml b/examples/trials/mnist-annotation/config_gpu.yml
deleted file mode 100644
index df8abd3a3b..0000000000
--- a/examples/trials/mnist-annotation/config_gpu.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-authorName: default
-experimentName: example_mnist
-trialConcurrency: 4
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-#choice: true, false
-useAnnotation: true
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 1
diff --git a/examples/trials/mnist-annotation/config_kubeflow.yml b/examples/trials/mnist-annotation/config_kubeflow.yml
deleted file mode 100644
index 37ff6f89a5..0000000000
--- a/examples/trials/mnist-annotation/config_kubeflow.yml
+++ /dev/null
@@ -1,31 +0,0 @@
-authorName: default
-experimentName: example_dist
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 1
-#choice: local, remote, pai, kubeflow
-trainingServicePlatform: kubeflow
-#choice: true, false
-useAnnotation: true
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- codeDir: .
- worker:
- replicas: 1
- command: python3 mnist.py
- gpuNum: 0
- cpuNum: 1
- memoryMB: 8192
- image: msranni/nni:latest
-kubeflowConfig:
- operator: tf-operator
- apiVersion: v1alpha2
- storage: nfs
- nfs:
- server: 10.10.10.10
- path: /var/nfs/general
\ No newline at end of file
diff --git a/examples/trials/mnist-annotation/config_pai.yml b/examples/trials/mnist-annotation/config_pai.yml
deleted file mode 100644
index ad27baae86..0000000000
--- a/examples/trials/mnist-annotation/config_pai.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-authorName: default
-experimentName: example_mnist
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-#choice: true, false
-useAnnotation: true
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: /home/user/mnt
- containerNFSMountPath: /mnt/data/user
- paiStorageConfigName: confignfs-data
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
\ No newline at end of file
diff --git a/examples/trials/mnist-annotation/config_remote.yml b/examples/trials/mnist-annotation/config_remote.yml
deleted file mode 100644
index 359b63044c..0000000000
--- a/examples/trials/mnist-annotation/config_remote.yml
+++ /dev/null
@@ -1,33 +0,0 @@
-authorName: default
-experimentName: example_mnist
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: remote
-#choice: true, false
-useAnnotation: true
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
-#machineList can be empty if the platform is local
-machineList:
- - ip: 10.1.1.1
- username: bob
- passwd: bob123
- #port can be skip if using default ssh port 22
- #port: 22
- - ip: 10.1.1.2
- username: bob
- passwd: bob123
- - ip: 10.1.1.3
- username: bob
- passwd: bob123
diff --git a/examples/trials/mnist-batch-tune-keras/config.yml b/examples/trials/mnist-batch-tune-keras/config.yml
index 92a87a4f7c..fae372a7aa 100644
--- a/examples/trials/mnist-batch-tune-keras/config.yml
+++ b/examples/trials/mnist-batch-tune-keras/config.yml
@@ -1,18 +1,10 @@
-authorName: default
-experimentName: example_mnist-keras
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist-keras.py
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
+maxExperimentDuration: 1h
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: BatchTuner
-trial:
- command: python3 mnist-keras.py
- codeDir: .
- gpuNum: 0
+ name: BatchTuner
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/mnist-batch-tune-keras/config_pai.yml b/examples/trials/mnist-batch-tune-keras/config_pai.yml
deleted file mode 100644
index f3c7586e54..0000000000
--- a/examples/trials/mnist-batch-tune-keras/config_pai.yml
+++ /dev/null
@@ -1,32 +0,0 @@
-authorName: default
-experimentName: example_mnist-keras
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: BatchTuner
-trial:
- command: python3 mnist-keras.py
- codeDir: .
- gpuNum: 0
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
diff --git a/examples/trials/mnist-distributed/config_kubeflow.yml b/examples/trials/mnist-distributed-tfv1/config_kubeflow.yml
similarity index 100%
rename from examples/trials/mnist-distributed/config_kubeflow.yml
rename to examples/trials/mnist-distributed-tfv1/config_kubeflow.yml
diff --git a/examples/trials/mnist-distributed/dist_mnist.py b/examples/trials/mnist-distributed-tfv1/dist_mnist.py
similarity index 100%
rename from examples/trials/mnist-distributed/dist_mnist.py
rename to examples/trials/mnist-distributed-tfv1/dist_mnist.py
diff --git a/examples/trials/mnist-distributed/search_space.json b/examples/trials/mnist-distributed-tfv1/search_space.json
similarity index 100%
rename from examples/trials/mnist-distributed/search_space.json
rename to examples/trials/mnist-distributed-tfv1/search_space.json
diff --git a/examples/trials/mnist-nested-search-space/config.yml b/examples/trials/mnist-nested-search-space/config.yml
index 7c1715b97c..2cff01c655 100644
--- a/examples/trials/mnist-nested-search-space/config.yml
+++ b/examples/trials/mnist-nested-search-space/config.yml
@@ -1,20 +1,14 @@
-authorName: default
-experimentName: mnist-nested-search-space
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
trialConcurrency: 2
-maxExecDuration: 1h
-maxTrialNum: 100
-#choice: local, remote
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 100
+maxExperimentDuration: 1h
tuner:
- #choice: TPE, Random, Anneal, Evolution
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
+ useActiveGpu: false # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+ # Check the doc for details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu
diff --git a/examples/trials/mnist-nested-search-space/search_space.json b/examples/trials/mnist-nested-search-space/search_space.json
index 4f35ddb354..185bc00253 100644
--- a/examples/trials/mnist-nested-search-space/search_space.json
+++ b/examples/trials/mnist-nested-search-space/search_space.json
@@ -1,114 +1,114 @@
{
- "layer0": {
- "_type": "choice",
- "_value": [{
- "_name": "Empty"
- },
- {
- "_name": "Conv",
- "kernel_size": {
- "_type": "choice",
- "_value": [1, 2, 3, 5]
- }
- },
- {
- "_name": "Max_pool",
- "pooling_size": {
- "_type": "choice",
- "_value": [2, 3, 5]
- }
- },
- {
- "_name": "Avg_pool",
- "pooling_size": {
- "_type": "choice",
- "_value": [2, 3, 5]
- }
- }
- ]
- },
- "layer1": {
- "_type": "choice",
- "_value": [{
- "_name": "Empty"
- },
- {
- "_name": "Conv",
- "kernel_size": {
- "_type": "choice",
- "_value": [1, 2, 3, 5]
- }
- },
- {
- "_name": "Max_pool",
- "pooling_size": {
- "_type": "choice",
- "_value": [2, 3, 5]
- }
- },
- {
- "_name": "Avg_pool",
- "pooling_size": {
- "_type": "choice",
- "_value": [2, 3, 5]
- }
- }
- ]
- },
- "layer2": {
- "_type": "choice",
- "_value": [{
- "_name": "Empty"
- },
- {
- "_name": "Conv",
- "kernel_size": {
- "_type": "choice",
- "_value": [1, 2, 3, 5]
- }
- },
- {
- "_name": "Max_pool",
- "pooling_size": {
- "_type": "choice",
- "_value": [2, 3, 5]
- }
- },
- {
- "_name": "Avg_pool",
- "pooling_size": {
- "_type": "choice",
- "_value": [2, 3, 5]
- }
- }
- ]
- },
- "layer3": {
- "_type": "choice",
- "_value": [{
- "_name": "Empty"
- },
- {
- "_name": "Conv",
- "kernel_size": {
- "_type": "choice",
- "_value": [1, 2, 3, 5]
- }
- },
- {
- "_name": "Max_pool",
- "pooling_size": {
- "_type": "choice",
- "_value": [2, 3, 5]
- }
- },
- {
- "_name": "Avg_pool",
- "pooling_size": {
- "_type": "choice",
- "_value": [2, 3, 5]
- }
- }
- ]
- }
-}
\ No newline at end of file
+ "layer0": {
+ "_type": "choice",
+ "_value": [{
+ "_name": "Empty"
+ },
+ {
+ "_name": "Conv",
+ "kernel_size": {
+ "_type": "choice",
+ "_value": [1, 2, 3, 5]
+ }
+ },
+ {
+ "_name": "Max_pool",
+ "pooling_size": {
+ "_type": "choice",
+ "_value": [2, 3, 5]
+ }
+ },
+ {
+ "_name": "Avg_pool",
+ "pooling_size": {
+ "_type": "choice",
+ "_value": [2, 3, 5]
+ }
+ }
+ ]
+ },
+ "layer1": {
+ "_type": "choice",
+ "_value": [{
+ "_name": "Empty"
+ },
+ {
+ "_name": "Conv",
+ "kernel_size": {
+ "_type": "choice",
+ "_value": [1, 2, 3, 5]
+ }
+ },
+ {
+ "_name": "Max_pool",
+ "pooling_size": {
+ "_type": "choice",
+ "_value": [2, 3, 5]
+ }
+ },
+ {
+ "_name": "Avg_pool",
+ "pooling_size": {
+ "_type": "choice",
+ "_value": [2, 3, 5]
+ }
+ }
+ ]
+ },
+ "layer2": {
+ "_type": "choice",
+ "_value": [{
+ "_name": "Empty"
+ },
+ {
+ "_name": "Conv",
+ "kernel_size": {
+ "_type": "choice",
+ "_value": [1, 2, 3, 5]
+ }
+ },
+ {
+ "_name": "Max_pool",
+ "pooling_size": {
+ "_type": "choice",
+ "_value": [2, 3, 5]
+ }
+ },
+ {
+ "_name": "Avg_pool",
+ "pooling_size": {
+ "_type": "choice",
+ "_value": [2, 3, 5]
+ }
+ }
+ ]
+ },
+ "layer3": {
+ "_type": "choice",
+ "_value": [{
+ "_name": "Empty"
+ },
+ {
+ "_name": "Conv",
+ "kernel_size": {
+ "_type": "choice",
+ "_value": [1, 2, 3, 5]
+ }
+ },
+ {
+ "_name": "Max_pool",
+ "pooling_size": {
+ "_type": "choice",
+ "_value": [2, 3, 5]
+ }
+ },
+ {
+ "_name": "Avg_pool",
+ "pooling_size": {
+ "_type": "choice",
+ "_value": [2, 3, 5]
+ }
+ }
+ ]
+ }
+}
diff --git a/examples/trials/mnist-pbt-tuner-pytorch/config.yml b/examples/trials/mnist-pbt-tuner-pytorch/config.yml
index 508278e69f..8d648c8893 100644
--- a/examples/trials/mnist-pbt-tuner-pytorch/config.yml
+++ b/examples/trials/mnist-pbt-tuner-pytorch/config.yml
@@ -1,22 +1,14 @@
-authorName: default
-experimentName: example_mnist_pbt_tuner_pytorch
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 1
trialConcurrency: 3
-maxExecDuration: 2h
-maxTrialNum: 100
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 100
+maxExperimentDuration: 2h
tuner:
-# codeDir: ~/nni/src/sdk/pynni/nni/pbt_tuner
-# classFileName: pbt_tuner.py
-# className: PBTTuner
- builtinTunerName: PBTTuner
+ name: PBTTuner
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 1
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
+ useActiveGpu: false # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+ # Check the doc for details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu
diff --git a/examples/trials/mnist-pytorch/.gitignore b/examples/trials/mnist-pytorch/.gitignore
new file mode 100644
index 0000000000..1269488f7f
--- /dev/null
+++ b/examples/trials/mnist-pytorch/.gitignore
@@ -0,0 +1 @@
+data
diff --git a/examples/trials/mnist-pytorch/.nniignore b/examples/trials/mnist-pytorch/.nniignore
new file mode 100644
index 0000000000..b22de6bbe6
--- /dev/null
+++ b/examples/trials/mnist-pytorch/.nniignore
@@ -0,0 +1,4 @@
+# Exclude the following directory when uploading codeDir
+data
+
+# They can also be files
\ No newline at end of file
diff --git a/examples/trials/mnist-pytorch/config.yml b/examples/trials/mnist-pytorch/config.yml
index 00a95216aa..7fd35c0e9a 100644
--- a/examples/trials/mnist-pytorch/config.yml
+++ b/examples/trials/mnist-pytorch/config.yml
@@ -1,21 +1,14 @@
-authorName: default
-experimentName: example_mnist_pytorch
+# This is the minimal config file for an NNI experiment.
+# Use "nnictl create --config config.yml" to launch this experiment.
+# Afterwards, you can check "config_detailed.yml" for more explanation.
+
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py # NOTE: change "python3" to "python" if you are using Windows
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
+trainingService:
+ platform: local
diff --git a/examples/trials/mnist-pytorch/config_aml.yml b/examples/trials/mnist-pytorch/config_aml.yml
index 8a5618606f..3c87d38123 100644
--- a/examples/trials/mnist-pytorch/config_aml.yml
+++ b/examples/trials/mnist-pytorch/config_aml.yml
@@ -1,25 +1,15 @@
-authorName: default
-experimentName: example_mnist_pytorch
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-trainingServicePlatform: aml
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- image: msranni/nni
-amlConfig:
- subscriptionId: ${replace_to_your_subscriptionId}
- resourceGroup: ${replace_to_your_resourceGroup}
- workspaceName: ${replace_to_your_workspaceName}
- computeTarget: ${replace_to_your_computeTarget}
+trainingService:
+ platform: aml
+ dockerImage: msranni/nni
+ subscriptionId: ${your subscription ID}
+ resourceGroup: ${your resource group}
+ workspaceName: ${your workspace name}
+ computeTarget: ${your compute target}
diff --git a/examples/trials/mnist-pytorch/config_detailed.yml b/examples/trials/mnist-pytorch/config_detailed.yml
new file mode 100644
index 0000000000..2bf65fd126
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_detailed.yml
@@ -0,0 +1,44 @@
+# This example shows more configurable fields comparing to the minimal "config.yml"
+# You can use "nnictl create --config config_detailed.yml" to launch this experiment.
+# If you see an error message saying "port 8080 is used", use "nnictl stop --all" to stop previous experiments.
+
+experimentName: MNIST # An optional name to help you distinguish experiments.
+
+# Hyper-parameter search space can either be configured here or in a seperate file.
+# "config.yml" shows how to specify a seperate search space file.
+# The common schema of search space is documented here:
+# https://nni.readthedocs.io/en/stable/Tutorial/SearchSpaceSpec.html
+searchSpace:
+ batch_size:
+ _type: choice
+ _value: [16, 32, 64, 128]
+ hidden_size:
+ _type: choice
+ _value: [128, 256, 512, 1024]
+ lr:
+ _type: choice
+ _value: [0.0001, 0.001, 0.01, 0.1]
+ momentum:
+ _type: uniform
+ _value: [0, 1]
+
+trialCommand: python3 mnist.py # The command to launch a trial. NOTE: change "python3" to "python" if you are using Windows.
+trialCodeDirectory: . # The path of trial code. By default it's ".", which means the same directory of this config file.
+trialGpuNumber: 1 # How many GPUs should each trial use. CUDA is required when it's greater than zero.
+
+trialConcurrency: 4 # Run 4 trials concurrently.
+maxTrialNumber: 10 # Generate at most 10 trials.
+maxExperimentDuration: 1h # Stop generating trials after 1 hour.
+
+tuner: # Configure the tuning alogrithm.
+ name: TPE # Supported algorithms: TPE, Random, Anneal, Evolution, GridSearch, GPTuner, PBTTuner, etc.
+ # Full list: https://nni.readthedocs.io/en/latest/Tuner/BuiltinTuner.html
+ classArgs: # Algorithm specific arguments. See the tuner's doc for details.
+ optimize_mode: maximize # "minimize" or "maximize"
+
+# Configure the training platform.
+# Supported platforms: local, remote, openpai, aml, kubeflow, kubernetes, adl.
+trainingService:
+ platform: local
+ useActiveGpu: false # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+ # Reason and details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu
diff --git a/examples/trials/mnist-pytorch/config_hybrid.yml b/examples/trials/mnist-pytorch/config_hybrid.yml
new file mode 100644
index 0000000000..1ae3a2cc3f
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_hybrid.yml
@@ -0,0 +1,23 @@
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
+trialConcurrency: 5
+maxTrialNumber: 20
+tuner:
+ name: TPE
+ classArgs:
+ optimize_mode: maximize
+# For local, remote, openpai, and aml, NNI can use multiple training services at one time
+trainingService:
+ - platform: local
+ - platform: remote
+ machineList:
+ - host: ${your server's IP or domain name}
+ user: ${your user name}
+ ssh_key_file: ~/.ssh/id_rsa
+ - platform: aml
+ dockerImage: msranni/nni
+ subscriptionId: ${your subscription ID}
+ resourceGroup: ${your resource group}
+ workspaceName: ${your workspace name}
+ computeTarget: ${your compute target}
diff --git a/examples/trials/mnist-pytorch/config_openpai.yml b/examples/trials/mnist-pytorch/config_openpai.yml
new file mode 100644
index 0000000000..01f90ecfae
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_openpai.yml
@@ -0,0 +1,20 @@
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
+trialConcurrency: 1
+maxTrialNumber: 10
+tuner:
+ name: TPE
+ classArgs:
+ optimize_mode: maximize
+trainingService:
+ platform: openpai
+ host: http://123.123.123.123
+ username: ${your user name}
+ token: ${your token}
+ dockerImage: msranni/nni
+ trialCpuNumber: 1
+ trialMemorySize: 8GB
+ storageConfigName: ${your storage config name}
+ localStorageMountPoint: ${NFS mount point on local machine}
+ containerStorageMountPoint: ${NFS mount point inside Docker container}
diff --git a/examples/trials/mnist-pytorch/config_pai.yml b/examples/trials/mnist-pytorch/config_pai.yml
deleted file mode 100644
index f821fa3ba2..0000000000
--- a/examples/trials/mnist-pytorch/config_pai.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-authorName: default
-experimentName: example_mnist_pytorch
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
\ No newline at end of file
diff --git a/examples/trials/mnist-pytorch/config_remote.yml b/examples/trials/mnist-pytorch/config_remote.yml
new file mode 100644
index 0000000000..42a8546848
--- /dev/null
+++ b/examples/trials/mnist-pytorch/config_remote.yml
@@ -0,0 +1,24 @@
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
+trialConcurrency: 4
+maxTrialNumber: 20
+tuner:
+ name: TPE
+ classArgs:
+ optimize_mode: maximize
+trainingService:
+ platform: remote
+ machineList:
+ - host: ${your server's IP or domain name}
+ user: ${your user name}
+ ssh_key_file: ~/.ssh/id_rsa # We recommend public key over password, it's more secure and convenient.
+ # You can specify more than one SSH servers:
+ - host: 123.123.123.123
+ port: 10022
+ user: nniuser
+ password: 12345
+ pythonPath: /usr/bin # Other examples:
+ # /opt/python3.9/bin
+ # C:/Python39
+ # C:/Users/USERNAME/.conda/envs/ENVNAME;C:/Users/USERNAME/.conda/envs/ENVNAME/Scripts;C:/Users/USERNAME/.conda/envs/ENVNAME/Library/bin
diff --git a/examples/trials/mnist-pytorch/config_tensorboard.yml b/examples/trials/mnist-pytorch/config_tensorboard.yml
index c067cd16c4..9c8839bca2 100644
--- a/examples/trials/mnist-pytorch/config_tensorboard.yml
+++ b/examples/trials/mnist-pytorch/config_tensorboard.yml
@@ -1,21 +1,11 @@
-authorName: default
-experimentName: example_mnist_pytorch
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist_tensorboard.py # NOTE: change "python3" to "python" if you are using Windows
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist_tensorboard.py
- codeDir: .
- gpuNum: 0
+trainingService:
+ platform: local
diff --git a/examples/trials/mnist-pytorch/config_v2.yml b/examples/trials/mnist-pytorch/config_v2.yml
deleted file mode 100644
index 370f22a7ea..0000000000
--- a/examples/trials/mnist-pytorch/config_v2.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-searchSpace:
- momentum:
- _type: uniform
- _value: [0, 1]
- hidden_size:
- _type: choice
- _value: [128, 256, 512, 1024]
- batch_size:
- _type: choice
- _value: [16, 32, 64, 128]
- lr:
- _type: choice
- _value: [0.0001, 0.001, 0.01, 0.1]
-trainingService:
- platform: local
-trialCodeDirectory: .
-trialCommand: python3 mnist.py
-trialConcurrency: 1
-trialGpuNumber: 0
-tuner:
- name: TPE
- classArgs:
- optimize_mode: maximize
diff --git a/examples/trials/mnist-pytorch/config_windows.yml b/examples/trials/mnist-pytorch/config_windows.yml
deleted file mode 100644
index ae27d4517b..0000000000
--- a/examples/trials/mnist-pytorch/config_windows.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-authorName: default
-experimentName: example_mnist_pytorch
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python mnist.py
- codeDir: .
- gpuNum: 0
diff --git a/examples/trials/mnist-sharedstorage/config_azureblob.yml b/examples/trials/mnist-sharedstorage/config_azureblob.yml
index 16994b3a0a..b070732fdf 100644
--- a/examples/trials/mnist-sharedstorage/config_azureblob.yml
+++ b/examples/trials/mnist-sharedstorage/config_azureblob.yml
@@ -1,28 +1,27 @@
-authorName: default
-experimentName: example_mnist
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-trainingServicePlatform: aml
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- image: msranni/nni
-amlConfig:
- subscriptionId: ${replace_to_your_subscriptionId}
- resourceGroup: ${replace_to_your_resourceGroup}
- workspaceName: ${replace_to_your_workspaceName}
- computeTarget: ${replace_to_your_computeTarget}
+trainingService:
+ platform: remote
+ machineList:
+ - host: ${your server's IP or domain name}
+ user: ${your user name}
+ ssh_key_file: ~/.ssh/id_rsa # We recommend public key over password, it's more secure and convenient.
+ # You can specify more than one SSH servers:
+ - host: 123.123.123.123
+ port: 10022
+ user: nniuser
+ password: 12345
+ pythonPath: /usr/bin # Other examples:
+ # /opt/python3.9/bin
+ # C:/Python39
+ # C:/Users/USERNAME/.conda/envs/ENVNAME;C:/Users/USERNAME/.conda/envs/ENVNAME/Scripts;C:/Users/USERNAME/.conda/envs/ENVNAME/Library/bin
sharedStorage:
storageType: AzureBlob
localMountPoint: ${your/local/mount/point}
diff --git a/examples/trials/mnist-sharedstorage/config_nfs.yml b/examples/trials/mnist-sharedstorage/config_nfs.yml
index 0a2872c920..1713982983 100644
--- a/examples/trials/mnist-sharedstorage/config_nfs.yml
+++ b/examples/trials/mnist-sharedstorage/config_nfs.yml
@@ -1,28 +1,27 @@
-authorName: default
-experimentName: example_mnist
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-trainingServicePlatform: aml
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- image: msranni/nni
-amlConfig:
- subscriptionId: ${replace_to_your_subscriptionId}
- resourceGroup: ${replace_to_your_resourceGroup}
- workspaceName: ${replace_to_your_workspaceName}
- computeTarget: ${replace_to_your_computeTarget}
+trainingService:
+ platform: remote
+ machineList:
+ - host: ${your server's IP or domain name}
+ user: ${your user name}
+ ssh_key_file: ~/.ssh/id_rsa # We recommend public key over password, it's more secure and convenient.
+ # You can specify more than one SSH servers:
+ - host: 123.123.123.123
+ port: 10022
+ user: nniuser
+ password: 12345
+ pythonPath: /usr/bin # Other examples:
+ # /opt/python3.9/bin
+ # C:/Python39
+ # C:/Users/USERNAME/.conda/envs/ENVNAME;C:/Users/USERNAME/.conda/envs/ENVNAME/Scripts;C:/Users/USERNAME/.conda/envs/ENVNAME/Library/bin
sharedStorage:
storageType: NFS
localMountPoint: ${your/local/mount/point}
@@ -32,4 +31,4 @@ sharedStorage:
# usermount means you have already mount this storage on localMountPoint
# nnimount means nni will try to mount this storage on localMountPoint
# nomount means storage will not mount in local machine, will support partial storages in the future
- localMounted: nnimount
\ No newline at end of file
+ localMounted: nnimount
diff --git a/examples/trials/mnist-tfv2/config.yml b/examples/trials/mnist-tfv2/config.yml
index 06e9af6be3..7fd35c0e9a 100644
--- a/examples/trials/mnist-tfv2/config.yml
+++ b/examples/trials/mnist-tfv2/config.yml
@@ -1,17 +1,14 @@
-authorName: NNI Example
-experimentName: MNIST TF v2.x
+# This is the minimal config file for an NNI experiment.
+# Use "nnictl create --config config.yml" to launch this experiment.
+# Afterwards, you can check "config_detailed.yml" for more explanation.
+
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py # NOTE: change "python3" to "python" if you are using Windows
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-trainingServicePlatform: local # choices: local, remote, pai
-searchSpacePath: search_space.json
-useAnnotation: false
tuner:
- builtinTunerName: TPE # choices: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner,
- # GPTuner, SMAC (SMAC should be installed through nnictl)
- classArgs:
- optimize_mode: maximize # choices: maximize, minimize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
+ name: TPE
+ classArgs:
+ optimize_mode: maximize
+trainingService:
+ platform: local
diff --git a/examples/trials/mnist-tfv2/config_assessor.yml b/examples/trials/mnist-tfv2/config_assessor.yml
index be9ec740ab..1a138d8e17 100644
--- a/examples/trials/mnist-tfv2/config_assessor.yml
+++ b/examples/trials/mnist-tfv2/config_assessor.yml
@@ -1,27 +1,16 @@
-authorName: NNI Example
-experimentName: MNIST TF v2.x with assessor
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 50
-#choice: local, remote
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
+trialConcurrency: 2
+maxTrialNumber: 50
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-assessor:
- #choice: Medianstop, Curvefitting
- builtinAssessorName: Curvefitting
+assessor: # Specify early-stop algorithm
+ name: Curvefitting
classArgs:
epoch_num: 20
threshold: 0.9
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
+trainingService:
+ platform: local
diff --git a/examples/trials/mnist-tfv2/config_detailed.yml b/examples/trials/mnist-tfv2/config_detailed.yml
new file mode 100644
index 0000000000..6c2fd1a487
--- /dev/null
+++ b/examples/trials/mnist-tfv2/config_detailed.yml
@@ -0,0 +1,48 @@
+# This example shows more configurable fields comparing to the minimal "config.yml"
+# You can use "nnictl create --config config_detailed.yml" to launch this experiment.
+# If you see an error message saying "port 8080 is used", use "nnictl stop --all" to stop previous experiments.
+
+experimentName: MNIST # An optional name to help you distinguish experiments.
+
+# Hyper-parameter search space can either be configured here or in a seperate file.
+# "config.yml" shows how to specify a seperate search space file.
+# The common schema of search space is documented here:
+# https://nni.readthedocs.io/en/stable/Tutorial/SearchSpaceSpec.html
+searchSpace:
+ dropout_rate:
+ _type: uniform
+ _value: [0.5, 0.9]
+ conv_size:
+ _type: choice
+ _value: [2, 3, 5, 7]
+ hidden_size:
+ _type: choice
+ _value: [128, 512, 1024]
+ batch_size:
+ _type: choice
+ _value: [16, 32]
+ learning_rate:
+ _type: choice
+ _value: [0.0001, 0.001, 0.01, 0.1]
+
+trialCommand: python3 mnist.py # The command to launch a trial. NOTE: change "python3" to "python" if you are using Windows.
+trialCodeDirectory: . # The path of trial code. By default it's ".", which means the same directory of this config file.
+trialGpuNumber: 1 # How many GPUs should each trial use. CUDA is required when it's greater than zero.
+
+trialConcurrency: 4 # Run 4 trials concurrently.
+maxTrialNumber: 10 # Generate at most 10 trials.
+maxExperimentDuration: 1h # Stop generating trials after 1 hour.
+
+tuner: # Configure the tuning alogrithm.
+ name: TPE # Supported algorithms: TPE, Random, Anneal, Evolution, GridSearch, GPTuner, PBTTuner, etc.
+ # Full list: https://nni.readthedocs.io/en/latest/Tuner/BuiltinTuner.html
+ classArgs: # Algorithm specific arguments. See the tuner's doc for details.
+ optimize_mode: maximize # "minimize" or "maximize"
+
+# Configure the training platform.
+# Supported platforms: local, remote, openpai, aml, kubeflow, kubernetes, adl.
+# You can find config template of some platforms in this directory, and others in mnist-pytorch example.
+trainingService:
+ platform: local
+ useActiveGpu: false # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+ # Reason and details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu
diff --git a/examples/trials/mnist-tfv2/config_hybrid.yml b/examples/trials/mnist-tfv2/config_hybrid.yml
new file mode 100644
index 0000000000..1ae3a2cc3f
--- /dev/null
+++ b/examples/trials/mnist-tfv2/config_hybrid.yml
@@ -0,0 +1,23 @@
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
+trialConcurrency: 5
+maxTrialNumber: 20
+tuner:
+ name: TPE
+ classArgs:
+ optimize_mode: maximize
+# For local, remote, openpai, and aml, NNI can use multiple training services at one time
+trainingService:
+ - platform: local
+ - platform: remote
+ machineList:
+ - host: ${your server's IP or domain name}
+ user: ${your user name}
+ ssh_key_file: ~/.ssh/id_rsa
+ - platform: aml
+ dockerImage: msranni/nni
+ subscriptionId: ${your subscription ID}
+ resourceGroup: ${your resource group}
+ workspaceName: ${your workspace name}
+ computeTarget: ${your compute target}
diff --git a/examples/trials/mnist-tfv2/config_remote.yml b/examples/trials/mnist-tfv2/config_remote.yml
index 09fb8634fe..42a8546848 100644
--- a/examples/trials/mnist-tfv2/config_remote.yml
+++ b/examples/trials/mnist-tfv2/config_remote.yml
@@ -1,32 +1,24 @@
-authorName: default
-experimentName: example_mnist
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: remote
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+searchSpaceFile: search_space.json
+trialCommand: python3 mnist.py
+trialGpuNumber: 0
+trialConcurrency: 4
+maxTrialNumber: 20
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 mnist.py
- codeDir: .
- gpuNum: 0
-#machineList can be empty if the platform is local
-machineList:
- - ip: ${replace_to_your_remote_machine_ip}
- username: ${replace_to_your_remote_machine_username}
- sshKeyPath: ${replace_to_your_remote_machine_sshKeyPath}
- # Below are examples of specifying python environment.
- # pythonPath: /opt/python3.7/bin
- # pythonPath: C:/Python37
- # Below is an example of specifying python environment for windows anaconda user. Multiple paths separated by ';'.
- # pythonPath: C:/Users/yourname/.conda/envs/myenv;C:/Users/yourname/.conda/envs/myenv/Scripts;C:/Users/yourname/.conda/envs/myenv/Library/bin
- pythonPath: ${replace_to_python_environment_path_in_your_remote_machine}
+trainingService:
+ platform: remote
+ machineList:
+ - host: ${your server's IP or domain name}
+ user: ${your user name}
+ ssh_key_file: ~/.ssh/id_rsa # We recommend public key over password, it's more secure and convenient.
+ # You can specify more than one SSH servers:
+ - host: 123.123.123.123
+ port: 10022
+ user: nniuser
+ password: 12345
+ pythonPath: /usr/bin # Other examples:
+ # /opt/python3.9/bin
+ # C:/Python39
+ # C:/Users/USERNAME/.conda/envs/ENVNAME;C:/Users/USERNAME/.conda/envs/ENVNAME/Scripts;C:/Users/USERNAME/.conda/envs/ENVNAME/Library/bin
diff --git a/examples/trials/mnist-tfv2/config_v2.yml b/examples/trials/mnist-tfv2/config_v2.yml
deleted file mode 100644
index 64ba5c7ec8..0000000000
--- a/examples/trials/mnist-tfv2/config_v2.yml
+++ /dev/null
@@ -1,26 +0,0 @@
-searchSpace:
- dropout_rate:
- _type: uniform
- _value: [0.5, 0.9]
- conv_size:
- _type: choice
- _value: [2, 3, 5, 7]
- hidden_size:
- _type: choice
- _value: [128, 512, 1024]
- batch_size:
- _type: choice
- _value: [16, 32]
- learning_rate:
- _type: choice
- _value: [0.0001, 0.001, 0.01, 0.1]
-trainingService:
- platform: local
-trialCodeDirectory: .
-trialCommand: python3 mnist.py
-trialConcurrency: 1
-trialGpuNumber: 0
-tuner:
- name: TPE
- classArgs:
- optimize_mode: maximize
diff --git a/examples/trials/mnist-tfv2/config_windows.yml b/examples/trials/mnist-tfv2/config_windows.yml
deleted file mode 100644
index f1c12aa135..0000000000
--- a/examples/trials/mnist-tfv2/config_windows.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-authorName: NNI Example
-experimentName: MNIST TF v2.x
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python mnist.py
- codeDir: .
- gpuNum: 0
diff --git a/examples/trials/network_morphism/FashionMNIST/config.yml b/examples/trials/network_morphism/FashionMNIST/config.yml
index f88f7dc57a..70a6d720a9 100644
--- a/examples/trials/network_morphism/FashionMNIST/config.yml
+++ b/examples/trials/network_morphism/FashionMNIST/config.yml
@@ -1,29 +1,18 @@
-authorName: default
-experimentName: example_FashionMNIST-network-morphism
+trialCommand: python3 FashionMNIST_keras.py
+trialGpuNumber: 1
trialConcurrency: 4
-maxExecDuration: 48h
-maxTrialNum: 200
-#choice: local, remote, pai
-trainingServicePlatform: local
-#searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxExperimentDuration: 48h
+maxTrialNumber: 200
+searchSpace: {} # search space of NetworkMorphism is provided via classArgs
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, NetworkMorphism
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: NetworkMorphism
+ name: NetworkMorphism
classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
- #for now, this tuner only supports cv domain
- task: cv
- #input image width
- input_width: 28
- #input image channel
- input_channel: 1
- #number of classes
- n_output_node: 10
-trial:
- command: python3 FashionMNIST_keras.py
- codeDir: .
- gpuNum: 1
+ optimize_mode: maximize # maximize or minimize
+ task: cv # for now, this tuner only supports cv domain
+ input_width: 28 # input image width
+ input_channel: 1 # input image channel
+ n_output_node: 10 # number of classes
+trainingService:
+ platform: local
+ useActiveGpu: false # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+ # Check the doc for details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu
diff --git a/examples/trials/network_morphism/FashionMNIST/config_pai.yml b/examples/trials/network_morphism/FashionMNIST/config_pai.yml
deleted file mode 100644
index 935180163c..0000000000
--- a/examples/trials/network_morphism/FashionMNIST/config_pai.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-authorName: default
-experimentName: example_FashionMNIST-network-morphism
-trialConcurrency: 1
-maxExecDuration: 24h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, NetworkMorphism
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: NetworkMorphism
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
- # for now, this tuner only supports cv domain
- task: cv
- #input image width
- input_width: 28
- #input image channel
- input_channel: 1
- #number of classes
- n_output_node: 10
-trial:
- command: python3 FashionMNIST_keras.py
- codeDir: .
- gpuNum: 1
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
\ No newline at end of file
diff --git a/examples/trials/network_morphism/cifar10/config.yml b/examples/trials/network_morphism/cifar10/config.yml
index 35a96bb41c..0e6cc00b6f 100644
--- a/examples/trials/network_morphism/cifar10/config.yml
+++ b/examples/trials/network_morphism/cifar10/config.yml
@@ -1,29 +1,18 @@
-authorName: default
-experimentName: example_cifar10-network-morphism
+trialCommand: python3 cifar10_keras.py
+trialGpuNumber: 1
trialConcurrency: 4
-maxExecDuration: 48h
-maxTrialNum: 200
-#choice: local, remote, pai
-trainingServicePlatform: local
-#searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxExperimentDuration: 48h
+maxTrialNumber: 200
+searchSpace: {} # search space of NetworkMorphism is provided via classArgs
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, NetworkMorphism
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: NetworkMorphism
+ name: NetworkMorphism
classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
- #for now, this tuner only supports cv domain
- task: cv
- #input image width
- input_width: 32
- #input image channel
- input_channel: 3
- #number of classes
- n_output_node: 10
-trial:
- command: python3 cifar10_keras.py
- codeDir: .
- gpuNum: 1
+ optimize_mode: maximize # maximize or minimize
+ task: cv # for now, this tuner only supports cv domain
+ input_width: 32 # input image width
+ input_channel: 3 # input image channel
+ n_output_node: 10 # number of classes
+trainingService:
+ platform: local
+ useActiveGpu: false # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+ # Check the doc for details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu
diff --git a/examples/trials/network_morphism/cifar10/config_pai.yml b/examples/trials/network_morphism/cifar10/config_pai.yml
deleted file mode 100644
index a377cc004a..0000000000
--- a/examples/trials/network_morphism/cifar10/config_pai.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-authorName: default
-experimentName: example_cifar10-network-morphism
-trialConcurrency: 1
-maxExecDuration: 24h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: pai
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, NetworkMorphism
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: NetworkMorphism
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
- # for now, this tuner only supports cv domain
- task: cv
- #input image width
- input_width: 32
- #input image channel
- input_channel: 3
- #number of classes
- n_output_node: 10
-trial:
- command: python3 cifar10_keras.py
- codeDir: .
- gpuNum: 1
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
\ No newline at end of file
diff --git a/examples/trials/sklearn/classification/config.yml b/examples/trials/sklearn/classification/config.yml
index 2071ee0385..a1e6bec0f6 100644
--- a/examples/trials/sklearn/classification/config.yml
+++ b/examples/trials/sklearn/classification/config.yml
@@ -1,20 +1,11 @@
-authorName: default
-experimentName: example_sklearn-classification
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 100
-#choice: local, remote
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 100
+maxExperimentDuration: 1h
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
\ No newline at end of file
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/sklearn/classification/config_pai.yml b/examples/trials/sklearn/classification/config_pai.yml
deleted file mode 100644
index 764cfbedae..0000000000
--- a/examples/trials/sklearn/classification/config_pai.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-authorName: default
-experimentName: example_sklearn
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 100
-#choice: local, remote, pai
-trainingServicePlatform: pai
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner,MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
\ No newline at end of file
diff --git a/examples/trials/sklearn/classification/python_api_start.ipynb b/examples/trials/sklearn/classification/python_api_start.ipynb
index 22ca8a4fb1..95ceb6d99b 100644
--- a/examples/trials/sklearn/classification/python_api_start.ipynb
+++ b/examples/trials/sklearn/classification/python_api_start.ipynb
@@ -79,7 +79,7 @@
"output_type": "stream",
"text": [
"[2021-03-05 12:12:19] Creating experiment, Experiment ID: wdt0le3v\n",
- "[2021-03-05 12:12:19] Statring web server...\n",
+ "[2021-03-05 12:12:19] Starting web server...\n",
"[2021-03-05 12:12:20] Setting up...\n",
"[2021-03-05 12:12:20] Web UI URLs: http://127.0.0.1:8080 http://10.0.1.5:8080 http://172.17.0.1:8080\n"
]
diff --git a/examples/trials/sklearn/regression/config.yml b/examples/trials/sklearn/regression/config.yml
index 35f0c5d617..c3fcf52c3e 100644
--- a/examples/trials/sklearn/regression/config.yml
+++ b/examples/trials/sklearn/regression/config.yml
@@ -1,20 +1,11 @@
-authorName: default
-experimentName: example_sklearn-regression
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 30
-#choice: local, remote
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 30
+maxExperimentDuration: 1h
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
\ No newline at end of file
+trainingService: # For other platforms, check mnist-pytorch example
+ platform: local
diff --git a/examples/trials/sklearn/regression/config_pai.yml b/examples/trials/sklearn/regression/config_pai.yml
deleted file mode 100644
index 89c6d49c42..0000000000
--- a/examples/trials/sklearn/regression/config_pai.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-authorName: default
-experimentName: example_sklearn
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 100
-#choice: local, remote, pai
-trainingServicePlatform: pai
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
-tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
- classArgs:
- #choice: maximize, minimize
- optimize_mode: maximize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
- cpuNum: 1
- memoryMB: 8196
- #The docker image to run nni job on pai
- image: msranni/nni:latest
- nniManagerNFSMountPath: {replace_to_your_nfs_mount_path}
- containerNFSMountPath: {replace_to_your_container_mount_path}
- paiStorageConfigName: {replace_to_your_storage_config_name}
-paiConfig:
- #The username to login pai
- userName: username
- #The token to login pai
- token: token
- #The host of restful server of pai
- host: 10.10.10.10
\ No newline at end of file
diff --git a/examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_smac.yml b/examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_smac.yml
index 0dfa1be326..5eb66ad909 100644
--- a/examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_smac.yml
+++ b/examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_smac.yml
@@ -1,21 +1,15 @@
-authorName: default
experimentName: auto_rocksdb_SMAC
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
+trialCodeDirectory: .
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 12h
-maxTrialNum: 256
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxExperimentDuration: 24h
+maxTrialNumber: 256
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: SMAC
+ name: SMAC
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
+trainingService:
+ platform: local
+ useActiveGpu: False
diff --git a/examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_tpe.yml b/examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_tpe.yml
index a77d24842e..acbb000193 100644
--- a/examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_tpe.yml
+++ b/examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_tpe.yml
@@ -1,21 +1,15 @@
-authorName: default
experimentName: auto_rocksdb_TPE
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
+trialCodeDirectory: .
+trialGpuNumber: 0
trialConcurrency: 1
-maxExecDuration: 12h
-maxTrialNum: 256
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxExperimentDuration: 24h
+maxTrialNumber: 256
tuner:
- #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
- #SMAC (SMAC should be installed through nnictl)
- builtinTunerName: TPE
+ name: TPE
classArgs:
- #choice: maximize, minimize
optimize_mode: maximize
-trial:
- command: python3 main.py
- codeDir: .
- gpuNum: 0
+trainingService:
+ platform: local
+ useActiveGpu: False
diff --git a/nni/algorithms/compression/pytorch/auto_compress/__init__.py b/nni/algorithms/compression/pytorch/auto_compress/__init__.py
new file mode 100644
index 0000000000..8026bdd94c
--- /dev/null
+++ b/nni/algorithms/compression/pytorch/auto_compress/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from .experiment import AutoCompressionExperiment
+from .interface import AbstractAutoCompressionModule
+from .utils import AutoCompressionSearchSpaceGenerator
diff --git a/nni/algorithms/compression/pytorch/auto_compress/auto_compress_engine.py b/nni/algorithms/compression/pytorch/auto_compress/auto_compress_engine.py
new file mode 100644
index 0000000000..b1af71ec7f
--- /dev/null
+++ b/nni/algorithms/compression/pytorch/auto_compress/auto_compress_engine.py
@@ -0,0 +1,164 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import logging
+from typing import Optional, Callable
+
+import json_tricks
+from torch.nn import Module
+from torch.optim import Optimizer
+
+import nni
+from .constants import PRUNER_DICT, QUANTIZER_DICT
+from .interface import BaseAutoCompressionEngine, AbstractAutoCompressionModule
+from .utils import import_
+
+_logger = logging.getLogger(__name__)
+_logger.setLevel(logging.INFO)
+
+class AutoCompressionEngine(BaseAutoCompressionEngine):
+ @classmethod
+ def __convert_compact_pruner_params_to_config_list(cls, compact_config: dict) -> list:
+ config_dict = {}
+ for key, value in compact_config.items():
+ _, op_types, op_names, var_name = key.split('::')
+ config_dict.setdefault((op_types, op_names), {})
+ config_dict[(op_types, op_names)][var_name] = value
+
+ config_list = []
+ for key, config in config_dict.items():
+ op_types, op_names = key
+ op_types = op_types.split(':') if op_types else []
+ op_names = op_names.split(':') if op_names else []
+ if op_types:
+ config['op_types'] = op_types
+ if op_names:
+ config['op_names'] = op_names
+ if 'op_types' in config or 'op_names' in config:
+ config_list.append(config)
+
+ return config_list
+
+ @classmethod
+ def __convert_compact_quantizer_params_to_config_list(cls, compact_config: dict) -> list:
+ config_dict = {}
+ for key, value in compact_config.items():
+ _, quant_types, op_types, op_names, var_name = key.split('::')
+ config_dict.setdefault((quant_types, op_types, op_names), {})
+ config_dict[(quant_types, op_types, op_names)][var_name] = value
+
+ config_list = []
+ for key, config in config_dict.items():
+ quant_types, op_types, op_names = key
+ quant_types = quant_types.split(':')
+ op_types = op_types.split(':')
+ op_names = op_names.split(':')
+ if quant_types:
+ config['quant_types'] = quant_types
+ else:
+ continue
+ if op_types:
+ config['op_types'] = op_types
+ if op_names:
+ config['op_names'] = op_names
+ if 'op_types' in config or 'op_names' in config:
+ config_list.append(config)
+
+ return config_list
+
+ @classmethod
+ def _convert_compact_params_to_config_list(cls, compressor_type: str, compact_config: dict) -> list:
+ func_dict = {
+ 'pruner': cls.__convert_compact_pruner_params_to_config_list,
+ 'quantizer': cls.__convert_compact_quantizer_params_to_config_list
+ }
+ return func_dict[compressor_type](compact_config)
+
+ @classmethod
+ def __compress_pruning(cls, algorithm_name: str,
+ model: Module,
+ config_list: list,
+ optimizer_factory: Optional[Callable],
+ criterion: Optional[Callable],
+ sparsifying_trainer: Optional[Callable[[Module, Optimizer, Callable, int], None]],
+ finetuning_trainer: Optional[Callable[[Module, Optimizer, Callable, int], None]],
+ finetuning_epochs: int,
+ **compressor_parameter_dict) -> Module:
+ if algorithm_name in ['level', 'l1', 'l2', 'fpgm']:
+ pruner = PRUNER_DICT[algorithm_name](model, config_list, **compressor_parameter_dict)
+ elif algorithm_name in ['slim', 'taylorfo', 'apoz', 'mean_activation']:
+ optimizer = None if optimizer_factory is None else optimizer_factory(model.parameters())
+ pruner = PRUNER_DICT[algorithm_name](model, config_list, optimizer, sparsifying_trainer, criterion, **compressor_parameter_dict)
+ else:
+ raise ValueError('Unsupported compression algorithm: {}.'.format(algorithm_name))
+ compressed_model = pruner.compress()
+ if finetuning_trainer is not None:
+ # note that in pruning process, finetuning will use an un-patched optimizer
+ optimizer = optimizer_factory(compressed_model.parameters())
+ for i in range(finetuning_epochs):
+ finetuning_trainer(compressed_model, optimizer, criterion, i)
+ pruner.get_pruned_weights()
+ return compressed_model
+
+ @classmethod
+ def __compress_quantization(cls, algorithm_name: str,
+ model: Module,
+ config_list: list,
+ optimizer_factory: Optional[Callable],
+ criterion: Optional[Callable],
+ sparsifying_trainer: Optional[Callable[[Module, Optimizer, Callable, int], None]],
+ finetuning_trainer: Optional[Callable[[Module, Optimizer, Callable, int], None]],
+ finetuning_epochs: int,
+ **compressor_parameter_dict) -> Module:
+ optimizer = None if optimizer_factory is None else optimizer_factory(model.parameters())
+ quantizer = QUANTIZER_DICT[algorithm_name](model, config_list, optimizer, **compressor_parameter_dict)
+ compressed_model = quantizer.compress()
+ if finetuning_trainer is not None:
+ # note that in quantization process, finetuning will use a patched optimizer
+ for i in range(finetuning_epochs):
+ finetuning_trainer(compressed_model, optimizer, criterion, i)
+ return compressed_model
+
+ @classmethod
+ def _compress(cls, compressor_type: str,
+ algorithm_name: str,
+ model: Module,
+ config_list: list,
+ optimizer_factory: Optional[Callable],
+ criterion: Optional[Callable],
+ sparsifying_trainer: Optional[Callable[[Module, Optimizer, Callable, int], None]],
+ finetuning_trainer: Optional[Callable[[Module, Optimizer, Callable, int], None]],
+ finetuning_epochs: int,
+ **compressor_parameter_dict) -> Module:
+ func_dict = {
+ 'pruner': cls.__compress_pruning,
+ 'quantizer': cls.__compress_quantization
+ }
+ _logger.info('%s compressor config_list:\n%s', algorithm_name, json_tricks.dumps(config_list, indent=4))
+ compressed_model = func_dict[compressor_type](algorithm_name, model, config_list, optimizer_factory, criterion, sparsifying_trainer,
+ finetuning_trainer, finetuning_epochs, **compressor_parameter_dict)
+ return compressed_model
+
+ @classmethod
+ def trial_execute_compress(cls, module_name):
+ auto_compress_module: AbstractAutoCompressionModule = import_(module_name)
+
+ algorithm_config = nni.get_next_parameter()['algorithm_name']
+ algorithm_name = algorithm_config['_name']
+ compact_config = {k: v for k, v in algorithm_config.items() if k.startswith('config_list::')}
+ parameter_dict = {k.split('parameter::')[1]: v for k, v in algorithm_config.items() if k.startswith('parameter::')}
+
+ compressor_type = 'quantizer' if algorithm_name in QUANTIZER_DICT else 'pruner'
+
+ config_list = cls._convert_compact_params_to_config_list(compressor_type, compact_config)
+
+ model, evaluator = auto_compress_module.model(), auto_compress_module.evaluator()
+ optimizer_factory, criterion = auto_compress_module.optimizer_factory(), auto_compress_module.criterion()
+ sparsifying_trainer = auto_compress_module.sparsifying_trainer(algorithm_name)
+ finetuning_trainer = auto_compress_module.post_compress_finetuning_trainer(algorithm_name)
+ finetuning_epochs = auto_compress_module.post_compress_finetuning_epochs(algorithm_name)
+
+ compressed_model = cls._compress(compressor_type, algorithm_name, model, config_list, optimizer_factory,
+ criterion, sparsifying_trainer, finetuning_trainer, finetuning_epochs, **parameter_dict)
+
+ nni.report_final_result(evaluator(compressed_model))
diff --git a/nni/algorithms/compression/pytorch/auto_compress/constants.py b/nni/algorithms/compression/pytorch/auto_compress/constants.py
new file mode 100644
index 0000000000..bd36871cd8
--- /dev/null
+++ b/nni/algorithms/compression/pytorch/auto_compress/constants.py
@@ -0,0 +1,25 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from ..pruning import LevelPruner, SlimPruner, L1FilterPruner, L2FilterPruner, FPGMPruner, TaylorFOWeightFilterPruner, \
+ ActivationAPoZRankFilterPruner, ActivationMeanRankFilterPruner
+from ..quantization.quantizers import NaiveQuantizer, QAT_Quantizer, DoReFaQuantizer, BNNQuantizer
+
+
+PRUNER_DICT = {
+ 'level': LevelPruner,
+ 'slim': SlimPruner,
+ 'l1': L1FilterPruner,
+ 'l2': L2FilterPruner,
+ 'fpgm': FPGMPruner,
+ 'taylorfo': TaylorFOWeightFilterPruner,
+ 'apoz': ActivationAPoZRankFilterPruner,
+ 'mean_activation': ActivationMeanRankFilterPruner
+}
+
+QUANTIZER_DICT = {
+ 'naive': NaiveQuantizer,
+ 'qat': QAT_Quantizer,
+ 'dorefa': DoReFaQuantizer,
+ 'bnn': BNNQuantizer
+}
diff --git a/nni/algorithms/compression/pytorch/auto_compress/experiment.py b/nni/algorithms/compression/pytorch/auto_compress/experiment.py
new file mode 100644
index 0000000000..6859e60a12
--- /dev/null
+++ b/nni/algorithms/compression/pytorch/auto_compress/experiment.py
@@ -0,0 +1,70 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import inspect
+from pathlib import Path, PurePath
+from typing import overload, Union, List
+
+from numpy import tri
+
+from nni.experiment import Experiment, ExperimentConfig
+from nni.algorithms.compression.pytorch.auto_compress.interface import AbstractAutoCompressionModule
+
+
+class AutoCompressionExperiment(Experiment):
+
+ @overload
+ def __init__(self, auto_compress_module: AbstractAutoCompressionModule, config: ExperimentConfig) -> None:
+ """
+ Prepare an experiment.
+
+ Use `Experiment.run()` to launch it.
+
+ Parameters
+ ----------
+ auto_compress_module
+ The module provided by the user implements the `AbstractAutoCompressionModule` interfaces.
+ Remember put the module file under `trial_code_directory`.
+ config
+ Experiment configuration.
+ """
+ ...
+
+ @overload
+ def __init__(self, auto_compress_module: AbstractAutoCompressionModule, training_service: Union[str, List[str]]) -> None:
+ """
+ Prepare an experiment, leaving configuration fields to be set later.
+
+ Example usage::
+
+ experiment = Experiment(auto_compress_module, 'remote')
+ experiment.config.trial_command = 'python3 trial.py'
+ experiment.config.machines.append(RemoteMachineConfig(ip=..., user_name=...))
+ ...
+ experiment.run(8080)
+
+ Parameters
+ ----------
+ auto_compress_module
+ The module provided by the user implements the `AbstractAutoCompressionModule` interfaces.
+ Remember put the module file under `trial_code_directory`.
+ training_service
+ Name of training service.
+ Supported value: "local", "remote", "openpai", "aml", "kubeflow", "frameworkcontroller", "adl" and hybrid training service.
+ """
+ ...
+
+ def __init__(self, auto_compress_module: AbstractAutoCompressionModule, config=None, training_service=None):
+ super().__init__(config, training_service)
+
+ self.module_file_path = str(PurePath(inspect.getfile(auto_compress_module)))
+ self.module_name = auto_compress_module.__name__
+
+ def start(self, port: int, debug: bool) -> None:
+ trial_code_directory = str(PurePath(Path(self.config.trial_code_directory).absolute())) + '/'
+ assert self.module_file_path.startswith(trial_code_directory), 'The file path of the user-provided module should under trial_code_directory.'
+ relative_module_path = self.module_file_path.split(trial_code_directory)[1]
+ # only support linux, need refactor?
+ command = 'python3 -m nni.algorithms.compression.pytorch.auto_compress.trial_entry --module_file_name {} --module_class_name {}'
+ self.config.trial_command = command.format(relative_module_path, self.module_name)
+ super().start(port=port, debug=debug)
diff --git a/nni/algorithms/compression/pytorch/auto_compress/interface.py b/nni/algorithms/compression/pytorch/auto_compress/interface.py
new file mode 100644
index 0000000000..208d0ef6a1
--- /dev/null
+++ b/nni/algorithms/compression/pytorch/auto_compress/interface.py
@@ -0,0 +1,122 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from abc import ABC, abstractmethod
+from typing import Optional, Callable, Iterable
+
+from torch.nn import Module
+from torch.optim import Optimizer
+
+
+class BaseAutoCompressionEngine(ABC):
+ @classmethod
+ @abstractmethod
+ def trial_execute_compress(cls):
+ """
+ Execute the compressing trial.
+ """
+ pass
+
+
+class AbstractAutoCompressionModule(ABC):
+ """
+ The abstract container that user need to implement.
+ """
+ @classmethod
+ @abstractmethod
+ def model(cls) -> Module:
+ """
+ Returns
+ -------
+ torch.nn.Module
+ Model to be compress.
+ """
+ pass
+
+ @classmethod
+ @abstractmethod
+ def evaluator(cls) -> Callable[[Module], float]:
+ """
+ Returns
+ -------
+ function
+ The function used to evaluate the compressed model, return a scalar.
+ """
+ pass
+
+ @classmethod
+ @abstractmethod
+ def optimizer_factory(cls) -> Optional[Callable[[Iterable], Optimizer]]:
+ """
+ Returns
+ -------
+ Optional[Callable[[Iterable], Optimizer]]
+ Optimizer factory function. Input is a iterable value, i.e. `model.parameters()`.
+ Output is the `torch.optim.Optimizer` instance.
+ """
+ pass
+
+ @classmethod
+ @abstractmethod
+ def criterion(cls) -> Optional[Callable]:
+ """
+ Returns
+ -------
+ Optional[Callable]
+ The criterion function used to train the model.
+ """
+ pass
+
+ @classmethod
+ @abstractmethod
+ def sparsifying_trainer(cls, compress_algorithm_name: str) -> Optional[Callable[[Module, Optimizer, Callable, int], None]]:
+ """
+ The trainer is used in sparsifying process.
+
+ Parameters
+ ----------
+ compress_algorithm_name: str
+ The name of pruner and quantizer, i.e. 'level', 'l1', 'qat'.
+
+ Returns
+ -------
+ Optional[Callable[[Module, Optimizer, Callable, int], None]]
+ Used to train model in compress stage, include `model, optimizer, criterion, current_epoch` as function arguments.
+ """
+ pass
+
+ @classmethod
+ @abstractmethod
+ def post_compress_finetuning_trainer(cls, compress_algorithm_name: str) -> Optional[Callable[[Module, Optimizer, Callable, int], None]]:
+ """
+ The trainer is used in post-compress finetuning process.
+
+ Parameters
+ ----------
+ compress_algorithm_name: str
+ The name of pruner and quantizer, i.e. 'level', 'l1', 'qat'.
+
+ Returns
+ -------
+ Optional[Callable[[Module, Optimizer, Callable, int], None]]
+ Used to train model in finetune stage, include `model, optimizer, criterion, current_epoch` as function arguments.
+ """
+ pass
+
+ @classmethod
+ @abstractmethod
+ def post_compress_finetuning_epochs(cls, compress_algorithm_name: str) -> int:
+ """
+ The epochs in post-compress finetuning process.
+
+ Parameters
+ ----------
+ compress_algorithm_name: str
+ The name of pruner and quantizer, i.e. 'level', 'l1', 'qat'.
+
+ Returns
+ -------
+ int
+ The finetuning epoch number.
+ """
+ pass
diff --git a/nni/algorithms/compression/pytorch/auto_compress/trial_entry.py b/nni/algorithms/compression/pytorch/auto_compress/trial_entry.py
new file mode 100644
index 0000000000..c502f4a185
--- /dev/null
+++ b/nni/algorithms/compression/pytorch/auto_compress/trial_entry.py
@@ -0,0 +1,21 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""
+Entrypoint for trials.
+"""
+import argparse
+from pathlib import Path
+import re
+
+from .auto_compress_engine import AutoCompressionEngine
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description='trial entry for auto compression.')
+ parser.add_argument('--module_file_name', required=True, dest='module_file_name', help='the path of auto compression module file')
+ parser.add_argument('--module_class_name', required=True, dest='module_class_name', help='the name of auto compression module')
+ args = parser.parse_args()
+
+ module_name = Path(args.module_file_name).as_posix()
+ module_name = re.sub(re.escape('.py') + '$', '', module_name).replace('/', '.') + '.' + args.module_class_name
+ AutoCompressionEngine.trial_execute_compress(module_name)
diff --git a/nni/algorithms/compression/pytorch/auto_compress/utils.py b/nni/algorithms/compression/pytorch/auto_compress/utils.py
new file mode 100644
index 0000000000..fe46db63e9
--- /dev/null
+++ b/nni/algorithms/compression/pytorch/auto_compress/utils.py
@@ -0,0 +1,116 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from typing import Any
+
+from .constants import PRUNER_DICT, QUANTIZER_DICT
+
+
+class AutoCompressionSearchSpaceGenerator:
+ """
+ For convenient generation of search space that can be used by tuner.
+ """
+
+ def __init__(self):
+ self.algorithm_choice_list = []
+
+ def add_config(self, algorithm_name: str, config_list: list, **algo_kwargs):
+ """
+ This function used for distinguish algorithm type is pruning or quantization.
+ Then call `self._add_pruner_config()` or `self._add_quantizer_config()`.
+ """
+ if algorithm_name in PRUNER_DICT:
+ self._add_pruner_config(algorithm_name, config_list, **algo_kwargs)
+ if algorithm_name in QUANTIZER_DICT:
+ self._add_quantizer_config(algorithm_name, config_list, **algo_kwargs)
+
+ def _add_pruner_config(self, pruner_name: str, config_list: list, **algo_kwargs):
+ """
+ Parameters
+ ----------
+ pruner_name
+ Supported pruner name: 'level', 'slim', 'l1', 'l2', 'fpgm', 'taylorfo', 'apoz', 'mean_activation'.
+ config_list
+ Except 'op_types' and 'op_names', other config value can be written as `{'_type': ..., '_value': ...}`.
+ **algo_kwargs
+ The additional pruner parameters except 'model', 'config_list', 'optimizer', 'trainer', 'criterion'.
+ i.e., you can set `statistics_batch_num={'_type': 'choice', '_value': [1, 2, 3]}` in TaylorFOWeightFilterPruner or just `statistics_batch_num=1`.
+ """
+ sub_search_space = {'_name': pruner_name}
+ for config in config_list:
+ op_types = config.pop('op_types', [])
+ op_names = config.pop('op_names', [])
+ key_prefix = 'config_list::{}::{}'.format(':'.join(op_types), ':'.join(op_names))
+ for var_name, var_search_space in config.items():
+ sub_search_space['{}::{}'.format(key_prefix, var_name)] = self._wrap_single_value(var_search_space)
+ for parameter_name, parameter_search_space in algo_kwargs.items():
+ key_prefix = 'parameter'
+ sub_search_space['{}::{}'.format(key_prefix, parameter_name)] = self._wrap_single_value(parameter_search_space)
+ self.algorithm_choice_list.append(sub_search_space)
+
+ def _add_quantizer_config(self, quantizer_name: str, config_list: list, **algo_kwargs):
+ """
+ Parameters
+ ----------
+ quantizer_name
+ Supported pruner name: 'naive', 'qat', 'dorefa', 'bnn'.
+ config_list
+ Except 'quant_types', 'op_types' and 'op_names', other config value can be written as `{'_type': ..., '_value': ...}`.
+ **algo_kwargs
+ The additional pruner parameters except 'model', 'config_list', 'optimizer'.
+ """
+ sub_search_space = {'_name': quantizer_name}
+ for config in config_list:
+ quant_types = config.pop('quant_types', [])
+ op_types = config.pop('op_types', [])
+ op_names = config.pop('op_names', [])
+ key_prefix = 'config_list::{}::{}::{}'.format(':'.join(quant_types), ':'.join(op_types), ':'.join(op_names))
+ for var_name, var_search_space in config.items():
+ sub_search_space['{}::{}'.format(key_prefix, var_name)] = self._wrap_single_value(var_search_space)
+ for parameter_name, parameter_search_space in algo_kwargs.items():
+ key_prefix = 'parameter'
+ sub_search_space['{}::{}'.format(key_prefix, parameter_name)] = self._wrap_single_value(parameter_search_space)
+ self.algorithm_choice_list.append(sub_search_space)
+
+ def dumps(self) -> dict:
+ """
+ Dump the search space as a dict.
+ """
+ search_space = {
+ 'algorithm_name': {
+ '_type': 'choice',
+ '_value': self.algorithm_choice_list
+ }
+ }
+ return search_space
+
+ @classmethod
+ def loads(cls, search_space: dict):
+ """
+ Return a AutoCompressionSearchSpaceGenerator instance load from a search space dict.
+ """
+ generator = AutoCompressionSearchSpaceGenerator()
+ generator.algorithm_choice_list = search_space['algorithm_name']['_value']
+ return generator
+
+ def _wrap_single_value(self, value) -> dict:
+ if not isinstance(value, dict):
+ converted_value = {
+ '_type': 'choice',
+ '_value': [value]
+ }
+ elif '_type' not in value:
+ converted_value = {}
+ for k, v in value.items():
+ converted_value[k] = self._wrap_single_value(v)
+ else:
+ converted_value = value
+ return converted_value
+
+
+def import_(target: str, allow_none: bool = False) -> Any:
+ if target is None:
+ return None
+ path, identifier = target.rsplit('.', 1)
+ module = __import__(path, globals(), locals(), [identifier])
+ return getattr(module, identifier)
diff --git a/nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py b/nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py
index 82a8f1cb98..207a8aa2f9 100644
--- a/nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py
+++ b/nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py
@@ -34,6 +34,9 @@ class AutoCompressPruner(Pruner):
Function used for the first subproblem of ADMM Pruner.
Users should write this function as a normal function to train the Pytorch model
and include `model, optimizer, criterion, epoch` as function arguments.
+ criterion: function
+ Function used to calculate the loss between the target and the output. By default, we use CrossEntropyLoss.
+ For example, you can use ``torch.nn.CrossEntropyLoss()`` as input.
evaluator : function
function to evaluate the pruned model.
This function should include `model` as the only parameter, and returns a scalar value.
@@ -80,7 +83,7 @@ def evaluator(model):
PATH to store temporary experiment data.
"""
- def __init__(self, model, config_list, trainer, criterion, evaluator, dummy_input,
+ def __init__(self, model, config_list, trainer, evaluator, dummy_input, criterion=torch.nn.CrossEntropyLoss(),
num_iterations=3, optimize_mode='maximize', base_algo='l1',
# SimulatedAnnealing related
start_temperature=100, stop_temperature=20, cool_down_rate=0.9, perturbation_magnitude=0.35,
diff --git a/nni/algorithms/compression/pytorch/pruning/iterative_pruner.py b/nni/algorithms/compression/pytorch/pruning/iterative_pruner.py
index 9651e9e35a..d5cd5dfccb 100644
--- a/nni/algorithms/compression/pytorch/pruning/iterative_pruner.py
+++ b/nni/algorithms/compression/pytorch/pruning/iterative_pruner.py
@@ -40,6 +40,7 @@ def __init__(self, model, config_list, optimizer=None, pruning_algorithm='slim',
and include `model, optimizer, criterion, epoch` as function arguments.
criterion: function
Function used to calculate the loss between the target and the output.
+ For example, you can use ``torch.nn.CrossEntropyLoss()`` as input.
num_iterations: int
Total number of iterations in pruning process. We will calculate mask at the end of an iteration.
epochs_per_iteration: Union[int, list]
@@ -59,8 +60,11 @@ def __init__(self, model, config_list, optimizer=None, pruning_algorithm='slim',
assert len(epochs_per_iteration) == num_iterations, 'num_iterations should equal to the length of epochs_per_iteration'
self.epochs_per_iteration = epochs_per_iteration
else:
+ assert num_iterations > 0, 'num_iterations should >= 1'
self.epochs_per_iteration = [epochs_per_iteration] * num_iterations
+ self._validate_iteration_params()
+
self._trainer = trainer
self._criterion = criterion
@@ -68,6 +72,9 @@ def _fresh_calculated(self):
for wrapper in self.get_modules_wrapper():
wrapper.if_calculated = False
+ def _validate_iteration_params(self):
+ assert all(num >= 0 for num in self.epochs_per_iteration), 'all epoch number need >= 0'
+
def compress(self):
training = self.bound_model.training
self.bound_model.train()
@@ -75,6 +82,10 @@ def compress(self):
self._fresh_calculated()
for epoch in range(epochs_num):
self._trainer(self.bound_model, optimizer=self.optimizer, criterion=self._criterion, epoch=epoch)
+ # NOTE: workaround for statistics_batch_num bigger than max batch number in one epoch, need refactor
+ if hasattr(self.masker, 'statistics_batch_num') and hasattr(self, 'iterations'):
+ if self.iterations < self.masker.statistics_batch_num:
+ self.iterations = self.masker.statistics_batch_num
self.update_mask()
self.bound_model.train(training)
@@ -97,6 +108,7 @@ class AGPPruner(IterativePruner):
Function to train the model
criterion: function
Function used to calculate the loss between the target and the output.
+ For example, you can use ``torch.nn.CrossEntropyLoss()`` as input.
num_iterations: int
Total number of iterations in pruning process. We will calculate mask at the end of an iteration.
epochs_per_iteration: int
@@ -245,6 +257,7 @@ class ADMMPruner(IterativePruner):
and include `model, optimizer, criterion, epoch` as function arguments.
criterion: function
Function used to calculate the loss between the target and the output. By default, we use CrossEntropyLoss in ADMMPruner.
+ For example, you can use ``torch.nn.CrossEntropyLoss()`` as input.
num_iterations: int
Total number of iterations in pruning process. We will calculate mask after we finish all iterations in ADMMPruner.
epochs_per_iteration: int
@@ -254,7 +267,6 @@ class ADMMPruner(IterativePruner):
base_algo : str
Base pruning algorithm. `level`, `l1`, `l2` or `fpgm`, by default `l1`. Given the sparsity distribution among
the ops, the assigned `base_algo` is used to decide which filters/channels/weights to prune.
-
"""
def __init__(self, model, config_list, trainer, criterion=torch.nn.CrossEntropyLoss(),
@@ -396,7 +408,8 @@ class SlimPruner(IterativePruner):
and include `model, optimizer, criterion, epoch` as function arguments.
criterion : function
Function used to calculate the loss between the target and the output.
- sparsity_training_epochs: int
+ For example, you can use ``torch.nn.CrossEntropyLoss()`` as input.
+ sparsifying_training_epochs: int
The number of channel sparsity regularization training epochs before pruning.
scale : float
Penalty parameters for sparsification.
@@ -413,10 +426,10 @@ class SlimPruner(IterativePruner):
should on the same device with the model.
"""
- def __init__(self, model, config_list, optimizer, trainer, criterion, sparsity_training_epochs=10, scale=0.0001,
+ def __init__(self, model, config_list, optimizer, trainer, criterion, sparsifying_training_epochs=10, scale=0.0001,
dependency_aware=False, dummy_input=None):
super().__init__(model, config_list, optimizer=optimizer, pruning_algorithm='slim', trainer=trainer, criterion=criterion,
- num_iterations=1, epochs_per_iteration=sparsity_training_epochs, dependency_aware=dependency_aware,
+ num_iterations=1, epochs_per_iteration=sparsifying_training_epochs, dependency_aware=dependency_aware,
dummy_input=dummy_input)
self.scale = scale
self.patch_optimizer_before(self._callback)
@@ -459,8 +472,9 @@ class TaylorFOWeightFilterPruner(IterativePruner):
and include `model, optimizer, criterion, epoch` as function arguments.
criterion : function
Function used to calculate the loss between the target and the output.
- sparsity_training_epochs: int
- The number of epochs to collect the contributions.
+ For example, you can use ``torch.nn.CrossEntropyLoss()`` as input.
+ sparsifying_training_batches: int
+ The number of batches to collect the contributions. Note that the number need to be less than the maximum batch number in one epoch.
dependency_aware: bool
If prune the model in a dependency-aware way. If it is `True`, this pruner will
prune the model according to the l2-norm of weights and the channel-dependency or
@@ -472,14 +486,14 @@ class TaylorFOWeightFilterPruner(IterativePruner):
dummy_input : torch.Tensor
The dummy input to analyze the topology constraints. Note that, the dummy_input
should on the same device with the model.
-
"""
- def __init__(self, model, config_list, optimizer, trainer, criterion, sparsity_training_epochs=1, dependency_aware=False,
- dummy_input=None):
+ def __init__(self, model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1,
+ dependency_aware=False, dummy_input=None):
super().__init__(model, config_list, optimizer=optimizer, pruning_algorithm='taylorfo', trainer=trainer,
- criterion=criterion, num_iterations=1, epochs_per_iteration=sparsity_training_epochs,
- dependency_aware=dependency_aware, dummy_input=dummy_input)
+ criterion=criterion, statistics_batch_num=sparsifying_training_batches, num_iterations=1,
+ epochs_per_iteration=1, dependency_aware=dependency_aware,
+ dummy_input=dummy_input)
def _supported_dependency_aware(self):
return True
@@ -503,10 +517,11 @@ class ActivationAPoZRankFilterPruner(IterativePruner):
and include `model, optimizer, criterion, epoch` as function arguments.
criterion : function
Function used to calculate the loss between the target and the output.
+ For example, you can use ``torch.nn.CrossEntropyLoss()`` as input.
activation: str
The activation type.
- sparsity_training_epochs: int
- The number of epochs to statistic the activation.
+ sparsifying_training_batches: int
+ The number of batches to collect the contributions. Note that the number need to be less than the maximum batch number in one epoch.
dependency_aware: bool
If prune the model in a dependency-aware way. If it is `True`, this pruner will
prune the model according to the l2-norm of weights and the channel-dependency or
@@ -522,10 +537,12 @@ class ActivationAPoZRankFilterPruner(IterativePruner):
"""
def __init__(self, model, config_list, optimizer, trainer, criterion, activation='relu',
- sparsity_training_epochs=1, dependency_aware=False, dummy_input=None):
+ sparsifying_training_batches=1, dependency_aware=False, dummy_input=None):
super().__init__(model, config_list, pruning_algorithm='apoz', optimizer=optimizer, trainer=trainer,
criterion=criterion, dependency_aware=dependency_aware, dummy_input=dummy_input,
- activation=activation, num_iterations=1, epochs_per_iteration=sparsity_training_epochs)
+ activation=activation, statistics_batch_num=sparsifying_training_batches, num_iterations=1,
+ epochs_per_iteration=1)
+ self.patch_optimizer(self.update_mask)
def _supported_dependency_aware(self):
return True
@@ -549,10 +566,11 @@ class ActivationMeanRankFilterPruner(IterativePruner):
and include `model, optimizer, criterion, epoch` as function arguments.
criterion : function
Function used to calculate the loss between the target and the output.
+ For example, you can use ``torch.nn.CrossEntropyLoss()`` as input.
activation: str
The activation type.
- sparsity_training_epochs: int
- The number of batches to statistic the activation.
+ sparsifying_training_batches: int
+ The number of batches to collect the contributions. Note that the number need to be less than the maximum batch number in one epoch.
dependency_aware: bool
If prune the model in a dependency-aware way. If it is `True`, this pruner will
prune the model according to the l2-norm of weights and the channel-dependency or
@@ -567,10 +585,12 @@ class ActivationMeanRankFilterPruner(IterativePruner):
"""
def __init__(self, model, config_list, optimizer, trainer, criterion, activation='relu',
- sparsity_training_epochs=1, dependency_aware=False, dummy_input=None):
+ sparsifying_training_batches=1, dependency_aware=False, dummy_input=None):
super().__init__(model, config_list, pruning_algorithm='mean_activation', optimizer=optimizer, trainer=trainer,
criterion=criterion, dependency_aware=dependency_aware, dummy_input=dummy_input,
- activation=activation, num_iterations=1, epochs_per_iteration=sparsity_training_epochs)
+ activation=activation, statistics_batch_num=sparsifying_training_batches, num_iterations=1,
+ epochs_per_iteration=1)
+ self.patch_optimizer(self.update_mask)
def _supported_dependency_aware(self):
return True
diff --git a/nni/algorithms/compression/pytorch/pruning/structured_pruning_masker.py b/nni/algorithms/compression/pytorch/pruning/structured_pruning_masker.py
index 671811e138..eb3cc06ebd 100644
--- a/nni/algorithms/compression/pytorch/pruning/structured_pruning_masker.py
+++ b/nni/algorithms/compression/pytorch/pruning/structured_pruning_masker.py
@@ -473,7 +473,7 @@ class TaylorFOWeightFilterPrunerMasker(StructuredWeightMasker):
def __init__(self, model, pruner, statistics_batch_num=1):
super().__init__(model, pruner)
- self.pruner.statistics_batch_num = statistics_batch_num
+ self.statistics_batch_num = statistics_batch_num
self.pruner.iterations = 0
self.pruner.set_wrappers_attribute("contribution", None)
self.pruner.patch_optimizer(self.calc_contributions)
@@ -497,13 +497,13 @@ def calc_contributions(self):
Calculate the estimated importance of filters as a sum of individual contribution
based on the first order taylor expansion.
"""
- if self.pruner.iterations >= self.pruner.statistics_batch_num:
+ if self.pruner.iterations >= self.statistics_batch_num:
return
for wrapper in self.pruner.get_modules_wrapper():
filters = wrapper.module.weight.size(0)
contribution = (
- wrapper.module.weight*wrapper.module.weight.grad).data.pow(2).view(filters, -1).sum(dim=1)
+ wrapper.module.weight * wrapper.module.weight.grad).data.pow(2).view(filters, -1).sum(dim=1)
if wrapper.contribution is None:
wrapper.contribution = contribution
else:
@@ -512,7 +512,7 @@ def calc_contributions(self):
self.pruner.iterations += 1
def get_channel_sum(self, wrapper, wrapper_idx):
- if self.pruner.iterations < self.pruner.statistics_batch_num:
+ if self.pruner.iterations < self.statistics_batch_num:
return None
if wrapper.contribution is None:
return None
@@ -524,6 +524,8 @@ def __init__(self, model, pruner, statistics_batch_num=1, activation='relu'):
super().__init__(model, pruner)
self.statistics_batch_num = statistics_batch_num
self.pruner.hook_id = self._add_activation_collector(self.pruner)
+ self.pruner.iterations = 0
+ self.pruner.patch_optimizer(self._iteration_counter)
assert activation in ['relu', 'relu6']
if activation == 'relu':
@@ -533,6 +535,9 @@ def __init__(self, model, pruner, statistics_batch_num=1, activation='relu'):
else:
self.pruner.activation = None
+ def _iteration_counter(self):
+ self.pruner.iterations += 1
+
def _add_activation_collector(self, pruner):
def collector(collected_activation):
def hook(module_, input_, output):
diff --git a/nni/algorithms/compression/tensorflow/pruning/one_shot_pruner.py b/nni/algorithms/compression/tensorflow/pruning/one_shot_pruner.py
index 76dc1915ce..2c1e1e3e0f 100644
--- a/nni/algorithms/compression/tensorflow/pruning/one_shot_pruner.py
+++ b/nni/algorithms/compression/tensorflow/pruning/one_shot_pruner.py
@@ -3,15 +3,15 @@
from nni.compression.tensorflow import Pruner
__all__ = [
- 'OneshotPruner',
'LevelPruner',
+ 'SlimPruner',
]
class OneshotPruner(Pruner):
- def __init__(self, model, config_list, pruning_algorithm='level', **algo_kwargs):
+ def __init__(self, model, config_list, masker_class, **algo_kwargs):
super().__init__(model, config_list)
self.set_wrappers_attribute('calculated', False)
- self.masker = MASKER_DICT[pruning_algorithm](model, self, **algo_kwargs)
+ self.masker = masker_class(model, self, **algo_kwargs)
def validate_config(self, model, config_list):
pass # TODO
@@ -28,7 +28,12 @@ def calc_masks(self, wrapper, wrapper_idx=None):
class LevelPruner(OneshotPruner):
def __init__(self, model, config_list):
- super().__init__(model, config_list, pruning_algorithm='level')
+ super().__init__(model, config_list, LevelPrunerMasker)
+
+
+class SlimPruner(OneshotPruner):
+ def __init__(self, model, config_list):
+ super().__init__(model, config_list, SlimPrunerMasker)
class WeightMasker:
@@ -57,7 +62,7 @@ def calc_masks(self, sparsity, wrapper, wrapper_idx=None):
w_abs = tf.math.abs(weight)
k = tf.size(weight) - num_prune
- topk = tf.math.top_k(tf.reshape(w_abs, [-1]), k)[0]
+ topk = tf.math.top_k(tf.reshape(w_abs, [-1]), k).values
if tf.size(topk) == 0:
mask = tf.zeros_like(weight)
else:
@@ -65,7 +70,41 @@ def calc_masks(self, sparsity, wrapper, wrapper_idx=None):
masks[weight_variable.name] = tf.cast(mask, weight.dtype)
return masks
+class SlimPrunerMasker(WeightMasker):
+ def __init__(self, model, pruner, **kwargs):
+ super().__init__(model, pruner)
+ weight_list = []
+ for wrapper in pruner.wrappers:
+ weights = [w for w in wrapper.layer.weights if '/gamma:' in w.name]
+ assert len(weights) == 1, f'Bad weights: {[w.name for w in wrapper.layer.weights]}'
+ weight_list.append(tf.math.abs(weights[0].read_value()))
+ all_bn_weights = tf.concat(weight_list, 0)
+ k = int(all_bn_weights.shape[0] * pruner.wrappers[0].config['sparsity'])
+ top_k = -tf.math.top_k(-tf.reshape(all_bn_weights, [-1]), k).values
+ self.global_threshold = top_k.numpy()[-1]
+
+ def calc_masks(self, sparsity, wrapper, wrapper_idx=None):
+ assert isinstance(wrapper.layer, tf.keras.layers.BatchNormalization), \
+ 'SlimPruner only supports 2D batch normalization layer pruning'
-MASKER_DICT = {
- 'level': LevelPrunerMasker,
-}
+ weight = None
+ weight_name = None
+ bias_name = None
+
+ for variable in wrapper.layer.weights:
+ if '/gamma:' in variable.name:
+ weight = variable.read_value()
+ weight_name = variable.name
+ elif '/beta:' in variable.name:
+ bias_name = variable.name
+
+ assert weight is not None
+ if wrapper.masks.get(weight_name) is not None:
+ weight *= wrapper.masks[weight_name]
+
+ mask = tf.cast(tf.math.abs(weight) > self.global_threshold, weight.dtype)
+
+ masks = {weight_name: mask}
+ if bias_name:
+ masks[bias_name] = mask
+ return masks
diff --git a/nni/algorithms/hpo/dngo_tuner.py b/nni/algorithms/hpo/dngo_tuner.py
new file mode 100644
index 0000000000..276ec75168
--- /dev/null
+++ b/nni/algorithms/hpo/dngo_tuner.py
@@ -0,0 +1,117 @@
+import logging
+
+import numpy as np
+import torch
+import nni.parameter_expressions as parameter_expressions
+from nni import ClassArgsValidator
+from nni.tuner import Tuner
+from pybnn import DNGO
+from torch.distributions import Normal
+
+_logger = logging.getLogger(__name__)
+
+
+def _random_config(search_space, random_state):
+ chosen_config = {}
+ for key, val in search_space.items():
+ if val['_type'] == 'choice':
+ choices = val['_value']
+ index = random_state.randint(len(choices))
+ if all([isinstance(c, (int, float)) for c in choices]):
+ chosen_config[key] = choices[index]
+ else:
+ raise ValueError('Choices with type other than int and float is not supported.')
+ elif val['_type'] == 'uniform':
+ chosen_config[key] = random_state.uniform(val['_value'][0], val['_value'][1])
+ elif val['_type'] == 'randint':
+ chosen_config[key] = random_state.randint(
+ val['_value'][0], val['_value'][1])
+ elif val['_type'] == 'quniform':
+ chosen_config[key] = parameter_expressions.quniform(
+ val['_value'][0], val['_value'][1], val['_value'][2], random_state)
+ elif val['_type'] == 'loguniform':
+ chosen_config[key] = parameter_expressions.loguniform(
+ val['_value'][0], val['_value'][1], random_state)
+ elif val['_type'] == 'qloguniform':
+ chosen_config[key] = parameter_expressions.qloguniform(
+ val['_value'][0], val['_value'][1], val['_value'][2], random_state)
+ else:
+ raise ValueError('Unknown key %s and value %s' % (key, val))
+ return chosen_config
+
+
+class DNGOTuner(Tuner):
+
+ def __init__(self, optimize_mode='maximize', sample_size=1000, trials_per_update=20, num_epochs_per_training=500):
+ self.searchspace_json = None
+ self.random_state = None
+ self.model = DNGO(do_mcmc=False, num_epochs=num_epochs_per_training)
+ self._model_initialized = False
+ self.sample_size = sample_size
+ self.trials_per_update = trials_per_update
+ self.optimize_mode = optimize_mode
+
+ self.x = []
+ self.y = []
+
+ def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
+ self.x.append(parameters)
+ self.y.append(self._get_default_value(value))
+ if len(self.y) % self.trials_per_update == 0:
+ self._update_model()
+
+ def generate_parameters(self, parameter_id, **kwargs):
+ if not self._model_initialized:
+ return _random_config(self.searchspace_json, self.random_state)
+ else:
+ # random samples and pick best with model
+ candidate_x = [_random_config(self.searchspace_json, self.random_state) for _ in range(self.sample_size)]
+
+ x_test = np.array([np.array(list(xi.values())) for xi in candidate_x])
+ m, v = self.model.predict(x_test)
+ mean = torch.Tensor(m)
+ sigma = torch.Tensor(v)
+ u = (mean - torch.Tensor([0.95]).expand_as(mean)) / sigma
+ normal = Normal(torch.zeros_like(u), torch.ones_like(u))
+ ucdf = normal.cdf(u)
+ updf = torch.exp(normal.log_prob(u))
+ ei = sigma * (updf + u * ucdf)
+
+ if self.optimize_mode == 'maximize':
+ ind = torch.argmax(ei)
+ else:
+ ind = torch.argmin(ei)
+ new_x = candidate_x[ind]
+ return new_x
+
+ def update_search_space(self, search_space):
+ self.searchspace_json = search_space
+ self.random_state = np.random.RandomState()
+
+ def import_data(self, data):
+ for d in data:
+ self.x.append(d['parameter'])
+ self.y.append(self._get_default_value(d['value']))
+ self._update_model()
+
+ def _update_model(self):
+ _logger.info('Updating model on %d samples', len(self.x))
+ x_arr = []
+ for x in self.x:
+ x_arr.append([x[k] for k in sorted(x.keys())])
+ self.model.train(np.array(x_arr), np.array(self.y), do_optimize=True)
+ self._model_initialized = True
+
+ def _get_default_value(self, value):
+ if isinstance(value, dict) and 'default' in value:
+ return value['default']
+ elif isinstance(value, float):
+ return value
+ else:
+ raise ValueError(f'Unsupported value: {value}')
+
+
+class DNGOClassArgsValidator(ClassArgsValidator):
+ # DNGO tuner do not have much input arg, so the validation is actually hardly used
+ def validate_class_args(self, **kwargs):
+ pass
diff --git a/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py b/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py
index c7849774cc..95af8358b1 100644
--- a/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py
+++ b/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py
@@ -290,6 +290,9 @@ def _tensorrt_build_withcalib(self, onnx_path):
calib_data_set.append(data)
calib_data = np.concatenate(calib_data_set)
elif type(self.calib_data_loader) == torch.Tensor:
+ # trt need numpy as calibration data, only cpu data can convert to numpy directly
+ if self.calib_data_loader.device != torch.device("cpu"):
+ self.calib_data_loader = self.calib_data_loader.to("cpu")
calib_data = self.calib_data_loader.numpy()
else:
raise ValueError("Not support calibration datatype")
@@ -326,6 +329,8 @@ def inference(self, test_data):
Model input tensor
"""
# convert pytorch tensor to numpy darray
+ if test_data.device != torch.device("cpu"):
+ test_data = test_data.to("cpu")
test_data = test_data.numpy()
# Numpy dtype should be float32
assert test_data.dtype == np.float32
diff --git a/nni/compression/pytorch/utils/counter.py b/nni/compression/pytorch/utils/counter.py
index 0302c98055..3bf043f340 100644
--- a/nni/compression/pytorch/utils/counter.py
+++ b/nni/compression/pytorch/utils/counter.py
@@ -313,12 +313,13 @@ def format_results(self):
table.field_names = headers
for i, result in enumerate(self.results):
+ flops_count = int(result['flops'].item()) if isinstance(result['flops'], torch.Tensor) else int(result['flops'])
row_values = [
i,
result['name'],
result['module_type'],
str(result['weight_shape']),
- result['flops'],
+ flops_count,
result['params'],
]
name_counter[result['name']] += 1
@@ -408,4 +409,4 @@ def count_flops_params(model, x, custom_ops=None, verbose=True, mode='default'):
print(f'FLOPs total: {profiler.sum_flops()}')
print(f'#Params total: {profiler.sum_params()}')
- return profiler.sum_flops(), profiler.sum_params(), profiler.results
\ No newline at end of file
+ return profiler.sum_flops(), profiler.sum_params(), profiler.results
diff --git a/nni/experiment/config/base.py b/nni/experiment/config/base.py
index 739adcbc38..d1fc92a26f 100644
--- a/nni/experiment/config/base.py
+++ b/nni/experiment/config/base.py
@@ -6,7 +6,7 @@
from pathlib import Path
from typing import Any, Dict, Optional, Type, TypeVar
-import ruamel.yaml as yaml
+import yaml
from . import util
@@ -72,7 +72,7 @@ def load(cls: Type[T], path: PathLike) -> T:
Load config from YAML (or JSON) file.
Keys in YAML file can either be camelCase or snake_case.
"""
- data = yaml.load(open(path), Loader=yaml.SafeLoader)
+ data = yaml.safe_load(open(path))
if not isinstance(data, dict):
raise ValueError(f'Content of config file {path} is not a dict/object')
return cls(**data, _base_path=Path(path).parent)
@@ -124,7 +124,7 @@ def validate(self) -> None:
type_name = str(field.type).replace('typing.', '')
optional = any([
type_name.startswith('Optional['),
- type_name.startswith('Union[') and 'NoneType' in type_name,
+ type_name.startswith('Union[') and 'None' in type_name,
type_name == 'Any'
])
if value is None:
diff --git a/nni/experiment/config/common.py b/nni/experiment/config/common.py
index 7143a8571f..c7cd64a7da 100644
--- a/nni/experiment/config/common.py
+++ b/nni/experiment/config/common.py
@@ -5,7 +5,7 @@
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
-from ruamel.yaml import YAML
+import yaml
from .base import ConfigBase, PathLike
from . import util
@@ -29,6 +29,8 @@ def validate(self):
super().validate()
_validate_algo(self)
+ _canonical_rules = {'code_directory': util.canonical_path}
+
@dataclass(init=False)
class AlgorithmConfig(_AlgorithmConfig):
name: str
@@ -37,7 +39,7 @@ class AlgorithmConfig(_AlgorithmConfig):
@dataclass(init=False)
class CustomAlgorithmConfig(_AlgorithmConfig):
class_name: str
- class_directory: Optional[PathLike] = '.'
+ code_directory: Optional[PathLike] = '.'
class_args: Optional[Dict[str, Any]] = None
@@ -67,7 +69,7 @@ class ExperimentConfig(ConfigBase):
debug: bool = False
log_level: Optional[str] = None
experiment_working_directory: PathLike = '~/nni-experiments'
- tuner_gpu_indices: Optional[Union[List[int], str]] = None
+ tuner_gpu_indices: Union[List[int], str, int, None] = None
tuner: Optional[_AlgorithmConfig] = None
assessor: Optional[_AlgorithmConfig] = None
advisor: Optional[_AlgorithmConfig] = None
@@ -118,7 +120,7 @@ def validate(self, initialized_tuner: bool = False) -> None:
def json(self) -> Dict[str, Any]:
obj = super().json()
if obj.get('searchSpaceFile'):
- obj['searchSpace'] = YAML().load(open(obj.pop('searchSpaceFile')))
+ obj['searchSpace'] = yaml.safe_load(open(obj.pop('searchSpaceFile')))
return obj
## End of public API ##
@@ -137,7 +139,7 @@ def _validation_rules(self):
'trial_code_directory': util.canonical_path,
'max_experiment_duration': lambda value: f'{util.parse_time(value)}s' if value is not None else None,
'experiment_working_directory': util.canonical_path,
- 'tuner_gpu_indices': lambda value: [int(idx) for idx in value.split(',')] if isinstance(value, str) else value,
+ 'tuner_gpu_indices': util.canonical_gpu_indices,
'tuner': lambda config: None if config is None or config.name == '_none_' else config.canonical(),
'assessor': lambda config: None if config is None or config.name == '_none_' else config.canonical(),
'advisor': lambda config: None if config is None or config.name == '_none_' else config.canonical(),
diff --git a/nni/experiment/config/convert.py b/nni/experiment/config/convert.py
index 06db0670f7..0b4b332d08 100644
--- a/nni/experiment/config/convert.py
+++ b/nni/experiment/config/convert.py
@@ -249,13 +249,13 @@ def convert_algo(algo_type, v1, v2):
v2_algo = AlgorithmConfig(name=builtin_name, class_args=class_args)
else:
- class_directory = util.canonical_path(v1_algo.pop('codeDir'))
+ code_directory = util.canonical_path(v1_algo.pop('codeDir'))
class_file_name = v1_algo.pop('classFileName')
assert class_file_name.endswith('.py')
class_name = class_file_name[:-3] + '.' + v1_algo.pop('className')
v2_algo = CustomAlgorithmConfig(
class_name=class_name,
- class_directory=class_directory,
+ code_directory=code_directory,
class_args=class_args
)
diff --git a/nni/experiment/config/local.py b/nni/experiment/config/local.py
index 01654c7231..90b92093fd 100644
--- a/nni/experiment/config/local.py
+++ b/nni/experiment/config/local.py
@@ -5,6 +5,7 @@
from typing import List, Optional, Union
from .common import TrainingServiceConfig
+from . import util
__all__ = ['LocalConfig']
@@ -13,10 +14,10 @@ class LocalConfig(TrainingServiceConfig):
platform: str = 'local'
use_active_gpu: Optional[bool] = None
max_trial_number_per_gpu: int = 1
- gpu_indices: Optional[Union[List[int], str]] = None
+ gpu_indices: Union[List[int], str, int, None] = None
_canonical_rules = {
- 'gpu_indices': lambda value: [int(idx) for idx in value.split(',')] if isinstance(value, str) else value
+ 'gpu_indices': util.canonical_gpu_indices
}
_validation_rules = {
diff --git a/nni/experiment/config/remote.py b/nni/experiment/config/remote.py
index d2ee34eff5..29c47fcaa8 100644
--- a/nni/experiment/config/remote.py
+++ b/nni/experiment/config/remote.py
@@ -22,12 +22,12 @@ class RemoteMachineConfig(ConfigBase):
ssh_passphrase: Optional[str] = None
use_active_gpu: bool = False
max_trial_number_per_gpu: int = 1
- gpu_indices: Optional[Union[List[int], str]] = None
+ gpu_indices: Union[List[int], str, int, None] = None
python_path: Optional[str] = None
_canonical_rules = {
'ssh_key_file': util.canonical_path,
- 'gpu_indices': lambda value: [int(idx) for idx in value.split(',')] if isinstance(value, str) else value,
+ 'gpu_indices': util.canonical_gpu_indices
}
_validation_rules = {
diff --git a/nni/experiment/config/util.py b/nni/experiment/config/util.py
index b855772da2..62a56f6b00 100644
--- a/nni/experiment/config/util.py
+++ b/nni/experiment/config/util.py
@@ -5,11 +5,15 @@
Miscellaneous utility functions.
"""
+import importlib
+import json
import math
import os.path
from pathlib import Path
from typing import Any, Dict, Optional, Union, List
+import nni.runtime.config
+
PathLike = Union[Path, str]
def case_insensitive(key_or_kwargs: Union[str, Dict[str, Any]]) -> Union[str, Dict[str, Any]]:
@@ -34,6 +38,14 @@ def training_service_config_factory(
config: Union[List, Dict] = None,
base_path: Optional[Path] = None): # -> TrainingServiceConfig
from .common import TrainingServiceConfig
+
+ # import all custom config classes so they can be found in TrainingServiceConfig.__subclasses__()
+ custom_ts_config_path = nni.runtime.config.get_config_file('training_services.json')
+ custom_ts_config = json.load(custom_ts_config_path.open())
+ for custom_ts_pkg in custom_ts_config.keys():
+ pkg = importlib.import_module(custom_ts_pkg)
+ _config_class = pkg.nni_training_service_info.config_class
+
ts_configs = []
if platform is not None:
assert config is None
@@ -42,7 +54,8 @@ def training_service_config_factory(
if cls.platform in platforms:
ts_configs.append(cls())
if len(ts_configs) < len(platforms):
- raise RuntimeError('There is unrecognized platform!')
+ bad = ', '.join(set(platforms) - set(ts_configs))
+ raise RuntimeError(f'Bad training service platform: {bad}')
else:
assert config is not None
supported_platforms = {cls.platform: cls for cls in TrainingServiceConfig.__subclasses__()}
@@ -79,3 +92,10 @@ def _parse_unit(string, target_unit, all_units):
value = float(number) * factor
return math.ceil(value / all_units[target_unit])
raise ValueError(f'Unsupported unit in "{string}"')
+
+def canonical_gpu_indices(indices: Union[List[int], str, int, None]) -> Optional[List[int]]:
+ if isinstance(indices, str):
+ return [int(idx) for idx in indices.split(',')]
+ if isinstance(indices, int):
+ return [indices]
+ return indices
diff --git a/nni/experiment/launcher.py b/nni/experiment/launcher.py
index e43df2f131..7ee9e43242 100644
--- a/nni/experiment/launcher.py
+++ b/nni/experiment/launcher.py
@@ -39,7 +39,7 @@ def start_experiment(exp_id: str, config: ExperimentConfig, port: int, debug: bo
try:
_logger.info('Creating experiment, Experiment ID: %s', colorama.Fore.CYAN + exp_id + colorama.Style.RESET_ALL)
start_time, proc = _start_rest_server(config, port, debug, exp_id, mode=mode)
- _logger.info('Statring web server...')
+ _logger.info('Starting web server...')
_check_rest_server(port)
platform = 'hybrid' if isinstance(config.training_service, list) else config.training_service.platform
_save_experiment_information(exp_id, port, start_time, platform,
@@ -74,7 +74,7 @@ def start_experiment_retiarii(exp_id: str, config: ExperimentConfig, port: int,
pipe_file = pipe.connect()
nni.runtime.protocol._in_file = pipe_file
nni.runtime.protocol._out_file = pipe_file
- _logger.info('Statring web server...')
+ _logger.info('Starting web server...')
_check_rest_server(port)
platform = 'hybrid' if isinstance(config.training_service, list) else config.training_service.platform
_save_experiment_information(exp_id, port, start_time, platform,
diff --git a/nni/experiment/pipe.py b/nni/experiment/pipe.py
index 79199c6cd1..e59fd8270b 100644
--- a/nni/experiment/pipe.py
+++ b/nni/experiment/pipe.py
@@ -1,7 +1,10 @@
from io import BufferedIOBase
+import logging
import os
import sys
+_logger = logging.getLogger(__name__)
+
if sys.platform == 'win32':
import _winapi
import msvcrt
@@ -29,8 +32,11 @@ def connect(self) -> BufferedIOBase:
return self.file
def close(self) -> None:
- if self.file is not None:
- self.file.close()
+ try:
+ if self.file is not None:
+ self.file.close()
+ except Exception as e:
+ _logger.debug('Error on closing Windows pipe: %s', e)
Pipe = WindowsPipe
@@ -55,9 +61,12 @@ def connect(self) -> BufferedIOBase:
return self.file
def close(self) -> None:
- if self.file is not None:
- self.file.close()
- self._socket.close()
- os.unlink(self.path)
+ try:
+ if self.file is not None:
+ self.file.close()
+ self._socket.close()
+ os.unlink(self.path)
+ except Exception as e:
+ _logger.debug('Error on closing POSIX pipe: %s', e)
Pipe = UnixPipe
diff --git a/nni/retiarii/codegen/pytorch.py b/nni/retiarii/codegen/pytorch.py
index c9d6b92562..17dc45e3a5 100644
--- a/nni/retiarii/codegen/pytorch.py
+++ b/nni/retiarii/codegen/pytorch.py
@@ -162,6 +162,8 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
import torch.nn.functional as F
import torch.optim as optim
+import nni.retiarii.nn.pytorch
+
{}
{}
diff --git a/nni/retiarii/evaluator/pytorch/lightning.py b/nni/retiarii/evaluator/pytorch/lightning.py
index d316ce857a..4399844ac6 100644
--- a/nni/retiarii/evaluator/pytorch/lightning.py
+++ b/nni/retiarii/evaluator/pytorch/lightning.py
@@ -202,7 +202,7 @@ class Classification(Lightning):
If the ``lightning_module`` has a predefined val_dataloaders method this will be skipped.
trainer_kwargs : dict
Optional keyword arguments passed to trainer. See
- `Lightning documentation `__ for details.
+ `Lightning documentation `__ for details.
"""
def __init__(self, criterion: nn.Module = nn.CrossEntropyLoss,
@@ -250,7 +250,7 @@ class Regression(Lightning):
If the ``lightning_module`` has a predefined val_dataloaders method this will be skipped.
trainer_kwargs : dict
Optional keyword arguments passed to trainer. See
- `Lightning documentation `__ for details.
+ `Lightning documentation `__ for details.
"""
def __init__(self, criterion: nn.Module = nn.MSELoss,
diff --git a/nni/retiarii/execution/python.py b/nni/retiarii/execution/python.py
index 93a4d10333..2a5e7a11a9 100644
--- a/nni/retiarii/execution/python.py
+++ b/nni/retiarii/execution/python.py
@@ -30,7 +30,7 @@ def load(data) -> 'PythonGraphData':
class PurePythonExecutionEngine(BaseExecutionEngine):
@classmethod
def pack_model_data(cls, model: Model) -> Any:
- mutation = {mut.mutator.label: _unpack_if_only_one(mut.samples) for mut in model.history}
+ mutation = get_mutation_dict(model)
graph_data = PythonGraphData(get_importable_name(model.python_class, relocate_module=True),
model.python_init_params, mutation, model.evaluator)
return graph_data
@@ -51,3 +51,7 @@ def _unpack_if_only_one(ele: List[Any]):
if len(ele) == 1:
return ele[0]
return ele
+
+
+def get_mutation_dict(model: Model):
+ return {mut.mutator.label: _unpack_if_only_one(mut.samples) for mut in model.history}
diff --git a/nni/retiarii/experiment/pytorch.py b/nni/retiarii/experiment/pytorch.py
index f15931dcaf..0780439cff 100644
--- a/nni/retiarii/experiment/pytorch.py
+++ b/nni/retiarii/experiment/pytorch.py
@@ -29,6 +29,7 @@
from ..codegen import model_to_pytorch_script
from ..converter import convert_to_graph
from ..execution import list_models, set_execution_engine
+from ..execution.python import get_mutation_dict
from ..graph import Model, Evaluator
from ..integration import RetiariiAdvisor
from ..mutator import Mutator
@@ -55,14 +56,14 @@ class RetiariiExeConfig(ConfigBase):
experiment_working_directory: PathLike = '~/nni-experiments'
# remove configuration of tuner/assessor/advisor
training_service: TrainingServiceConfig
- execution_engine: str = 'base'
+ execution_engine: str = 'py'
def __init__(self, training_service_platform: Optional[str] = None, **kwargs):
super().__init__(**kwargs)
if training_service_platform is not None:
assert 'training_service' not in kwargs
self.training_service = util.training_service_config_factory(platform = training_service_platform)
- self.__dict__['trial_command'] = 'python3 -m nni.retiarii.trial_entry base'
+ self.__dict__['trial_command'] = 'python3 -m nni.retiarii.trial_entry py'
def __setattr__(self, key, value):
fixed_attrs = {'search_space': '',
@@ -313,11 +314,11 @@ def stop(self) -> None:
self._dispatcher_thread = None
_logger.info('Experiment stopped')
- def export_top_models(self, top_k: int = 1, optimize_mode: str = 'maximize', formatter: str = 'code') -> Any:
+ def export_top_models(self, top_k: int = 1, optimize_mode: str = 'maximize', formatter: str = 'dict') -> Any:
"""
Export several top performing models.
- For one-shot algorithms, only top-1 is supported. For others, ``optimize_mode`` asnd ``formater`` is
+ For one-shot algorithms, only top-1 is supported. For others, ``optimize_mode`` and ``formatter`` are
available for customization.
top_k : int
@@ -326,8 +327,12 @@ def export_top_models(self, top_k: int = 1, optimize_mode: str = 'maximize', for
``maximize`` or ``minimize``. Not supported by one-shot algorithms.
``optimize_mode`` is likely to be removed and defined in strategy in future.
formatter : str
- Only model code is supported for now. Not supported by one-shot algorithms.
+ Support ``code`` and ``dict``. Not supported by one-shot algorithms.
+ If ``code``, the python code of model will be returned.
+ If ``dict``, the mutation history will be returned.
"""
+ if formatter == 'code':
+ assert self.config.execution_engine != 'py', 'You should use `dict` formatter when using Python execution engine.'
if isinstance(self.trainer, BaseOneShotTrainer):
assert top_k == 1, 'Only support top_k is 1 for now.'
return self.trainer.export()
@@ -335,9 +340,11 @@ def export_top_models(self, top_k: int = 1, optimize_mode: str = 'maximize', for
all_models = filter(lambda m: m.metric is not None, list_models())
assert optimize_mode in ['maximize', 'minimize']
all_models = sorted(all_models, key=lambda m: m.metric, reverse=optimize_mode == 'maximize')
- assert formatter == 'code', 'Export formatter other than "code" is not supported yet.'
+ assert formatter in ['code', 'dict'], 'Export formatter other than "code" and "dict" is not supported yet.'
if formatter == 'code':
return [model_to_pytorch_script(model) for model in all_models[:top_k]]
+ elif formatter == 'dict':
+ return [get_mutation_dict(model) for model in all_models[:top_k]]
def retrain_model(self, model):
"""
diff --git a/nni/retiarii/graph.py b/nni/retiarii/graph.py
index 2255e288f1..3eba65805d 100644
--- a/nni/retiarii/graph.py
+++ b/nni/retiarii/graph.py
@@ -6,7 +6,6 @@
"""
import abc
-import copy
import json
from enum import Enum
from typing import (Any, Dict, Iterable, List, Optional, Tuple, Type, Union, overload)
@@ -143,7 +142,7 @@ def fork(self) -> 'Model':
new_model.python_class = self.python_class
new_model.python_init_params = self.python_init_params
new_model.graphs = {name: graph._fork_to(new_model) for name, graph in self.graphs.items()}
- new_model.evaluator = copy.deepcopy(self.evaluator) # TODO this may be a problem when evaluator is large
+ new_model.evaluator = self.evaluator # TODO this needs a clever copy (not deepcopy) if we need mutation
new_model.history = [*self.history]
# Note: the history is not updated. It will be updated when the model is changed, that is in mutator.
return new_model
diff --git a/nni/retiarii/nn/pytorch/component.py b/nni/retiarii/nn/pytorch/component.py
index 4ae5dc03bb..20c6e30de5 100644
--- a/nni/retiarii/nn/pytorch/component.py
+++ b/nni/retiarii/nn/pytorch/component.py
@@ -72,7 +72,7 @@ def _replicate_and_instantiate(blocks, repeat):
class Cell(nn.Module):
"""
- Cell structure [1]_ [2]_ that is popularly used in NAS literature.
+ Cell structure [zophnas]_ [zophnasnet]_ that is popularly used in NAS literature.
A cell consists of multiple "nodes". Each node is a sum of multiple operators. Each operator is chosen from
``op_candidates``, and takes one input from previous nodes and predecessors. Predecessor means the input of cell.
@@ -95,8 +95,8 @@ class Cell(nn.Module):
References
----------
- .. [1] Barret Zoph, Quoc V. Le, "Neural Architecture Search with Reinforcement Learning". https://arxiv.org/abs/1611.01578
- .. [2] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le,
+ .. [zophnas] Barret Zoph, Quoc V. Le, "Neural Architecture Search with Reinforcement Learning". https://arxiv.org/abs/1611.01578
+ .. [zophnasnet] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le,
"Learning Transferable Architectures for Scalable Image Recognition". https://arxiv.org/abs/1707.07012
"""
diff --git a/nni/retiarii/nn/pytorch/mutator.py b/nni/retiarii/nn/pytorch/mutator.py
index 6ef2ef19af..4307aeac68 100644
--- a/nni/retiarii/nn/pytorch/mutator.py
+++ b/nni/retiarii/nn/pytorch/mutator.py
@@ -15,7 +15,7 @@
class LayerChoiceMutator(Mutator):
def __init__(self, nodes: List[Node]):
- super().__init__()
+ super().__init__(label=nodes[0].operation.parameters['label'])
self.nodes = nodes
def mutate(self, model):
@@ -33,14 +33,14 @@ def mutate(self, model):
model.get_node_by_name(node.name).update_operation(Cell(node.operation.cell_name))
# remove redundant nodes
- for rm_node in target.hidden_nodes:
+ for rm_node in list(target.hidden_nodes): # remove from a list on the fly will cause issues
if rm_node.name != chosen_node.name:
rm_node.remove()
class InputChoiceMutator(Mutator):
def __init__(self, nodes: List[Node]):
- super().__init__()
+ super().__init__(label=nodes[0].operation.parameters['label'])
self.nodes = nodes
def mutate(self, model):
@@ -56,7 +56,7 @@ def mutate(self, model):
class ValueChoiceMutator(Mutator):
def __init__(self, nodes: List[Node], candidates: List[Any]):
- super().__init__()
+ super().__init__(label=nodes[0].operation.parameters['label'])
self.nodes = nodes
self.candidates = candidates
@@ -69,7 +69,8 @@ def mutate(self, model):
class ParameterChoiceMutator(Mutator):
def __init__(self, nodes: List[Tuple[Node, str]], candidates: List[Any]):
- super().__init__()
+ node, argname = nodes[0]
+ super().__init__(label=node.operation.parameters[argname].label)
self.nodes = nodes
self.candidates = candidates
@@ -84,7 +85,7 @@ def mutate(self, model):
class RepeatMutator(Mutator):
def __init__(self, nodes: List[Node]):
# nodes is a subgraph consisting of repeated blocks.
- super().__init__()
+ super().__init__(label=nodes[0].operation.parameters['label'])
self.nodes = nodes
def _retrieve_chain_from_graph(self, graph: Graph) -> List[Node]:
diff --git a/nni/retiarii/serializer.py b/nni/retiarii/serializer.py
index e0c2a26115..efa78243bd 100644
--- a/nni/retiarii/serializer.py
+++ b/nni/retiarii/serializer.py
@@ -139,6 +139,7 @@ def serialize(cls, *args, **kwargs):
To create an serializable instance inline without decorator. For example,
.. code-block:: python
+
self.op = serialize(MyCustomOp, hidden_units=128)
"""
return serialize_cls(cls)(*args, **kwargs)
diff --git a/nni/retiarii/strategy/_rl_impl.py b/nni/retiarii/strategy/_rl_impl.py
index 43b07a3136..7e038aab69 100644
--- a/nni/retiarii/strategy/_rl_impl.py
+++ b/nni/retiarii/strategy/_rl_impl.py
@@ -1,6 +1,8 @@
# This file might cause import error for those who didn't install RL-related dependencies
import logging
+import threading
+from multiprocessing.pool import ThreadPool
import gym
import numpy as np
@@ -9,6 +11,7 @@
from gym import spaces
from tianshou.data import to_torch
+from tianshou.env.worker import EnvWorker
from .utils import get_targeted_model
from ..graph import ModelStatus
@@ -16,6 +19,42 @@
_logger = logging.getLogger(__name__)
+_thread_lock = threading.Lock()
+
+
+class MultiThreadEnvWorker(EnvWorker):
+ def __init__(self, env_fn):
+ self.env = env_fn()
+ self.pool = ThreadPool(processes=1)
+ super().__init__(env_fn)
+
+ def __getattr__(self, key):
+ return getattr(self.env, key)
+
+ def reset(self):
+ return self.env.reset()
+
+ @staticmethod
+ def wait(*args, **kwargs):
+ raise NotImplementedError('Async collect is not supported yet.')
+
+ def send_action(self, action) -> None:
+ # self.result is actually a handle
+ self.result = self.pool.apply_async(self.env.step, (action,))
+
+ def get_result(self):
+ return self.result.get()
+
+ def seed(self, seed):
+ super().seed(seed)
+ return self.env.seed(seed)
+
+ def render(self, **kwargs):
+ return self.env.render(**kwargs)
+
+ def close_env(self) -> None:
+ self.pool.terminate()
+ return self.env.close()
class ModelEvaluationEnv(gym.Env):
@@ -63,7 +102,8 @@ def step(self, action):
if self.cur_step < self.num_steps else self.action_dim
}
if self.cur_step == self.num_steps:
- model = get_targeted_model(self.base_model, self.mutators, self.sample)
+ with _thread_lock:
+ model = get_targeted_model(self.base_model, self.mutators, self.sample)
_logger.info(f'New model created: {self.sample}')
submit_models(model)
wait_models(model)
@@ -107,7 +147,7 @@ def forward(self, obs, **kwargs):
# to take care of choices with different number of options
mask = torch.arange(self.action_dim).expand(len(out), self.action_dim) >= obs['action_dim'].unsqueeze(1)
out[mask.to(out.device)] = float('-inf')
- return nn.functional.softmax(out), kwargs.get('state', None)
+ return nn.functional.softmax(out, dim=-1), kwargs.get('state', None)
class Critic(nn.Module):
diff --git a/nni/retiarii/strategy/bruteforce.py b/nni/retiarii/strategy/bruteforce.py
index 379b159ba6..971711f0d9 100644
--- a/nni/retiarii/strategy/bruteforce.py
+++ b/nni/retiarii/strategy/bruteforce.py
@@ -8,7 +8,7 @@
import time
from typing import Any, Dict, List
-from .. import Sampler, submit_models, query_available_resources
+from .. import Sampler, submit_models, query_available_resources, budget_exhausted
from .base import BaseStrategy
from .utils import dry_run_for_search_space, get_targeted_model
@@ -62,7 +62,9 @@ def run(self, base_model, applied_mutators):
search_space = dry_run_for_search_space(base_model, applied_mutators)
for sample in grid_generator(search_space, shuffle=self.shuffle):
_logger.debug('New model created. Waiting for resource. %s', str(sample))
- if query_available_resources() <= 0:
+ while query_available_resources() <= 0:
+ if budget_exhausted():
+ return
time.sleep(self._polling_interval)
submit_models(get_targeted_model(base_model, applied_mutators, sample))
@@ -106,6 +108,8 @@ def run(self, base_model, applied_mutators):
model = mutator.apply(model)
_logger.debug('New model created. Applied mutators are: %s', str(applied_mutators))
submit_models(model)
+ elif budget_exhausted():
+ break
else:
time.sleep(self._polling_interval)
else:
@@ -113,6 +117,8 @@ def run(self, base_model, applied_mutators):
search_space = dry_run_for_search_space(base_model, applied_mutators)
for sample in random_generator(search_space, dedup=self.dedup):
_logger.debug('New model created. Waiting for resource. %s', str(sample))
- if query_available_resources() <= 0:
+ while query_available_resources() <= 0:
+ if budget_exhausted():
+ return
time.sleep(self._polling_interval)
submit_models(get_targeted_model(base_model, applied_mutators, sample))
diff --git a/nni/retiarii/strategy/rl.py b/nni/retiarii/strategy/rl.py
index 50052471eb..23a05ed1ed 100644
--- a/nni/retiarii/strategy/rl.py
+++ b/nni/retiarii/strategy/rl.py
@@ -8,10 +8,10 @@
try:
has_tianshou = True
import torch
- from tianshou.data import AsyncCollector, Collector, VectorReplayBuffer
- from tianshou.env import SubprocVectorEnv
+ from tianshou.data import Collector, VectorReplayBuffer
+ from tianshou.env import BaseVectorEnv
from tianshou.policy import BasePolicy, PPOPolicy # pylint: disable=unused-import
- from ._rl_impl import ModelEvaluationEnv, Preprocessor, Actor, Critic
+ from ._rl_impl import ModelEvaluationEnv, MultiThreadEnvWorker, Preprocessor, Actor, Critic
except ImportError:
has_tianshou = False
@@ -25,8 +25,6 @@ class PolicyBasedRL(BaseStrategy):
This is a wrapper of algorithms provided in tianshou (PPO by default),
and can be easily customized with other algorithms that inherit ``BasePolicy`` (e.g., REINFORCE [1]_).
- Note that RL algorithms are known to have issues on Windows and MacOS. They will be supported in future.
-
Parameters
----------
max_collect : int
@@ -36,12 +34,6 @@ class PolicyBasedRL(BaseStrategy):
After each collect, trainer will sample batch from replay buffer and do the update. Default: 20.
policy_fn : function
Takes ``ModelEvaluationEnv`` as input and return a policy. See ``_default_policy_fn`` for an example.
- asynchronous : bool
- If true, in each step, collector won't wait for all the envs to complete.
- This should generally not affect the result, but might affect the efficiency. Note that a slightly more trials
- than expected might be collected if this is enabled.
- If asynchronous is false, collector will wait for all parallel environments to complete in each step.
- See ``tianshou.data.AsyncCollector`` for more details.
References
----------
@@ -51,7 +43,7 @@ class PolicyBasedRL(BaseStrategy):
"""
def __init__(self, max_collect: int = 100, trial_per_collect = 20,
- policy_fn: Optional[Callable[['ModelEvaluationEnv'], 'BasePolicy']] = None, asynchronous: bool = True):
+ policy_fn: Optional[Callable[['ModelEvaluationEnv'], 'BasePolicy']] = None):
if not has_tianshou:
raise ImportError('`tianshou` is required to run RL-based strategy. '
'Please use "pip install tianshou" to install it beforehand.')
@@ -59,7 +51,6 @@ def __init__(self, max_collect: int = 100, trial_per_collect = 20,
self.policy_fn = policy_fn or self._default_policy_fn
self.max_collect = max_collect
self.trial_per_collect = trial_per_collect
- self.asynchronous = asynchronous
@staticmethod
def _default_policy_fn(env):
@@ -77,13 +68,8 @@ def run(self, base_model, applied_mutators):
env_fn = lambda: ModelEvaluationEnv(base_model, applied_mutators, search_space)
policy = self.policy_fn(env_fn())
- if self.asynchronous:
- # wait for half of the env complete in each step
- env = SubprocVectorEnv([env_fn for _ in range(concurrency)], wait_num=int(concurrency * 0.5))
- collector = AsyncCollector(policy, env, VectorReplayBuffer(20000, len(env)))
- else:
- env = SubprocVectorEnv([env_fn for _ in range(concurrency)])
- collector = Collector(policy, env, VectorReplayBuffer(20000, len(env)))
+ env = BaseVectorEnv([env_fn for _ in range(concurrency)], MultiThreadEnvWorker)
+ collector = Collector(policy, env, VectorReplayBuffer(20000, len(env)))
for cur_collect in range(1, self.max_collect + 1):
_logger.info('Collect [%d] Running...', cur_collect)
diff --git a/nni/retiarii/strategy/tpe_strategy.py b/nni/retiarii/strategy/tpe_strategy.py
index 7d897cbfb3..7f55ad302e 100644
--- a/nni/retiarii/strategy/tpe_strategy.py
+++ b/nni/retiarii/strategy/tpe_strategy.py
@@ -40,6 +40,18 @@ def choice(self, candidates, mutator, model, index):
class TPEStrategy(BaseStrategy):
+ """
+ The Tree-structured Parzen Estimator (TPE) [bergstrahpo]_ is a sequential model-based optimization (SMBO) approach.
+ SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements,
+ and then subsequently choose new hyperparameters to test based on this model.
+
+ References
+ ----------
+
+ .. [bergstrahpo] Bergstra et al., "Algorithms for Hyper-Parameter Optimization".
+ https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
+ """
+
def __init__(self):
self.tpe_sampler = TPESampler()
self.model_id = 0
diff --git a/nni/runtime/default_config/registered_algorithms.yml b/nni/runtime/default_config/registered_algorithms.yml
index 99a6c3b9b0..04c87e8de8 100644
--- a/nni/runtime/default_config/registered_algorithms.yml
+++ b/nni/runtime/default_config/registered_algorithms.yml
@@ -76,3 +76,7 @@ tuners:
classArgsValidator: nni.algorithms.hpo.regularized_evolution_tuner.EvolutionClassArgsValidator
className: nni.algorithms.hpo.regularized_evolution_tuner.RegularizedEvolutionTuner
source: nni
+- builtinName: DNGOTuner
+ classArgsValidator: nni.algorithms.hpo.dngo_tuner.DNGOClassArgsValidator
+ className: nni.algorithms.hpo.dngo_tuner.DNGOTuner
+ source: nni
diff --git a/nni/runtime/default_config/training_services.json b/nni/runtime/default_config/training_services.json
new file mode 100644
index 0000000000..0967ef424b
--- /dev/null
+++ b/nni/runtime/default_config/training_services.json
@@ -0,0 +1 @@
+{}
diff --git a/nni/runtime/platform/__init__.py b/nni/runtime/platform/__init__.py
index 7a1d82b466..9f42a16f43 100644
--- a/nni/runtime/platform/__init__.py
+++ b/nni/runtime/platform/__init__.py
@@ -9,7 +9,5 @@
from .standalone import *
elif trial_env_vars.NNI_PLATFORM == 'unittest':
from .test import *
-elif trial_env_vars.NNI_PLATFORM in ('local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller', 'dlts', 'aml', 'adl', 'hybrid'):
- from .local import *
else:
- raise RuntimeError('Unknown platform %s' % trial_env_vars.NNI_PLATFORM)
+ from .local import *
diff --git a/nni/tools/nnictl/algo_management.py b/nni/tools/nnictl/algo_management.py
index f91468335b..1715e01300 100644
--- a/nni/tools/nnictl/algo_management.py
+++ b/nni/tools/nnictl/algo_management.py
@@ -1,7 +1,6 @@
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
-import os
import importlib
import json
from nni.tools.package_utils import read_registerd_algo_meta, get_registered_algo_meta, \
diff --git a/nni/tools/nnictl/common_utils.py b/nni/tools/nnictl/common_utils.py
index 3667f8adfd..25df67cf2e 100644
--- a/nni/tools/nnictl/common_utils.py
+++ b/nni/tools/nnictl/common_utils.py
@@ -9,11 +9,11 @@
import socket
import string
import random
-import ruamel.yaml as yaml
-import psutil
-import filelock
import glob
from colorama import Fore
+import filelock
+import psutil
+import yaml
from .constants import ERROR_INFO, NORMAL_INFO, WARNING_INFO
@@ -21,7 +21,7 @@ def get_yml_content(file_path):
'''Load yaml file content'''
try:
with open(file_path, 'r') as file:
- return yaml.load(file, Loader=yaml.SafeLoader)
+ return yaml.safe_load(file)
except yaml.scanner.ScannerError as err:
print_error('yaml file format error!')
print_error(err)
diff --git a/nni/tools/nnictl/config_schema.py b/nni/tools/nnictl/config_schema.py
index 11d540812d..65e645b560 100644
--- a/nni/tools/nnictl/config_schema.py
+++ b/nni/tools/nnictl/config_schema.py
@@ -6,12 +6,12 @@
import os
import netifaces
+from schema import And, Optional, Or, Regex, Schema, SchemaError
from nni.tools.package_utils import (
create_validator_instance,
get_all_builtin_names,
get_registered_algo_meta,
)
-from schema import And, Optional, Or, Regex, Schema, SchemaError
from .common_utils import get_yml_content, print_warning
from .constants import SCHEMA_PATH_ERROR, SCHEMA_RANGE_ERROR, SCHEMA_TYPE_ERROR
@@ -625,7 +625,7 @@ def validate_frameworkcontroller_trial_config(self, experiment_config):
raise SchemaError("""If no taskRoles are specified a valid custom frameworkcontroller config should
be set using the configPath attribute in frameworkcontrollerConfig!""")
config_content = get_yml_content(experiment_config.get('frameworkcontrollerConfig').get('configPath'))
- if not config_content.get('spec').get('taskRoles') or not len(config_content.get('spec').get('taskRoles')):
+ if not config_content.get('spec').get('taskRoles') or not config_content.get('spec').get('taskRoles'):
raise SchemaError('Invalid frameworkcontroller config! No taskRoles were specified!')
if not config_content.get('spec').get('taskRoles')[0].get('task'):
raise SchemaError('Invalid frameworkcontroller config! No task was specified for taskRole!')
diff --git a/nni/tools/nnictl/config_utils.py b/nni/tools/nnictl/config_utils.py
index 154b1d8ccb..78f884e6f0 100644
--- a/nni/tools/nnictl/config_utils.py
+++ b/nni/tools/nnictl/config_utils.py
@@ -2,12 +2,9 @@
# Licensed under the MIT license.
import os
-import json_tricks
-import shutil
import sqlite3
-import time
+import json_tricks
from .constants import NNI_HOME_DIR
-from .command_utils import print_error
from .common_utils import get_file_lock
def config_v0_to_v1(config: dict) -> dict:
diff --git a/nni/tools/nnictl/launcher.py b/nni/tools/nnictl/launcher.py
index e4d6a0966a..9144bc0267 100644
--- a/nni/tools/nnictl/launcher.py
+++ b/nni/tools/nnictl/launcher.py
@@ -17,10 +17,9 @@
import nni_node # pylint: disable=import-error
from .launcher_utils import validate_all_content
from .rest_utils import rest_put, rest_post, check_rest_server, check_response
-from .url_utils import cluster_metadata_url, experiment_url, get_local_urls, setPrefixUrl, formatURLPath
+from .url_utils import cluster_metadata_url, experiment_url, get_local_urls, set_prefix_url
from .config_utils import Config, Experiments
-from .common_utils import get_yml_content, get_json_content, print_error, print_normal, print_warning, \
- detect_port, get_user
+from .common_utils import get_yml_content, get_json_content, print_error, print_normal, detect_port, get_user
from .constants import NNI_HOME_DIR, ERROR_INFO, REST_TIME_OUT, EXPERIMENT_SUCCESS_INFO, LOG_HEADER
from .command_utils import check_output_command, kill_command
@@ -84,7 +83,7 @@ def start_rest_server(port, platform, mode, experiment_id, foreground=False, log
cmds += ['--foreground', 'true']
if url_prefix:
_validate_prefix_path(url_prefix)
- setPrefixUrl(url_prefix)
+ set_prefix_url(url_prefix)
cmds += ['--url_prefix', url_prefix]
stdout_full_path, stderr_full_path = get_log_path(experiment_id)
@@ -167,7 +166,8 @@ def set_V1_common_config(experiment_config, port, config_file_name):
response = rest_put(cluster_metadata_url(port), json.dumps({'version_check': version_check}), REST_TIME_OUT)
validate_response(response, config_file_name)
if experiment_config.get('logCollection'):
- response = rest_put(cluster_metadata_url(port), json.dumps({'log_collection': experiment_config.get('logCollection')}), REST_TIME_OUT)
+ data = json.dumps({'log_collection': experiment_config.get('logCollection')})
+ response = rest_put(cluster_metadata_url(port), data, REST_TIME_OUT)
validate_response(response, config_file_name)
def setNNIManagerIp(experiment_config, port, config_file_name):
@@ -229,7 +229,8 @@ def set_frameworkcontroller_config(experiment_config, port, config_file_name):
def set_shared_storage(experiment_config, port, config_file_name):
if 'sharedStorage' in experiment_config:
- response = rest_put(cluster_metadata_url(port), json.dumps({'shared_storage_config': experiment_config['sharedStorage']}), REST_TIME_OUT)
+ data = json.dumps({'shared_storage_config': experiment_config['sharedStorage']})
+ response = rest_put(cluster_metadata_url(port), data, REST_TIME_OUT)
err_message = None
if not response or not response.status_code == 200:
if response is not None:
@@ -402,9 +403,13 @@ def launch_experiment(args, experiment_config, mode, experiment_id, config_versi
if not os.path.isdir(path):
os.makedirs(path)
path = tempfile.mkdtemp(dir=path)
- nas_mode = experiment_config['trial'].get('nasMode', 'classic_mode')
- code_dir = expand_annotations(experiment_config['trial']['codeDir'], path, nas_mode=nas_mode)
- experiment_config['trial']['codeDir'] = code_dir
+ if config_version == 1:
+ nas_mode = experiment_config['trial'].get('nasMode', 'classic_mode')
+ code_dir = expand_annotations(experiment_config['trial']['codeDir'], path, nas_mode=nas_mode)
+ experiment_config['trial']['codeDir'] = code_dir
+ else:
+ code_dir = expand_annotations(experiment_config['trialCodeDirectory'], path)
+ experiment_config['trialCodeDirectory'] = code_dir
search_space = generate_search_space(code_dir)
experiment_config['searchSpace'] = search_space
assert search_space, ERROR_INFO % 'Generated search space is empty'
@@ -452,10 +457,11 @@ def launch_experiment(args, experiment_config, mode, experiment_id, config_versi
except Exception:
raise Exception(ERROR_INFO % 'Restful server stopped!')
exit(1)
+ url_prefix_format = '' if args.url_prefix is None else '/{0}'.format(args.url_prefix)
if experiment_config.get('nniManagerIp'):
- web_ui_url_list = ['http://{0}:{1}{2}'.format(experiment_config['nniManagerIp'], str(args.port), formatURLPath(args.url_prefix))]
+ web_ui_url_list = ['http://{0}:{1}{2}'.format(experiment_config['nniManagerIp'], str(args.port), url_prefix_format)]
else:
- web_ui_url_list = get_local_urls(args.port, args.url_prefix)
+ web_ui_url_list = get_local_urls(args.port, url_prefix_format)
Experiments().update_experiment(experiment_id, 'webuiUrl', web_ui_url_list)
print_normal(EXPERIMENT_SUCCESS_INFO % (experiment_id, ' '.join(web_ui_url_list)))
@@ -484,7 +490,10 @@ def _validate_v2(config, path):
print_error(f'Config V2 validation failed: {repr(e)}')
def _validate_prefix_path(path):
- assert re.match("^[A-Za-z0-9_-]*$", path), "prefix url is invalid."
+ assert not path.startswith('/'), 'URL prefix should not start with "/".'
+ parts = path.split('/')
+ valid = all(re.match('^[A-Za-z0-9_-]*$', part) for part in parts)
+ assert valid, 'URL prefix should only contain letter, number, underscore, and hyphen.'
def create_experiment(args):
'''start a new experiment'''
@@ -503,7 +512,6 @@ def create_experiment(args):
config_v1 = config_yml
else:
schema = 2
- from nni.experiment.config import convert
config_v2 = convert.to_v2(config_yml).json()
else:
config_v2 = _validate_v2(config_yml, config_path)
diff --git a/nni/tools/nnictl/nnictl.py b/nni/tools/nnictl/nnictl.py
index d4b55d506a..a5f02c8c9e 100644
--- a/nni/tools/nnictl/nnictl.py
+++ b/nni/tools/nnictl/nnictl.py
@@ -16,6 +16,8 @@
save_experiment, load_experiment
from .algo_management import algo_reg, algo_unreg, algo_show, algo_list
from .constants import DEFAULT_REST_PORT
+from .import ts_management
+
init(autoreset=True)
if os.environ.get('COVERAGE_PROCESS_START'):
@@ -242,6 +244,22 @@ def parse_args():
parser_algo_list = parser_algo_subparsers.add_parser('list', help='list registered algorithms')
parser_algo_list.set_defaults(func=algo_list)
+ #parse trainingservice command
+ parser_ts = subparsers.add_parser('trainingservice', help='control training service')
+ # add subparsers for parser_ts
+ parser_ts_subparsers = parser_ts.add_subparsers()
+
+ parser_ts_reg = parser_ts_subparsers.add_parser('register', help='register training service')
+ parser_ts_reg.add_argument('--package', dest='package', help='package name', required=True)
+ parser_ts_reg.set_defaults(func=ts_management.register)
+
+ parser_ts_unreg = parser_ts_subparsers.add_parser('unregister', help='unregister training service')
+ parser_ts_unreg.add_argument('--package', dest='package', help='package name', required=True)
+ parser_ts_unreg.set_defaults(func=ts_management.unregister)
+
+ parser_ts_list = parser_ts_subparsers.add_parser('list', help='list custom training services')
+ parser_ts_list.set_defaults(func=ts_management.list_services)
+
# To show message that nnictl package command is replaced by nnictl algo, to be remove in the future release.
def show_messsage_for_nnictl_package(args):
print_error('nnictl package command is replaced by nnictl algo, please run nnictl algo -h to show the usage')
diff --git a/nni/tools/nnictl/nnictl_utils.py b/nni/tools/nnictl/nnictl_utils.py
index 16942b74ef..7ef0fe7dd7 100644
--- a/nni/tools/nnictl/nnictl_utils.py
+++ b/nni/tools/nnictl/nnictl_utils.py
@@ -6,7 +6,6 @@
import sys
import json
import time
-import re
import shutil
import subprocess
from functools import cmp_to_key
@@ -528,7 +527,7 @@ def experiment_clean(args):
print_warning('platform {0} clean up not supported yet.'.format(platform))
exit(0)
# clean local data
- local_base_dir = experiments_config[experiment_id]['logDir']
+ local_base_dir = experiments_config.experiments[experiment_id]['logDir']
if not local_base_dir:
local_base_dir = NNI_HOME_DIR
local_experiment_dir = os.path.join(local_base_dir, experiment_id)
diff --git a/nni/tools/nnictl/ts_management.py b/nni/tools/nnictl/ts_management.py
new file mode 100644
index 0000000000..151b053591
--- /dev/null
+++ b/nni/tools/nnictl/ts_management.py
@@ -0,0 +1,77 @@
+import importlib
+import json
+
+from nni.runtime.config import get_config_file
+from .common_utils import print_error, print_green
+
+_builtin_training_services = [
+ 'local',
+ 'remote',
+ 'openpai', 'pai',
+ 'aml',
+ 'kubeflow',
+ 'frameworkcontroller',
+ 'adl',
+]
+
+def register(args):
+ if args.package in _builtin_training_services:
+ print_error(f'{args.package} is a builtin training service')
+ return
+
+ try:
+ module = importlib.import_module(args.package)
+ except Exception:
+ print_error(f'Cannot import package {args.package}')
+ return
+
+ try:
+ info = module.nni_training_service_info
+ except Exception:
+ print_error(f'Cannot read nni_training_service_info from {args.package}')
+ return
+
+ try:
+ info.config_class()
+ except Exception:
+ print_error('Bad experiment config class')
+ return
+
+ try:
+ service_config = {
+ 'nodeModulePath': str(info.node_module_path),
+ 'nodeClassName': info.node_class_name,
+ }
+ json.dumps(service_config)
+ except Exception:
+ print_error('Bad node_module_path or bad node_class_name')
+ return
+
+ config = _load()
+ update = args.package in config
+
+ config[args.package] = service_config
+ _save(config)
+
+ if update:
+ print_green(f'Sucessfully updated {args.package}')
+ else:
+ print_green(f'Sucessfully registered {args.package}')
+
+def unregister(args):
+ config = _load()
+ if args.package not in config:
+ print_error(f'{args.package} is not a registered training service')
+ return
+ config.pop(args.package, None)
+ _save(config)
+ print_green(f'Sucessfully unregistered {args.package}')
+
+def list_services(_):
+ print('\n'.join(_load().keys()))
+
+def _load():
+ return json.load(get_config_file('training_services.json').open())
+
+def _save(config):
+ json.dump(config, get_config_file('training_services.json').open('w'), indent=4)
diff --git a/nni/tools/nnictl/url_utils.py b/nni/tools/nnictl/url_utils.py
index 379ac8e729..a4f1b807d2 100644
--- a/nni/tools/nnictl/url_utils.py
+++ b/nni/tools/nnictl/url_utils.py
@@ -24,12 +24,12 @@
METRIC_DATA_API = '/metric-data'
-def formatURLPath(path):
- return '' if path is None else '/{0}'.format(path)
+def format_url_path(path):
+ return API_ROOT_URL if path is None else f'/{path}{API_ROOT_URL}'
-def setPrefixUrl(prefix_path):
+def set_prefix_url(prefix_path):
global API_ROOT_URL
- API_ROOT_URL = formatURLPath(prefix_path)
+ API_ROOT_URL = format_url_path(prefix_path)
def metric_data_url(port):
'''get metric_data url'''
@@ -81,5 +81,5 @@ def get_local_urls(port,prefix):
for _, info in psutil.net_if_addrs().items():
for addr in info:
if socket.AddressFamily.AF_INET == addr.family:
- url_list.append('http://{0}:{1}{2}'.format(addr.address, port, formatURLPath(prefix)))
+ url_list.append('http://{0}:{1}{2}'.format(addr.address, port, prefix))
return url_list
diff --git a/nni/tools/package_utils/__init__.py b/nni/tools/package_utils/__init__.py
index 1fecdd9008..7f8f441965 100644
--- a/nni/tools/package_utils/__init__.py
+++ b/nni/tools/package_utils/__init__.py
@@ -6,7 +6,7 @@
import os
from pathlib import Path
import sys
-import ruamel.yaml as yaml
+import yaml
from nni.runtime.config import get_config_file
ALGO_TYPES = ['tuners', 'assessors', 'advisors']
@@ -187,7 +187,7 @@ def create_customized_class_instance(class_params):
Returns customized class instance.
"""
- code_dir = class_params.get('classDirectory')
+ code_dir = class_params.get('codeDirectory')
qualified_class_name = class_params.get('className')
class_args = class_params.get('classArgs')
@@ -215,7 +215,7 @@ def read_registerd_algo_meta():
config_file = get_registered_algo_config_path()
if os.path.exists(config_file):
with open(config_file, 'r') as f:
- config = yaml.load(f, Loader=yaml.SafeLoader)
+ config = yaml.safe_load(f)
else:
config = defaultdict(list)
for t in ALGO_TYPES:
@@ -226,4 +226,4 @@ def read_registerd_algo_meta():
def write_registered_algo_meta(config):
config_file = get_registered_algo_config_path()
with open(config_file, 'w') as f:
- f.write(yaml.dump(dict(config), default_flow_style=False))
+ f.write(yaml.safe_dump(dict(config), default_flow_style=False))
diff --git a/pipelines/fast-test.yml b/pipelines/fast-test.yml
index a93b62944f..fdf004e31c 100644
--- a/pipelines/fast-test.yml
+++ b/pipelines/fast-test.yml
@@ -87,6 +87,10 @@ stages:
variables:
YARN_CACHE_FOLDER: $(Pipeline.Workspace)/.yarn
steps:
+ - task: NodeTool@0
+ inputs:
+ versionSpec: 16.3.0
+ displayName: Configure Node.js version
- task: Cache@2
inputs:
key: 'yarn | "$(Agent.OS)" | ts/**/yarn.lock, !**/node_modules/**'
@@ -123,6 +127,11 @@ stages:
versionSpec: 3.8
displayName: Configure Python version
+ - task: NodeTool@0
+ inputs:
+ versionSpec: 16.3.0
+ displayName: Configure Node.js version
+
- script: |
sudo apt-get install -y pandoc
sudo apt-get remove swig -y
@@ -161,7 +170,7 @@ stages:
- script: |
set -e
python -m pip install -r dependencies/recommended.txt
- python -m pip install -e .[SMAC,BOHB,PPOTuner]
+ python -m pip install -e .[SMAC,BOHB,PPOTuner,DNGO]
displayName: Install extra dependencies
# Need del later
@@ -201,13 +210,17 @@ stages:
PIP_CACHE_DIR: $(Pipeline.Workspace)/.pip
YARN_CACHE_FOLDER: $(Pipeline.Workspace)/.yarn
- # This platform runs integration test first.
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: 3.6
displayName: Configure Python version
+ - task: NodeTool@0
+ inputs:
+ versionSpec: 16.3.0
+ displayName: Configure Node.js version
+
- script: |
sudo apt-get install -y pandoc
sudo apt-get remove swig -y
@@ -246,7 +259,7 @@ stages:
- script: |
set -e
python -m pip install -r dependencies/recommended_legacy.txt
- python -m pip install -e .[SMAC,BOHB,PPOTuner]
+ python -m pip install -e .[SMAC,BOHB,PPOTuner,DNGO]
displayName: Install extra dependencies
# Need del later
@@ -283,14 +296,17 @@ stages:
PIP_CACHE_DIR: $(Pipeline.Workspace)/.pip
YARN_CACHE_FOLDER: $(Pipeline.Workspace)/.yarn
- # This platform runs TypeScript unit test first.
-
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: 3.8
displayName: Configure Python version
+ - task: NodeTool@0
+ inputs:
+ versionSpec: 16.3.0
+ displayName: Configure Node.js version
+
- script: |
brew install swig@3
rm -f /usr/local/bin/swig
@@ -335,7 +351,7 @@ stages:
- script: |
set -e
python -m pip install -r dependencies/recommended.txt
- python -m pip install -e .[SMAC,BOHB,PPOTuner]
+ python -m pip install -e .[SMAC,BOHB,PPOTuner,DNGO]
displayName: Install extra dependencies
# Need del later
@@ -361,14 +377,17 @@ stages:
PIP_CACHE_DIR: $(Pipeline.Workspace)/.pip
YARN_CACHE_FOLDER: $(Pipeline.Workspace)/.yarn
- # This platform runs Python unit test first.
-
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: 3.8
displayName: Configure Python version
+ - task: NodeTool@0
+ inputs:
+ versionSpec: 16.3.0
+ displayName: Configure Node.js version
+
- task: Cache@2
inputs:
key: 'python | "$(Agent.OS)" | dependencies/*.txt'
@@ -398,6 +417,7 @@ stages:
- script: |
python -m pip install -r dependencies/recommended.txt
+ python -m pip install -e .[DNGO]
displayName: Install extra dependencies
# Need del later
diff --git a/pipelines/full-test-linux.yml b/pipelines/full-test-linux.yml
index 97f8ab415a..0a6b801283 100644
--- a/pipelines/full-test-linux.yml
+++ b/pipelines/full-test-linux.yml
@@ -35,6 +35,7 @@ jobs:
python3 -m pip install keras==2.1.6
python3 -m pip install tensorflow==2.3.1 tensorflow-estimator==2.3.0
python3 -m pip install thop
+ python3 -m pip install pybnn
python3 -m pip install tianshou>=0.4.1 gym
sudo apt-get install swig -y
displayName: Install extra dependencies
diff --git a/pipelines/full-test-windows.yml b/pipelines/full-test-windows.yml
index 30dd072ddb..fd3efdc277 100644
--- a/pipelines/full-test-windows.yml
+++ b/pipelines/full-test-windows.yml
@@ -30,6 +30,7 @@ jobs:
python -m pip install torch==1.6.0 torchvision==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
python -m pip install 'pytorch-lightning>=1.1.1'
python -m pip install tensorflow==2.3.1 tensorflow-estimator==2.3.0
+ python -m pip install pybnn
python -m pip install tianshou>=0.4.1 gym
displayName: Install extra dependencies
diff --git a/setup.py b/setup.py
index f43b15fde0..5a09186034 100644
--- a/setup.py
+++ b/setup.py
@@ -88,7 +88,8 @@ def _setup():
extras_require = {
'SMAC': _read_requirements_txt('dependencies/required_extra.txt', 'SMAC'),
'BOHB': _read_requirements_txt('dependencies/required_extra.txt', 'BOHB'),
- 'PPOTuner': _read_requirements_txt('dependencies/required_extra.txt', 'PPOTuner')
+ 'PPOTuner': _read_requirements_txt('dependencies/required_extra.txt', 'PPOTuner'),
+ 'DNGO': _read_requirements_txt('dependencies/required_extra.txt', 'DNGO'),
},
setup_requires = ['requests'],
diff --git a/setup_ts.py b/setup_ts.py
index 1ffb95f3c7..136243a8cf 100644
--- a/setup_ts.py
+++ b/setup_ts.py
@@ -22,7 +22,7 @@
from zipfile import ZipFile
-node_version = 'v10.23.0'
+node_version = 'v16.3.0'
yarn_version = 'v1.22.10'
diff --git a/test/config/tuners/gp.yml b/test/config/tuners/gp.yml
index 7bc3888ae2..30835223bf 100644
--- a/test/config/tuners/gp.yml
+++ b/test/config/tuners/gp.yml
@@ -13,7 +13,7 @@ tuner:
kappa: 5.0
xi: 0.0
nu: 2.5
- alpha: 1e-6
+ alpha: 1.0e-6
cold_start_num: 10
selection_num_warm_up: 100000
selection_num_starting_points: 250
diff --git a/test/nni_test/nnitest/run_tests.py b/test/nni_test/nnitest/run_tests.py
index f517f0a816..f87416a5fd 100644
--- a/test/nni_test/nnitest/run_tests.py
+++ b/test/nni_test/nnitest/run_tests.py
@@ -9,7 +9,7 @@
import sys
import time
-import ruamel.yaml as yaml
+import yaml
import validators
from utils import (CLEAR, EXPERIMENT_URL, GREEN, RED, REST_ENDPOINT,
@@ -80,7 +80,7 @@ def prepare_config_file(test_case_config, it_config, args):
# generate temporary config yml file to launch experiment
new_config_file = config_path + '.tmp'
dump_yml_content(new_config_file, test_yml_config)
- print(yaml.dump(test_yml_config, default_flow_style=False), flush=True)
+ print(yaml.safe_dump(test_yml_config, default_flow_style=False), flush=True)
return new_config_file
diff --git a/test/nni_test/nnitest/utils.py b/test/nni_test/nnitest/utils.py
index 566cb8e97e..300799003f 100644
--- a/test/nni_test/nnitest/utils.py
+++ b/test/nni_test/nnitest/utils.py
@@ -9,7 +9,7 @@
import subprocess
import requests
import time
-import ruamel.yaml as yaml
+import yaml
import shlex
EXPERIMENT_DONE_SIGNAL = 'Experiment done'
@@ -43,12 +43,12 @@ def remove_files(file_list):
def get_yml_content(file_path):
'''Load yaml file content'''
with open(file_path, 'r') as file:
- return yaml.load(file, Loader=yaml.Loader)
+ return yaml.safe_load(file)
def dump_yml_content(file_path, content):
'''Dump yaml file content'''
with open(file_path, 'w') as file:
- file.write(yaml.dump(content, default_flow_style=False))
+ file.write(yaml.safe_dump(content, default_flow_style=False))
def setup_experiment(installed=True):
'''setup the experiment if nni is not installed'''
diff --git a/test/retiarii_test/darts/darts_model.py b/test/retiarii_test/darts/darts_model.py
index 1354bdc14b..9a01e78b40 100644
--- a/test/retiarii_test/darts/darts_model.py
+++ b/test/retiarii_test/darts/darts_model.py
@@ -1,4 +1,5 @@
from collections import OrderedDict
+from nni.retiarii.serializer import model_wrapper
from typing import (List, Optional)
import torch
@@ -7,7 +8,7 @@
import ops
import nni.retiarii.nn.pytorch as nn
-from nni.retiarii import basic_unit
+from nni.retiarii import basic_unit, model_wrapper
@basic_unit
class AuxiliaryHead(nn.Module):
@@ -98,6 +99,7 @@ def forward(self, s0, s1):
output = torch.cat(new_tensors, dim=1)
return output
+@model_wrapper
class CNN(nn.Module):
def __init__(self, input_size, in_channels, channels, n_classes, n_layers, n_nodes=4,
diff --git a/test/scripts/model_compression.sh b/test/scripts/model_compression.sh
index a5a5c5da1b..a8b4c10756 100644
--- a/test/scripts/model_compression.sh
+++ b/test/scripts/model_compression.sh
@@ -4,35 +4,35 @@ CWD=${PWD}
echo ""
echo "===========================Testing: pruning and speedup==========================="
-cd ${CWD}/../examples/model_compress
+cd ${CWD}/../examples/model_compress/pruning
echo "testing fpgm pruning and speedup..."
-python3 pruning/basic_pruners_torch.py --pruner fpgm --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10
-python3 pruning/model_speedup.py --example_name fpgm
+python3 basic_pruners_torch.py --pruner fpgm --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10
+python3 model_speedup.py --example_name fpgm
echo "testing slim pruning and speedup..."
-python3 pruning/basic_pruners_torch.py --pruner slim --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg19 --dataset cifar10 --sparsity 0.7
-python3 pruning/model_speedup.py --example_name slim
+python3 basic_pruners_torch.py --pruner slim --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg19 --dataset cifar10 --sparsity 0.7
+python3 model_speedup.py --example_name slim
echo "testing l1filter pruning and speedup..."
-python3 pruning/basic_pruners_torch.py --pruner l1filter --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10
-python3 pruning/model_speedup.py --example_name l1filter
+python3 basic_pruners_torch.py --pruner l1filter --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10 --pretrained-model-dir experiment_data/pretrain_cifar10_vgg16.pth
+python3 model_speedup.py --example_name l1filter
echo "testing apoz pruning and speedup..."
-python3 pruning/basic_pruners_torch.py --pruner apoz --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10
-python3 pruning/model_speedup.py --example_name apoz
+python3 basic_pruners_torch.py --pruner apoz --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10 --pretrained-model-dir experiment_data/pretrain_cifar10_vgg16.pth
+python3 model_speedup.py --example_name apoz
echo 'testing level pruner pruning'
-python3 pruning/basic_pruners_torch.py --pruner level --pretrain-epochs 1 --fine-tune-epochs 1 --model lenet --dataset mnist
+python3 basic_pruners_torch.py --pruner level --pretrain-epochs 1 --fine-tune-epochs 1 --model lenet --dataset mnist
echo 'testing agp pruning'
-python3 pruning/basic_pruners_torch.py --pruner agp --pretrain-epochs 1 --fine-tune-epochs 1 --model lenet --dataset mnist
+python3 basic_pruners_torch.py --pruner agp --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10 --pretrained-model-dir experiment_data/pretrain_cifar10_vgg16.pth
echo 'testing mean_activation pruning'
-python3 pruning/basic_pruners_torch.py --pruner mean_activation --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10
+python3 basic_pruners_torch.py --pruner mean_activation --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10 --pretrained-model-dir experiment_data/pretrain_cifar10_vgg16.pth
echo "testing lottery ticket pruning..."
-python3 pruning/lottery_torch_mnist_fc.py --train_epochs 1
+python3 lottery_torch_mnist_fc.py --train_epochs 1
echo ""
echo "===========================Testing: quantizers==========================="
diff --git a/test/ut/retiarii/debug_mnist_pytorch.py b/test/ut/retiarii/debug_mnist_pytorch.py
index a15977e5f2..c7511a5dbd 100644
--- a/test/ut/retiarii/debug_mnist_pytorch.py
+++ b/test/ut/retiarii/debug_mnist_pytorch.py
@@ -3,6 +3,8 @@
import torch.nn.functional as F
import torch.optim as optim
+import nni.retiarii.nn.pytorch
+
import torch
diff --git a/test/ut/retiarii/test_highlevel_apis.py b/test/ut/retiarii/test_highlevel_apis.py
index a3ff2b2d5d..e246e748f2 100644
--- a/test/ut/retiarii/test_highlevel_apis.py
+++ b/test/ut/retiarii/test_highlevel_apis.py
@@ -93,6 +93,24 @@ def forward(self, x):
self.assertEqual(self._get_converted_pytorch_model(model2)(torch.randn(1, 3, 3, 3)).size(),
torch.Size([1, 5, 3, 3]))
+ def test_layer_choice_multiple(self):
+ @self.get_serializer()
+ class Net(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.module = nn.LayerChoice([nn.Conv2d(3, i, kernel_size=1) for i in range(1, 11)])
+
+ def forward(self, x):
+ return self.module(x)
+
+ model, mutators = self._get_model_with_mutators(Net())
+ self.assertEqual(len(mutators), 1)
+ mutator = mutators[0].bind_sampler(EnumerateSampler())
+ for i in range(1, 11):
+ model_new = mutator.apply(model)
+ self.assertEqual(self._get_converted_pytorch_model(model_new)(torch.randn(1, 3, 3, 3)).size(),
+ torch.Size([1, i, 3, 3]))
+
def test_input_choice(self):
@self.get_serializer()
class Net(nn.Module):
diff --git a/test/ut/retiarii/test_strategy.py b/test/ut/retiarii/test_strategy.py
index 02fefdd3a9..2dd688b4fb 100644
--- a/test/ut/retiarii/test_strategy.py
+++ b/test/ut/retiarii/test_strategy.py
@@ -141,7 +141,6 @@ def test_evolution():
_reset_execution_engine()
-@pytest.mark.skipif(sys.platform in ('win32', 'darwin'), reason='Does not run on Windows and MacOS')
def test_rl():
rl = strategy.PolicyBasedRL(max_collect=2, trial_per_collect=10)
engine = MockExecutionEngine(failure_prob=0.2)
@@ -150,7 +149,7 @@ def test_rl():
wait_models(*engine.models)
_reset_execution_engine()
- rl = strategy.PolicyBasedRL(max_collect=2, trial_per_collect=10, asynchronous=False)
+ rl = strategy.PolicyBasedRL(max_collect=2, trial_per_collect=10)
engine = MockExecutionEngine(failure_prob=0.2)
_reset_execution_engine(engine)
rl.run(*_get_model_and_mutators())
diff --git a/test/ut/sdk/test_builtin_tuners.py b/test/ut/sdk/test_builtin_tuners.py
index 59739dad16..a60da8ecb6 100644
--- a/test/ut/sdk/test_builtin_tuners.py
+++ b/test/ut/sdk/test_builtin_tuners.py
@@ -12,6 +12,7 @@
from unittest import TestCase, main
from nni.algorithms.hpo.batch_tuner import BatchTuner
+from nni.algorithms.hpo.dngo_tuner import DNGOTuner
from nni.algorithms.hpo.evolution_tuner import EvolutionTuner
from nni.algorithms.hpo.gp_tuner import GPTuner
from nni.algorithms.hpo.gridsearch_tuner import GridSearchTuner
@@ -388,6 +389,12 @@ def test_pbt(self):
))
self.import_data_test_for_pbt()
+ def test_dngo(self):
+ tuner_fn = lambda: DNGOTuner(trials_per_update=100, num_epochs_per_training=1)
+ self.search_space_test_all(tuner_fn, fail_types=["choice_str", "choice_mixed",
+ "normal", "lognormal", "qnormal", "qlognormal"])
+ self.import_data_test(tuner_fn, stype='choice_num')
+
def tearDown(self):
file_list = glob.glob("smac3*") + ["param_config_space.pcs", "scenario.txt", "model_path"]
for file in file_list:
diff --git a/test/ut/sdk/test_compressor_torch.py b/test/ut/sdk/test_compressor_torch.py
index 350e46025e..e6e34d30e6 100644
--- a/test/ut/sdk/test_compressor_torch.py
+++ b/test/ut/sdk/test_compressor_torch.py
@@ -201,7 +201,7 @@ def test_torch_taylorFOweight_pruner(self):
model = TorchModel()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
- pruner = torch_pruner.TaylorFOWeightFilterPruner(model, config_list, optimizer, trainer=None, criterion=None, sparsity_training_epochs=1)
+ pruner = torch_pruner.TaylorFOWeightFilterPruner(model, config_list, optimizer, trainer=None, criterion=None, sparsifying_training_batches=1)
x = torch.rand((1, 1, 28, 28), requires_grad=True)
model.conv1.module.weight.data = torch.tensor(w1).float()
diff --git a/ts/nni_manager/.eslintrc b/ts/nni_manager/.eslintrc
index 2f8b5ac991..5cdba21a4f 100644
--- a/ts/nni_manager/.eslintrc
+++ b/ts/nni_manager/.eslintrc
@@ -24,14 +24,16 @@
"@typescript-eslint/no-inferrable-types": 0,
"no-inner-declarations": 0,
"@typescript-eslint/explicit-function-return-type": "error",
+ "@typescript-eslint/no-var-requires": 0,
+ "@typescript-eslint/no-non-null-assertion": 0,
+
"@typescript-eslint/no-unused-vars": [
- "error",
+ "off",
{
"argsIgnorePattern": "^_"
}
],
- "@typescript-eslint/no-var-requires": 0,
- "@typescript-eslint/no-non-null-assertion": 0
+ "@typescript-eslint/no-use-before-define": 0
},
"ignorePatterns": [
"node_modules/",
diff --git a/ts/nni_manager/common/experimentStartupInfo.ts b/ts/nni_manager/common/experimentStartupInfo.ts
index 18d412869d..ee3e80584e 100644
--- a/ts/nni_manager/common/experimentStartupInfo.ts
+++ b/ts/nni_manager/common/experimentStartupInfo.ts
@@ -6,36 +6,43 @@
import * as assert from 'assert';
import * as os from 'os';
import * as path from 'path';
-import * as component from '../common/component';
-
-@component.Singleton
-class ExperimentStartupInfo {
- private readonly API_ROOT_URL: string = '/api/v1/nni';
-
- private experimentId: string = '';
- private newExperiment: boolean = true;
- private basePort: number = -1;
- private initialized: boolean = false;
- private logDir: string = '';
- private logLevel: string = '';
- private readonly: boolean = false;
- private dispatcherPipe: string | null = null;
- private platform: string = '';
- private urlprefix: string = '';
-
- public setStartupInfo(newExperiment: boolean, experimentId: string, basePort: number, platform: string, logDir?: string, logLevel?: string, readonly?: boolean, dispatcherPipe?: string, urlprefix?: string): void {
- assert(!this.initialized);
- assert(experimentId.trim().length > 0);
+
+const API_ROOT_URL: string = '/api/v1/nni';
+
+let singleton: ExperimentStartupInfo | null = null;
+
+export class ExperimentStartupInfo {
+
+ public experimentId: string = '';
+ public newExperiment: boolean = true;
+ public basePort: number = -1;
+ public initialized: boolean = false;
+ public logDir: string = '';
+ public logLevel: string = '';
+ public readonly: boolean = false;
+ public dispatcherPipe: string | null = null;
+ public platform: string = '';
+ public urlprefix: string = '';
+
+ constructor(
+ newExperiment: boolean,
+ experimentId: string,
+ basePort: number,
+ platform: string,
+ logDir?: string,
+ logLevel?: string,
+ readonly?: boolean,
+ dispatcherPipe?: string,
+ urlprefix?: string) {
this.newExperiment = newExperiment;
this.experimentId = experimentId;
this.basePort = basePort;
- this.initialized = true;
this.platform = platform;
if (logDir !== undefined && logDir.length > 0) {
- this.logDir = path.join(path.normalize(logDir), this.getExperimentId());
+ this.logDir = path.join(path.normalize(logDir), experimentId);
} else {
- this.logDir = path.join(os.homedir(), 'nni-experiments', this.getExperimentId());
+ this.logDir = path.join(os.homedir(), 'nni-experiments', experimentId);
}
if (logLevel !== undefined && logLevel.length > 1) {
@@ -55,98 +62,67 @@ class ExperimentStartupInfo {
}
}
- public getExperimentId(): string {
- assert(this.initialized);
-
- return this.experimentId;
- }
-
- public getBasePort(): number {
- assert(this.initialized);
-
- return this.basePort;
- }
-
- public isNewExperiment(): boolean {
- assert(this.initialized);
-
- return this.newExperiment;
- }
-
- public getPlatform(): string {
- assert(this.initialized);
-
- return this.platform;
+ public get apiRootUrl(): string {
+ return this.urlprefix === '' ? API_ROOT_URL : `/${this.urlprefix}${API_ROOT_URL}`;
}
- public getLogDir(): string {
- assert(this.initialized);
-
- return this.logDir;
- }
-
- public getLogLevel(): string {
- assert(this.initialized);
-
- return this.logLevel;
- }
-
- public isReadonly(): boolean {
- assert(this.initialized);
-
- return this.readonly;
- }
-
- public getDispatcherPipe(): string | null {
- assert(this.initialized);
- return this.dispatcherPipe;
- }
-
- public getAPIRootUrl(): string {
- assert(this.initialized);
- return this.urlprefix==''?this.API_ROOT_URL:`/${this.urlprefix}`;
+ public static getInstance(): ExperimentStartupInfo {
+ assert(singleton !== null);
+ return singleton!;
}
}
-function getExperimentId(): string {
- return component.get(ExperimentStartupInfo).getExperimentId();
+export function getExperimentStartupInfo(): ExperimentStartupInfo {
+ return ExperimentStartupInfo.getInstance();
}
-function getBasePort(): number {
- return component.get(ExperimentStartupInfo).getBasePort();
+export function setExperimentStartupInfo(
+ newExperiment: boolean,
+ experimentId: string,
+ basePort: number,
+ platform: string,
+ logDir?: string,
+ logLevel?: string,
+ readonly?: boolean,
+ dispatcherPipe?: string,
+ urlprefix?: string): void {
+ singleton = new ExperimentStartupInfo(
+ newExperiment,
+ experimentId,
+ basePort,
+ platform,
+ logDir,
+ logLevel,
+ readonly,
+ dispatcherPipe,
+ urlprefix
+ );
}
-function isNewExperiment(): boolean {
- return component.get(ExperimentStartupInfo).isNewExperiment();
+export function getExperimentId(): string {
+ return getExperimentStartupInfo().experimentId;
}
-function getPlatform(): string {
- return component.get(ExperimentStartupInfo).getPlatform();
+export function getBasePort(): number {
+ return getExperimentStartupInfo().basePort;
}
-function getExperimentStartupInfo(): ExperimentStartupInfo {
- return component.get(ExperimentStartupInfo);
+export function isNewExperiment(): boolean {
+ return getExperimentStartupInfo().newExperiment;
}
-function setExperimentStartupInfo(
- newExperiment: boolean, experimentId: string, basePort: number, platform: string, logDir?: string, logLevel?: string, readonly?: boolean, dispatcherPipe?: string, urlprefix?: string): void {
- component.get(ExperimentStartupInfo)
- .setStartupInfo(newExperiment, experimentId, basePort, platform, logDir, logLevel, readonly, dispatcherPipe, urlprefix);
+export function getPlatform(): string {
+ return getExperimentStartupInfo().platform;
}
-function isReadonly(): boolean {
- return component.get(ExperimentStartupInfo).isReadonly();
+export function isReadonly(): boolean {
+ return getExperimentStartupInfo().readonly;
}
-function getDispatcherPipe(): string | null {
- return component.get(ExperimentStartupInfo).getDispatcherPipe();
+export function getDispatcherPipe(): string | null {
+ return getExperimentStartupInfo().dispatcherPipe;
}
-function getAPIRootUrl(): string {
- return component.get(ExperimentStartupInfo).getAPIRootUrl();
+export function getAPIRootUrl(): string {
+ return getExperimentStartupInfo().apiRootUrl;
}
-
-export {
- ExperimentStartupInfo, getBasePort, getExperimentId, isNewExperiment, getPlatform, getExperimentStartupInfo,
- setExperimentStartupInfo, isReadonly, getDispatcherPipe, getAPIRootUrl
-};
diff --git a/ts/nni_manager/common/log.ts b/ts/nni_manager/common/log.ts
index dff76028e1..bcb01145cb 100644
--- a/ts/nni_manager/common/log.ts
+++ b/ts/nni_manager/common/log.ts
@@ -5,6 +5,7 @@
import * as fs from 'fs';
import { Writable } from 'stream';
+import * as util from 'util';
/* log level constants */
@@ -28,7 +29,6 @@ const levelNames = new Map([
/* global_ states */
-let logFile: Writable | null = null;
let logLevel: number = 0;
const loggers = new Map();
@@ -70,7 +70,8 @@ export class Logger {
}
private log(level: number, args: any[]): void {
- if (level < logLevel || logFile === null) {
+ const logFile: Writable | undefined = (global as any).logFile;
+ if (level < logLevel) {
return;
}
@@ -80,23 +81,15 @@ export class Logger {
const levelName = levelNames.has(level) ? levelNames.get(level) : level.toString();
- const words = [];
- for (const arg of args) {
- if (arg === undefined) {
- words.push('undefined');
- } else if (arg === null) {
- words.push('null');
- } else if (typeof arg === 'object') {
- const json = JSON.stringify(arg);
- words.push(json === undefined ? arg : json);
- } else {
- words.push(arg);
- }
- }
- const message = words.join(' ');
+ const message = args.map(arg => (typeof arg === 'string' ? arg : util.inspect(arg))).join(' ');
const record = `[${time}] ${levelName} (${this.name}) ${message}\n`;
- logFile.write(record);
+
+ if (logFile === undefined) {
+ console.log(record);
+ } else {
+ logFile.write(record);
+ }
}
}
@@ -124,7 +117,7 @@ export function setLogLevel(levelName: string): void {
}
export function startLogging(logPath: string): void {
- logFile = fs.createWriteStream(logPath, {
+ (global as any).logFile = fs.createWriteStream(logPath, {
flags: 'a+',
encoding: 'utf8',
autoClose: true
@@ -132,8 +125,8 @@ export function startLogging(logPath: string): void {
}
export function stopLogging(): void {
- if (logFile !== null) {
- logFile.end();
- logFile = null;
+ if ((global as any).logFile !== undefined) {
+ (global as any).logFile.end();
+ (global as any).logFile = undefined;
}
}
diff --git a/ts/nni_manager/common/nniConfig.ts b/ts/nni_manager/common/nniConfig.ts
new file mode 100644
index 0000000000..1e0a5c4c33
--- /dev/null
+++ b/ts/nni_manager/common/nniConfig.ts
@@ -0,0 +1,37 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT license.
+
+'use strict';
+
+import * as fs from 'fs';
+import * as path from 'path';
+import { promisify } from 'util';
+import { runPythonScript } from './pythonScript';
+
+export interface CustomEnvironmentServiceConfig {
+ name: string;
+ nodeModulePath: string;
+ nodeClassName: string;
+}
+
+const readFile = promisify(fs.readFile);
+
+async function readConfigFile(fileName: string): Promise {
+ const script = 'import nni.runtime.config ; print(nni.runtime.config.get_config_directory())';
+ const configDir = (await runPythonScript(script)).trim();
+ const stream = await readFile(path.join(configDir, fileName));
+ return stream.toString();
+}
+
+export async function getCustomEnvironmentServiceConfig(name: string): Promise {
+ const configJson = await readConfigFile('training_services.json');
+ const config = JSON.parse(configJson);
+ if (config[name] === undefined) {
+ return null;
+ }
+ return {
+ name,
+ nodeModulePath: config[name].nodeModulePath as string,
+ nodeClassName: config[name].nodeClassName as string,
+ }
+}
diff --git a/ts/nni_manager/common/pythonScript.ts b/ts/nni_manager/common/pythonScript.ts
new file mode 100644
index 0000000000..12a2bf2112
--- /dev/null
+++ b/ts/nni_manager/common/pythonScript.ts
@@ -0,0 +1,38 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT license.
+
+'use strict';
+
+import { spawn } from 'child_process';
+import { Logger, getLogger } from './log';
+
+const logger: Logger = getLogger('pythonScript');
+
+const python: string = process.platform === 'win32' ? 'python.exe' : 'python3';
+
+export async function runPythonScript(script: string, logTag?: string): Promise {
+ const proc = spawn(python, [ '-c', script ]);
+
+ let stdout: string = '';
+ let stderr: string = '';
+ proc.stdout.on('data', (data: string) => { stdout += data; });
+ proc.stderr.on('data', (data: string) => { stderr += data; });
+
+ const procPromise = new Promise((resolve, reject) => {
+ proc.on('error', (err: Error) => { reject(err); });
+ proc.on('exit', () => { resolve(); });
+ });
+ await procPromise;
+
+ if (stderr) {
+ if (logTag) {
+ logger.warning(`Python script [${logTag}] has stderr:`, stderr);
+ } else {
+ logger.warning('Python script has stderr.');
+ logger.warning(' script:', script);
+ logger.warning(' stderr:', stderr);
+ }
+ }
+
+ return stdout;
+}
diff --git a/ts/nni_manager/common/restServer.ts b/ts/nni_manager/common/restServer.ts
index 368aff977c..efa1d056eb 100644
--- a/ts/nni_manager/common/restServer.ts
+++ b/ts/nni_manager/common/restServer.ts
@@ -25,7 +25,7 @@ export abstract class RestServer {
protected hostName: string = '0.0.0.0';
protected port?: number;
protected app: express.Application = express();
- protected log: Logger = getLogger();
+ protected log: Logger = getLogger('RestServer');
protected basePort?: number;
constructor() {
diff --git a/ts/nni_manager/common/trainingService.ts b/ts/nni_manager/common/trainingService.ts
index 68133e7ae7..2e460bd1db 100644
--- a/ts/nni_manager/common/trainingService.ts
+++ b/ts/nni_manager/common/trainingService.ts
@@ -8,7 +8,7 @@
*/
type TrialJobStatus = 'UNKNOWN' | 'WAITING' | 'RUNNING' | 'SUCCEEDED' | 'FAILED' | 'USER_CANCELED' | 'SYS_CANCELED' | 'EARLY_STOPPED';
-type LogType = 'TRIAL_LOG' | 'TRIAL_ERROR';
+type LogType = 'TRIAL_LOG' | 'TRIAL_STDOUT' | 'TRIAL_ERROR';
interface TrainingServiceMetadata {
readonly key: string;
diff --git a/ts/nni_manager/common/utils.ts b/ts/nni_manager/common/utils.ts
index 8e0a5b4667..917eb3ba52 100644
--- a/ts/nni_manager/common/utils.ts
+++ b/ts/nni_manager/common/utils.ts
@@ -19,14 +19,13 @@ import * as util from 'util';
import * as glob from 'glob';
import { Database, DataStore } from './datastore';
-import { ExperimentStartupInfo, getExperimentStartupInfo, setExperimentStartupInfo } from './experimentStartupInfo';
+import { getExperimentStartupInfo, setExperimentStartupInfo } from './experimentStartupInfo';
import { ExperimentConfig, Manager } from './manager';
import { ExperimentManager } from './experimentManager';
import { HyperParameters, TrainingService, TrialJobStatus } from './trainingService';
function getExperimentRootDir(): string {
- return getExperimentStartupInfo()
- .getLogDir();
+ return getExperimentStartupInfo().logDir;
}
function getLogDir(): string {
@@ -34,8 +33,7 @@ function getLogDir(): string {
}
function getLogLevel(): string {
- return getExperimentStartupInfo()
- .getLogLevel();
+ return getExperimentStartupInfo().logLevel;
}
function getDefaultDatabaseDir(): string {
@@ -58,7 +56,7 @@ function mkDirP(dirPath: string): Promise {
} else {
const parent: string = path.dirname(dirPath);
mkDirP(parent).then(() => {
- fs.mkdir(dirPath, (err: Error) => {
+ fs.mkdir(dirPath, (err: Error | null) => {
if (err) {
deferred.reject(err);
} else {
@@ -186,7 +184,6 @@ function generateParamFileName(hyperParameters: HyperParameters): string {
* Must be paired with `cleanupUnitTest()`.
*/
function prepareUnitTest(): void {
- Container.snapshot(ExperimentStartupInfo);
Container.snapshot(Database);
Container.snapshot(DataStore);
Container.snapshot(TrainingService);
@@ -215,8 +212,9 @@ function cleanupUnitTest(): void {
Container.restore(TrainingService);
Container.restore(DataStore);
Container.restore(Database);
- Container.restore(ExperimentStartupInfo);
Container.restore(ExperimentManager);
+ const logLevel: string = parseArg(['--log_level', '-ll']);
+ setExperimentStartupInfo(true, 'unittest', 8080, 'unittest', undefined, logLevel);
}
let cachedipv4Address: string = '';
@@ -287,40 +285,6 @@ function countFilesRecursively(directory: string): Promise {
});
}
-export function validateFileName(fileName: string): boolean {
- const pattern: string = '^[a-z0-9A-Z._-]+$';
- const validateResult = fileName.match(pattern);
- if (validateResult) {
- return true;
- }
- return false;
-}
-
-async function validateFileNameRecursively(directory: string): Promise {
- if (!fs.existsSync(directory)) {
- throw Error(`Direcotory ${directory} doesn't exist`);
- }
-
- const fileNameArray: string[] = fs.readdirSync(directory);
- let result = true;
- for (const name of fileNameArray) {
- const fullFilePath: string = path.join(directory, name);
- try {
- // validate file names and directory names
- result = validateFileName(name);
- if (fs.lstatSync(fullFilePath).isDirectory()) {
- result = result && await validateFileNameRecursively(fullFilePath);
- }
- if (!result) {
- return Promise.reject(new Error(`file name in ${fullFilePath} is not valid!`));
- }
- } catch (error) {
- return Promise.reject(error);
- }
- }
- return Promise.resolve(result);
-}
-
/**
* get the version of current package
*/
@@ -481,8 +445,13 @@ async function getFreePort(host: string, start: number, end: number): Promise;
public init(): Promise {
@@ -71,7 +71,6 @@ class NNIDataStore implements DataStore {
public storeTrialJobEvent(
event: TrialJobEvent, trialJobId: string, hyperParameter?: string, jobDetail?: TrialJobDetail): Promise {
- //this.log.debug(`storeTrialJobEvent: event: ${event}, data: ${hyperParameter}, jobDetail: ${JSON.stringify(jobDetail)}`);
// Use the timestamp in jobDetail as TrialJobEvent timestamp for different events
let timestamp: number | undefined;
@@ -243,7 +242,7 @@ class NNIDataStore implements DataStore {
for (const metric of metrics) {
const existMetrics: MetricDataRecord[] | undefined = map.get(metric.trialJobId);
if (existMetrics !== undefined) {
- this.log.error(`Found multiple FINAL results for trial job ${trialJobId}, metrics: ${JSON.stringify(metrics)}`);
+ this.log.error(`Found multiple FINAL results for trial job ${trialJobId}, metrics:`, metrics);
} else {
map.set(metric.trialJobId, [metric]);
}
diff --git a/ts/nni_manager/core/nniExperimentsManager.ts b/ts/nni_manager/core/nniExperimentsManager.ts
index e345817406..10712d2f2e 100644
--- a/ts/nni_manager/core/nniExperimentsManager.ts
+++ b/ts/nni_manager/core/nniExperimentsManager.ts
@@ -30,7 +30,7 @@ class NNIExperimentsManager implements ExperimentManager {
constructor() {
this.experimentsPath = getExperimentsInfoPath();
- this.log = getLogger();
+ this.log = getLogger('NNIExperimentsManager');
this.profileUpdateTimer = {};
}
diff --git a/ts/nni_manager/core/nniTensorboardManager.ts b/ts/nni_manager/core/nniTensorboardManager.ts
index f3993b9dd7..323fc14fb5 100644
--- a/ts/nni_manager/core/nniTensorboardManager.ts
+++ b/ts/nni_manager/core/nniTensorboardManager.ts
@@ -37,7 +37,7 @@ class NNITensorboardManager implements TensorboardManager {
private nniManager: Manager;
constructor() {
- this.log = getLogger();
+ this.log = getLogger('NNITensorboardManager');
this.tensorboardTaskMap = new Map();
this.setTensorboardVersion();
this.nniManager = component.get(Manager);
@@ -70,7 +70,7 @@ class NNITensorboardManager implements TensorboardManager {
this.log.error(error);
const alive: boolean = await isAlive(tensorboardProc.pid);
if (alive) {
- process.kill(-tensorboardProc.pid);
+ process.kill(-tensorboardProc.pid!);
}
this.setTensorboardTaskStatus(tensorboardTask, 'ERROR');
});
diff --git a/ts/nni_manager/core/nnimanager.ts b/ts/nni_manager/core/nnimanager.ts
index e535052c91..ee5aa0784e 100644
--- a/ts/nni_manager/core/nnimanager.ts
+++ b/ts/nni_manager/core/nnimanager.ts
@@ -61,7 +61,7 @@ class NNIManager implements Manager {
this.trialDataForTuner = '';
this.readonly = false;
- this.log = getLogger();
+ this.log = getLogger('NNIManager');
this.dataStore = component.get(DataStore);
this.status = {
status: 'INITIALIZED',
@@ -445,10 +445,7 @@ class NNIManager implements Manager {
throw new Error('Cannot detect training service platform');
}
- if (['remote', 'pai', 'aml', 'hybrid'].includes(platform)) {
- const module_ = await import('../training_service/reusable/routerTrainingService');
- return new module_.RouterTrainingService(config);
- } else if (platform === 'local') {
+ if (platform === 'local') {
const module_ = await import('../training_service/local/localTrainingService');
return new module_.LocalTrainingService(config);
} else if (platform === 'kubeflow') {
@@ -460,6 +457,9 @@ class NNIManager implements Manager {
} else if (platform === 'adl') {
const module_ = await import('../training_service/kubernetes/adl/adlTrainingService');
return new module_.AdlTrainingService();
+ } else {
+ const module_ = await import('../training_service/reusable/routerTrainingService');
+ return await module_.RouterTrainingService.construct(config);
}
throw new Error(`Unsupported training service platform "${platform}"`);
@@ -490,7 +490,7 @@ class NNIManager implements Manager {
};
const newEnv = Object.assign({}, process.env, nniEnv);
const tunerProc: ChildProcess = getTunerProc(command, stdio, newCwd, newEnv);
- this.dispatcherPid = tunerProc.pid;
+ this.dispatcherPid = tunerProc.pid!;
this.dispatcher = createDispatcherInterface(tunerProc);
return;
@@ -659,7 +659,7 @@ class NNIManager implements Manager {
}
const form = this.waitingTrials.shift() as TrialJobApplicationForm;
this.currSubmittedTrialNum++;
- this.log.info(`submitTrialJob: form: ${JSON.stringify(form)}`);
+ this.log.info('submitTrialJob: form:', form);
const trialJobDetail: TrialJobDetail = await this.trainingService.submitTrialJob(form);
const Snapshot: TrialJobDetail = Object.assign({}, trialJobDetail);
await this.storeExperimentProfile();
@@ -732,7 +732,7 @@ class NNIManager implements Manager {
}
private async onTrialJobMetrics(metric: TrialJobMetric): Promise {
- this.log.debug(`NNIManager received trial job metrics: ${JSON.stringify(metric)}`);
+ this.log.debug('NNIManager received trial job metrics:', metric);
if (this.trialJobs.has(metric.id)){
await this.dataStore.storeMetricData(metric.id, metric.data);
if (this.dispatcher === undefined) {
@@ -740,7 +740,7 @@ class NNIManager implements Manager {
}
this.dispatcher.sendCommand(REPORT_METRIC_DATA, metric.data);
} else {
- this.log.warning(`NNIManager received non-existent trial job metrics: ${metric}`);
+ this.log.warning('NNIManager received non-existent trial job metrics:', metric);
}
}
@@ -804,7 +804,7 @@ class NNIManager implements Manager {
index: tunerCommand.parameter_index
}
};
- this.log.info(`updateTrialJob: job id: ${tunerCommand.trial_job_id}, form: ${JSON.stringify(trialJobForm)}`);
+ this.log.info('updateTrialJob: job id:', tunerCommand.trial_job_id, 'form:', trialJobForm);
await this.trainingService.updateTrialJob(tunerCommand.trial_job_id, trialJobForm);
if (tunerCommand['parameters'] !== null) {
// parameters field is set as empty string if no more hyper parameter can be generated by tuner.
@@ -820,7 +820,7 @@ class NNIManager implements Manager {
break;
}
case KILL_TRIAL_JOB: {
- this.log.info(`cancelTrialJob: ${JSON.parse(content)}`);
+ this.log.info('cancelTrialJob:', content);
await this.trainingService.cancelTrialJob(JSON.parse(content), true);
break;
}
diff --git a/ts/nni_manager/core/sqlDatabase.ts b/ts/nni_manager/core/sqlDatabase.ts
index 5ccb29c06d..4a15c28b87 100644
--- a/ts/nni_manager/core/sqlDatabase.ts
+++ b/ts/nni_manager/core/sqlDatabase.ts
@@ -80,7 +80,7 @@ function loadMetricData(row: any): MetricDataRecord {
class SqlDB implements Database {
private db!: sqlite3.Database;
- private log: Logger = getLogger();
+ private log: Logger = getLogger('SqlDB');
private initTask!: Deferred;
public init(createNew: boolean, dbDir: string): Promise {
@@ -130,7 +130,7 @@ class SqlDB implements Database {
exp.nextSequenceId,
exp.revision
];
- this.log.trace(`storeExperimentProfile: SQL: ${sql}, args: ${JSON.stringify(args)}`);
+ this.log.trace(`storeExperimentProfile: SQL: ${sql}, args:`, args);
const deferred: Deferred = new Deferred();
this.db.run(sql, args, (err: Error | null) => { this.resolve(deferred, err); });
@@ -147,7 +147,7 @@ class SqlDB implements Database {
sql = 'select * from ExperimentProfile where id=? and revision=?';
args = [experimentId, revision];
}
- this.log.trace(`queryExperimentProfile: SQL: ${sql}, args: ${JSON.stringify(args)}`);
+ this.log.trace(`queryExperimentProfile: SQL: ${sql}, args:`, args);
const deferred: Deferred = new Deferred();
this.db.all(sql, args, (err: Error | null, rows: any[]) => {
this.resolve(deferred, err, rows, loadExperimentProfile);
@@ -170,7 +170,7 @@ class SqlDB implements Database {
const message: string | undefined = jobDetail === undefined ? undefined : jobDetail.message;
const args: any[] = [timestamp, trialJobId, event, hyperParameter, logPath, sequenceId, message];
- this.log.trace(`storeTrialJobEvent: SQL: ${sql}, args: ${JSON.stringify(args)}`);
+ this.log.trace(`storeTrialJobEvent: SQL: ${sql}, args:`, args);
const deferred: Deferred = new Deferred();
this.db.run(sql, args, (err: Error | null) => { this.resolve(deferred, err); });
@@ -193,7 +193,7 @@ class SqlDB implements Database {
args = [trialJobId, event];
}
- this.log.trace(`queryTrialJobEvent: SQL: ${sql}, args: ${JSON.stringify(args)}`);
+ this.log.trace(`queryTrialJobEvent: SQL: ${sql}, args:`, args);
const deferred: Deferred = new Deferred();
this.db.all(sql, args, (err: Error | null, rows: any[]) => {
this.resolve(deferred, err, rows, loadTrialJobEvent);
@@ -207,7 +207,7 @@ class SqlDB implements Database {
const json: MetricDataRecord = JSON.parse(data);
const args: any[] = [Date.now(), json.trialJobId, json.parameterId, json.type, json.sequence, JSON.stringify(json.data)];
- this.log.trace(`storeMetricData: SQL: ${sql}, args: ${JSON.stringify(args)}`);
+ this.log.trace(`storeMetricData: SQL: ${sql}, args:`, args);
const deferred: Deferred = new Deferred();
this.db.run(sql, args, (err: Error | null) => { this.resolve(deferred, err); });
@@ -230,7 +230,7 @@ class SqlDB implements Database {
args = [trialJobId, metricType];
}
- this.log.trace(`queryMetricData: SQL: ${sql}, args: ${JSON.stringify(args)}`);
+ this.log.trace(`queryMetricData: SQL: ${sql}, args:`, args);
const deferred: Deferred = new Deferred();
this.db.all(sql, args, (err: Error | null, rows: any[]) => {
this.resolve(deferred, err, rows, loadMetricData);
@@ -259,7 +259,7 @@ class SqlDB implements Database {
for (const row of (rows)) {
data.push(rowLoader(row));
}
- this.log.trace(`sql query result: ${JSON.stringify(data)}`);
+ this.log.trace(`sql query result:`, data);
(>deferred).resolve(data);
}
}
diff --git a/ts/nni_manager/core/test/ipcInterface.test.ts b/ts/nni_manager/core/test/ipcInterface.test.ts
index fbe11b3f3d..f186aa8c92 100644
--- a/ts/nni_manager/core/test/ipcInterface.test.ts
+++ b/ts/nni_manager/core/test/ipcInterface.test.ts
@@ -30,7 +30,7 @@ function runProcess(): Promise {
if (code !== 0) {
deferred.resolve(new Error(`return code: ${code}`));
} else {
- let str = proc.stdout.read().toString();
+ let str = proc.stdout!.read().toString();
if(str.search("\r\n")!=-1){
sentCommands = str.split("\r\n");
}
diff --git a/ts/nni_manager/main.ts b/ts/nni_manager/main.ts
index c104c3a190..360dc8148c 100644
--- a/ts/nni_manager/main.ts
+++ b/ts/nni_manager/main.ts
@@ -53,9 +53,9 @@ async function initContainer(foreground: boolean, platformMode: string, logFileN
} else {
startLogging(logFileName);
}
- // eslint-disable-next-line @typescript-eslint/no-use-before-define
- setLogLevel(logLevel);
}
+ // eslint-disable-next-line @typescript-eslint/no-use-before-define
+ setLogLevel(logLevel);
const ds: DataStore = component.get(DataStore);
await ds.init();
@@ -83,11 +83,6 @@ const foreground: boolean = foregroundArg.toLowerCase() === 'true' ? true : fals
const port: number = parseInt(strPort, 10);
const mode: string = parseArg(['--mode', '-m']);
-if (!['local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller', 'dlts', 'aml', 'adl', 'hybrid'].includes(mode)) {
- console.log(`FATAL: unknown mode: ${mode}`);
- usage();
- process.exit(1);
-}
const startMode: string = parseArg(['--start_mode', '-s']);
if (![ExperimentStartUpMode.NEW, ExperimentStartUpMode.RESUME].includes(startMode)) {
diff --git a/ts/nni_manager/package.json b/ts/nni_manager/package.json
index 409876e487..c8b0eae512 100644
--- a/ts/nni_manager/package.json
+++ b/ts/nni_manager/package.json
@@ -11,79 +11,71 @@
},
"license": "MIT",
"dependencies": {
- "azure-storage": "^2.10.2",
+ "azure-storage": "^2.10.4",
"child-process-promise": "^2.2.1",
- "express": "^4.16.3",
- "express-joi-validator": "^2.0.0",
- "ignore": "^5.1.4",
- "js-base64": "^2.4.9",
- "kubernetes-client": "^6.5.0",
+ "express": "^4.17.1",
+ "express-joi-validator": "^2.0.1",
+ "ignore": "^5.1.8",
+ "js-base64": "^3.6.1",
+ "kubernetes-client": "^6.12.1",
"lockfile": "^1.0.4",
- "python-shell": "^2.0.1",
+ "python-shell": "^3.0.0",
"rx": "^4.1.0",
- "sqlite3": "5.0.0",
- "ssh2": "^0.8.9",
+ "sqlite3": "5.0.2",
+ "ssh2": "^1.1.0",
"stream-buffers": "^3.0.2",
"tail-stream": "^0.3.4",
- "tar": "^6.0.2",
+ "tar": "^6.1.0",
"tree-kill": "^1.2.2",
"ts-deferred": "^1.0.4",
- "typescript-ioc": "^1.2.4",
- "typescript-string-operations": "^1.3.1",
- "webhdfs": "^1.2.0",
- "ws": "^7.3.0"
+ "typescript-ioc": "^1.2.6",
+ "typescript-string-operations": "^1.4.1",
+ "ws": "^7.4.6"
},
"devDependencies": {
- "@types/chai": "^4.1.4",
+ "@types/chai": "^4.2.18",
"@types/chai-as-promised": "^7.1.0",
- "@types/express": "^4.16.0",
- "@types/glob": "^7.1.1",
- "@types/js-base64": "^2.3.1",
- "@types/js-yaml": "^3.12.5",
+ "@types/express": "^4.17.2",
+ "@types/glob": "^7.1.3",
+ "@types/js-base64": "^3.3.1",
+ "@types/js-yaml": "^4.0.1",
"@types/lockfile": "^1.0.0",
- "@types/mocha": "^8.0.3",
- "@types/node": "10.12.18",
- "@types/request": "^2.47.1",
- "@types/rx": "^4.1.1",
- "@types/sqlite3": "^3.1.3",
- "@types/ssh2": "^0.5.35",
- "@types/stream-buffers": "^3.0.2",
- "@types/tar": "^4.0.3",
- "@types/tmp": "^0.0.33",
- "@types/ws": "^7.2.5",
+ "@types/mocha": "^8.2.2",
+ "@types/node": "^15.12.1",
+ "@types/request": "^2.48.5",
+ "@types/rx": "^4.1.2",
+ "@types/sqlite3": "^3.1.7",
+ "@types/ssh2": "^0.5.46",
+ "@types/stream-buffers": "^3.0.3",
+ "@types/tar": "^4.0.4",
+ "@types/tmp": "^0.2.0",
+ "@types/ws": "^7.4.4",
"@typescript-eslint/eslint-plugin": "^2.10.0",
- "@typescript-eslint/parser": "^2.10.0",
- "chai": "^4.1.2",
+ "@typescript-eslint/parser": "^4.26.0",
+ "chai": "^4.3.4",
"chai-as-promised": "^7.1.1",
- "eslint": "^6.7.2",
- "glob": "^7.1.3",
- "mocha": "^8.1.3",
+ "eslint": "^7.28.0",
+ "glob": "^7.1.7",
+ "mocha": "^8.4.0",
"npx": "^10.2.2",
- "nyc": "^15.0.0",
- "request": "^2.87.0",
+ "nyc": "^15.1.0",
+ "request": "^2.88.2",
"rmdir": "^1.2.0",
- "tmp": "^0.0.33",
- "ts-node": "^7.0.0",
- "typescript": "^3.2.2"
+ "tmp": "^0.2.1",
+ "ts-node": "^10.0.0",
+ "typescript": "^4.3.2"
},
"resolutions": {
- "mem": "^4.0.0",
- "lodash": ">=4.17.13",
- "lodash.merge": ">=4.6.2",
- "node.extend": "^1.1.7",
- "hoek": "^4.2.1",
- "js-yaml": "^3.13.1",
- "node-forge": ">=0.10.0",
- "dot-prop": "^4.2.1",
- "npm": ">=6.14.8",
- "yargs": "~16.0.3",
- "yargs-parser": ">=20.2.0",
- "y18n": ">=5.0.5",
- "acorn": ">=8.0.4",
- "serialize-javascript": ">=5.0.1"
+ "acorn": ">=8.3.0",
+ "hoek": ">=6.1.3",
+ "node.extend": ">=1.1.8",
+ "npm": ">=7.16.0",
+ "y18n": ">=5.0.8",
+ "yargs-parser": ">=20.2.7",
+ "joi": ">=17.4.0"
},
"engines": {
- "node": ">=10.0.0"
+ "node": "^16.3.0"
},
"nyc": {
"include": [
diff --git a/ts/nni_manager/rest_server/restHandler.ts b/ts/nni_manager/rest_server/restHandler.ts
index 9d528dab06..851276c745 100644
--- a/ts/nni_manager/rest_server/restHandler.ts
+++ b/ts/nni_manager/rest_server/restHandler.ts
@@ -17,6 +17,9 @@ import { TensorboardManager, TensorboardTaskInfo } from '../common/tensorboardMa
import { ValidationSchemas } from './restValidationSchemas';
import { NNIRestServer } from './nniRestServer';
import { getVersion } from '../common/utils';
+import { MetricType } from '../common/datastore';
+import { ProfileUpdateType } from '../common/manager';
+import { LogType, TrialJobStatus } from '../common/trainingService';
const expressJoi = require('express-joi-validator');
@@ -32,14 +35,14 @@ class NNIRestHandler {
this.experimentsManager = component.get(ExperimentManager);
this.tensorboardManager = component.get(TensorboardManager);
this.restServer = rs;
- this.log = getLogger();
+ this.log = getLogger('NNIRestHandler');
}
public createRestHandler(): Router {
const router: Router = Router();
router.use((req: Request, res: Response, next) => {
- this.log.debug(`${req.method}: ${req.url}: body:\n${JSON.stringify(req.body, undefined, 4)}`);
+ this.log.debug(`${req.method}: ${req.url}: body:`, req.body);
res.header('Access-Control-Allow-Headers', 'Origin, X-Requested-With, Content-Type, Accept');
res.header('Access-Control-Allow-Methods', 'PUT,POST,GET,DELETE,OPTIONS');
@@ -139,7 +142,7 @@ class NNIRestHandler {
private updateExperimentProfile(router: Router): void {
router.put('/experiment', (req: Request, res: Response) => {
- this.nniManager.updateExperimentProfile(req.body, req.query.update_type).then(() => {
+ this.nniManager.updateExperimentProfile(req.body, req.query.update_type as ProfileUpdateType).then(() => {
res.send();
}).catch((err: Error) => {
this.handleError(err, res);
@@ -219,7 +222,7 @@ class NNIRestHandler {
private listTrialJobs(router: Router): void {
router.get('/trial-jobs', (req: Request, res: Response) => {
- this.nniManager.listTrialJobs(req.query.status).then((jobInfos: TrialJobInfo[]) => {
+ this.nniManager.listTrialJobs(req.query.status as TrialJobStatus).then((jobInfos: TrialJobInfo[]) => {
jobInfos.forEach((trialJob: TrialJobInfo) => {
this.setErrorPathForFailedJob(trialJob);
});
@@ -263,7 +266,7 @@ class NNIRestHandler {
private getMetricData(router: Router): void {
router.get('/metric-data/:job_id*?', async (req: Request, res: Response) => {
- this.nniManager.getMetricData(req.params.job_id, req.query.type).then((metricsData: MetricDataRecord[]) => {
+ this.nniManager.getMetricData(req.params.job_id, req.query.type as MetricType).then((metricsData: MetricDataRecord[]) => {
res.send(metricsData);
}).catch((err: Error) => {
this.handleError(err, res);
@@ -295,7 +298,7 @@ class NNIRestHandler {
private getTrialLog(router: Router): void {
router.get('/trial-log/:id/:type', async(req: Request, res: Response) => {
- this.nniManager.getTrialLog(req.params.id, req.params.type).then((log: string) => {
+ this.nniManager.getTrialLog(req.params.id, req.params.type as LogType).then((log: string) => {
if (log === '') {
log = 'No logs available.'
}
diff --git a/ts/nni_manager/rest_server/restValidationSchemas.ts b/ts/nni_manager/rest_server/restValidationSchemas.ts
index f2e0a453e4..bd8c856c53 100644
--- a/ts/nni_manager/rest_server/restValidationSchemas.ts
+++ b/ts/nni_manager/rest_server/restValidationSchemas.ts
@@ -82,7 +82,7 @@ export namespace ValidationSchemas {
gpuNum: joi.number().min(0).required(),
command: joi.string().min(1).required()
}),
- taskRoles: joi.array({
+ taskRoles: joi.array().items({
name: joi.string().min(1),
taskNum: joi.number().min(1).required(),
image: joi.string().min(1),
@@ -98,7 +98,7 @@ export namespace ValidationSchemas {
minSucceededTaskCount: joi.number()
})
}),
- imagePullSecrets: joi.array({
+ imagePullSecrets: joi.array().items({
name: joi.string().min(1).required()
}),
// ############## adl ###############
diff --git a/ts/nni_manager/training_service/common/clusterJobRestServer.ts b/ts/nni_manager/training_service/common/clusterJobRestServer.ts
index 49298744a3..43501046a9 100644
--- a/ts/nni_manager/training_service/common/clusterJobRestServer.ts
+++ b/ts/nni_manager/training_service/common/clusterJobRestServer.ts
@@ -76,7 +76,7 @@ export abstract class ClusterJobRestServer extends RestServer {
const router: Router = Router();
router.use((req: Request, res: Response, next: any) => {
- this.log.info(`${req.method}: ${req.url}: body:\n${JSON.stringify(req.body, undefined, 4)}`);
+ this.log.info(`${req.method}: ${req.url}: body:`, req.body);
res.setHeader('Content-Type', 'application/json');
next();
});
@@ -109,7 +109,7 @@ export abstract class ClusterJobRestServer extends RestServer {
router.post(`/update-metrics/${this.expId}/:trialId`, (req: Request, res: Response) => {
try {
this.log.info(`Get update-metrics request, trial job id is ${req.params.trialId}`);
- this.log.info(`update-metrics body is ${JSON.stringify(req.body)}`);
+ this.log.info('update-metrics body is', req.body);
this.handleTrialMetrics(req.body.jobId, req.body.metrics);
diff --git a/ts/nni_manager/training_service/common/util.ts b/ts/nni_manager/training_service/common/util.ts
index c88c9cd957..28f5aa515c 100644
--- a/ts/nni_manager/training_service/common/util.ts
+++ b/ts/nni_manager/training_service/common/util.ts
@@ -11,7 +11,6 @@ import * as path from 'path';
import * as tar from 'tar';
import { getLogger } from '../../common/log';
import { String } from 'typescript-string-operations';
-import { validateFileName } from '../../common/utils';
import { GPU_INFO_COLLECTOR_FORMAT_WINDOWS } from './gpuData';
/**
@@ -54,7 +53,6 @@ export function* listDirWithIgnoredFiles(root: string, relDir: string, ignoreFil
export async function validateCodeDir(codeDir: string): Promise {
let fileCount: number = 0;
let fileTotalSize: number = 0;
- let fileNameValid: boolean = true;
for (const relPath of listDirWithIgnoredFiles(codeDir, '', [])) {
const d = path.join(codeDir, relPath);
fileCount += 1;
@@ -66,13 +64,16 @@ export async function validateCodeDir(codeDir: string): Promise {
if (fileTotalSize > 300 * 1024 * 1024) {
throw new Error(`File total size too large in code dir (${fileTotalSize} bytes already scanned, exceeds 300MB).`);
}
- fileNameValid = true;
- relPath.split(path.sep).forEach(fpart => {
- if (fpart !== '' && !validateFileName(fpart))
- fileNameValid = false;
- });
+ // NOTE: We added this test in case any training service or shared storage (e.g. HDFS) does not support complex file name.
+ // If there is no bug found for long time, feel free to remove it.
+ const fileNameValid = relPath.split(path.sep).every(fpart => (fpart.match('^[a-z0-9A-Z._-]*$') !== null));
if (!fileNameValid) {
- throw new Error(`Validate file name error: '${d}' is an invalid file name.`);
+ const message = [
+ `File ${relPath} in directory ${codeDir} contains spaces or special characters in its name.`,
+ 'This might cause problem when uploading to cloud or remote machine.',
+ 'If you encounter any error, please report an issue: https://github.com/microsoft/nni/issues'
+ ].join(' ');
+ getLogger('validateCodeDir').warning(message);
}
}
@@ -111,7 +112,7 @@ export async function execCopydir(source: string, destination: string): Promise<
await fs.promises.mkdir(destPath);
}
} else {
- getLogger().debug(`Copying file from ${sourcePath} to ${destPath}`);
+ getLogger('execCopydir').debug(`Copying file from ${sourcePath} to ${destPath}`);
await fs.promises.copyFile(sourcePath, destPath);
}
}
diff --git a/ts/nni_manager/training_service/dlts/dltsClusterConfig.ts b/ts/nni_manager/training_service/dlts/dltsClusterConfig.ts
deleted file mode 100644
index 1ee3f6a7fe..0000000000
--- a/ts/nni_manager/training_service/dlts/dltsClusterConfig.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-// Copyright (c) Microsoft Corporation.
-// Licensed under the MIT license.
-
-export interface DLTSClusterConfig {
- dashboard: string;
-
- cluster: string;
- team: string;
-
- email: string;
- password: string;
-
- gpuType?: string;
-}
diff --git a/ts/nni_manager/training_service/dlts/dltsData.ts b/ts/nni_manager/training_service/dlts/dltsData.ts
deleted file mode 100644
index 3a59c3d8d2..0000000000
--- a/ts/nni_manager/training_service/dlts/dltsData.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-// Copyright (c) Microsoft Corporation.
-// Licensed under the MIT license.
-
-export const DLTS_TRIAL_COMMAND_FORMAT: string =
-`export NNI_PLATFORM=dlts NNI_SYS_DIR={0} NNI_OUTPUT_DIR={1} NNI_TRIAL_JOB_ID={2} NNI_EXP_ID={3} NNI_TRIAL_SEQ_ID={4} MULTI_PHASE={5} \
-&& cd $NNI_SYS_DIR && sh install_nni.sh \
-&& cd '{6}' && python3 -m nni.tools.trial_tool.trial_keeper --trial_command '{7}' \
---nnimanager_ip '{8}' --nnimanager_port '{9}' --nni_manager_version '{10}' --log_collection '{11}'`;
diff --git a/ts/nni_manager/training_service/dlts/dltsJobConfig.ts b/ts/nni_manager/training_service/dlts/dltsJobConfig.ts
deleted file mode 100644
index d517d6bfc5..0000000000
--- a/ts/nni_manager/training_service/dlts/dltsJobConfig.ts
+++ /dev/null
@@ -1,45 +0,0 @@
-// Copyright (c) Microsoft Corporation.
-// Licensed under the MIT license.
-
-import { DLTSClusterConfig } from "./dltsClusterConfig";
-
-export class DLTSJobConfig {
- public readonly team: string;
- public readonly userName: string;
- public readonly vcName: string;
- public readonly gpuType: string;
- public readonly jobType = "training";
- public readonly jobtrainingtype = "RegularJob";
- public readonly ssh = false;
- public readonly ipython = false;
- public readonly tensorboard = false;
- public readonly workPath = '';
- public readonly enableworkpath = true;
- public readonly dataPath = '';
- public readonly enabledatapath = false;
- public readonly jobPath = '';
- public readonly enablejobpath = true;
- public readonly mountpoints = [];
- public readonly env = [{ name: 'TMPDIR', value: '$HOME/tmp' }]
- public readonly hostNetwork = false;
- public readonly useGPUTopology = false;
- public readonly isPrivileged = false;
- public readonly hostIPC = false;
- public readonly preemptionAllowed = "False"
-
- public constructor(
- clusterConfig: DLTSClusterConfig,
- public readonly jobName: string,
- public readonly resourcegpu: number,
- public readonly image: string,
- public readonly cmd: string,
- public readonly interactivePorts: number[],
- ) {
- if (clusterConfig.gpuType === undefined) {
- throw Error('GPU type not fetched')
- }
- this.vcName = this.team = clusterConfig.team
- this.gpuType = clusterConfig.gpuType
- this.userName = clusterConfig.email
- }
-}
diff --git a/ts/nni_manager/training_service/dlts/dltsJobRestServer.ts b/ts/nni_manager/training_service/dlts/dltsJobRestServer.ts
deleted file mode 100644
index 818e8dd538..0000000000
--- a/ts/nni_manager/training_service/dlts/dltsJobRestServer.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-// Copyright (c) Microsoft Corporation.
-// Licensed under the MIT license.
-
-'use strict';
-
-import { Request, Response, Router } from 'express';
-import { Inject } from 'typescript-ioc';
-import * as component from '../../common/component';
-import { ClusterJobRestServer } from '../common/clusterJobRestServer';
-import { DLTSTrainingService } from './dltsTrainingService';
-
-export interface ParameterFileMeta {
- readonly experimentId: string;
- readonly trialId: string;
- readonly filePath: string;
-}
-
-/**
- * DLTS Training service Rest server, provides rest API to support DLTS job metrics update
- *
- */
-@component.Singleton
-export class DLTSJobRestServer extends ClusterJobRestServer {
- private parameterFileMetaList: ParameterFileMeta[] = [];
-
- @Inject
- private readonly dltsTrainingService: DLTSTrainingService;
-
- /**
- * constructor to provide NNIRestServer's own rest property, e.g. port
- */
- constructor() {
- super();
- this.dltsTrainingService = component.get(DLTSTrainingService);
- }
-
- // tslint:disable-next-line:no-any
- protected handleTrialMetrics(jobId: string, metrics: any[]): void {
- // Split metrics array into single metric, then emit
- // Warning: If not split metrics into single ones, the behavior will be UNKNOWN
- for (const singleMetric of metrics) {
- this.dltsTrainingService.MetricsEmitter.emit('metric', {
- id : jobId,
- data : singleMetric
- });
- }
- }
-
- protected createRestHandler(): Router {
- const router: Router = super.createRestHandler();
-
- router.post(`/parameter-file-meta`, (req: Request, res: Response) => {
- try {
- this.log.info(`POST /parameter-file-meta, body is ${JSON.stringify(req.body)}`);
- this.parameterFileMetaList.push(req.body);
- res.send();
- } catch (err) {
- this.log.error(`POST parameter-file-meta error: ${err}`);
- res.status(500);
- res.send(err.message);
- }
- });
-
- router.get(`/parameter-file-meta`, (req: Request, res: Response) => {
- try {
- this.log.info(`GET /parameter-file-meta`);
- res.send(this.parameterFileMetaList);
- } catch (err) {
- this.log.error(`GET parameter-file-meta error: ${err}`);
- res.status(500);
- res.send(err.message);
- }
- });
-
- return router;
- }
-}
diff --git a/ts/nni_manager/training_service/dlts/dltsTrainingService.ts b/ts/nni_manager/training_service/dlts/dltsTrainingService.ts
deleted file mode 100644
index 6380375fd4..0000000000
--- a/ts/nni_manager/training_service/dlts/dltsTrainingService.ts
+++ /dev/null
@@ -1,578 +0,0 @@
-// Copyright (c) Microsoft Corporation.
-// Licensed under the MIT license.
-
-'use strict';
-
-import * as fs from 'fs';
-import * as path from 'path';
-import * as request from 'request';
-
-import * as component from '../../common/component';
-import { EventEmitter } from 'events';
-import { String } from 'typescript-string-operations';
-import { getExperimentId } from '../../common/experimentStartupInfo';
-import { getLogger, Logger } from '../../common/log';
-import { MethodNotImplementedError } from '../../common/errors';
-import {
- NNIManagerIpConfig, TrainingService,
- TrialJobApplicationForm, TrialJobDetail, TrialJobMetric, LogType
-} from '../../common/trainingService';
-import { DLTS_TRIAL_COMMAND_FORMAT } from './dltsData';
-import { CONTAINER_INSTALL_NNI_SHELL_FORMAT } from '../common/containerJobData';
-import { execMkdir, validateCodeDir } from '../common/util';
-import { delay, uniqueString, getIPV4Address, getExperimentRootDir, getVersion, generateParamFileName } from '../../common/utils';
-import { DLTSJobRestServer } from './dltsJobRestServer';
-import { TrialConfigMetadataKey } from '../../training_service/common/trialConfigMetadataKey';
-import { DLTSJobConfig } from './dltsJobConfig';
-import { DLTSClusterConfig } from './dltsClusterConfig';
-import { DLTSTrialConfig } from './dltsTrialConfig';
-import { DLTSTrialJobDetail } from './dltsTrialJobDetail';
-
-@component.Singleton
-class DLTSTrainingService implements TrainingService {
- private readonly log!: Logger;
- private readonly metricsEmitter: EventEmitter;
- //private readonly expRootDir: string;
- private readonly jobQueue: string[];
- private stopping: boolean = false;
- private readonly experimentId!: string;
- private versionCheck: boolean = true;
- private logCollection: string = 'none';
- private isMultiPhase: boolean = false;
- private dltsRestServerHost: string;
- private dltsRestServerPort?: number;
- private jobMode: boolean;
-
- private readonly trialJobsMap: Map;
- private nniManagerIpConfig?: NNIManagerIpConfig;
- private dltsClusterConfig?: DLTSClusterConfig;
- private dltsTrialConfig?: DLTSTrialConfig;
-
- constructor() {
- this.log = getLogger();
- this.metricsEmitter = new EventEmitter();
- this.trialJobsMap = new Map();
- this.jobQueue = [];
- this.experimentId = getExperimentId();
- this.dltsRestServerHost = getIPV4Address();
- this.jobMode = 'DLTS_JOB_ID' in process.env;
- this.log.info(`Construct DLTS training service in ${this.jobMode ? 'job mode' : 'local mode'}.`);
- }
-
- public async run(): Promise {
- this.log.info('Run DLTS training service.');
- const restServer: DLTSJobRestServer = component.get(DLTSJobRestServer);
- await restServer.start();
- restServer.setEnableVersionCheck = this.versionCheck;
- this.log.info(`DLTS Training service rest server listening on: ${restServer.endPoint}`);
- if (this.jobMode) {
- await this.exposeRestServerPort(restServer.clusterRestServerPort);
- } else {
- this.dltsRestServerPort = restServer.clusterRestServerPort
- }
- await Promise.all([
- this.statusCheckingLoop(),
- this.submitJobLoop()]);
- this.log.info('DLTS training service exit.');
- }
-
- private async exposeRestServerPort(port: number): Promise {
- if (this.dltsClusterConfig == null) {
- throw Error('Cluster config is not set');
- }
- const { dashboard, cluster, email, password } = this.dltsClusterConfig;
- const jobId = process.env['DLTS_JOB_ID'] + '';
- const uri = `${dashboard}api/clusters/${cluster}/jobs/${jobId}/endpoints`;
- const qs = { email, password };
-
- do {
- this.log.debug('Checking endpoints');
- const endpoints = await new Promise((resolve, reject) => {
- request.get(uri, { qs, json: true }, function (error, response, body) {
- if (error) {
- reject(error);
- } else {
- resolve(body);
- }
- });
- });
- this.log.debug('Endpoints: %o', endpoints);
- if (Array.isArray(endpoints)) {
- const restServerEndpoint = endpoints.find(({ podPort }) => podPort === port);
- if (restServerEndpoint == null) {
- this.log.debug('Exposing %d', port);
- await new Promise((resolve, reject) => {
- request.post(uri, {
- qs,
- json: true,
- body: {
- endpoints: [{
- name: "nni-rest-server",
- podPort: port
- }]
- }
- }, function (error) {
- if (error) {
- reject(error);
- } else {
- resolve();
- }
- });
- });
- } else if (restServerEndpoint['status'] === 'running') {
- // We get an exposed restserver port
- this.dltsRestServerHost = restServerEndpoint['nodeName'];
- this.dltsRestServerPort = restServerEndpoint['port'];
- break;
- }
- }
- } while (await new Promise(resolve => setTimeout(resolve, 1000, true)));
- }
-
- private async statusCheckingLoop(): Promise {
- while (!this.stopping) {
- const updateDLTSTrialJobs: Promise[] = [];
- for (const dltsTrialJob of this.trialJobsMap.values()) {
- updateDLTSTrialJobs.push(this.getDLTSTrialJobInfo(dltsTrialJob));
- }
-
- await Promise.all(updateDLTSTrialJobs);
-
- // Calcel paused dlts job
- const cancelPausedJobPromises: Promise[] = [];
- for (const [trialJobId, dltsTrialJob] of this.trialJobsMap) {
- if (dltsTrialJob.dltsPaused && dltsTrialJob.status === 'RUNNING') {
- cancelPausedJobPromises.push(this.cancelTrialJob(trialJobId));
- }
- }
- await Promise.all(cancelPausedJobPromises);
-
- const restServer: DLTSJobRestServer = component.get(DLTSJobRestServer);
- if (restServer.getErrorMessage !== undefined) {
- throw new Error(restServer.getErrorMessage);
- }
- await delay(3000);
- }
- }
-
- private async getDLTSTrialJobInfo(dltsTrialJob: DLTSTrialJobDetail): Promise {
- if (this.dltsClusterConfig == null) {
- throw Error('Cluster config is not set');
- }
- const requestOptions: request.Options = {
- uri: `${this.dltsClusterConfig.dashboard}api/v2/clusters/${this.dltsClusterConfig.cluster}/jobs/${dltsTrialJob.dltsJobId}`,
- qs: {
- email: this.dltsClusterConfig.email,
- password: this.dltsClusterConfig.password
- },
- json: true
- };
- const body = await new Promise((resolve, reject) => {
- request(requestOptions, (error, response, body) => {
- if (error != null) {
- reject(error)
- } else {
- resolve(body)
- }
- })
- }) as any;
- void ((): void => {
- switch (body['jobStatus']) {
- case 'unapproved':
- case 'queued':
- case 'scheduling':
- dltsTrialJob.status = "WAITING";
- break;
- case 'running':
- dltsTrialJob.status = "RUNNING";
- if (dltsTrialJob.startTime === undefined) {
- dltsTrialJob.startTime = Date.parse(body['jobStatusDetail'][0]['startedAt'])
- }
- if (dltsTrialJob.url === undefined) {
- dltsTrialJob.url = `${this.dltsClusterConfig.dashboard}job/${this.dltsClusterConfig.team}/${this.dltsClusterConfig.cluster}/${dltsTrialJob.dltsJobId}`
- }
- break;
- case 'finished':
- dltsTrialJob.status = "SUCCEEDED";
- break;
- case 'failed':
- dltsTrialJob.status = "FAILED";
- break;
- case 'pausing':
- case 'paused':
- dltsTrialJob.status = "RUNNING";
- dltsTrialJob.dltsPaused = true;
- break;
- case 'killing':
- case 'killed':
- if (dltsTrialJob.isEarlyStopped !== undefined) {
- dltsTrialJob.status = dltsTrialJob.isEarlyStopped === true
- ? 'EARLY_STOPPED' : 'USER_CANCELED';
- } else {
- dltsTrialJob.status = 'SYS_CANCELED';
- }
- break;
- default:
- dltsTrialJob.status = "UNKNOWN";
- }
- }) ();
- }
-
- private async submitJobLoop(): Promise {
- while (!this.stopping) {
- while (!this.stopping && this.jobQueue.length > 0) {
- const trialJobId: string = this.jobQueue[0];
- this.log.info(`Got job ${trialJobId}`);
- if (await this.submitTrialJobToDLTS(trialJobId)) {
- // Remove trial job with trialJobId from job queue
- this.jobQueue.shift();
- } else {
- // Break the while loop since failed to submitJob
- break;
- }
- }
- await delay(3000);
- }
- }
-
- public async listTrialJobs(): Promise {
- return Array.from(this.trialJobsMap.values());
- }
-
- public async getTrialJob(trialJobId: string): Promise {
- const trialJob = this.trialJobsMap.get(trialJobId);
- if (trialJob === undefined) {
- throw Error(`Trial job ${trialJobId} not found.`)
- }
- return trialJob
- }
-
- public async getTrialLog(_trialJobId: string, _logType: LogType): Promise {
- throw new MethodNotImplementedError();
- }
-
- public addTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void {
- this.metricsEmitter.on('metric', listener);
- }
-
- public removeTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void {
- this.metricsEmitter.off('metric', listener);
- }
-
- public get MetricsEmitter(): EventEmitter {
- return this.metricsEmitter;
- }
-
- public async submitTrialJob(form: TrialJobApplicationForm): Promise {
- const trialJobId: string = uniqueString(5);
- const trialWorkingFolder: string = path.join(
- '/nni-experiments', getExperimentId(),
- '/trials/', trialJobId);
- const trialJobDetail = new DLTSTrialJobDetail(
- trialJobId, // id
- 'WAITING', // status
- Date.now(), // submitTime
- trialWorkingFolder, // workingDirectory
- form,
- `nni_exp_${this.experimentId}_trial_${trialJobId}`
- );
-
- this.trialJobsMap.set(trialJobId, trialJobDetail);
- this.jobQueue.push(trialJobId);
-
- return trialJobDetail;
- }
-
- public async cancelTrialJob(trialJobId: string, isEarlyStopped: boolean = false): Promise {
- const trialJobDetail: DLTSTrialJobDetail | undefined = this.trialJobsMap.get(trialJobId);
- if (trialJobDetail === undefined) {
- throw Error(`cancelTrialJob: trial job id ${trialJobId} not found`);
- }
-
- if (this.dltsClusterConfig === undefined) {
- throw Error('DLTS Cluster config is not initialized');
- }
-
- const options: request.Options = {
- method: 'PUT',
- uri: `${this.dltsClusterConfig.dashboard}api/clusters/${this.dltsClusterConfig.cluster}/jobs/${trialJobDetail.dltsJobId}/status`,
- qs: {
- email: this.dltsClusterConfig.email,
- password: this.dltsClusterConfig.password
- },
- body: {
- status: 'killing'
- },
- json: true
- };
-
- // Set trialjobDetail's early stopped field, to mark the job's cancellation source
- trialJobDetail.isEarlyStopped = isEarlyStopped;
-
- await new Promise((resolve, reject) => {
- request(options, (error: Error, response: request.Response, body: any) => {
- if (error) {
- reject(error);
- } else {
- resolve(body);
- }
- });
- });
- }
-
- private async getGpuType(): Promise {
- if (this.dltsClusterConfig === undefined) {
- throw new Error('DLTS Cluster config is not initialized');
- }
- const gpuRequestOptions: request.Options = {
- method: 'GET',
- qs: {
- email: this.dltsClusterConfig.email,
- password: this.dltsClusterConfig.password
- },
- uri: `${this.dltsClusterConfig.dashboard}api/teams/${this.dltsClusterConfig.team}/clusters/${this.dltsClusterConfig.cluster}`,
- json: true
- };
- return new Promise((resolve, reject) => {
- request(gpuRequestOptions, (error, response, data) => {
- if (error) {
- return reject(error)
- }
- try {
- const metadata = JSON.parse(data['metadata'])
- resolve(Object.keys(metadata)[0])
- } catch (error) {
- reject(error)
- }
- })
- });
- }
-
- public async setClusterMetadata(key: string, value: string): Promise {
- switch (key) {
- case TrialConfigMetadataKey.NNI_MANAGER_IP:
- this.nniManagerIpConfig = JSON.parse(value);
- break;
-
- case TrialConfigMetadataKey.DLTS_CLUSTER_CONFIG:
- this.dltsClusterConfig = JSON.parse(value);
- if (!this.dltsClusterConfig.cluster) {
- this.dltsClusterConfig.cluster = '.default'
- }
- if (!this.dltsClusterConfig.email) {
- if (process.env['DLWS_USER_EMAIL']) {
- this.dltsClusterConfig.email = process.env['DLWS_USER_EMAIL'] as string
- } else {
- throw Error('`email` field in `dltsConfig` is not configured.')
- }
- }
- if (!this.dltsClusterConfig.password) {
- if (process.env['DLTS_JOB_TOKEN']) {
- this.dltsClusterConfig.password = process.env['DLTS_JOB_TOKEN'] as string
- } else {
- throw Error('`password` field in `dltsConfig` is not configured.')
- }
- }
- if (!this.dltsClusterConfig.team) {
- if (process.env['DLWS_VC_NAME']) {
- this.dltsClusterConfig.team = process.env['DLWS_VC_NAME'] as string
- } else {
- throw Error('`team` field in `dltsConfig` is not configured.')
- }
- }
- this.dltsClusterConfig.gpuType = await this.getGpuType();
- break;
-
- case TrialConfigMetadataKey.TRIAL_CONFIG:
- this.dltsTrialConfig = JSON.parse(value);
-
- // Validate to make sure codeDir doesn't have too many files
- try {
- await validateCodeDir(this.dltsTrialConfig.codeDir);
- } catch (error) {
- this.log.error(error);
- throw error;
- }
-
- break;
- case TrialConfigMetadataKey.VERSION_CHECK:
- this.versionCheck = (value === 'true' || value === 'True');
- break;
- case TrialConfigMetadataKey.LOG_COLLECTION:
- this.logCollection = value;
- break;
- case TrialConfigMetadataKey.MULTI_PHASE:
- this.isMultiPhase = (value === 'true' || value === 'True');
- break;
- default:
- //Reject for unknown keys
- throw new Error(`Uknown key: ${key}`);
- }
- }
-
- public async getClusterMetadata(_key: string): Promise {
- return '';
- }
-
- public async cleanUp(): Promise {
- this.log.info('Stopping DLTS training service...');
- this.stopping = true;
-
- const restServer: DLTSJobRestServer = component.get(DLTSJobRestServer);
- try {
- await restServer.stop();
- this.log.info('DLTS Training service rest server stopped successfully.');
- return;
- } catch (error) {
- // tslint:disable-next-line: no-unsafe-any
- this.log.error(`DLTS Training service rest server stopped failed, error: ${error.message}`);
- throw error;
- }
- }
-
- private async submitTrialJobToDLTS(trialJobId: string): Promise {
- const trialJobDetail: DLTSTrialJobDetail | undefined = this.trialJobsMap.get(trialJobId);
-
- if (trialJobDetail === undefined) {
- throw new Error(`Failed to find DLTSTrialJobDetail for job ${trialJobId}`);
- }
-
- if (this.dltsClusterConfig === undefined) {
- throw new Error('DLTS Cluster config is not initialized');
- }
- if (this.dltsTrialConfig === undefined) {
- throw new Error('trial config is not initialized');
- }
-
- if (this.dltsRestServerPort === undefined) {
- const restServer: DLTSJobRestServer = component.get(DLTSJobRestServer);
- this.dltsRestServerPort = restServer.clusterRestServerPort;
- }
-
- // Step 1. Prepare DLTS job configuration
-
- const trialLocalFolder = path.join(getExperimentRootDir(), 'trials-local', trialJobId);
- //create tmp trial working folder locally.
- await execMkdir(trialLocalFolder);
-
- const runScriptContent: string = CONTAINER_INSTALL_NNI_SHELL_FORMAT;
- // Write NNI installation file to local tmp files
- await fs.promises.writeFile(path.join(trialLocalFolder, 'install_nni.sh'), runScriptContent, { encoding: 'utf8' });
-
- // Write file content ( parameter.cfg ) to local tmp folders
- if (trialJobDetail.form !== undefined) {
- await fs.promises.writeFile(
- path.join(trialLocalFolder, generateParamFileName(trialJobDetail.form.hyperParameters)),
- trialJobDetail.form.hyperParameters.value, { encoding: 'utf8' }
- );
- }
- // tslint:disable-next-line: strict-boolean-expressions
- const nniManagerIp: string = this.nniManagerIpConfig ? this.nniManagerIpConfig.nniManagerIp : this.dltsRestServerHost;
- const version: string = this.versionCheck ? await getVersion() : '';
- const nniDLTSTrialCommand: string = String.Format(
- DLTS_TRIAL_COMMAND_FORMAT,
- trialLocalFolder,
- path.join(trialLocalFolder, 'nnioutput'),
- trialJobId,
- this.experimentId,
- trialJobDetail.form.sequenceId,
- false,
- this.dltsTrialConfig.codeDir,
- this.dltsTrialConfig.command,
- nniManagerIp,
- this.dltsRestServerPort,
- version,
- this.logCollection
- )
- .replace(/\r\n|\n|\r/gm, '');
-
- // Step 2. Submit DLTS job via Rest call
- const dltsJobConfig: DLTSJobConfig = new DLTSJobConfig(
- this.dltsClusterConfig,
- trialJobDetail.dltsJobName,
- this.dltsTrialConfig.gpuNum,
- this.dltsTrialConfig.image,
- nniDLTSTrialCommand,
- []
- );
- const submitJobRequest: request.Options = {
- method: 'POST',
- uri: `${this.dltsClusterConfig.dashboard}api/clusters/${this.dltsClusterConfig.cluster}/jobs`,
- qs: {
- email: this.dltsClusterConfig.email,
- password: this.dltsClusterConfig.password
- },
- body: dltsJobConfig,
- json: true
- }
- const responseData = await new Promise((resolve, reject) => {
- request(submitJobRequest, function (error, response, data) {
- if (error) {
- return reject(error)
- } else {
- return resolve(data)
- }
- })
- });
-
- trialJobDetail.dltsJobId = responseData['jobId']
-
- return true;
- }
-
- public async updateTrialJob(trialJobId: string, form: TrialJobApplicationForm): Promise {
- const trialJobDetail: undefined | TrialJobDetail = this.trialJobsMap.get(trialJobId);
- if (trialJobDetail === undefined) {
- throw new Error(`updateTrialJob failed: ${trialJobId} not found`);
- }
- if (this.dltsClusterConfig === undefined) {
- throw new Error('DLTS Cluster config is not initialized');
- }
- if (this.dltsTrialConfig === undefined) {
- throw new Error('DLTS trial config is not initialized');
- }
-
- const hyperParameters = form.hyperParameters;
- const trialLocalTempFolder: string = path.join(getExperimentRootDir(), 'trials-local', trialJobId);
- const hpFileName: string = generateParamFileName(hyperParameters);
- const localFilepath: string = path.join(trialLocalTempFolder, hpFileName);
- await fs.promises.writeFile(localFilepath, hyperParameters.value, { encoding: 'utf8' });
-
- const parameterFileMeta = {
- experimentId: this.experimentId,
- trialId: trialJobId
- };
- const restServer: DLTSJobRestServer = component.get(DLTSJobRestServer);
- const req: request.Options = {
- uri: `${restServer.endPoint}${restServer.apiRootUrl}/parameter-file-meta`,
- method: 'POST',
- json: true,
- body: parameterFileMeta
- };
- await new Promise((resolve, reject) => {
- request(req, (err: Error, _res: request.Response) => {
- if (err) {
- reject(err);
- } else {
- resolve();
- }
- });
- });
-
- return trialJobDetail;
- }
-
- public get isMultiPhaseJobSupported(): boolean {
- return false;
- }
-
- public getTrialOutputLocalPath(_trialJobId: string): Promise {
- throw new MethodNotImplementedError();
- }
-
- public fetchTrialOutput(_trialJobId: string, _subpath: string): Promise {
- throw new MethodNotImplementedError();
- }
-}
-
-export { DLTSTrainingService };
diff --git a/ts/nni_manager/training_service/dlts/dltsTrialConfig.ts b/ts/nni_manager/training_service/dlts/dltsTrialConfig.ts
deleted file mode 100644
index bbf35d011b..0000000000
--- a/ts/nni_manager/training_service/dlts/dltsTrialConfig.ts
+++ /dev/null
@@ -1,15 +0,0 @@
-// Copyright (c) Microsoft Corporation.
-// Licensed under the MIT license.
-
-import { TrialConfig } from "training_service/common/trialConfig";
-
-export class DLTSTrialConfig extends TrialConfig {
- public constructor(
- command: string,
- codeDir: string,
- gpuNum: number,
- public readonly image: string
- ) {
- super(command, codeDir, gpuNum);
- }
-}
diff --git a/ts/nni_manager/training_service/dlts/dltsTrialJobDetail.ts b/ts/nni_manager/training_service/dlts/dltsTrialJobDetail.ts
deleted file mode 100644
index 2168102465..0000000000
--- a/ts/nni_manager/training_service/dlts/dltsTrialJobDetail.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-// Copyright (c) Microsoft Corporation.
-// Licensed under the MIT license.
-
-import {
- TrialJobDetail,
- TrialJobStatus,
- TrialJobApplicationForm
-} from "../../common/trainingService";
-
-export class DLTSTrialJobDetail implements TrialJobDetail {
- public startTime?: number;
- public endTime?: number;
- public tags?: string[];
- public url?: string;
- public isEarlyStopped?: boolean;
-
- // DLTS staff
- public dltsJobId?: string;
- public dltsPaused: boolean = false;
-
- public constructor (
- public id: string,
- public status: TrialJobStatus,
- public submitTime: number,
- public workingDirectory: string,
- public form: TrialJobApplicationForm,
-
- // DLTS staff
- public dltsJobName: string,
- ) {}
-}
diff --git a/ts/nni_manager/training_service/kubernetes/azureStorageClientUtils.ts b/ts/nni_manager/training_service/kubernetes/azureStorageClientUtils.ts
index fb393d186c..c589f64e92 100644
--- a/ts/nni_manager/training_service/kubernetes/azureStorageClientUtils.ts
+++ b/ts/nni_manager/training_service/kubernetes/azureStorageClientUtils.ts
@@ -22,7 +22,7 @@ export namespace AzureStorageClientUtility {
const deferred: Deferred = new Deferred();
fileServerClient.createShareIfNotExists(azureShare, (error: any, _result: any, _response: any) => {
if (error) {
- getLogger()
+ getLogger('AzureStorageClientUtility')
.error(`Create share failed:, ${error}`);
deferred.resolve(false);
} else {
@@ -43,7 +43,7 @@ export namespace AzureStorageClientUtility {
const deferred: Deferred = new Deferred();
fileServerClient.createDirectoryIfNotExists(azureShare, azureFoler, (error: any, _result: any, _response: any) => {
if (error) {
- getLogger()
+ getLogger('AzureStorageClientUtility')
.error(`Create directory failed:, ${error}`);
deferred.resolve(false);
} else {
@@ -91,7 +91,7 @@ export namespace AzureStorageClientUtility {
await fileServerClient.createFileFromLocalFile(azureShare, azureDirectory, azureFileName, localFilePath,
(error: any, _result: any, _response: any) => {
if (error) {
- getLogger()
+ getLogger('AzureStorageClientUtility')
.error(`Upload file failed:, ${error}`);
deferred.resolve(false);
} else {
@@ -116,7 +116,7 @@ export namespace AzureStorageClientUtility {
await fileServerClient.getFileToStream(azureShare, azureDirectory, azureFileName, fs.createWriteStream(localFilePath),
(error: any, _result: any, _response: any) => {
if (error) {
- getLogger()
+ getLogger('AzureStorageClientUtility')
.error(`Download file failed:, ${error}`);
deferred.resolve(false);
} else {
@@ -185,19 +185,19 @@ export namespace AzureStorageClientUtility {
fileServerClient.listFilesAndDirectoriesSegmented(azureShare, azureDirectory, 'null',
async (_error: any, result: any, _response: any) => {
if (('entries' in result) === false) {
- getLogger()
+ getLogger('AzureStorageClientUtility')
.error(`list files failed, can't get entries in result`);
throw new Error(`list files failed, can't get entries in result`);
}
if (('files' in result.entries) === false) {
- getLogger()
+ getLogger('AzureStorageClientUtility')
.error(`list files failed, can't get files in result['entries']`);
throw new Error(`list files failed, can't get files in result['entries']`);
}
if (('directories' in result.directories) === false) {
- getLogger()
+ getLogger('AzureStorageClientUtility')
.error(`list files failed, can't get directories in result['entries']`);
throw new Error(`list files failed, can't get directories in result['entries']`);
}
diff --git a/ts/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerConfig.ts b/ts/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerConfig.ts
index 622756e2d9..ee47c384b7 100644
--- a/ts/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerConfig.ts
+++ b/ts/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerConfig.ts
@@ -27,7 +27,7 @@ export class FrameworkControllerTrialConfigTemplate extends KubernetesTrialConfi
public readonly frameworkAttemptCompletionPolicy: FrameworkAttemptCompletionPolicy;
public readonly name: string;
public readonly taskNum: number;
- constructor(taskNum: number, command: string, gpuNum: number,
+ constructor(name: string, taskNum: number, command: string, gpuNum: number,
cpuNum: number, memoryMB: number, image: string,
frameworkAttemptCompletionPolicy: FrameworkAttemptCompletionPolicy, privateRegistryFilePath?: string | undefined) {
super(command, gpuNum, cpuNum, memoryMB, image, privateRegistryFilePath);
diff --git a/ts/nni_manager/training_service/kubernetes/kubernetesApiClient.ts b/ts/nni_manager/training_service/kubernetes/kubernetesApiClient.ts
index ea3dc459f8..459050e72b 100644
--- a/ts/nni_manager/training_service/kubernetes/kubernetesApiClient.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubernetesApiClient.ts
@@ -7,16 +7,31 @@
import {Client1_10, config} from 'kubernetes-client';
import {getLogger, Logger} from '../../common/log';
+/**
+ * This function uses the environment variable
+ * 'KUBERNETES_SERVICE_HOST' to determine whether
+ * the code is running from within a kubernetes container.
+ * If it is, it returns the in-cluster config
+ * instead of the kubeconfig.
+ */
+function getKubernetesConfig(): any {
+ if ('KUBERNETES_SERVICE_HOST' in process.env) {
+ return config.getInCluster();
+ } else {
+ return config.fromKubeconfig();
+ }
+}
+
/**
* Generic Kubernetes client, target version >= 1.9
*/
class GeneralK8sClient {
protected readonly client: any;
- protected readonly log: Logger = getLogger();
+ protected readonly log: Logger = getLogger('GeneralK8sClient');
protected namespace: string = 'default';
constructor() {
- this.client = new Client1_10({config: config.fromKubeconfig(), version: '1.9'});
+ this.client = new Client1_10({config: getKubernetesConfig(), version: '1.9'});
this.client.loadSpec();
}
@@ -135,11 +150,11 @@ class GeneralK8sClient {
*/
abstract class KubernetesCRDClient {
protected readonly client: any;
- protected readonly log: Logger = getLogger();
+ protected readonly log: Logger = getLogger('KubernetesCRDClient');
protected crdSchema: any;
constructor() {
- this.client = new Client1_10({config: config.fromKubeconfig()});
+ this.client = new Client1_10({config: getKubernetesConfig()});
this.client.loadSpec();
}
diff --git a/ts/nni_manager/training_service/kubernetes/kubernetesJobInfoCollector.ts b/ts/nni_manager/training_service/kubernetes/kubernetesJobInfoCollector.ts
index 2865ff19fd..231f088efe 100644
--- a/ts/nni_manager/training_service/kubernetes/kubernetesJobInfoCollector.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubernetesJobInfoCollector.ts
@@ -15,7 +15,7 @@ import { KubernetesTrialJobDetail } from './kubernetesData';
*/
export class KubernetesJobInfoCollector {
protected readonly trialJobsMap: Map;
- protected readonly log: Logger = getLogger();
+ protected readonly log: Logger = getLogger('KubernetesJobInfoCollector');
protected readonly statusesNeedToCheck: TrialJobStatus[];
constructor(jobMap: Map) {
diff --git a/ts/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts b/ts/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
index 147895ea28..b747ad84fc 100644
--- a/ts/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
@@ -54,7 +54,7 @@ abstract class KubernetesTrainingService {
protected expContainerCodeFolder: string;
constructor() {
- this.log = getLogger();
+ this.log = getLogger('KubernetesTrainingService');
this.metricsEmitter = new EventEmitter();
this.trialJobsMap = new Map();
this.trialLocalTempFolder = path.join(getExperimentRootDir(), 'trials-nfs-tmp');
diff --git a/ts/nni_manager/training_service/local/gpuScheduler.ts b/ts/nni_manager/training_service/local/gpuScheduler.ts
index c437a2708d..0283988b0f 100644
--- a/ts/nni_manager/training_service/local/gpuScheduler.ts
+++ b/ts/nni_manager/training_service/local/gpuScheduler.ts
@@ -24,7 +24,7 @@ class GPUScheduler {
constructor() {
this.stopping = false;
- this.log = getLogger();
+ this.log = getLogger('GPUScheduler');
this.gpuMetricCollectorScriptFolder = `${os.tmpdir()}/${os.userInfo().username}/nni/script`;
}
diff --git a/ts/nni_manager/training_service/local/localTrainingService.ts b/ts/nni_manager/training_service/local/localTrainingService.ts
index 92a1c44da6..1bc33a8340 100644
--- a/ts/nni_manager/training_service/local/localTrainingService.ts
+++ b/ts/nni_manager/training_service/local/localTrainingService.ts
@@ -98,7 +98,7 @@ class LocalTrainingService implements TrainingService {
this.jobMap = new Map();
this.jobQueue = [];
this.stopping = false;
- this.log = getLogger();
+ this.log = getLogger('LocalTrainingService');
this.experimentId = getExperimentId();
this.jobStreamMap = new Map();
this.log.info('Construct local machine training service.');
@@ -174,6 +174,8 @@ class LocalTrainingService implements TrainingService {
let logPath: string;
if (logType === 'TRIAL_LOG') {
logPath = path.join(this.rootDir, 'trials', trialJobId, 'trial.log');
+ } else if (logType === 'TRIAL_STDOUT'){
+ logPath = path.join(this.rootDir, 'trials', trialJobId, 'stdout');
} else if (logType === 'TRIAL_ERROR') {
logPath = path.join(this.rootDir, 'trials', trialJobId, 'stderr');
} else {
@@ -202,7 +204,7 @@ class LocalTrainingService implements TrainingService {
this.jobQueue.push(trialJobId);
this.jobMap.set(trialJobId, trialJobDetail);
- this.log.debug(`submitTrialJob: return: ${JSON.stringify(trialJobDetail)} `);
+ this.log.debug('submitTrialJob: return:', trialJobDetail);
return Promise.resolve(trialJobDetail);
}
@@ -412,15 +414,16 @@ class LocalTrainingService implements TrainingService {
private getScript(workingDirectory: string): string[] {
const script: string[] = [];
if (process.platform === 'win32') {
+ script.push(`$PSDefaultParameterValues = @{'Out-File:Encoding' = 'utf8'}`);
script.push(`cd $env:NNI_CODE_DIR`);
script.push(
- `cmd.exe /c ${this.config.trialCommand} 2>&1 | Out-File "${path.join(workingDirectory, 'stderr')}" -encoding utf8`,
+ `cmd.exe /c ${this.config.trialCommand} 1>${path.join(workingDirectory, 'stdout')} 2>${path.join(workingDirectory, 'stderr')}`,
`$NOW_DATE = [int64](([datetime]::UtcNow)-(get-date "1/1/1970")).TotalSeconds`,
`$NOW_DATE = "$NOW_DATE" + (Get-Date -Format fff).ToString()`,
`Write $LASTEXITCODE " " $NOW_DATE | Out-File "${path.join(workingDirectory, '.nni', 'state')}" -NoNewline -encoding utf8`);
} else {
script.push(`cd $NNI_CODE_DIR`);
- script.push(`eval ${this.config.trialCommand} 2>"${path.join(workingDirectory, 'stderr')}"`);
+ script.push(`eval ${this.config.trialCommand} 1>${path.join(workingDirectory, 'stdout')} 2>${path.join(workingDirectory, 'stderr')}`);
if (process.platform === 'darwin') {
// https://superuser.com/questions/599072/how-to-get-bash-execution-time-in-milliseconds-under-mac-os-x
// Considering the worst case, write 999 to avoid negative duration
diff --git a/ts/nni_manager/training_service/pai/paiJobInfoCollector.ts b/ts/nni_manager/training_service/pai/paiJobInfoCollector.ts
index cf3974618e..24eaf7b3b8 100644
--- a/ts/nni_manager/training_service/pai/paiJobInfoCollector.ts
+++ b/ts/nni_manager/training_service/pai/paiJobInfoCollector.ts
@@ -18,7 +18,7 @@ interface FlattenOpenpaiConfig extends ExperimentConfig, OpenpaiConfig { }
*/
export class PAIJobInfoCollector {
private readonly trialJobsMap: Map;
- private readonly log: Logger = getLogger();
+ private readonly log: Logger = getLogger('PAIJobInfoCollector');
private readonly statusesNeedToCheck: TrialJobStatus[];
private readonly finalStatuses: TrialJobStatus[];
diff --git a/ts/nni_manager/training_service/pai/paiJobRestServer.ts b/ts/nni_manager/training_service/pai/paiJobRestServer.ts
index 00e960cdce..1ba7930736 100644
--- a/ts/nni_manager/training_service/pai/paiJobRestServer.ts
+++ b/ts/nni_manager/training_service/pai/paiJobRestServer.ts
@@ -46,7 +46,7 @@ export class PAIJobRestServer extends ClusterJobRestServer {
router.post(`/parameter-file-meta`, (req: Request, res: Response) => {
try {
- this.log.info(`POST /parameter-file-meta, body is ${JSON.stringify(req.body)}`);
+ this.log.info('POST /parameter-file-meta, body is', req.body);
this.parameterFileMetaList.push(req.body);
res.send();
} catch (err) {
diff --git a/ts/nni_manager/training_service/pai/paiTrainingService.ts b/ts/nni_manager/training_service/pai/paiTrainingService.ts
index 11f24cea5b..36cf78835a 100644
--- a/ts/nni_manager/training_service/pai/paiTrainingService.ts
+++ b/ts/nni_manager/training_service/pai/paiTrainingService.ts
@@ -63,7 +63,7 @@ class PAITrainingService implements TrainingService {
private config: FlattenOpenpaiConfig;
constructor(config: ExperimentConfig) {
- this.log = getLogger();
+ this.log = getLogger('PAITrainingService');
this.metricsEmitter = new EventEmitter();
this.trialJobsMap = new Map();
this.jobQueue = [];
@@ -308,7 +308,7 @@ class PAITrainingService implements TrainingService {
}
public async submitTrialJob(form: TrialJobApplicationForm): Promise {
- this.log.info(`submitTrialJob: form: ${JSON.stringify(form)}`);
+ this.log.info('submitTrialJob: form:', form);
const trialJobId: string = uniqueString(5);
//TODO: use HDFS working folder instead
diff --git a/ts/nni_manager/training_service/remote_machine/gpuScheduler.ts b/ts/nni_manager/training_service/remote_machine/gpuScheduler.ts
index f520e96c45..a78e841cb2 100644
--- a/ts/nni_manager/training_service/remote_machine/gpuScheduler.ts
+++ b/ts/nni_manager/training_service/remote_machine/gpuScheduler.ts
@@ -18,7 +18,7 @@ type SCHEDULE_POLICY_NAME = 'random' | 'round-robin';
export class GPUScheduler {
private readonly machineExecutorMap: Map;
- private readonly log: Logger = getLogger();
+ private readonly log: Logger = getLogger('GPUScheduler');
private readonly policyName: SCHEDULE_POLICY_NAME = 'round-robin';
private roundRobinIndex: number = 0;
private configuredRMs: RemoteMachineMeta[] = [];
diff --git a/ts/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts b/ts/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts
index f2f789d194..da6c5551bb 100644
--- a/ts/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts
+++ b/ts/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts
@@ -67,7 +67,7 @@ class RemoteMachineTrainingService implements TrainingService {
this.sshConnectionPromises = [];
this.expRootDir = getExperimentRootDir();
this.timer = component.get(ObservableTimer);
- this.log = getLogger();
+ this.log = getLogger('RemoteMachineTrainingService');
this.log.info('Construct remote machine training service.');
this.config = flattenConfig(config, 'remote');
diff --git a/ts/nni_manager/training_service/remote_machine/shellExecutor.ts b/ts/nni_manager/training_service/remote_machine/shellExecutor.ts
index 0baa5bae6f..7c92055e59 100644
--- a/ts/nni_manager/training_service/remote_machine/shellExecutor.ts
+++ b/ts/nni_manager/training_service/remote_machine/shellExecutor.ts
@@ -36,7 +36,7 @@ class ShellExecutor {
public isWindows: boolean = false;
constructor() {
- this.log = getLogger();
+ this.log = getLogger('ShellExecutor');
this.sshClient = new Client();
}
@@ -277,7 +277,7 @@ class ShellExecutor {
this.log.debug(`copyFileToRemote(${commandIndex}): localFilePath: ${localFilePath}, remoteFilePath: ${remoteFilePath}`);
const deferred: Deferred = new Deferred();
- this.sshClient.sftp((err: Error, sftp: SFTPWrapper) => {
+ this.sshClient.sftp((err: Error | undefined, sftp: SFTPWrapper) => {
if (err !== undefined && err !== null) {
this.log.error(`copyFileToRemote(${commandIndex}): ${err}`);
deferred.reject(err);
@@ -328,7 +328,7 @@ class ShellExecutor {
const commandIndex = randomInt(10000);
this.log.debug(`getRemoteFileContent(${commandIndex}): filePath: ${filePath}`);
const deferred: Deferred = new Deferred();
- this.sshClient.sftp((err: Error, sftp: SFTPWrapper) => {
+ this.sshClient.sftp((err: Error | undefined, sftp: SFTPWrapper) => {
if (err !== undefined && err !== null) {
this.log.error(`getRemoteFileContent(${commandIndex}) sftp: ${err}`);
deferred.reject(new Error(`SFTP error: ${err}`));
@@ -376,7 +376,7 @@ class ShellExecutor {
// Windows always uses shell, and it needs to disable to get it works.
useShell = useShell && !this.isWindows;
- const callback = (err: Error, channel: ClientChannel): void => {
+ const callback = (err: Error | undefined, channel: ClientChannel): void => {
if (err !== undefined && err !== null) {
this.log.error(`remoteExeCommand(${commandIndex}): ${err.message}`);
deferred.reject(err);
diff --git a/ts/nni_manager/training_service/reusable/channels/webCommandChannel.ts b/ts/nni_manager/training_service/reusable/channels/webCommandChannel.ts
index f292b5451a..ed55885fe6 100644
--- a/ts/nni_manager/training_service/reusable/channels/webCommandChannel.ts
+++ b/ts/nni_manager/training_service/reusable/channels/webCommandChannel.ts
@@ -61,7 +61,7 @@ export class WebCommandChannel extends CommandChannel {
this.webSocketServer.on('connection', (client: WebSocket) => {
this.log.debug(`WebCommandChannel: received connection`);
client.onerror = (event): void => {
- this.log.error(`error on client ${JSON.stringify(event)}`);
+ this.log.error('error on client', event);
}
this.clients.set(client, undefined);
@@ -109,7 +109,7 @@ export class WebCommandChannel extends CommandChannel {
// undefined means it's expecting initializing message.
const commands = this.parseCommands(rawCommands);
let isValid = false;
- this.log.debug(`WebCommandChannel: received initialize message: ${JSON.stringify(rawCommands)}`);
+ this.log.debug('WebCommandChannel: received initialize message:', rawCommands);
if (commands.length > 0) {
const commandType = commands[0][0];
diff --git a/ts/nni_manager/training_service/reusable/commandChannel.ts b/ts/nni_manager/training_service/reusable/commandChannel.ts
index 1cf836f093..a16d0e27aa 100644
--- a/ts/nni_manager/training_service/reusable/commandChannel.ts
+++ b/ts/nni_manager/training_service/reusable/commandChannel.ts
@@ -50,7 +50,7 @@ export abstract class CommandChannel {
private readonly commandPattern: RegExp = /(?[\w]{2})(?[\d]{14})(?.*)\n?/gm;
public constructor(commandEmitter: EventEmitter) {
- this.log = getLogger();
+ this.log = getLogger('CommandChannel');
this.commandEmitter = commandEmitter;
}
diff --git a/ts/nni_manager/training_service/reusable/environment.ts b/ts/nni_manager/training_service/reusable/environment.ts
index 7c399c2a06..c89bc5edd6 100644
--- a/ts/nni_manager/training_service/reusable/environment.ts
+++ b/ts/nni_manager/training_service/reusable/environment.ts
@@ -82,7 +82,7 @@ export class EnvironmentInformation {
public useSharedStorage?: boolean;
constructor(id: string, name: string, envId?: string) {
- this.log = getLogger();
+ this.log = getLogger('EnvironmentInformation');
this.id = id;
this.name = name;
this.envId = envId ? envId : name;
@@ -116,7 +116,7 @@ export class EnvironmentInformation {
const gpuSummary = this.gpuSummaries.get(this.defaultNodeId);
if (gpuSummary === undefined) {
if (false === this.isNoGpuWarned) {
- this.log.warning(`EnvironmentInformation: ${this.envId} no default gpu found. current gpu info ${JSON.stringify(this.gpuSummaries)}`);
+ this.log.warning(`EnvironmentInformation: ${this.envId} no default gpu found. current gpu info`, this.gpuSummaries);
this.isNoGpuWarned = true;
}
} else {
diff --git a/ts/nni_manager/training_service/reusable/environments/amlEnvironmentService.ts b/ts/nni_manager/training_service/reusable/environments/amlEnvironmentService.ts
index 2cecfc63d5..58d25e144e 100644
--- a/ts/nni_manager/training_service/reusable/environments/amlEnvironmentService.ts
+++ b/ts/nni_manager/training_service/reusable/environments/amlEnvironmentService.ts
@@ -6,10 +6,9 @@
import * as fs from 'fs';
import * as path from 'path';
import * as component from '../../../common/component';
-import { getExperimentId } from '../../../common/experimentStartupInfo';
import { getLogger, Logger } from '../../../common/log';
-import { getExperimentRootDir } from '../../../common/utils';
import { ExperimentConfig, AmlConfig, flattenConfig } from '../../../common/experimentConfig';
+import { ExperimentStartupInfo } from '../../../common/experimentStartupInfo';
import { validateCodeDir } from '../../common/util';
import { AMLClient } from '../aml/amlClient';
import { AMLEnvironmentInformation } from '../aml/amlConfig';
@@ -26,15 +25,15 @@ interface FlattenAmlConfig extends ExperimentConfig, AmlConfig { }
@component.Singleton
export class AMLEnvironmentService extends EnvironmentService {
- private readonly log: Logger = getLogger();
+ private readonly log: Logger = getLogger('AMLEnvironmentService');
private experimentId: string;
private experimentRootDir: string;
private config: FlattenAmlConfig;
- constructor(config: ExperimentConfig) {
+ constructor(config: ExperimentConfig, info: ExperimentStartupInfo) {
super();
- this.experimentId = getExperimentId();
- this.experimentRootDir = getExperimentRootDir();
+ this.experimentId = info.experimentId;
+ this.experimentRootDir = info.logDir;
this.config = flattenConfig(config, 'aml');
validateCodeDir(this.config.trialCodeDirectory);
}
diff --git a/ts/nni_manager/training_service/reusable/environments/environmentServiceFactory.ts b/ts/nni_manager/training_service/reusable/environments/environmentServiceFactory.ts
index 1a32407bd4..24d7b8c088 100644
--- a/ts/nni_manager/training_service/reusable/environments/environmentServiceFactory.ts
+++ b/ts/nni_manager/training_service/reusable/environments/environmentServiceFactory.ts
@@ -4,20 +4,29 @@ import { LocalEnvironmentService } from './localEnvironmentService';
import { RemoteEnvironmentService } from './remoteEnvironmentService';
import { EnvironmentService } from '../environment';
import { ExperimentConfig } from '../../../common/experimentConfig';
+import { ExperimentStartupInfo } from '../../../common/experimentStartupInfo';
+import { getCustomEnvironmentServiceConfig } from '../../../common/nniConfig';
+import { importModule } from '../../../common/utils';
-export class EnvironmentServiceFactory {
- public static createEnvironmentService(name: string, config: ExperimentConfig): EnvironmentService {
- switch(name) {
- case 'local':
- return new LocalEnvironmentService(config);
- case 'remote':
- return new RemoteEnvironmentService(config);
- case 'aml':
- return new AMLEnvironmentService(config);
- case 'openpai':
- return new OpenPaiEnvironmentService(config);
- default:
- throw new Error(`${name} not supported!`);
- }
+export async function createEnvironmentService(name: string, config: ExperimentConfig): Promise {
+ const info = ExperimentStartupInfo.getInstance();
+
+ switch(name) {
+ case 'local':
+ return new LocalEnvironmentService(config, info);
+ case 'remote':
+ return new RemoteEnvironmentService(config, info);
+ case 'aml':
+ return new AMLEnvironmentService(config, info);
+ case 'openpai':
+ return new OpenPaiEnvironmentService(config, info);
+ }
+
+ const esConfig = await getCustomEnvironmentServiceConfig(name);
+ if (esConfig === null) {
+ throw new Error(`${name} is not a supported training service!`);
}
+ const esModule = importModule(esConfig.nodeModulePath);
+ const esClass = esModule[esConfig.nodeClassName] as any;
+ return new esClass(config, info);
}
diff --git a/ts/nni_manager/training_service/reusable/environments/localEnvironmentService.ts b/ts/nni_manager/training_service/reusable/environments/localEnvironmentService.ts
index b44aa03c6d..f75a14d805 100644
--- a/ts/nni_manager/training_service/reusable/environments/localEnvironmentService.ts
+++ b/ts/nni_manager/training_service/reusable/environments/localEnvironmentService.ts
@@ -7,25 +7,25 @@ import * as fs from 'fs';
import * as path from 'path';
import * as tkill from 'tree-kill';
import * as component from '../../../common/component';
-import { getExperimentId } from '../../../common/experimentStartupInfo';
import { getLogger, Logger } from '../../../common/log';
import { ExperimentConfig } from '../../../common/experimentConfig';
+import { ExperimentStartupInfo } from '../../../common/experimentStartupInfo';
import { EnvironmentInformation, EnvironmentService } from '../environment';
-import { getExperimentRootDir, isAlive, getNewLine } from '../../../common/utils';
+import { isAlive, getNewLine } from '../../../common/utils';
import { execMkdir, runScript, getScriptName, execCopydir } from '../../common/util';
import { SharedStorageService } from '../sharedStorage'
@component.Singleton
export class LocalEnvironmentService extends EnvironmentService {
- private readonly log: Logger = getLogger();
+ private readonly log: Logger = getLogger('LocalEnvironmentService');
private experimentRootDir: string;
private experimentId: string;
- constructor(_config: ExperimentConfig) {
+ constructor(_config: ExperimentConfig, info: ExperimentStartupInfo) {
super();
- this.experimentId = getExperimentId();
- this.experimentRootDir = getExperimentRootDir();
+ this.experimentId = info.experimentId;
+ this.experimentRootDir = info.logDir;
}
public get environmentMaintenceLoopInterval(): number {
@@ -110,8 +110,6 @@ export class LocalEnvironmentService extends EnvironmentService {
const sharedStorageService = component.get(SharedStorageService);
if (environment.useSharedStorage && sharedStorageService.canLocalMounted) {
this.experimentRootDir = sharedStorageService.localWorkingRoot;
- } else {
- this.experimentRootDir = getExperimentRootDir();
}
const localEnvCodeFolder: string = path.join(this.experimentRootDir, "envs");
if (environment.useSharedStorage && !sharedStorageService.canLocalMounted) {
diff --git a/ts/nni_manager/training_service/reusable/environments/openPaiEnvironmentService.ts b/ts/nni_manager/training_service/reusable/environments/openPaiEnvironmentService.ts
index e4c85a87ad..222b0b9e53 100644
--- a/ts/nni_manager/training_service/reusable/environments/openPaiEnvironmentService.ts
+++ b/ts/nni_manager/training_service/reusable/environments/openPaiEnvironmentService.ts
@@ -7,8 +7,8 @@ import * as yaml from 'js-yaml';
import * as request from 'request';
import { Deferred } from 'ts-deferred';
import * as component from '../../../common/component';
-import { getExperimentId } from '../../../common/experimentStartupInfo';
import { ExperimentConfig, OpenpaiConfig, flattenConfig, toMegaBytes } from '../../../common/experimentConfig';
+import { ExperimentStartupInfo } from '../../../common/experimentStartupInfo';
import { getLogger, Logger } from '../../../common/log';
import { PAIClusterConfig } from '../../pai/paiConfig';
import { NNIPAITrialConfig } from '../../pai/paiConfig';
@@ -24,7 +24,7 @@ interface FlattenOpenpaiConfig extends ExperimentConfig, OpenpaiConfig { }
@component.Singleton
export class OpenPaiEnvironmentService extends EnvironmentService {
- private readonly log: Logger = getLogger();
+ private readonly log: Logger = getLogger('OpenPaiEnvironmentService');
private paiClusterConfig: PAIClusterConfig | undefined;
private paiTrialConfig: NNIPAITrialConfig | undefined;
private paiToken: string;
@@ -32,9 +32,9 @@ export class OpenPaiEnvironmentService extends EnvironmentService {
private experimentId: string;
private config: FlattenOpenpaiConfig;
- constructor(config: ExperimentConfig) {
+ constructor(config: ExperimentConfig, info: ExperimentStartupInfo) {
super();
- this.experimentId = getExperimentId();
+ this.experimentId = info.experimentId;
this.config = flattenConfig(config, 'openpai');
this.paiToken = this.config.token;
this.protocol = this.config.host.toLowerCase().startsWith('https://') ? 'https' : 'http';
@@ -78,7 +78,7 @@ export class OpenPaiEnvironmentService extends EnvironmentService {
// Status code 200 for success
if ((error !== undefined && error !== null) || response.statusCode >= 400) {
const errorMessage: string = (error !== undefined && error !== null) ? error.message :
- `OpenPAI: get environment list from PAI Cluster failed!, http code:${response.statusCode}, http body: ${JSON.stringify(body)}`;
+ `OpenPAI: get environment list from PAI Cluster failed!, http code:${response.statusCode}, http body:' ${JSON.stringify(body)}`;
this.log.error(`${errorMessage}`);
deferred.reject(errorMessage);
} else {
@@ -114,7 +114,7 @@ export class OpenPaiEnvironmentService extends EnvironmentService {
this.log.debug(`OpenPAI: job ${environment.envId} change status ${oldEnvironmentStatus} to ${environment.status} due to job is ${jobResponse.state}.`)
}
} else {
- this.log.error(`OpenPAI: job ${environment.envId} has no state returned. body:${JSON.stringify(jobResponse)}`);
+ this.log.error(`OpenPAI: job ${environment.envId} has no state returned. body:`, jobResponse);
// some error happens, and mark this environment
environment.status = 'FAILED';
}
@@ -310,7 +310,7 @@ export class OpenPaiEnvironmentService extends EnvironmentService {
}
}
}
- return yaml.safeDump(nniJobConfig);
+ return yaml.dump(nniJobConfig);
}
protected formatPAIHost(host: string): string {
diff --git a/ts/nni_manager/training_service/reusable/environments/remoteEnvironmentService.ts b/ts/nni_manager/training_service/reusable/environments/remoteEnvironmentService.ts
index 193e38f5d6..5add571275 100644
--- a/ts/nni_manager/training_service/reusable/environments/remoteEnvironmentService.ts
+++ b/ts/nni_manager/training_service/reusable/environments/remoteEnvironmentService.ts
@@ -6,11 +6,11 @@
import * as fs from 'fs';
import * as path from 'path';
import * as component from '../../../common/component';
-import { getExperimentId } from '../../../common/experimentStartupInfo';
import { getLogger, Logger } from '../../../common/log';
import { EnvironmentInformation, EnvironmentService } from '../environment';
-import { getExperimentRootDir, getLogLevel } from '../../../common/utils';
+import { getLogLevel } from '../../../common/utils';
import { ExperimentConfig, RemoteConfig, RemoteMachineConfig, flattenConfig } from '../../../common/experimentConfig';
+import { ExperimentStartupInfo } from '../../../common/experimentStartupInfo';
import { execMkdir } from '../../common/util';
import { ExecutorManager } from '../../remote_machine/remoteMachineData';
import { ShellExecutor } from 'training_service/remote_machine/shellExecutor';
@@ -33,15 +33,14 @@ export class RemoteEnvironmentService extends EnvironmentService {
private experimentId: string;
private config: FlattenRemoteConfig;
- constructor(config: ExperimentConfig) {
+ constructor(config: ExperimentConfig, info: ExperimentStartupInfo) {
super();
- this.experimentId = getExperimentId();
+ this.experimentId = info.experimentId;
this.environmentExecutorManagerMap = new Map();
this.machineExecutorManagerMap = new Map();
this.remoteMachineMetaOccupiedMap = new Map();
- this.experimentRootDir = getExperimentRootDir();
- this.experimentId = getExperimentId();
- this.log = getLogger();
+ this.experimentRootDir = info.logDir;
+ this.log = getLogger('RemoteEnvironmentService');
this.config = flattenConfig(config, 'remote');
// codeDir is not a valid directory, throw Error
@@ -103,10 +102,10 @@ export class RemoteEnvironmentService extends EnvironmentService {
// Create root working directory after executor is ready
const nniRootDir: string = executor.joinPath(executor.getTempPath(), 'nni-experiments');
- await executor.createFolder(executor.getRemoteExperimentRootDir(getExperimentId()));
+ await executor.createFolder(executor.getRemoteExperimentRootDir(this.experimentId));
// the directory to store temp scripts in remote machine
- const remoteGpuScriptCollectorDir: string = executor.getRemoteScriptsPath(getExperimentId());
+ const remoteGpuScriptCollectorDir: string = executor.getRemoteScriptsPath(this.experimentId);
// clean up previous result.
await executor.createFolder(remoteGpuScriptCollectorDir, true);
@@ -245,7 +244,7 @@ export class RemoteEnvironmentService extends EnvironmentService {
throw new Error(`Mount shared storage on remote machine failed.\n ERROR: ${result.stderr}`);
}
} else {
- this.remoteExperimentRootDir = executor.getRemoteExperimentRootDir(getExperimentId());
+ this.remoteExperimentRootDir = executor.getRemoteExperimentRootDir(this.experimentId);
}
environment.command = await this.getScript(environment);
diff --git a/ts/nni_manager/training_service/reusable/gpuScheduler.ts b/ts/nni_manager/training_service/reusable/gpuScheduler.ts
index 86f590746d..2e94c29a58 100644
--- a/ts/nni_manager/training_service/reusable/gpuScheduler.ts
+++ b/ts/nni_manager/training_service/reusable/gpuScheduler.ts
@@ -29,7 +29,7 @@ export type GpuScheduleResult = {
export class GpuScheduler {
// private readonly machineExecutorMap: Set;
- private readonly log: Logger = getLogger();
+ private readonly log: Logger = getLogger('GpuScheduler');
private readonly policyName: SCHEDULE_POLICY_NAME = 'recently-idle';
private defaultSetting: GpuSchedulerSetting;
private roundRobinIndex: number = 0;
diff --git a/ts/nni_manager/training_service/reusable/routerTrainingService.ts b/ts/nni_manager/training_service/reusable/routerTrainingService.ts
index ab8691a577..bc9f413d05 100644
--- a/ts/nni_manager/training_service/reusable/routerTrainingService.ts
+++ b/ts/nni_manager/training_service/reusable/routerTrainingService.ts
@@ -3,7 +3,6 @@
'use strict';
-import * as component from '../../common/component';
import { getLogger, Logger } from '../../common/log';
import { MethodNotImplementedError } from '../../common/errors';
import { ExperimentConfig, RemoteConfig, OpenpaiConfig } from '../../common/experimentConfig';
@@ -18,23 +17,27 @@ import { TrialDispatcher } from './trialDispatcher';
* It's a intermedia implementation to support reusable training service.
* The final goal is to support reusable training job in higher level than training service.
*/
-@component.Singleton
class RouterTrainingService implements TrainingService {
- protected readonly log: Logger;
- private internalTrainingService: TrainingService;
+ private log!: Logger;
+ private internalTrainingService!: TrainingService;
- constructor(config: ExperimentConfig) {
- this.log = getLogger();
+ public static async construct(config: ExperimentConfig): Promise {
+ const instance = new RouterTrainingService();
+ instance.log = getLogger('RouterTrainingService');
const platform = Array.isArray(config.trainingService) ? 'hybrid' : config.trainingService.platform;
if (platform === 'remote' && !(config.trainingService).reuseMode) {
- this.internalTrainingService = new RemoteMachineTrainingService(config);
+ instance.internalTrainingService = new RemoteMachineTrainingService(config);
} else if (platform === 'openpai' && !(config.trainingService).reuseMode) {
- this.internalTrainingService = new PAITrainingService(config);
+ instance.internalTrainingService = new PAITrainingService(config);
} else {
- this.internalTrainingService = new TrialDispatcher(config);
+ instance.internalTrainingService = await TrialDispatcher.construct(config);
}
+ return instance;
}
+ // eslint-disable-next-line @typescript-eslint/no-empty-function
+ private constructor() { }
+
public async listTrialJobs(): Promise {
if (this.internalTrainingService === undefined) {
throw new Error("TrainingService is not assigned!");
diff --git a/ts/nni_manager/training_service/reusable/shared_storages/azureblobStorageService.ts b/ts/nni_manager/training_service/reusable/shared_storages/azureblobStorageService.ts
index 46165a307b..7a0185f861 100644
--- a/ts/nni_manager/training_service/reusable/shared_storages/azureblobStorageService.ts
+++ b/ts/nni_manager/training_service/reusable/shared_storages/azureblobStorageService.ts
@@ -66,7 +66,7 @@ export class AzureBlobSharedStorageService extends SharedStorageService {
constructor() {
super();
- this.log = getLogger();
+ this.log = getLogger('AzureBlobSharedStorageService');
this.internalStorageService = new MountedStorageService();
this.experimentId = getExperimentId();
}
diff --git a/ts/nni_manager/training_service/reusable/shared_storages/nfsStorageService.ts b/ts/nni_manager/training_service/reusable/shared_storages/nfsStorageService.ts
index 212ea837d9..db7687196b 100644
--- a/ts/nni_manager/training_service/reusable/shared_storages/nfsStorageService.ts
+++ b/ts/nni_manager/training_service/reusable/shared_storages/nfsStorageService.ts
@@ -50,7 +50,7 @@ export class NFSSharedStorageService extends SharedStorageService {
constructor() {
super();
- this.log = getLogger();
+ this.log = getLogger('NFSSharedStorageService');
this.internalStorageService = new MountedStorageService();
this.experimentId = getExperimentId();
}
diff --git a/ts/nni_manager/training_service/reusable/storageService.ts b/ts/nni_manager/training_service/reusable/storageService.ts
index ec54a6792a..78bcad9d1b 100644
--- a/ts/nni_manager/training_service/reusable/storageService.ts
+++ b/ts/nni_manager/training_service/reusable/storageService.ts
@@ -16,21 +16,21 @@ export abstract class StorageService {
protected logger: Logger;
protected abstract internalConfig(key: string, value: string): void;
- protected abstract async internalRemove(remotePath: string, isDirectory: boolean, isRecursive: boolean): Promise;
- protected abstract async internalRename(remotePath: string, newName: string): Promise;
- protected abstract async internalMkdir(remotePath: string): Promise;
- protected abstract async internalCopy(sourcePath: string, targetPath: string, isDirectory: boolean, isFromRemote: boolean, isToRemote: boolean): Promise;
- protected abstract async internalExists(remotePath: string): Promise;
- protected abstract async internalRead(remotePath: string, offset: number, length: number): Promise;
- protected abstract async internalList(remotePath: string): Promise;
- protected abstract async internalAttach(remotePath: string, content: string): Promise;
+ protected abstract internalRemove(remotePath: string, isDirectory: boolean, isRecursive: boolean): Promise;
+ protected abstract internalRename(remotePath: string, newName: string): Promise;
+ protected abstract internalMkdir(remotePath: string): Promise;
+ protected abstract internalCopy(sourcePath: string, targetPath: string, isDirectory: boolean, isFromRemote: boolean, isToRemote: boolean): Promise;
+ protected abstract internalExists(remotePath: string): Promise;
+ protected abstract internalRead(remotePath: string, offset: number, length: number): Promise;
+ protected abstract internalList(remotePath: string): Promise;
+ protected abstract internalAttach(remotePath: string, content: string): Promise;
protected abstract internalIsRelativePath(path: string): boolean;
protected abstract internalJoin(...paths: string[]): string;
protected abstract internalDirname(...paths: string[]): string;
protected abstract internalBasename(...paths: string[]): string;
constructor() {
- this.logger = getLogger();
+ this.logger = getLogger('StorageService');
}
public initialize(localRoot: string, remoteRoot: string): void {
diff --git a/ts/nni_manager/training_service/reusable/test/trialDispatcher.test.ts b/ts/nni_manager/training_service/reusable/test/trialDispatcher.test.ts
index b2a166f017..bf711cc9c0 100644
--- a/ts/nni_manager/training_service/reusable/test/trialDispatcher.test.ts
+++ b/ts/nni_manager/training_service/reusable/test/trialDispatcher.test.ts
@@ -203,7 +203,7 @@ describe('Unit Test for TrialDispatcher', () => {
});
beforeEach(async () => {
- trialDispatcher = new TrialDispatcher(config);
+ trialDispatcher = await TrialDispatcher.construct(config);
// set ut environment
let environmentServiceList: EnvironmentService[] = [];
diff --git a/ts/nni_manager/training_service/reusable/trialDispatcher.ts b/ts/nni_manager/training_service/reusable/trialDispatcher.ts
index 40e98360de..576e6f0786 100644
--- a/ts/nni_manager/training_service/reusable/trialDispatcher.ts
+++ b/ts/nni_manager/training_service/reusable/trialDispatcher.ts
@@ -24,7 +24,7 @@ import { TrialConfig } from '../common/trialConfig';
import { validateCodeDir } from '../common/util';
import { Command, CommandChannel } from './commandChannel';
import { EnvironmentInformation, EnvironmentService, NodeInformation, RunnerSettings, TrialGpuSummary } from './environment';
-import { EnvironmentServiceFactory } from './environments/environmentServiceFactory';
+import { createEnvironmentService } from './environments/environmentServiceFactory';
import { GpuScheduler } from './gpuScheduler';
import { MountedStorageService } from './storages/mountedStorageService';
import { StorageService } from './storageService';
@@ -39,20 +39,20 @@ import { TrialDetail } from './trial';
**/
@component.Singleton
class TrialDispatcher implements TrainingService {
- private readonly log: Logger;
- private readonly isDeveloping: boolean = false;
+ private log: Logger;
+ private isDeveloping: boolean = false;
private stopping: boolean = false;
- private readonly metricsEmitter: EventEmitter;
- private readonly experimentId: string;
- private readonly experimentRootDir: string;
+ private metricsEmitter: EventEmitter;
+ private experimentId: string;
+ private experimentRootDir: string;
private enableVersionCheck: boolean = true;
private trialConfig: TrialConfig | undefined;
- private readonly trials: Map;
- private readonly environments: Map;
+ private trials: Map;
+ private environments: Map;
// make public for ut
public environmentServiceList: EnvironmentService[] = [];
public commandChannelSet: Set;
@@ -82,8 +82,14 @@ class TrialDispatcher implements TrainingService {
private config: ExperimentConfig;
- constructor(config: ExperimentConfig) {
- this.log = getLogger();
+ public static async construct(config: ExperimentConfig): Promise {
+ const instance = new TrialDispatcher(config);
+ await instance.asyncConstructor(config);
+ return instance;
+ }
+
+ private constructor(config: ExperimentConfig) {
+ this.log = getLogger('TrialDispatcher');
this.trials = new Map();
this.environments = new Map();
this.metricsEmitter = new EventEmitter();
@@ -109,18 +115,14 @@ class TrialDispatcher implements TrainingService {
if (this.enableGpuScheduler) {
this.log.info(`TrialDispatcher: GPU scheduler is enabled.`)
}
+ }
- validateCodeDir(config.trialCodeDirectory);
+ private async asyncConstructor(config: ExperimentConfig): Promise {
+ await validateCodeDir(config.trialCodeDirectory);
- if (Array.isArray(config.trainingService)) {
- config.trainingService.forEach(trainingService => {
- const env = EnvironmentServiceFactory.createEnvironmentService(trainingService.platform, config);
- this.environmentServiceList.push(env);
- });
- } else {
- const env = EnvironmentServiceFactory.createEnvironmentService(config.trainingService.platform, config);
- this.environmentServiceList.push(env);
- }
+ const serviceConfigs = Array.isArray(config.trainingService) ? config.trainingService : [ config.trainingService ];
+ const servicePromises = serviceConfigs.map(serviceConfig => createEnvironmentService(serviceConfig.platform, config));
+ this.environmentServiceList = await Promise.all(servicePromises);
this.environmentMaintenceLoopInterval = Math.max(
...this.environmentServiceList.map((env) => env.environmentMaintenceLoopInterval)
@@ -132,7 +134,7 @@ class TrialDispatcher implements TrainingService {
}
if (this.config.sharedStorage !== undefined) {
- this.initializeSharedStorage(this.config.sharedStorage);
+ await this.initializeSharedStorage(this.config.sharedStorage);
}
}
diff --git a/ts/nni_manager/yarn.lock b/ts/nni_manager/yarn.lock
index 4681e5c278..b805155705 100644
--- a/ts/nni_manager/yarn.lock
+++ b/ts/nni_manager/yarn.lock
@@ -2,402 +2,668 @@
# yarn lockfile v1
-"@babel/code-frame@^7.0.0":
- version "7.0.0"
- resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.0.0.tgz#06e2ab19bdb535385559aabb5ba59729482800f8"
+"@babel/code-frame@7.12.11":
+ version "7.12.11"
+ resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.12.11.tgz#f4ad435aa263db935b8f10f2c552d23fb716a63f"
+ integrity sha512-Zt1yodBx1UcyiePMSkWnU4hPqhwq7hGi2nFL1LeA3EUl+q2LQx16MISgJ0+z7dnmgvP9QtIleuETGOiOH1RcIw==
dependencies:
- "@babel/highlight" "^7.0.0"
+ "@babel/highlight" "^7.10.4"
-"@babel/code-frame@^7.8.3":
- version "7.8.3"
- resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.8.3.tgz#33e25903d7481181534e12ec0a25f16b6fcf419e"
+"@babel/code-frame@^7.12.13":
+ version "7.12.13"
+ resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.12.13.tgz#dcfc826beef65e75c50e21d3837d7d95798dd658"
+ integrity sha512-HV1Cm0Q3ZrpCR93tkWOYiuYIgLxZXZFVG2VgK+MBWjUqZTundupbfx2aXarXuw5Ko5aMcjtJgbSs4vUGBS5v6g==
dependencies:
- "@babel/highlight" "^7.8.3"
+ "@babel/highlight" "^7.12.13"
+
+"@babel/compat-data@^7.14.4":
+ version "7.14.4"
+ resolved "https://registry.yarnpkg.com/@babel/compat-data/-/compat-data-7.14.4.tgz#45720fe0cecf3fd42019e1d12cc3d27fadc98d58"
+ integrity sha512-i2wXrWQNkH6JplJQGn3Rd2I4Pij8GdHkXwHMxm+zV5YG/Jci+bCNrWZEWC4o+umiDkRrRs4dVzH3X4GP7vyjQQ==
"@babel/core@^7.7.5":
- version "7.9.0"
- resolved "https://registry.yarnpkg.com/@babel/core/-/core-7.9.0.tgz#ac977b538b77e132ff706f3b8a4dbad09c03c56e"
- dependencies:
- "@babel/code-frame" "^7.8.3"
- "@babel/generator" "^7.9.0"
- "@babel/helper-module-transforms" "^7.9.0"
- "@babel/helpers" "^7.9.0"
- "@babel/parser" "^7.9.0"
- "@babel/template" "^7.8.6"
- "@babel/traverse" "^7.9.0"
- "@babel/types" "^7.9.0"
+ version "7.14.3"
+ resolved "https://registry.yarnpkg.com/@babel/core/-/core-7.14.3.tgz#5395e30405f0776067fbd9cf0884f15bfb770a38"
+ integrity sha512-jB5AmTKOCSJIZ72sd78ECEhuPiDMKlQdDI/4QRI6lzYATx5SSogS1oQA2AoPecRCknm30gHi2l+QVvNUu3wZAg==
+ dependencies:
+ "@babel/code-frame" "^7.12.13"
+ "@babel/generator" "^7.14.3"
+ "@babel/helper-compilation-targets" "^7.13.16"
+ "@babel/helper-module-transforms" "^7.14.2"
+ "@babel/helpers" "^7.14.0"
+ "@babel/parser" "^7.14.3"
+ "@babel/template" "^7.12.13"
+ "@babel/traverse" "^7.14.2"
+ "@babel/types" "^7.14.2"
convert-source-map "^1.7.0"
debug "^4.1.0"
- gensync "^1.0.0-beta.1"
+ gensync "^1.0.0-beta.2"
json5 "^2.1.2"
- lodash "^4.17.13"
- resolve "^1.3.2"
- semver "^5.4.1"
+ semver "^6.3.0"
source-map "^0.5.0"
-"@babel/generator@^7.9.0":
- version "7.9.4"
- resolved "https://registry.yarnpkg.com/@babel/generator/-/generator-7.9.4.tgz#12441e90c3b3c4159cdecf312075bf1a8ce2dbce"
+"@babel/generator@^7.14.2", "@babel/generator@^7.14.3":
+ version "7.14.3"
+ resolved "https://registry.yarnpkg.com/@babel/generator/-/generator-7.14.3.tgz#0c2652d91f7bddab7cccc6ba8157e4f40dcedb91"
+ integrity sha512-bn0S6flG/j0xtQdz3hsjJ624h3W0r3llttBMfyHX3YrZ/KtLYr15bjA0FXkgW7FpvrDuTuElXeVjiKlYRpnOFA==
dependencies:
- "@babel/types" "^7.9.0"
+ "@babel/types" "^7.14.2"
jsesc "^2.5.1"
- lodash "^4.17.13"
source-map "^0.5.0"
-"@babel/helper-function-name@^7.8.3":
- version "7.8.3"
- resolved "https://registry.yarnpkg.com/@babel/helper-function-name/-/helper-function-name-7.8.3.tgz#eeeb665a01b1f11068e9fb86ad56a1cb1a824cca"
+"@babel/helper-compilation-targets@^7.13.16":
+ version "7.14.4"
+ resolved "https://registry.yarnpkg.com/@babel/helper-compilation-targets/-/helper-compilation-targets-7.14.4.tgz#33ebd0ffc34248051ee2089350a929ab02f2a516"
+ integrity sha512-JgdzOYZ/qGaKTVkn5qEDV/SXAh8KcyUVkCoSWGN8T3bwrgd6m+/dJa2kVGi6RJYJgEYPBdZ84BZp9dUjNWkBaA==
dependencies:
- "@babel/helper-get-function-arity" "^7.8.3"
- "@babel/template" "^7.8.3"
- "@babel/types" "^7.8.3"
+ "@babel/compat-data" "^7.14.4"
+ "@babel/helper-validator-option" "^7.12.17"
+ browserslist "^4.16.6"
+ semver "^6.3.0"
-"@babel/helper-get-function-arity@^7.8.3":
- version "7.8.3"
- resolved "https://registry.yarnpkg.com/@babel/helper-get-function-arity/-/helper-get-function-arity-7.8.3.tgz#b894b947bd004381ce63ea1db9f08547e920abd5"
- dependencies:
- "@babel/types" "^7.8.3"
+"@babel/helper-function-name@^7.14.2":
+ version "7.14.2"
+ resolved "https://registry.yarnpkg.com/@babel/helper-function-name/-/helper-function-name-7.14.2.tgz#397688b590760b6ef7725b5f0860c82427ebaac2"
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+"@typescript-eslint/typescript-estree@4.26.0":
+ version "4.26.0"
+ resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-4.26.0.tgz#aea17a40e62dc31c63d5b1bbe9a75783f2ce7109"
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+ dependencies:
+ "@typescript-eslint/types" "4.26.0"
+ "@typescript-eslint/visitor-keys" "4.26.0"
+ debug "^4.3.1"
+ globby "^11.0.3"
+ is-glob "^4.0.1"
+ semver "^7.3.5"
+ tsutils "^3.21.0"
+
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+ version "4.26.0"
+ resolved "https://registry.yarnpkg.com/@typescript-eslint/visitor-keys/-/visitor-keys-4.26.0.tgz#26d2583169222815be4dcd1da4fe5459bc3bcc23"
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- jsonparse "^1.2.0"
- through ">=2.2.7 <3"
+ "@typescript-eslint/types" "4.26.0"
+ eslint-visitor-keys "^2.0.0"
+
+"@ungap/promise-all-settled@1.1.2":
+ version "1.1.2"
+ resolved "https://registry.yarnpkg.com/@ungap/promise-all-settled/-/promise-all-settled-1.1.2.tgz#aa58042711d6e3275dd37dc597e5d31e8c290a44"
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abbrev@1, abbrev@~1.1.1:
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resolved "https://registry.yarnpkg.com/abbrev/-/abbrev-1.1.1.tgz#f8f2c887ad10bf67f634f005b6987fed3179aac8"
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- version "1.3.5"
- resolved "https://registry.yarnpkg.com/accepts/-/accepts-1.3.5.tgz#eb777df6011723a3b14e8a72c0805c8e86746bd2"
+accepts@~1.3.7:
+ version "1.3.7"
+ resolved "https://registry.yarnpkg.com/accepts/-/accepts-1.3.7.tgz#531bc726517a3b2b41f850021c6cc15eaab507cd"
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dependencies:
- mime-types "~2.1.18"
- negotiator "0.6.1"
-
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- version "5.1.0"
- resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.1.0.tgz#294adb71b57398b0680015f0a38c563ee1db5384"
+ mime-types "~2.1.24"
+ negotiator "0.6.2"
-acorn@>=8.0.4, acorn@^7.1.0:
- version "8.0.4"
- resolved "https://registry.yarnpkg.com/acorn/-/acorn-8.0.4.tgz#7a3ae4191466a6984eee0fe3407a4f3aa9db8354"
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+acorn-jsx@^5.3.1:
+ version "5.3.1"
+ resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.3.1.tgz#fc8661e11b7ac1539c47dbfea2e72b3af34d267b"
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-agent-base@4, agent-base@^4.3.0:
- version "4.3.0"
- resolved "https://registry.yarnpkg.com/agent-base/-/agent-base-4.3.0.tgz#8165f01c436009bccad0b1d122f05ed770efc6ee"
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- dependencies:
- es6-promisify "^5.0.0"
+acorn@>=8.3.0, acorn@^7.4.0:
+ version "8.3.0"
+ resolved "https://registry.yarnpkg.com/acorn/-/acorn-8.3.0.tgz#1193f9b96c4e8232f00b11a9edff81b2c8b98b88"
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-agent-base@~4.2.1:
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+agent-base@6:
+ version "6.0.2"
+ resolved "https://registry.yarnpkg.com/agent-base/-/agent-base-6.0.2.tgz#49fff58577cfee3f37176feab4c22e00f86d7f77"
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dependencies:
- es6-promisify "^5.0.0"
+ debug "4"
-agentkeepalive@^3.4.1:
- version "3.5.2"
- resolved "https://registry.yarnpkg.com/agentkeepalive/-/agentkeepalive-3.5.2.tgz#a113924dd3fa24a0bc3b78108c450c2abee00f67"
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+agentkeepalive@^4.1.3:
+ version "4.1.4"
+ resolved "https://registry.yarnpkg.com/agentkeepalive/-/agentkeepalive-4.1.4.tgz#d928028a4862cb11718e55227872e842a44c945b"
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dependencies:
+ debug "^4.1.0"
+ depd "^1.1.2"
humanize-ms "^1.2.1"
aggregate-error@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/aggregate-error/-/aggregate-error-1.0.0.tgz#888344dad0220a72e3af50906117f48771925fac"
+ integrity sha1-iINE2tAiCnLjr1CQYRf0h3GSX6w=
dependencies:
clean-stack "^1.0.0"
indent-string "^3.0.0"
aggregate-error@^3.0.0:
- version "3.0.1"
- resolved "https://registry.yarnpkg.com/aggregate-error/-/aggregate-error-3.0.1.tgz#db2fe7246e536f40d9b5442a39e117d7dd6a24e0"
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/aggregate-error/-/aggregate-error-3.1.0.tgz#92670ff50f5359bdb7a3e0d40d0ec30c5737687a"
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dependencies:
clean-stack "^2.0.0"
indent-string "^4.0.0"
-ajv@^5.1.0, ajv@^5.3.0:
- version "5.5.2"
- resolved "https://registry.yarnpkg.com/ajv/-/ajv-5.5.2.tgz#73b5eeca3fab653e3d3f9422b341ad42205dc965"
+ajv@^6.10.0, ajv@^6.12.3, ajv@^6.12.4:
+ version "6.12.6"
+ resolved "https://registry.yarnpkg.com/ajv/-/ajv-6.12.6.tgz#baf5a62e802b07d977034586f8c3baf5adf26df4"
+ integrity sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g==
dependencies:
- co "^4.6.0"
- fast-deep-equal "^1.0.0"
+ fast-deep-equal "^3.1.1"
fast-json-stable-stringify "^2.0.0"
- json-schema-traverse "^0.3.0"
+ json-schema-traverse "^0.4.1"
+ uri-js "^4.2.2"
-ajv@^6.10.0, ajv@^6.10.2:
- version "6.10.2"
- resolved "https://registry.yarnpkg.com/ajv/-/ajv-6.10.2.tgz#d3cea04d6b017b2894ad69040fec8b623eb4bd52"
+ajv@^8.0.1:
+ version "8.6.0"
+ resolved "https://registry.yarnpkg.com/ajv/-/ajv-8.6.0.tgz#60cc45d9c46a477d80d92c48076d972c342e5720"
+ integrity sha512-cnUG4NSBiM4YFBxgZIj/In3/6KX+rQ2l2YPRVcvAMQGWEPKuXoPIhxzwqh31jA3IPbI4qEOp/5ILI4ynioXsGQ==
dependencies:
- fast-deep-equal "^2.0.1"
- fast-json-stable-stringify "^2.0.0"
- json-schema-traverse "^0.4.1"
+ fast-deep-equal "^3.1.1"
+ json-schema-traverse "^1.0.0"
+ require-from-string "^2.0.2"
uri-js "^4.2.2"
ansi-align@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/ansi-align/-/ansi-align-2.0.0.tgz#c36aeccba563b89ceb556f3690f0b1d9e3547f7f"
+ integrity sha1-w2rsy6VjuJzrVW82kPCx2eNUf38=
dependencies:
string-width "^2.0.0"
-ansi-colors@4.1.1:
+ansi-colors@4.1.1, ansi-colors@^4.1.1:
version "4.1.1"
resolved "https://registry.yarnpkg.com/ansi-colors/-/ansi-colors-4.1.1.tgz#cbb9ae256bf750af1eab344f229aa27fe94ba348"
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-ansi-escapes@^4.2.1:
- version "4.3.0"
- resolved "https://registry.yarnpkg.com/ansi-escapes/-/ansi-escapes-4.3.0.tgz#a4ce2b33d6b214b7950d8595c212f12ac9cc569d"
- dependencies:
- type-fest "^0.8.1"
-
ansi-regex@^2.0.0:
version "2.1.1"
resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-2.1.1.tgz#c3b33ab5ee360d86e0e628f0468ae7ef27d654df"
+ integrity sha1-w7M6te42DYbg5ijwRorn7yfWVN8=
ansi-regex@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-3.0.0.tgz#ed0317c322064f79466c02966bddb605ab37d998"
-
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- version "4.1.0"
- resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-4.1.0.tgz#8b9f8f08cf1acb843756a839ca8c7e3168c51997"
+ integrity sha1-7QMXwyIGT3lGbAKWa922Bas32Zg=
ansi-regex@^5.0.0:
version "5.0.0"
resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-5.0.0.tgz#388539f55179bf39339c81af30a654d69f87cb75"
+ integrity sha512-bY6fj56OUQ0hU1KjFNDQuJFezqKdrAyFdIevADiqrWHwSlbmBNMHp5ak2f40Pm8JTFyM2mqxkG6ngkHO11f/lg==
-ansi-styles@^3.2.0, ansi-styles@^3.2.1:
+ansi-styles@^3.2.1:
version "3.2.1"
resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-3.2.1.tgz#41fbb20243e50b12be0f04b8dedbf07520ce841d"
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color-convert "^1.9.0"
-ansi-styles@^4.0.0, ansi-styles@^4.1.0:
- version "4.2.1"
- resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-4.2.1.tgz#90ae75c424d008d2624c5bf29ead3177ebfcf359"
+ansi-styles@^4.0.0, ansi-styles@^4.1.0, ansi-styles@^4.3.0:
+ version "4.3.0"
+ resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-4.3.0.tgz#edd803628ae71c04c85ae7a0906edad34b648937"
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dependencies:
- "@types/color-name" "^1.1.1"
color-convert "^2.0.1"
ansicolors@~0.3.2:
version "0.3.2"
resolved "https://registry.yarnpkg.com/ansicolors/-/ansicolors-0.3.2.tgz#665597de86a9ffe3aa9bfbe6cae5c6ea426b4979"
+ integrity sha1-ZlWX3oap/+Oqm/vmyuXG6kJrSXk=
ansistyles@~0.1.3:
version "0.1.3"
resolved "https://registry.yarnpkg.com/ansistyles/-/ansistyles-0.1.3.tgz#5de60415bda071bb37127854c864f41b23254539"
+ integrity sha1-XeYEFb2gcbs3EnhUyGT0GyMlRTk=
anymatch@~3.1.1:
- version "3.1.1"
- resolved "https://registry.yarnpkg.com/anymatch/-/anymatch-3.1.1.tgz#c55ecf02185e2469259399310c173ce31233b142"
+ version "3.1.2"
+ resolved "https://registry.yarnpkg.com/anymatch/-/anymatch-3.1.2.tgz#c0557c096af32f106198f4f4e2a383537e378716"
+ integrity sha512-P43ePfOAIupkguHUycrc4qJ9kz8ZiuOUijaETwX7THt0Y/GNK7v0aa8rY816xWjZ7rJdA5XdMcpVFTKMq+RvWg==
dependencies:
normalize-path "^3.0.0"
picomatch "^2.0.4"
@@ -651,14 +959,16 @@ anymatch@~3.1.1:
append-transform@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/append-transform/-/append-transform-2.0.0.tgz#99d9d29c7b38391e6f428d28ce136551f0b77e12"
+ integrity sha512-7yeyCEurROLQJFv5Xj4lEGTy0borxepjFv1g22oAdqFu//SrAlDl1O1Nxx15SH1RoliUml6p8dwJW9jvZughhg==
dependencies:
default-require-extensions "^3.0.0"
-aproba@^1.0.3, aproba@^1.1.1, aproba@^1.1.2:
+aproba@^1.0.3:
version "1.2.0"
resolved "https://registry.yarnpkg.com/aproba/-/aproba-1.2.0.tgz#6802e6264efd18c790a1b0d517f0f2627bf2c94a"
+ integrity sha512-Y9J6ZjXtoYh8RnXVCMOU/ttDmk1aBjunq9vO0ta5x85WDQiQfUF9sIPBITdbiiIVcBo03Hi3jMxigBtsddlXRw==
-"aproba@^1.1.2 || 2", aproba@^2.0.0:
+aproba@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/aproba/-/aproba-2.0.0.tgz#52520b8ae5b569215b354efc0caa3fe1e45a8adc"
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@@ -666,91 +976,111 @@ aproba@^1.0.3, aproba@^1.1.1, aproba@^1.1.2:
archy@^1.0.0, archy@~1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/archy/-/archy-1.0.0.tgz#f9c8c13757cc1dd7bc379ac77b2c62a5c2868c40"
+ integrity sha1-+cjBN1fMHde8N5rHeyxipcKGjEA=
are-we-there-yet@~1.1.2:
version "1.1.5"
resolved "https://registry.yarnpkg.com/are-we-there-yet/-/are-we-there-yet-1.1.5.tgz#4b35c2944f062a8bfcda66410760350fe9ddfc21"
+ integrity sha512-5hYdAkZlcG8tOLujVDTgCT+uPX0VnpAH28gWsLfzpXYm7wP6mp5Q/gYyR7YQ0cKVJcXJnl3j2kpBan13PtQf6w==
dependencies:
delegates "^1.0.0"
readable-stream "^2.0.6"
+arg@^4.1.0:
+ version "4.1.3"
+ resolved "https://registry.yarnpkg.com/arg/-/arg-4.1.3.tgz#269fc7ad5b8e42cb63c896d5666017261c144089"
+ integrity sha512-58S9QDqG0Xx27YwPSt9fJxivjYl432YCwfDMfZ+71RAqUrZef7LrKQZ3LHLOwCS4FLNBplP533Zx895SeOCHvA==
+
argparse@^1.0.7:
version "1.0.10"
resolved "https://registry.yarnpkg.com/argparse/-/argparse-1.0.10.tgz#bcd6791ea5ae09725e17e5ad988134cd40b3d911"
+ integrity sha512-o5Roy6tNG4SL/FOkCAN6RzjiakZS25RLYFrcMttJqbdd8BWrnA+fGz57iN5Pb06pvBGvl5gQ0B48dJlslXvoTg==
dependencies:
sprintf-js "~1.0.2"
+argparse@^2.0.1:
+ version "2.0.1"
+ resolved "https://registry.yarnpkg.com/argparse/-/argparse-2.0.1.tgz#246f50f3ca78a3240f6c997e8a9bd1eac49e4b38"
+ integrity sha512-8+9WqebbFzpX9OR+Wa6O29asIogeRMzcGtAINdpMHHyAg10f05aSFVBbcEqGf/PXw1EjAZ+q2/bEBg3DvurK3Q==
+
+arr-union@^3.1.0:
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/arr-union/-/arr-union-3.1.0.tgz#e39b09aea9def866a8f206e288af63919bae39c4"
+ integrity sha1-45sJrqne+Gao8gbiiK9jkZuuOcQ=
+
array-flatten@1.1.1:
version "1.1.1"
resolved "https://registry.yarnpkg.com/array-flatten/-/array-flatten-1.1.1.tgz#9a5f699051b1e7073328f2a008968b64ea2955d2"
+ integrity sha1-ml9pkFGx5wczKPKgCJaLZOopVdI=
array-union@^1.0.1:
version "1.0.2"
resolved "https://registry.yarnpkg.com/array-union/-/array-union-1.0.2.tgz#9a34410e4f4e3da23dea375be5be70f24778ec39"
+ integrity sha1-mjRBDk9OPaI96jdb5b5w8kd47Dk=
dependencies:
array-uniq "^1.0.1"
+array-union@^2.1.0:
+ version "2.1.0"
+ resolved "https://registry.yarnpkg.com/array-union/-/array-union-2.1.0.tgz#b798420adbeb1de828d84acd8a2e23d3efe85e8d"
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+
array-uniq@^1.0.1:
version "1.0.3"
resolved "https://registry.yarnpkg.com/array-uniq/-/array-uniq-1.0.3.tgz#af6ac877a25cc7f74e058894753858dfdb24fdb6"
-
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- version "1.0.2"
- resolved "https://registry.yarnpkg.com/array.prototype.map/-/array.prototype.map-1.0.2.tgz#9a4159f416458a23e9483078de1106b2ef68f8ec"
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- dependencies:
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- es-abstract "^1.17.0-next.1"
- es-array-method-boxes-properly "^1.0.0"
- is-string "^1.0.4"
-
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- version "1.0.1"
- resolved "https://registry.yarnpkg.com/arrify/-/arrify-1.0.1.tgz#898508da2226f380df904728456849c1501a4b0d"
+ integrity sha1-r2rId6Jcx/dOBYiUdThY39sk/bY=
asap@^2.0.0:
version "2.0.6"
resolved "https://registry.yarnpkg.com/asap/-/asap-2.0.6.tgz#e50347611d7e690943208bbdafebcbc2fb866d46"
+ integrity sha1-5QNHYR1+aQlDIIu9r+vLwvuGbUY=
-asn1@~0.2.0, asn1@~0.2.3:
- version "0.2.3"
- resolved "https://registry.yarnpkg.com/asn1/-/asn1-0.2.3.tgz#dac8787713c9966849fc8180777ebe9c1ddf3b86"
+asn1@^0.2.4, asn1@~0.2.3:
+ version "0.2.4"
+ resolved "https://registry.yarnpkg.com/asn1/-/asn1-0.2.4.tgz#8d2475dfab553bb33e77b54e59e880bb8ce23136"
+ integrity sha512-jxwzQpLQjSmWXgwaCZE9Nz+glAG01yF1QnWgbhGwHI5A6FRIEY6IVqtHhIepHqI7/kyEyQEagBC5mBEFlIYvdg==
+ dependencies:
+ safer-buffer "~2.1.0"
assert-plus@1.0.0, assert-plus@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/assert-plus/-/assert-plus-1.0.0.tgz#f12e0f3c5d77b0b1cdd9146942e4e96c1e4dd525"
+ integrity sha1-8S4PPF13sLHN2RRpQuTpbB5N1SU=
-assertion-error@^1.0.1:
+assertion-error@^1.1.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/assertion-error/-/assertion-error-1.1.0.tgz#e60b6b0e8f301bd97e5375215bda406c85118c0b"
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-astral-regex@^1.0.0:
- version "1.0.0"
- resolved "https://registry.yarnpkg.com/astral-regex/-/astral-regex-1.0.0.tgz#6c8c3fb827dd43ee3918f27b82782ab7658a6fd9"
+astral-regex@^2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/astral-regex/-/astral-regex-2.0.0.tgz#483143c567aeed4785759c0865786dc77d7d2e31"
+ integrity sha512-Z7tMw1ytTXt5jqMcOP+OQteU1VuNK9Y02uuJtKQ1Sv69jXQKKg5cibLwGJow8yzZP+eAc18EmLGPal0bp36rvQ==
async-limiter@~1.0.0:
- version "1.0.0"
- resolved "https://registry.yarnpkg.com/async-limiter/-/async-limiter-1.0.0.tgz#78faed8c3d074ab81f22b4e985d79e8738f720f8"
+ version "1.0.1"
+ resolved "https://registry.yarnpkg.com/async-limiter/-/async-limiter-1.0.1.tgz#dd379e94f0db8310b08291f9d64c3209766617fd"
+ integrity sha512-csOlWGAcRFJaI6m+F2WKdnMKr4HhdhFVBk0H/QbJFMCr+uO2kwohwXQPxw/9OCxp05r5ghVBFSyioixx3gfkNQ==
asynckit@^0.4.0:
version "0.4.0"
resolved "https://registry.yarnpkg.com/asynckit/-/asynckit-0.4.0.tgz#c79ed97f7f34cb8f2ba1bc9790bcc366474b4b79"
+ integrity sha1-x57Zf380y48robyXkLzDZkdLS3k=
aws-sign2@~0.7.0:
version "0.7.0"
resolved "https://registry.yarnpkg.com/aws-sign2/-/aws-sign2-0.7.0.tgz#b46e890934a9591f2d2f6f86d7e6a9f1b3fe76a8"
-
-aws4@^1.6.0:
- version "1.7.0"
- resolved "https://registry.yarnpkg.com/aws4/-/aws4-1.7.0.tgz#d4d0e9b9dbfca77bf08eeb0a8a471550fe39e289"
+ integrity sha1-tG6JCTSpWR8tL2+G1+ap8bP+dqg=
aws4@^1.8.0:
- version "1.8.0"
- resolved "https://registry.yarnpkg.com/aws4/-/aws4-1.8.0.tgz#f0e003d9ca9e7f59c7a508945d7b2ef9a04a542f"
+ version "1.11.0"
+ resolved "https://registry.yarnpkg.com/aws4/-/aws4-1.11.0.tgz#d61f46d83b2519250e2784daf5b09479a8b41c59"
+ integrity sha512-xh1Rl34h6Fi1DC2WWKfxUTVqRsNnr6LsKz2+hfwDxQJWmrx8+c7ylaqBMcHfl1U1r2dsifOvKX3LQuLNZ+XSvA==
-azure-storage@^2.10.2:
- version "2.10.2"
- resolved "https://registry.yarnpkg.com/azure-storage/-/azure-storage-2.10.2.tgz#3bcabdbf10e72fd0990db81116e49023c4a675b6"
+azure-storage@^2.10.4:
+ version "2.10.4"
+ resolved "https://registry.yarnpkg.com/azure-storage/-/azure-storage-2.10.4.tgz#c481d207eabc05f57f019b209f7faa8737435104"
+ integrity sha512-zlfRPl4js92JC6+79C2EUmNGYjSknRl8pOiHQF78zy+pbOFOHtlBF6BU/OxPeHQX3gaa6NdEZnVydFxhhndkEw==
dependencies:
browserify-mime "~1.2.9"
extend "^3.0.2"
@@ -758,42 +1088,50 @@ azure-storage@^2.10.2:
md5.js "1.3.4"
readable-stream "~2.0.0"
request "^2.86.0"
- underscore "~1.8.3"
+ underscore "^1.12.1"
uuid "^3.0.0"
validator "~9.4.1"
xml2js "0.2.8"
xmlbuilder "^9.0.7"
balanced-match@^1.0.0:
- version "1.0.0"
- resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.0.tgz#89b4d199ab2bee49de164ea02b89ce462d71b767"
- integrity sha1-ibTRmasr7kneFk6gK4nORi1xt2c=
+ version "1.0.2"
+ resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
+ integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
+
+base64-js@^1.3.1:
+ version "1.5.1"
+ resolved "https://registry.yarnpkg.com/base64-js/-/base64-js-1.5.1.tgz#1b1b440160a5bf7ad40b650f095963481903930a"
+ integrity sha512-AKpaYlHn8t4SVbOHCy+b5+KKgvR4vrsD8vbvrbiQJps7fKDTkjkDry6ji0rUJjC0kzbNePLwzxq8iypo41qeWA==
-base64url@^3.0.0:
+base64url@^3.0.0, base64url@^3.0.1:
version "3.0.1"
resolved "https://registry.yarnpkg.com/base64url/-/base64url-3.0.1.tgz#6399d572e2bc3f90a9a8b22d5dbb0a32d33f788d"
+ integrity sha512-ir1UPr3dkwexU7FdV8qBBbNDRUhMmIekYMFZfi+C/sLNnRESKPl23nB9b2pltqfOQNnGzsDdId90AEtG5tCx4A==
bcrypt-pbkdf@^1.0.0, bcrypt-pbkdf@^1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/bcrypt-pbkdf/-/bcrypt-pbkdf-1.0.2.tgz#a4301d389b6a43f9b67ff3ca11a3f6637e360e9e"
+ integrity sha1-pDAdOJtqQ/m2f/PKEaP2Y342Dp4=
dependencies:
tweetnacl "^0.14.3"
-bin-links@^1.1.2, bin-links@^1.1.8:
- version "1.1.8"
- resolved "https://registry.yarnpkg.com/bin-links/-/bin-links-1.1.8.tgz#bd39aadab5dc4bdac222a07df5baf1af745b2228"
- integrity sha512-KgmVfx+QqggqP9dA3iIc5pA4T1qEEEL+hOhOhNPaUm77OTrJoOXE/C05SJLNJe6m/2wUK7F1tDSou7n5TfCDzQ==
+bin-links@^2.2.1:
+ version "2.2.1"
+ resolved "https://registry.yarnpkg.com/bin-links/-/bin-links-2.2.1.tgz#347d9dbb48f7d60e6c11fe68b77a424bee14d61b"
+ integrity sha512-wFzVTqavpgCCYAh8SVBdnZdiQMxTkGR+T3b14CNpBXIBe2neJWaMGAZ55XWWHELJJ89dscuq0VCBqcVaIOgCMg==
dependencies:
- bluebird "^3.5.3"
- cmd-shim "^3.0.0"
- gentle-fs "^2.3.0"
- graceful-fs "^4.1.15"
+ cmd-shim "^4.0.1"
+ mkdirp "^1.0.3"
npm-normalize-package-bin "^1.0.0"
- write-file-atomic "^2.3.0"
+ read-cmd-shim "^2.0.0"
+ rimraf "^3.0.0"
+ write-file-atomic "^3.0.3"
-binary-extensions@^2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.0.0.tgz#23c0df14f6a88077f5f986c0d167ec03c3d5537c"
+binary-extensions@^2.0.0, binary-extensions@^2.2.0:
+ version "2.2.0"
+ resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.2.0.tgz#75f502eeaf9ffde42fc98829645be4ea76bd9e2d"
+ integrity sha512-jDctJ/IVQbZoJykoeHbhXpOlNBqGNcwXJKJog42E5HDPUwQTSdjCHdihjj0DlnheQ7blbT6dHOafNAiS8ooQKA==
block-stream@*:
version "0.0.9"
@@ -802,35 +1140,33 @@ block-stream@*:
dependencies:
inherits "~2.0.0"
-bluebird@^3.5.1, bluebird@^3.5.3, bluebird@^3.5.5:
- version "3.7.2"
- resolved "https://registry.yarnpkg.com/bluebird/-/bluebird-3.7.2.tgz#9f229c15be272454ffa973ace0dbee79a1b0c36f"
- integrity sha512-XpNj6GDQzdfW+r2Wnn7xiSAd7TM3jzkxGXBGTtWKuSXv1xUV+azxAm8jdWZN06QTQk+2N2XB9jRDkvbmQmcRtg==
-
-body-parser@1.18.2:
- version "1.18.2"
- resolved "https://registry.yarnpkg.com/body-parser/-/body-parser-1.18.2.tgz#87678a19d84b47d859b83199bd59bce222b10454"
+body-parser@1.19.0:
+ version "1.19.0"
+ resolved "https://registry.yarnpkg.com/body-parser/-/body-parser-1.19.0.tgz#96b2709e57c9c4e09a6fd66a8fd979844f69f08a"
+ integrity sha512-dhEPs72UPbDnAQJ9ZKMNTP6ptJaionhP5cBb541nXPlW60Jepo9RV/a4fX4XWW9CuFNK22krhrj1+rgzifNCsw==
dependencies:
- bytes "3.0.0"
+ bytes "3.1.0"
content-type "~1.0.4"
debug "2.6.9"
- depd "~1.1.1"
- http-errors "~1.6.2"
- iconv-lite "0.4.19"
+ depd "~1.1.2"
+ http-errors "1.7.2"
+ iconv-lite "0.4.24"
on-finished "~2.3.0"
- qs "6.5.1"
- raw-body "2.3.2"
- type-is "~1.6.15"
+ qs "6.7.0"
+ raw-body "2.4.0"
+ type-is "~1.6.17"
boom@2.6.x:
version "2.6.1"
resolved "https://registry.yarnpkg.com/boom/-/boom-2.6.1.tgz#4dc8ef9b6dfad9c43bbbfbe71fa4c21419f22753"
+ integrity sha1-Tcjvm2362cQ7u/vnH6TCFBnyJ1M=
dependencies:
hoek "2.x.x"
boxen@^1.2.1:
version "1.3.0"
resolved "https://registry.yarnpkg.com/boxen/-/boxen-1.3.0.tgz#55c6c39a8ba58d9c61ad22cd877532deb665a20b"
+ integrity sha512-TNPjfTr432qx7yOjQyaXm3dSR0MH9vXp7eT1BFSl/C51g+EFnOR9hTg1IreahGBmDNCehscshe45f+C1TBZbLw==
dependencies:
ansi-align "^2.0.0"
camelcase "^4.0.0"
@@ -848,72 +1184,96 @@ brace-expansion@^1.1.7:
balanced-match "^1.0.0"
concat-map "0.0.1"
-braces@~3.0.2:
+braces@^3.0.1, braces@~3.0.2:
version "3.0.2"
resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.2.tgz#3454e1a462ee8d599e236df336cd9ea4f8afe107"
+ integrity sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==
dependencies:
fill-range "^7.0.1"
browser-stdout@1.3.1:
version "1.3.1"
resolved "https://registry.yarnpkg.com/browser-stdout/-/browser-stdout-1.3.1.tgz#baa559ee14ced73452229bad7326467c61fabd60"
+ integrity sha512-qhAVI1+Av2X7qelOfAIYwXONood6XlZE/fXaBSmW/T5SzLAmCgzi+eiWE7fUvbHaeNBQH13UftjpXxsfLkMpgw==
browserify-mime@~1.2.9:
version "1.2.9"
resolved "https://registry.yarnpkg.com/browserify-mime/-/browserify-mime-1.2.9.tgz#aeb1af28de6c0d7a6a2ce40adb68ff18422af31f"
+ integrity sha1-rrGvKN5sDXpqLOQK22j/GEIq8x8=
+
+browserify-zlib@^0.2.0:
+ version "0.2.0"
+ resolved "https://registry.yarnpkg.com/browserify-zlib/-/browserify-zlib-0.2.0.tgz#2869459d9aa3be245fe8fe2ca1f46e2e7f54d73f"
+ integrity sha512-Z942RysHXmJrhqk88FmKBVq/v5tqmSkDz7p54G/MGyjMnCFFnC79XWNbg+Vta8W6Wb2qtSZTSxIGkJrRpCFEiA==
+ dependencies:
+ pako "~1.0.5"
+
+browserslist@^4.16.6:
+ version "4.16.6"
+ resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.16.6.tgz#d7901277a5a88e554ed305b183ec9b0c08f66fa2"
+ integrity sha512-Wspk/PqO+4W9qp5iUTJsa1B/QrYn1keNCcEP5OvP7WBwT4KaDly0uONYmC6Xa3Z5IqnUgS0KcgLYu1l74x0ZXQ==
+ dependencies:
+ caniuse-lite "^1.0.30001219"
+ colorette "^1.2.2"
+ electron-to-chromium "^1.3.723"
+ escalade "^3.1.1"
+ node-releases "^1.1.71"
-buffer-from@^1.0.0, buffer-from@^1.1.0:
+buffer-from@^1.0.0:
version "1.1.1"
resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.1.tgz#32713bc028f75c02fdb710d7c7bcec1f2c6070ef"
integrity sha512-MQcXEUbCKtEo7bhqEs6560Hyd4XaovZlO/k9V3hjVUF/zwW7KBVdSK4gIt/bzwS9MbR5qob+F5jusZsb0YQK2A==
-buffer-stream-reader@^0.1.1:
- version "0.1.1"
- resolved "https://registry.yarnpkg.com/buffer-stream-reader/-/buffer-stream-reader-0.1.1.tgz#ca8bf93631deedd8b8f8c3bb44991cc30951e259"
+buffer@^5.5.0:
+ version "5.7.1"
+ resolved "https://registry.yarnpkg.com/buffer/-/buffer-5.7.1.tgz#ba62e7c13133053582197160851a8f648e99eed0"
+ integrity sha512-EHcyIPBQ4BSGlvjB16k5KgAJ27CIsHY/2JBmCRReo48y9rQ3MaUzWX3KVlBa4U7MyX02HdVj0K7C3WaB3ju7FQ==
+ dependencies:
+ base64-js "^1.3.1"
+ ieee754 "^1.1.13"
builtins@^1.0.3:
version "1.0.3"
resolved "https://registry.yarnpkg.com/builtins/-/builtins-1.0.3.tgz#cb94faeb61c8696451db36534e1422f94f0aee88"
integrity sha1-y5T662HIaWRR2zZTThQi+U8K7og=
-byline@^5.0.0:
- version "5.0.0"
- resolved "https://registry.yarnpkg.com/byline/-/byline-5.0.0.tgz#741c5216468eadc457b03410118ad77de8c1ddb1"
- integrity sha1-dBxSFkaOrcRXsDQQEYrXfejB3bE=
-
-byte-size@^5.0.1:
- version "5.0.1"
- resolved "https://registry.yarnpkg.com/byte-size/-/byte-size-5.0.1.tgz#4b651039a5ecd96767e71a3d7ed380e48bed4191"
- integrity sha512-/XuKeqWocKsYa/cBY1YbSJSWWqTi4cFgr9S6OyM7PBaPbr9zvNGwWP33vt0uqGhwDdN+y3yhbXVILEUpnwEWGw==
+byte-size@^7.0.1:
+ version "7.0.1"
+ resolved "https://registry.yarnpkg.com/byte-size/-/byte-size-7.0.1.tgz#b1daf3386de7ab9d706b941a748dbfc71130dee3"
+ integrity sha512-crQdqyCwhokxwV1UyDzLZanhkugAgft7vt0qbbdt60C6Zf3CAiGmtUCylbtYwrU6loOUw3euGrNtW1J651ot1A==
-bytes@3.0.0:
- version "3.0.0"
- resolved "https://registry.yarnpkg.com/bytes/-/bytes-3.0.0.tgz#d32815404d689699f85a4ea4fa8755dd13a96048"
+bytes@3.1.0:
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/bytes/-/bytes-3.1.0.tgz#f6cf7933a360e0588fa9fde85651cdc7f805d1f6"
+ integrity sha512-zauLjrfCG+xvoyaqLoV8bLVXXNGC4JqlxFCutSDWA6fJrTo2ZuvLYTqZ7aHBLZSMOopbzwv8f+wZcVzfVTI2Dg==
-cacache@^12.0.0, cacache@^12.0.2, cacache@^12.0.3:
- version "12.0.4"
- resolved "https://registry.yarnpkg.com/cacache/-/cacache-12.0.4.tgz#668bcbd105aeb5f1d92fe25570ec9525c8faa40c"
- integrity sha512-a0tMB40oefvuInr4Cwb3GerbL9xTj1D5yg0T5xrjGCGyfvbxseIXX7BAO/u/hIXdafzOI5JC3wDwHyf24buOAQ==
+cacache@^15.0.3, cacache@^15.0.5, cacache@^15.2.0:
+ version "15.2.0"
+ resolved "https://registry.yarnpkg.com/cacache/-/cacache-15.2.0.tgz#73af75f77c58e72d8c630a7a2858cb18ef523389"
+ integrity sha512-uKoJSHmnrqXgthDFx/IU6ED/5xd+NNGe+Bb+kLZy7Ku4P+BaiWEUflAKPZ7eAzsYGcsAGASJZsybXp+quEcHTw==
dependencies:
- bluebird "^3.5.5"
- chownr "^1.1.1"
- figgy-pudding "^3.5.1"
+ "@npmcli/move-file" "^1.0.1"
+ chownr "^2.0.0"
+ fs-minipass "^2.0.0"
glob "^7.1.4"
- graceful-fs "^4.1.15"
- infer-owner "^1.0.3"
- lru-cache "^5.1.1"
- mississippi "^3.0.0"
- mkdirp "^0.5.1"
- move-concurrently "^1.0.1"
+ infer-owner "^1.0.4"
+ lru-cache "^6.0.0"
+ minipass "^3.1.1"
+ minipass-collect "^1.0.2"
+ minipass-flush "^1.0.5"
+ minipass-pipeline "^1.2.2"
+ mkdirp "^1.0.3"
+ p-map "^4.0.0"
promise-inflight "^1.0.1"
- rimraf "^2.6.3"
- ssri "^6.0.1"
+ rimraf "^3.0.2"
+ ssri "^8.0.1"
+ tar "^6.0.2"
unique-filename "^1.1.1"
- y18n "^4.0.0"
cacheable-request@^2.1.1:
version "2.1.4"
resolved "https://registry.yarnpkg.com/cacheable-request/-/cacheable-request-2.1.4.tgz#0d808801b6342ad33c91df9d0b44dc09b91e5c3d"
+ integrity sha1-DYCIAbY0KtM8kd+dC0TcCbkeXD0=
dependencies:
clone-response "1.0.2"
get-stream "3.0.0"
@@ -926,102 +1286,115 @@ cacheable-request@^2.1.1:
caching-transform@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/caching-transform/-/caching-transform-4.0.0.tgz#00d297a4206d71e2163c39eaffa8157ac0651f0f"
+ integrity sha512-kpqOvwXnjjN44D89K5ccQC+RUrsy7jB/XLlRrx0D7/2HNcTPqzsb6XgYoErwko6QsV184CA2YgS1fxDiiDZMWA==
dependencies:
hasha "^5.0.0"
make-dir "^3.0.0"
package-hash "^4.0.0"
write-file-atomic "^3.0.0"
-call-limit@^1.1.1:
- version "1.1.1"
- resolved "https://registry.yarnpkg.com/call-limit/-/call-limit-1.1.1.tgz#ef15f2670db3f1992557e2d965abc459e6e358d4"
- integrity sha512-5twvci5b9eRBw2wCfPtN0GmlR2/gadZqyFpPhOK6CvMFoFgA+USnZ6Jpu1lhG9h85pQ3Ouil3PfXWRD4EUaRiQ==
+call-bind@^1.0.0:
+ version "1.0.2"
+ resolved "https://registry.yarnpkg.com/call-bind/-/call-bind-1.0.2.tgz#b1d4e89e688119c3c9a903ad30abb2f6a919be3c"
+ integrity sha512-7O+FbCihrB5WGbFYesctwmTKae6rOiIzmz1icreWJ+0aA7LJfuqhEso2T9ncpcFtzMQtzXf2QGGueWJGTYsqrA==
+ dependencies:
+ function-bind "^1.1.1"
+ get-intrinsic "^1.0.2"
callsites@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/callsites/-/callsites-1.0.1.tgz#c14c24188ce8e1d6a030b4c3c942e6ba895b6a1a"
+ integrity sha1-wUwkGIzo4dagMLTDyULmuolbaho=
callsites@^3.0.0:
version "3.1.0"
resolved "https://registry.yarnpkg.com/callsites/-/callsites-3.1.0.tgz#b3630abd8943432f54b3f0519238e33cd7df2f73"
+ integrity sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ==
camelcase@^4.0.0:
version "4.1.0"
resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-4.1.0.tgz#d545635be1e33c542649c69173e5de6acfae34dd"
+ integrity sha1-1UVjW+HjPFQmScaRc+Xeas+uNN0=
camelcase@^5.3.1:
version "5.3.1"
resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-5.3.1.tgz#e3c9b31569e106811df242f715725a1f4c494320"
+ integrity sha512-L28STB170nwWS63UjtlEOE3dldQApaJXZkOI1uMFfzf3rRuPegHaHesyee+YxQ+W6SvRDQV6UrdOdRiR153wJg==
+
+camelcase@^6.0.0:
+ version "6.2.0"
+ resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-6.2.0.tgz#924af881c9d525ac9d87f40d964e5cea982a1809"
+ integrity sha512-c7wVvbw3f37nuobQNtgsgG9POC9qMbNuMQmTCqZv23b6MIz0fcYpBiOlv9gEN/hdLdnZTDQhg6e9Dq5M1vKvfg==
+
+caniuse-lite@^1.0.30001219:
+ version "1.0.30001235"
+ resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001235.tgz#ad5ca75bc5a1f7b12df79ad806d715a43a5ac4ed"
+ integrity sha512-zWEwIVqnzPkSAXOUlQnPW2oKoYb2aLQ4Q5ejdjBcnH63rfypaW34CxaeBn1VMya2XaEU3P/R2qHpWyj+l0BT1A==
capture-stack-trace@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/capture-stack-trace/-/capture-stack-trace-1.0.1.tgz#a6c0bbe1f38f3aa0b92238ecb6ff42c344d4135d"
+ integrity sha512-mYQLZnx5Qt1JgB1WEiMCf2647plpGeQ2NMR/5L0HNZzGQo4fuSPnK+wjfPnKZV0aiJDgzmWqqkV/g7JD+DW0qw==
caseless@~0.12.0:
version "0.12.0"
resolved "https://registry.yarnpkg.com/caseless/-/caseless-0.12.0.tgz#1b681c21ff84033c826543090689420d187151dc"
+ integrity sha1-G2gcIf+EAzyCZUMJBolCDRhxUdw=
chai-as-promised@^7.1.1:
version "7.1.1"
resolved "https://registry.yarnpkg.com/chai-as-promised/-/chai-as-promised-7.1.1.tgz#08645d825deb8696ee61725dbf590c012eb00ca0"
+ integrity sha512-azL6xMoi+uxu6z4rhWQ1jbdUhOMhis2PvscD/xjLqNMkv3BPPp2JyyuTHOrf9BOosGpNQ11v6BKv/g57RXbiaA==
dependencies:
check-error "^1.0.2"
-chai@^4.1.2:
- version "4.1.2"
- resolved "https://registry.yarnpkg.com/chai/-/chai-4.1.2.tgz#0f64584ba642f0f2ace2806279f4f06ca23ad73c"
+chai@^4.3.4:
+ version "4.3.4"
+ resolved "https://registry.yarnpkg.com/chai/-/chai-4.3.4.tgz#b55e655b31e1eac7099be4c08c21964fce2e6c49"
+ integrity sha512-yS5H68VYOCtN1cjfwumDSuzn/9c+yza4f3reKXlE5rUg7SFcCEy90gJvydNgOYtblyf4Zi6jIWRnXOgErta0KA==
dependencies:
- assertion-error "^1.0.1"
- check-error "^1.0.1"
- deep-eql "^3.0.0"
+ assertion-error "^1.1.0"
+ check-error "^1.0.2"
+ deep-eql "^3.0.1"
get-func-name "^2.0.0"
- pathval "^1.0.0"
- type-detect "^4.0.0"
-
-chalk@^2.0.0:
- version "2.4.1"
- resolved "https://registry.yarnpkg.com/chalk/-/chalk-2.4.1.tgz#18c49ab16a037b6eb0152cc83e3471338215b66e"
- dependencies:
- ansi-styles "^3.2.1"
- escape-string-regexp "^1.0.5"
- supports-color "^5.3.0"
+ pathval "^1.1.1"
+ type-detect "^4.0.5"
-chalk@^2.0.1, chalk@^2.1.0, chalk@^2.4.2:
+chalk@^2.0.0, chalk@^2.0.1:
version "2.4.2"
resolved "https://registry.yarnpkg.com/chalk/-/chalk-2.4.2.tgz#cd42541677a54333cf541a49108c1432b44c9424"
+ integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==
dependencies:
ansi-styles "^3.2.1"
escape-string-regexp "^1.0.5"
supports-color "^5.3.0"
-chalk@^4.0.0:
- version "4.1.0"
- resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.0.tgz#4e14870a618d9e2edd97dd8345fd9d9dc315646a"
- integrity sha512-qwx12AxXe2Q5xQ43Ac//I6v5aXTipYrSESdOgzrN+9XjgEpyjpKuvSGaN4qE93f7TQTlerQQ8S+EQ0EyDoVL1A==
+chalk@^4.0.0, chalk@^4.1.0:
+ version "4.1.1"
+ resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.1.tgz#c80b3fab28bf6371e6863325eee67e618b77e6ad"
+ integrity sha512-diHzdDKxcU+bAsUboHLPEDQiw0qEe0qd7SYUn3HgcFlWgbDcfLGswOHYeGrHKzG9z6UYf01d9VFMfZxPM1xZSg==
dependencies:
ansi-styles "^4.1.0"
supports-color "^7.1.0"
-chardet@^0.7.0:
- version "0.7.0"
- resolved "https://registry.yarnpkg.com/chardet/-/chardet-0.7.0.tgz#90094849f0937f2eedc2425d0d28a9e5f0cbad9e"
-
-check-error@^1.0.1, check-error@^1.0.2:
+check-error@^1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/check-error/-/check-error-1.0.2.tgz#574d312edd88bb5dd8912e9286dd6c0aed4aac82"
+ integrity sha1-V00xLt2Iu13YkS6Sht1sCu1KrII=
child-process-promise@^2.2.1:
version "2.2.1"
resolved "https://registry.yarnpkg.com/child-process-promise/-/child-process-promise-2.2.1.tgz#4730a11ef610fad450b8f223c79d31d7bdad8074"
+ integrity sha1-RzChHvYQ+tRQuPIjx50x172tgHQ=
dependencies:
cross-spawn "^4.0.2"
node-version "^1.0.0"
promise-polyfill "^6.0.1"
-chokidar@3.4.2:
- version "3.4.2"
- resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.4.2.tgz#38dc8e658dec3809741eb3ef7bb0a47fe424232d"
- integrity sha512-IZHaDeBeI+sZJRX7lGcXsdzgvZqKv6sECqsbErJA4mHWfpRrD8B97kSFN4cQz6nGBGiuFia1MKR4d6c1o8Cv7A==
+chokidar@3.5.1:
+ version "3.5.1"
+ resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.5.1.tgz#ee9ce7bbebd2b79f49f304799d5468e31e14e68a"
+ integrity sha512-9+s+Od+W0VJJzawDma/gvBNQqkTiqYTWLuZoyAsivsI4AaWTCzHG06/TMjsf1cYe9Cb97UCEhjz7HvnPk2p/tw==
dependencies:
anymatch "~3.1.1"
braces "~3.0.2"
@@ -1029,15 +1402,11 @@ chokidar@3.4.2:
is-binary-path "~2.1.0"
is-glob "~4.0.1"
normalize-path "~3.0.0"
- readdirp "~3.4.0"
+ readdirp "~3.5.0"
optionalDependencies:
- fsevents "~2.1.2"
+ fsevents "~2.3.1"
-chownr@^1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/chownr/-/chownr-1.0.1.tgz#e2a75042a9551908bebd25b8523d5f9769d79181"
-
-chownr@^1.1.1, chownr@^1.1.2, chownr@^1.1.4:
+chownr@^1.1.1:
version "1.1.4"
resolved "https://registry.yarnpkg.com/chownr/-/chownr-1.1.4.tgz#6fc9d7b42d32a583596337666e7d08084da2cc6b"
integrity sha512-jJ0bqzaylmJtVnNgzTeSOs8DPavpbYgEr/b0YL8/2GO3xJEhInFmhKMUnEJQjZumK7KXGFhUy89PrsJWlakBVg==
@@ -1045,34 +1414,34 @@ chownr@^1.1.1, chownr@^1.1.2, chownr@^1.1.4:
chownr@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/chownr/-/chownr-2.0.0.tgz#15bfbe53d2eab4cf70f18a8cd68ebe5b3cb1dece"
+ integrity sha512-bIomtDF5KGpdogkLd9VspvFzk9KfpyyGlS8YFVZl7TGPBHL5snIOnxeshwVgPteQ9b4Eydl+pVbIyE1DcvCWgQ==
ci-info@^1.5.0:
version "1.6.0"
resolved "https://registry.yarnpkg.com/ci-info/-/ci-info-1.6.0.tgz#2ca20dbb9ceb32d4524a683303313f0304b1e497"
+ integrity sha512-vsGdkwSCDpWmP80ncATX7iea5DWQemg1UgCW5J8tqjU3lYw4FBYuj89J0CTVomA7BEfvSZd84GmHko+MxFQU2A==
-ci-info@^2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/ci-info/-/ci-info-2.0.0.tgz#67a9e964be31a51e15e5010d58e6f12834002f46"
- integrity sha512-5tK7EtrZ0N+OLFMthtqOj4fI2Jeb88C4CAZPu25LDVUgXJ0A3Js4PMGqrn0JU1W0Mh1/Z8wZzYPxqUrXeBboCQ==
-
-cidr-regex@^2.0.10:
- version "2.0.10"
- resolved "https://registry.yarnpkg.com/cidr-regex/-/cidr-regex-2.0.10.tgz#af13878bd4ad704de77d6dc800799358b3afa70d"
- integrity sha512-sB3ogMQXWvreNPbJUZMRApxuRYd+KoIo4RGQ81VatjmMW6WJPo+IJZ2846FGItr9VzKo5w7DXzijPLGtSd0N3Q==
+cidr-regex@^3.1.1:
+ version "3.1.1"
+ resolved "https://registry.yarnpkg.com/cidr-regex/-/cidr-regex-3.1.1.tgz#ba1972c57c66f61875f18fd7dd487469770b571d"
+ integrity sha512-RBqYd32aDwbCMFJRL6wHOlDNYJsPNTt8vC82ErHF5vKt8QQzxm1FrkW8s/R5pVrXMf17sba09Uoy91PKiddAsw==
dependencies:
- ip-regex "^2.1.0"
+ ip-regex "^4.1.0"
clean-stack@^1.0.0:
version "1.3.0"
resolved "https://registry.yarnpkg.com/clean-stack/-/clean-stack-1.3.0.tgz#9e821501ae979986c46b1d66d2d432db2fd4ae31"
+ integrity sha1-noIVAa6XmYbEax1m0tQy2y/UrjE=
clean-stack@^2.0.0:
version "2.2.0"
resolved "https://registry.yarnpkg.com/clean-stack/-/clean-stack-2.2.0.tgz#ee8472dbb129e727b31e8a10a427dee9dfe4008b"
+ integrity sha512-4diC9HaTE+KRAMWhDhrGOECgWZxoevMc5TlkObMqNSsVU62PYzXZ/SMTjzyGAFF1YusgxGcSWTEXBhp0CPwQ1A==
cli-boxes@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/cli-boxes/-/cli-boxes-1.0.0.tgz#4fa917c3e59c94a004cd61f8ee509da651687143"
+ integrity sha1-T6kXw+WclKAEzWH47lCdplFocUM=
cli-columns@^3.1.2:
version "3.1.2"
@@ -1082,27 +1451,35 @@ cli-columns@^3.1.2:
string-width "^2.0.0"
strip-ansi "^3.0.1"
-cli-cursor@^3.1.0:
- version "3.1.0"
- resolved "https://registry.yarnpkg.com/cli-cursor/-/cli-cursor-3.1.0.tgz#264305a7ae490d1d03bf0c9ba7c925d1753af307"
- dependencies:
- restore-cursor "^3.1.0"
-
-cli-table3@^0.5.0, cli-table3@^0.5.1:
- version "0.5.1"
- resolved "https://registry.yarnpkg.com/cli-table3/-/cli-table3-0.5.1.tgz#0252372d94dfc40dbd8df06005f48f31f656f202"
- integrity sha512-7Qg2Jrep1S/+Q3EceiZtQcDPWxhAvBw+ERf1162v4sikJrvojMHFqXt8QIVha8UlH9rgU0BeWPytZ9/TzYqlUw==
+cli-table3@^0.6.0:
+ version "0.6.0"
+ resolved "https://registry.yarnpkg.com/cli-table3/-/cli-table3-0.6.0.tgz#b7b1bc65ca8e7b5cef9124e13dc2b21e2ce4faee"
+ integrity sha512-gnB85c3MGC7Nm9I/FkiasNBOKjOiO1RNuXXarQms37q4QMpWdlbBgD/VnOStA2faG1dpXMv31RFApjX1/QdgWQ==
dependencies:
object-assign "^4.1.0"
- string-width "^2.1.1"
+ string-width "^4.2.0"
optionalDependencies:
colors "^1.1.2"
-cli-width@^2.0.0:
- version "2.2.0"
- resolved "https://registry.yarnpkg.com/cli-width/-/cli-width-2.2.0.tgz#ff19ede8a9a5e579324147b0c11f0fbcbabed639"
+cliui@^4.0.0:
+ version "4.1.0"
+ resolved "https://registry.yarnpkg.com/cliui/-/cliui-4.1.0.tgz#348422dbe82d800b3022eef4f6ac10bf2e4d1b49"
+ integrity sha512-4FG+RSG9DL7uEwRUZXZn3SS34DiDPfzP0VOiEwtUWlE+AR2EIg+hSyvrIgUUfhdgR/UkAeW2QHgeP+hWrXs7jQ==
+ dependencies:
+ string-width "^2.1.1"
+ strip-ansi "^4.0.0"
+ wrap-ansi "^2.0.0"
-cliui@^7.0.0:
+cliui@^6.0.0:
+ version "6.0.0"
+ resolved "https://registry.yarnpkg.com/cliui/-/cliui-6.0.0.tgz#511d702c0c4e41ca156d7d0e96021f23e13225b1"
+ integrity sha512-t6wbgtoCXvAzst7QgXxJYqPt0usEfbgQdftEPbLL/cvv6HPE5VgvqCuAIDR0NgU52ds6rFwqrgakNLrHEjCbrQ==
+ dependencies:
+ string-width "^4.2.0"
+ strip-ansi "^6.0.0"
+ wrap-ansi "^6.2.0"
+
+cliui@^7.0.2:
version "7.0.4"
resolved "https://registry.yarnpkg.com/cliui/-/cliui-7.0.4.tgz#a0265ee655476fc807aea9df3df8df7783808b4f"
integrity sha512-OcRE68cOsVMXp1Yvonl/fzkQOyjLSu/8bhPDfQt0e0/Eb283TKP20Fs2MqoPsr9SwA595rRCA+QMzYc9nBP+JQ==
@@ -1111,51 +1488,69 @@ cliui@^7.0.0:
strip-ansi "^6.0.0"
wrap-ansi "^7.0.0"
+clone-deep@^0.2.4:
+ version "0.2.4"
+ resolved "https://registry.yarnpkg.com/clone-deep/-/clone-deep-0.2.4.tgz#4e73dd09e9fb971cc38670c5dced9c1896481cc6"
+ integrity sha1-TnPdCen7lxzDhnDF3O2cGJZIHMY=
+ dependencies:
+ for-own "^0.1.3"
+ is-plain-object "^2.0.1"
+ kind-of "^3.0.2"
+ lazy-cache "^1.0.3"
+ shallow-clone "^0.1.2"
+
clone-response@1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/clone-response/-/clone-response-1.0.2.tgz#d1dc973920314df67fbeb94223b4ee350239e96b"
+ integrity sha1-0dyXOSAxTfZ/vrlCI7TuNQI56Ws=
dependencies:
mimic-response "^1.0.0"
clone@^1.0.2:
version "1.0.4"
resolved "https://registry.yarnpkg.com/clone/-/clone-1.0.4.tgz#da309cc263df15994c688ca902179ca3c7cd7c7e"
+ integrity sha1-2jCcwmPfFZlMaIypAheco8fNfH4=
-cmd-shim@^3.0.0, cmd-shim@^3.0.3:
- version "3.0.3"
- resolved "https://registry.yarnpkg.com/cmd-shim/-/cmd-shim-3.0.3.tgz#2c35238d3df37d98ecdd7d5f6b8dc6b21cadc7cb"
- integrity sha512-DtGg+0xiFhQIntSBRzL2fRQBnmtAVwXIDo4Qq46HPpObYquxMaZS4sb82U9nH91qJrlosC1wa9gwr0QyL/HypA==
+cmd-shim@^4.0.1:
+ version "4.1.0"
+ resolved "https://registry.yarnpkg.com/cmd-shim/-/cmd-shim-4.1.0.tgz#b3a904a6743e9fede4148c6f3800bf2a08135bdd"
+ integrity sha512-lb9L7EM4I/ZRVuljLPEtUJOP+xiQVknZ4ZMpMgEp4JzNldPb27HU03hi6K1/6CoIuit/Zm/LQXySErFeXxDprw==
dependencies:
- graceful-fs "^4.1.2"
- mkdirp "~0.5.0"
-
-co@^4.6.0:
- version "4.6.0"
- resolved "https://registry.yarnpkg.com/co/-/co-4.6.0.tgz#6ea6bdf3d853ae54ccb8e47bfa0bf3f9031fb184"
+ mkdirp-infer-owner "^2.0.0"
code-point-at@^1.0.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/code-point-at/-/code-point-at-1.1.0.tgz#0d070b4d043a5bea33a2f1a40e2edb3d9a4ccf77"
+ integrity sha1-DQcLTQQ6W+ozovGkDi7bPZpMz3c=
color-convert@^1.9.0:
- version "1.9.2"
- resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-1.9.2.tgz#49881b8fba67df12a96bdf3f56c0aab9e7913147"
+ version "1.9.3"
+ resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-1.9.3.tgz#bb71850690e1f136567de629d2d5471deda4c1e8"
+ integrity sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==
dependencies:
- color-name "1.1.1"
+ color-name "1.1.3"
color-convert@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
+ integrity sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==
dependencies:
color-name "~1.1.4"
-color-name@1.1.1:
- version "1.1.1"
- resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.1.tgz#4b1415304cf50028ea81643643bd82ea05803689"
+color-name@1.1.3:
+ version "1.1.3"
+ resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.3.tgz#a7d0558bd89c42f795dd42328f740831ca53bc25"
+ integrity sha1-p9BVi9icQveV3UIyj3QIMcpTvCU=
color-name@~1.1.4:
version "1.1.4"
resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
+ integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
+
+colorette@^1.2.2:
+ version "1.2.2"
+ resolved "https://registry.yarnpkg.com/colorette/-/colorette-1.2.2.tgz#cbcc79d5e99caea2dbf10eb3a26fd8b3e6acfa94"
+ integrity sha512-MKGMzyfeuutC/ZJ1cba9NqcNpfeqMUcYmyF1ZFY6/Cn7CNSAKx6a+s48sqLqyAiZuaP2TcqMhoo+dlwFnVxT9w==
colors@^1.1.2:
version "1.4.0"
@@ -1165,112 +1560,107 @@ colors@^1.1.2:
columnify@~1.5.4:
version "1.5.4"
resolved "https://registry.yarnpkg.com/columnify/-/columnify-1.5.4.tgz#4737ddf1c7b69a8a7c340570782e947eec8e78bb"
+ integrity sha1-Rzfd8ce2mop8NAVweC6UfuyOeLs=
dependencies:
strip-ansi "^3.0.0"
wcwidth "^1.0.0"
-combined-stream@1.0.6, combined-stream@~1.0.5:
- version "1.0.6"
- resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.6.tgz#723e7df6e801ac5613113a7e445a9b69cb632818"
+combined-stream@^1.0.6, combined-stream@~1.0.6:
+ version "1.0.8"
+ resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.8.tgz#c3d45a8b34fd730631a110a8a2520682b31d5a7f"
+ integrity sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==
dependencies:
delayed-stream "~1.0.0"
-combined-stream@~1.0.6:
- version "1.0.7"
- resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.7.tgz#2d1d24317afb8abe95d6d2c0b07b57813539d828"
- dependencies:
- delayed-stream "~1.0.0"
+common-ancestor-path@^1.0.1:
+ version "1.0.1"
+ resolved "https://registry.yarnpkg.com/common-ancestor-path/-/common-ancestor-path-1.0.1.tgz#4f7d2d1394d91b7abdf51871c62f71eadb0182a7"
+ integrity sha512-L3sHRo1pXXEqX8VU28kfgUY+YGsk09hPqZiZmLacNib6XNTCM8ubYeT7ryXQw8asB1sKgcU5lkB7ONug08aB8w==
commondir@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/commondir/-/commondir-1.0.1.tgz#ddd800da0c66127393cca5950ea968a3aaf1253b"
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resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
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-
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- version "3.1.2"
- resolved "https://registry.yarnpkg.com/configstore/-/configstore-3.1.2.tgz#c6f25defaeef26df12dd33414b001fe81a543f8f"
+ version "3.1.5"
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dependencies:
- dot-prop "^4.1.0"
+ dot-prop "^4.2.1"
graceful-fs "^4.1.2"
make-dir "^1.0.0"
unique-string "^1.0.0"
write-file-atomic "^2.0.0"
xdg-basedir "^3.0.0"
-console-control-strings@^1.0.0, console-control-strings@^1.1.0, console-control-strings@~1.1.0:
+console-control-strings@^1.0.0, console-control-strings@~1.1.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/console-control-strings/-/console-control-strings-1.1.0.tgz#3d7cf4464db6446ea644bf4b39507f9851008e8e"
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- resolved "https://registry.yarnpkg.com/content-disposition/-/content-disposition-0.5.2.tgz#0cf68bb9ddf5f2be7961c3a85178cb85dba78cb4"
+content-disposition@0.5.3:
+ version "0.5.3"
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+ dependencies:
+ safe-buffer "5.1.2"
content-type@~1.0.4:
version "1.0.4"
resolved "https://registry.yarnpkg.com/content-type/-/content-type-1.0.4.tgz#e138cc75e040c727b1966fe5e5f8c9aee256fe3b"
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convert-source-map@^1.7.0:
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resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-1.7.0.tgz#17a2cb882d7f77d3490585e2ce6c524424a3a442"
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safe-buffer "~5.1.1"
cookie-signature@1.0.6:
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resolved "https://registry.yarnpkg.com/cookie-signature/-/cookie-signature-1.0.6.tgz#e303a882b342cc3ee8ca513a79999734dab3ae2c"
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- iferr "^0.1.5"
- mkdirp "^0.5.1"
- rimraf "^2.5.4"
- run-queue "^1.0.0"
+cookie@0.4.0:
+ version "0.4.0"
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core-util-is@1.0.2, core-util-is@~1.0.0:
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integrity sha1-tf1UIgqivFq1eqtxQMlAdUUDwac=
+cpu-features@0.0.2:
+ version "0.0.2"
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+ dependencies:
+ nan "^2.14.1"
+
create-error-class@^3.0.0:
version "3.0.2"
resolved "https://registry.yarnpkg.com/create-error-class/-/create-error-class-3.0.2.tgz#06be7abef947a3f14a30fd610671d401bca8b7b6"
+ integrity sha1-Br56vvlHo/FKMP1hBnHUAbyot7Y=
dependencies:
capture-stack-trace "^1.0.0"
+create-require@^1.1.0:
+ version "1.1.1"
+ resolved "https://registry.yarnpkg.com/create-require/-/create-require-1.1.1.tgz#c1d7e8f1e5f6cfc9ff65f9cd352d37348756c333"
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+
cross-spawn@^4.0.2:
version "4.0.2"
resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-4.0.2.tgz#7b9247621c23adfdd3856004a823cbe397424d41"
+ integrity sha1-e5JHYhwjrf3ThWAEqCPL45dCTUE=
dependencies:
lru-cache "^4.0.1"
which "^1.2.9"
@@ -1278,14 +1668,16 @@ cross-spawn@^4.0.2:
cross-spawn@^5.0.1:
version "5.1.0"
resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-5.1.0.tgz#e8bd0efee58fcff6f8f94510a0a554bbfa235449"
+ integrity sha1-6L0O/uWPz/b4+UUQoKVUu/ojVEk=
dependencies:
lru-cache "^4.0.1"
shebang-command "^1.2.0"
which "^1.2.9"
-cross-spawn@^6.0.5:
+cross-spawn@^6.0.0:
version "6.0.5"
resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-6.0.5.tgz#4a5ec7c64dfae22c3a14124dbacdee846d80cbc4"
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dependencies:
nice-try "^1.0.4"
path-key "^2.0.1"
@@ -1293,9 +1685,10 @@ cross-spawn@^6.0.5:
shebang-command "^1.2.0"
which "^1.2.9"
-cross-spawn@^7.0.0:
- version "7.0.1"
- resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.1.tgz#0ab56286e0f7c24e153d04cc2aa027e43a9a5d14"
+cross-spawn@^7.0.0, cross-spawn@^7.0.2:
+ version "7.0.3"
+ resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.3.tgz#f73a85b9d5d41d045551c177e2882d4ac85728a6"
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dependencies:
path-key "^3.1.0"
shebang-command "^2.0.0"
@@ -1304,154 +1697,157 @@ cross-spawn@^7.0.0:
crypto-random-string@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/crypto-random-string/-/crypto-random-string-1.0.0.tgz#a230f64f568310e1498009940790ec99545bca7e"
-
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- version "1.0.1"
- resolved "https://registry.yarnpkg.com/cyclist/-/cyclist-1.0.1.tgz#596e9698fd0c80e12038c2b82d6eb1b35b6224d9"
- integrity sha1-WW6WmP0MgOEgOMK4LW6xs1tiJNk=
+ integrity sha1-ojD2T1aDEOFJgAmUB5DsmVRbyn4=
dashdash@^1.12.0:
version "1.14.1"
resolved "https://registry.yarnpkg.com/dashdash/-/dashdash-1.14.1.tgz#853cfa0f7cbe2fed5de20326b8dd581035f6e2f0"
+ integrity sha1-hTz6D3y+L+1d4gMmuN1YEDX24vA=
dependencies:
assert-plus "^1.0.0"
-debug@2.6.9, debug@^2.1.2:
+debug@2.6.9:
version "2.6.9"
resolved "https://registry.yarnpkg.com/debug/-/debug-2.6.9.tgz#5d128515df134ff327e90a4c93f4e077a536341f"
+ integrity sha512-bC7ElrdJaJnPbAP+1EotYvqZsb3ecl5wi6Bfi6BJTUcNowp6cvspg0jXznRTKDjm/E7AdgFBVeAPVMNcKGsHMA==
dependencies:
ms "2.0.0"
-debug@3.1.0:
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- dependencies:
- ms "2.0.0"
-
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- version "4.1.1"
- resolved "https://registry.yarnpkg.com/debug/-/debug-4.1.1.tgz#3b72260255109c6b589cee050f1d516139664791"
+debug@4, debug@4.3.1, debug@^4.0.1, debug@^4.1.0, debug@^4.1.1, debug@^4.3.1:
+ version "4.3.1"
+ resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.1.tgz#f0d229c505e0c6d8c49ac553d1b13dc183f6b2ee"
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dependencies:
- ms "^2.1.1"
+ ms "2.1.2"
-debug@^3.1.0:
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- resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.6.tgz#e83d17de16d8a7efb7717edbe5fb10135eee629b"
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+debug@^3.2.6:
+ version "3.2.7"
+ resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.7.tgz#72580b7e9145fb39b6676f9c5e5fb100b934179a"
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ms "^2.1.1"
debuglog@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/debuglog/-/debuglog-1.0.1.tgz#aa24ffb9ac3df9a2351837cfb2d279360cd78492"
+ integrity sha1-qiT/uaw9+aI1GDfPstJ5NgzXhJI=
-decamelize@^1.2.0:
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version "1.2.0"
resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-1.2.0.tgz#f6534d15148269b20352e7bee26f501f9a191290"
+ integrity sha1-9lNNFRSCabIDUue+4m9QH5oZEpA=
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+ version "4.0.0"
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decode-uri-component@^0.2.0:
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resolved "https://registry.yarnpkg.com/decode-uri-component/-/decode-uri-component-0.2.0.tgz#eb3913333458775cb84cd1a1fae062106bb87545"
+ integrity sha1-6zkTMzRYd1y4TNGh+uBiEGu4dUU=
decompress-response@^3.3.0:
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resolved "https://registry.yarnpkg.com/decompress-response/-/decompress-response-3.3.0.tgz#80a4dd323748384bfa248083622aedec982adff3"
+ integrity sha1-gKTdMjdIOEv6JICDYirt7Jgq3/M=
dependencies:
mimic-response "^1.0.0"
-deep-eql@^3.0.0:
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version "3.0.1"
resolved "https://registry.yarnpkg.com/deep-eql/-/deep-eql-3.0.1.tgz#dfc9404400ad1c8fe023e7da1df1c147c4b444df"
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dependencies:
type-detect "^4.0.0"
deep-extend@^0.6.0:
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resolved "https://registry.yarnpkg.com/deep-extend/-/deep-extend-0.6.0.tgz#c4fa7c95404a17a9c3e8ca7e1537312b736330ac"
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-deep-is@~0.1.3:
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version "0.1.3"
resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.3.tgz#b369d6fb5dbc13eecf524f91b070feedc357cf34"
+ integrity sha1-s2nW+128E+7PUk+RsHD+7cNXzzQ=
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- resolved "https://registry.yarnpkg.com/deepmerge/-/deepmerge-2.2.1.tgz#5d3ff22a01c00f645405a2fbc17d0778a1801170"
+deepmerge@^3.2.0:
+ version "3.3.0"
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default-require-extensions@^3.0.0:
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resolved "https://registry.yarnpkg.com/default-require-extensions/-/default-require-extensions-3.0.0.tgz#e03f93aac9b2b6443fc52e5e4a37b3ad9ad8df96"
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defaults@^1.0.3:
version "1.0.3"
resolved "https://registry.yarnpkg.com/defaults/-/defaults-1.0.3.tgz#c656051e9817d9ff08ed881477f3fe4019f3ef7d"
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dependencies:
clone "^1.0.2"
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resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
+ integrity sha1-3zrhmayt+31ECqrgsp4icrJOxhk=
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resolved "https://registry.yarnpkg.com/delegates/-/delegates-1.0.0.tgz#84c6e159b81904fdca59a0ef44cd870d31250f9a"
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- resolved "https://registry.yarnpkg.com/depd/-/depd-1.1.1.tgz#5783b4e1c459f06fa5ca27f991f3d06e7a310359"
-
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resolved "https://registry.yarnpkg.com/depd/-/depd-1.1.2.tgz#9bcd52e14c097763e749b274c4346ed2e560b5a9"
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resolved "https://registry.yarnpkg.com/destroy/-/destroy-1.0.4.tgz#978857442c44749e4206613e37946205826abd80"
-
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- resolved "https://registry.yarnpkg.com/detect-indent/-/detect-indent-5.0.0.tgz#3871cc0a6a002e8c3e5b3cf7f336264675f06b9d"
+ integrity sha1-l4hXRCxEdJ5CBmE+N5RiBYJqvYA=
detect-libc@^1.0.2:
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resolved "https://registry.yarnpkg.com/detect-libc/-/detect-libc-1.0.3.tgz#fa137c4bd698edf55cd5cd02ac559f91a4c4ba9b"
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version "1.0.3"
resolved "https://registry.yarnpkg.com/dezalgo/-/dezalgo-1.0.3.tgz#7f742de066fc748bc8db820569dddce49bf0d456"
+ integrity sha1-f3Qt4Gb8dIvI24IFad3c5Jvw1FY=
dependencies:
asap "^2.0.0"
wrappy "1"
-diff@4.0.2:
+diff@5.0.0, diff@^5.0.0:
+ version "5.0.0"
+ resolved "https://registry.yarnpkg.com/diff/-/diff-5.0.0.tgz#7ed6ad76d859d030787ec35855f5b1daf31d852b"
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- version "3.5.0"
- resolved "https://registry.yarnpkg.com/diff/-/diff-3.5.0.tgz#800c0dd1e0a8bfbc95835c202ad220fe317e5a12"
+dir-glob@^3.0.1:
+ version "3.0.1"
+ resolved "https://registry.yarnpkg.com/dir-glob/-/dir-glob-3.0.1.tgz#56dbf73d992a4a93ba1584f4534063fd2e41717f"
+ integrity sha512-WkrWp9GR4KXfKGYzOLmTuGVi1UWFfws377n9cc55/tb6DuqyF6pcQ5AbiHEshaDpY9v6oaSr2XCDidGmMwdzIA==
+ dependencies:
+ path-type "^4.0.0"
doctrine@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/doctrine/-/doctrine-3.0.0.tgz#addebead72a6574db783639dc87a121773973961"
+ integrity sha512-yS+Q5i3hBf7GBkd4KG8a7eBNNWNGLTaEwwYWUijIYM7zrlYDM0BFXHjjPWlWZ1Rg7UaddZeIDmi9jF3HmqiQ2w==
dependencies:
esutils "^2.0.2"
-dot-prop@^4.1.0, dot-prop@^4.2.1:
+dot-prop@^4.2.1:
version "4.2.1"
resolved "https://registry.yarnpkg.com/dot-prop/-/dot-prop-4.2.1.tgz#45884194a71fc2cda71cbb4bceb3a4dd2f433ba4"
integrity sha512-l0p4+mIuJIua0mhxGoh4a+iNL9bmeK5DvnSVQa6T0OhrVmaEa1XScX5Etc673FePCJOArq/4Pa2cLGODUWTPOQ==
@@ -1461,158 +1857,83 @@ dot-prop@^4.1.0, dot-prop@^4.2.1:
dotenv@^5.0.1:
version "5.0.1"
resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-5.0.1.tgz#a5317459bd3d79ab88cff6e44057a6a3fbb1fcef"
+ integrity sha512-4As8uPrjfwb7VXC+WnLCbXK7y+Ueb2B3zgNCePYfhxS1PYeaO1YTeplffTEcbfLhvFNGLAz90VvJs9yomG7bow==
duplexer3@^0.1.4:
version "0.1.4"
resolved "https://registry.yarnpkg.com/duplexer3/-/duplexer3-0.1.4.tgz#ee01dd1cac0ed3cbc7fdbea37dc0a8f1ce002ce2"
-
-duplexify@^3.4.2, duplexify@^3.6.0:
- version "3.7.1"
- resolved "https://registry.yarnpkg.com/duplexify/-/duplexify-3.7.1.tgz#2a4df5317f6ccfd91f86d6fd25d8d8a103b88309"
- integrity sha512-07z8uv2wMyS51kKhD1KsdXJg5WQ6t93RneqRxUHnskXVtlYYkLqM0gqStQZ3pj073g687jPCHrqNfCzawLYh5g==
- dependencies:
- end-of-stream "^1.0.0"
- inherits "^2.0.1"
- readable-stream "^2.0.0"
- stream-shift "^1.0.0"
+ integrity sha1-7gHdHKwO08vH/b6jfcCo8c4ALOI=
ecc-jsbn@~0.1.1:
- version "0.1.1"
- resolved "https://registry.yarnpkg.com/ecc-jsbn/-/ecc-jsbn-0.1.1.tgz#0fc73a9ed5f0d53c38193398523ef7e543777505"
+ version "0.1.2"
+ resolved "https://registry.yarnpkg.com/ecc-jsbn/-/ecc-jsbn-0.1.2.tgz#3a83a904e54353287874c564b7549386849a98c9"
+ integrity sha1-OoOpBOVDUyh4dMVkt1SThoSamMk=
dependencies:
jsbn "~0.1.0"
-
-editor@~1.0.0:
- version "1.0.0"
- resolved "https://registry.yarnpkg.com/editor/-/editor-1.0.0.tgz#60c7f87bd62bcc6a894fa8ccd6afb7823a24f742"
+ safer-buffer "^2.1.0"
ee-first@1.1.1:
version "1.1.1"
resolved "https://registry.yarnpkg.com/ee-first/-/ee-first-1.1.1.tgz#590c61156b0ae2f4f0255732a158b266bc56b21d"
+ integrity sha1-WQxhFWsK4vTwJVcyoViyZrxWsh0=
-emoji-regex@^7.0.1:
- version "7.0.3"
- resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-7.0.3.tgz#933a04052860c85e83c122479c4748a8e4c72156"
+electron-to-chromium@^1.3.723:
+ version "1.3.749"
+ resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.3.749.tgz#0ecebc529ceb49dd2a7c838ae425236644c3439a"
+ integrity sha512-F+v2zxZgw/fMwPz/VUGIggG4ZndDsYy0vlpthi3tjmDZlcfbhN5mYW0evXUsBr2sUtuDANFtle410A9u/sd/4A==
emoji-regex@^8.0.0:
version "8.0.0"
resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-8.0.0.tgz#e818fd69ce5ccfcb404594f842963bf53164cc37"
+ integrity sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==
encodeurl@~1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/encodeurl/-/encodeurl-1.0.2.tgz#ad3ff4c86ec2d029322f5a02c3a9a606c95b3f59"
+ integrity sha1-rT/0yG7C0CkyL1oCw6mmBslbP1k=
-encoding@^0.1.11:
- version "0.1.12"
- resolved "https://registry.yarnpkg.com/encoding/-/encoding-0.1.12.tgz#538b66f3ee62cd1ab51ec323829d1f9480c74beb"
- integrity sha1-U4tm8+5izRq1HsMjgp0flIDHS+s=
+encoding@^0.1.12:
+ version "0.1.13"
+ resolved "https://registry.yarnpkg.com/encoding/-/encoding-0.1.13.tgz#56574afdd791f54a8e9b2785c0582a2d26210fa9"
+ integrity sha512-ETBauow1T35Y/WZMkio9jiM0Z5xjHHmJ4XmjZOq1l/dXz3lr2sRn87nJy20RupqSh1F2m3HHPSp8ShIPQJrJ3A==
dependencies:
- iconv-lite "~0.4.13"
+ iconv-lite "^0.6.2"
-end-of-stream@^1.0.0, end-of-stream@^1.1.0:
+end-of-stream@^1.1.0:
version "1.4.4"
resolved "https://registry.yarnpkg.com/end-of-stream/-/end-of-stream-1.4.4.tgz#5ae64a5f45057baf3626ec14da0ca5e4b2431eb0"
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dependencies:
once "^1.4.0"
-env-paths@^2.2.0:
- version "2.2.0"
- resolved "https://registry.yarnpkg.com/env-paths/-/env-paths-2.2.0.tgz#cdca557dc009152917d6166e2febe1f039685e43"
- integrity sha512-6u0VYSCo/OW6IoD5WCLLy9JUGARbamfSavcNXry/eu8aHVFei6CD3Sw+VGX5alea1i9pgPHW0mbu6Xj0uBh7gA==
-
-err-code@^1.0.0:
- version "1.1.2"
- resolved "https://registry.yarnpkg.com/err-code/-/err-code-1.1.2.tgz#06e0116d3028f6aef4806849eb0ea6a748ae6960"
- integrity sha1-BuARbTAo9q70gGhJ6w6mp0iuaWA=
-
-errno@~0.1.7:
- version "0.1.7"
- resolved "https://registry.yarnpkg.com/errno/-/errno-0.1.7.tgz#4684d71779ad39af177e3f007996f7c67c852618"
- integrity sha512-MfrRBDWzIWifgq6tJj60gkAwtLNb6sQPlcFrSOflcP1aFmmruKQ2wRnze/8V6kgyz7H3FF8Npzv78mZ7XLLflg==
- dependencies:
- prr "~1.0.1"
-
-es-abstract@^1.17.0-next.1:
- version "1.17.0-next.1"
- resolved "https://registry.yarnpkg.com/es-abstract/-/es-abstract-1.17.0-next.1.tgz#94acc93e20b05a6e96dacb5ab2f1cb3a81fc2172"
+enquirer@^2.3.5:
+ version "2.3.6"
+ resolved "https://registry.yarnpkg.com/enquirer/-/enquirer-2.3.6.tgz#2a7fe5dd634a1e4125a975ec994ff5456dc3734d"
+ integrity sha512-yjNnPr315/FjS4zIsUxYguYUPP2e1NK4d7E7ZOLiyYCcbFBiTMyID+2wvm2w6+pZ/odMA7cRkjhsPbltwBOrLg==
dependencies:
- es-to-primitive "^1.2.1"
- function-bind "^1.1.1"
- has "^1.0.3"
- has-symbols "^1.0.1"
- is-callable "^1.1.4"
- is-regex "^1.0.4"
- object-inspect "^1.7.0"
- object-keys "^1.1.1"
- object.assign "^4.1.0"
- string.prototype.trimleft "^2.1.0"
- string.prototype.trimright "^2.1.0"
-
-es-abstract@^1.17.4, es-abstract@^1.17.5:
- version "1.17.6"
- resolved "https://registry.yarnpkg.com/es-abstract/-/es-abstract-1.17.6.tgz#9142071707857b2cacc7b89ecb670316c3e2d52a"
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- dependencies:
- es-to-primitive "^1.2.1"
- function-bind "^1.1.1"
- has "^1.0.3"
- has-symbols "^1.0.1"
- is-callable "^1.2.0"
- is-regex "^1.1.0"
- object-inspect "^1.7.0"
- object-keys "^1.1.1"
- object.assign "^4.1.0"
- string.prototype.trimend "^1.0.1"
- string.prototype.trimstart "^1.0.1"
-
-es-array-method-boxes-properly@^1.0.0:
- version "1.0.0"
- resolved "https://registry.yarnpkg.com/es-array-method-boxes-properly/-/es-array-method-boxes-properly-1.0.0.tgz#873f3e84418de4ee19c5be752990b2e44718d09e"
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+ ansi-colors "^4.1.1"
-es-get-iterator@^1.0.2:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/es-get-iterator/-/es-get-iterator-1.1.0.tgz#bb98ad9d6d63b31aacdc8f89d5d0ee57bcb5b4c8"
- integrity sha512-UfrmHuWQlNMTs35e1ypnvikg6jCz3SK8v8ImvmDsh36fCVUR1MqoFDiyn0/k52C8NqO3YsO8Oe0azeesNuqSsQ==
- dependencies:
- es-abstract "^1.17.4"
- has-symbols "^1.0.1"
- is-arguments "^1.0.4"
- is-map "^2.0.1"
- is-set "^2.0.1"
- is-string "^1.0.5"
- isarray "^2.0.5"
+env-paths@^2.2.0:
+ version "2.2.1"
+ resolved "https://registry.yarnpkg.com/env-paths/-/env-paths-2.2.1.tgz#420399d416ce1fbe9bc0a07c62fa68d67fd0f8f2"
+ integrity sha512-+h1lkLKhZMTYjog1VEpJNG7NZJWcuc2DDk/qsqSTRRCOXiLjeQ1d1/udrUGhqMxUgAlwKNZ0cf2uqan5GLuS2A==
-es-to-primitive@^1.2.1:
- version "1.2.1"
- resolved "https://registry.yarnpkg.com/es-to-primitive/-/es-to-primitive-1.2.1.tgz#e55cd4c9cdc188bcefb03b366c736323fc5c898a"
- dependencies:
- is-callable "^1.1.4"
- is-date-object "^1.0.1"
- is-symbol "^1.0.2"
+err-code@^2.0.2:
+ version "2.0.3"
+ resolved "https://registry.yarnpkg.com/err-code/-/err-code-2.0.3.tgz#23c2f3b756ffdfc608d30e27c9a941024807e7f9"
+ integrity sha512-2bmlRpNKBxT/CRmPOlyISQpNj+qSeYvcym/uT0Jx2bMOlKLtSy1ZmLuVxSEKKyor/N5yhvp/ZiG1oE3DEYMSFA==
es6-error@^4.0.1:
version "4.1.1"
resolved "https://registry.yarnpkg.com/es6-error/-/es6-error-4.1.1.tgz#9e3af407459deed47e9a91f9b885a84eb05c561d"
+ integrity sha512-Um/+FxMr9CISWh0bi5Zv0iOD+4cFh5qLeks1qhAopKVAJw3drgKbKySikp7wGhDL0HPeaja0P5ULZrxLkniUVg==
-es6-promise@^4.0.3:
+es6-promise@^4.2.8:
version "4.2.8"
resolved "https://registry.yarnpkg.com/es6-promise/-/es6-promise-4.2.8.tgz#4eb21594c972bc40553d276e510539143db53e0a"
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-es6-promise@^4.2.5:
- version "4.2.5"
- resolved "https://registry.yarnpkg.com/es6-promise/-/es6-promise-4.2.5.tgz#da6d0d5692efb461e082c14817fe2427d8f5d054"
-
-es6-promisify@^5.0.0:
- version "5.0.0"
- resolved "https://registry.yarnpkg.com/es6-promisify/-/es6-promisify-5.0.0.tgz#5109d62f3e56ea967c4b63505aef08291c8a5203"
- integrity sha1-UQnWLz5W6pZ8S2NQWu8IKRyKUgM=
- dependencies:
- es6-promise "^4.0.3"
-
-escalade@^3.0.2:
+escalade@^3.1.1:
version "3.1.1"
resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.1.tgz#d8cfdc7000965c5a0174b4a82eaa5c0552742e40"
integrity sha512-k0er2gUkLf8O0zKJiAhmkTnJlTvINGv7ygDNPbeIsX/TJjGJZHuh9B2UxbsaEkmlEo9MfhrSzmhIlhRlI2GXnw==
@@ -1620,8 +1941,9 @@ escalade@^3.0.2:
escape-html@~1.0.3:
version "1.0.3"
resolved "https://registry.yarnpkg.com/escape-html/-/escape-html-1.0.3.tgz#0258eae4d3d0c0974de1c169188ef0051d1d1988"
+ integrity sha1-Aljq5NPQwJdN4cFpGI7wBR0dGYg=
-escape-string-regexp@4.0.0:
+escape-string-regexp@4.0.0, escape-string-regexp@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/escape-string-regexp/-/escape-string-regexp-4.0.0.tgz#14ba83a5d373e3d311e5afca29cf5bfad965bf34"
integrity sha512-TtpcNJ3XAzx3Gq8sWRzJaVajRs0uVxA2YAkdb1jm2YkPz4G6egUFAyA3n5vtEIZefPk5Wa4UXbKuS5fKkJWdgA==
@@ -1629,105 +1951,130 @@ escape-string-regexp@4.0.0:
escape-string-regexp@^1.0.5:
version "1.0.5"
resolved "https://registry.yarnpkg.com/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz#1b61c0562190a8dff6ae3bb2cf0200ca130b86d4"
+ integrity sha1-G2HAViGQqN/2rjuyzwIAyhMLhtQ=
-eslint-scope@^5.0.0:
- version "5.0.0"
- resolved "https://registry.yarnpkg.com/eslint-scope/-/eslint-scope-5.0.0.tgz#e87c8887c73e8d1ec84f1ca591645c358bfc8fb9"
+eslint-scope@^5.0.0, eslint-scope@^5.1.1:
+ version "5.1.1"
+ resolved "https://registry.yarnpkg.com/eslint-scope/-/eslint-scope-5.1.1.tgz#e786e59a66cb92b3f6c1fb0d508aab174848f48c"
+ integrity sha512-2NxwbF/hZ0KpepYN0cNbo+FN6XoK7GaHlQhgx/hIZl6Va0bF45RQOOwhLIy8lQDbuCiadSLCBnH2CFYquit5bw==
dependencies:
- esrecurse "^4.1.0"
+ esrecurse "^4.3.0"
estraverse "^4.1.1"
-eslint-utils@^1.4.3:
- version "1.4.3"
- resolved "https://registry.yarnpkg.com/eslint-utils/-/eslint-utils-1.4.3.tgz#74fec7c54d0776b6f67e0251040b5806564e981f"
+eslint-utils@^2.0.0, eslint-utils@^2.1.0:
+ version "2.1.0"
+ resolved "https://registry.yarnpkg.com/eslint-utils/-/eslint-utils-2.1.0.tgz#d2de5e03424e707dc10c74068ddedae708741b27"
+ integrity sha512-w94dQYoauyvlDc43XnGB8lU3Zt713vNChgt4EWwhXAP2XkBvndfxF0AgIqKOOasjPIPzj9JqgwkwbCYD0/V3Zg==
dependencies:
eslint-visitor-keys "^1.1.0"
-eslint-visitor-keys@^1.1.0:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-1.1.0.tgz#e2a82cea84ff246ad6fb57f9bde5b46621459ec2"
+eslint-visitor-keys@^1.1.0, eslint-visitor-keys@^1.3.0:
+ version "1.3.0"
+ resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-1.3.0.tgz#30ebd1ef7c2fdff01c3a4f151044af25fab0523e"
+ integrity sha512-6J72N8UNa462wa/KFODt/PJ3IU60SDpC3QXC1Hjc1BXXpfL2C9R5+AU7jhe0F6GREqVMh4Juu+NY7xn+6dipUQ==
+
+eslint-visitor-keys@^2.0.0:
+ version "2.1.0"
+ resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-2.1.0.tgz#f65328259305927392c938ed44eb0a5c9b2bd303"
+ integrity sha512-0rSmRBzXgDzIsD6mGdJgevzgezI534Cer5L/vyMX0kHzT/jiB43jRhd9YUlMGYLQy2zprNmoT8qasCGtY+QaKw==
-eslint@^6.7.2:
- version "6.7.2"
- resolved "https://registry.yarnpkg.com/eslint/-/eslint-6.7.2.tgz#c17707ca4ad7b2d8af986a33feba71e18a9fecd1"
+eslint@^7.28.0:
+ version "7.28.0"
+ resolved "https://registry.yarnpkg.com/eslint/-/eslint-7.28.0.tgz#435aa17a0b82c13bb2be9d51408b617e49c1e820"
+ integrity sha512-UMfH0VSjP0G4p3EWirscJEQ/cHqnT/iuH6oNZOB94nBjWbMnhGEPxsZm1eyIW0C/9jLI0Fow4W5DXLjEI7mn1g==
dependencies:
- "@babel/code-frame" "^7.0.0"
+ "@babel/code-frame" "7.12.11"
+ "@eslint/eslintrc" "^0.4.2"
ajv "^6.10.0"
- chalk "^2.1.0"
- cross-spawn "^6.0.5"
+ chalk "^4.0.0"
+ cross-spawn "^7.0.2"
debug "^4.0.1"
doctrine "^3.0.0"
- eslint-scope "^5.0.0"
- eslint-utils "^1.4.3"
- eslint-visitor-keys "^1.1.0"
- espree "^6.1.2"
- esquery "^1.0.1"
+ enquirer "^2.3.5"
+ escape-string-regexp "^4.0.0"
+ eslint-scope "^5.1.1"
+ eslint-utils "^2.1.0"
+ eslint-visitor-keys "^2.0.0"
+ espree "^7.3.1"
+ esquery "^1.4.0"
esutils "^2.0.2"
- file-entry-cache "^5.0.1"
+ fast-deep-equal "^3.1.3"
+ file-entry-cache "^6.0.1"
functional-red-black-tree "^1.0.1"
- glob-parent "^5.0.0"
- globals "^12.1.0"
+ glob-parent "^5.1.2"
+ globals "^13.6.0"
ignore "^4.0.6"
import-fresh "^3.0.0"
imurmurhash "^0.1.4"
- inquirer "^7.0.0"
is-glob "^4.0.0"
js-yaml "^3.13.1"
json-stable-stringify-without-jsonify "^1.0.1"
- levn "^0.3.0"
- lodash "^4.17.14"
+ levn "^0.4.1"
+ lodash.merge "^4.6.2"
minimatch "^3.0.4"
- mkdirp "^0.5.1"
natural-compare "^1.4.0"
- optionator "^0.8.3"
+ optionator "^0.9.1"
progress "^2.0.0"
- regexpp "^2.0.1"
- semver "^6.1.2"
- strip-ansi "^5.2.0"
- strip-json-comments "^3.0.1"
- table "^5.2.3"
+ regexpp "^3.1.0"
+ semver "^7.2.1"
+ strip-ansi "^6.0.0"
+ strip-json-comments "^3.1.0"
+ table "^6.0.9"
text-table "^0.2.0"
v8-compile-cache "^2.0.3"
-espree@^6.1.2:
- version "6.1.2"
- resolved "https://registry.yarnpkg.com/espree/-/espree-6.1.2.tgz#6c272650932b4f91c3714e5e7b5f5e2ecf47262d"
+espree@^7.3.0, espree@^7.3.1:
+ version "7.3.1"
+ resolved "https://registry.yarnpkg.com/espree/-/espree-7.3.1.tgz#f2df330b752c6f55019f8bd89b7660039c1bbbb6"
+ integrity sha512-v3JCNCE64umkFpmkFGqzVKsOT0tN1Zr+ueqLZfpV1Ob8e+CEgPWa+OxCoGH3tnhimMKIaBm4m/vaRpJ/krRz2g==
dependencies:
- acorn "^7.1.0"
- acorn-jsx "^5.1.0"
- eslint-visitor-keys "^1.1.0"
+ acorn "^7.4.0"
+ acorn-jsx "^5.3.1"
+ eslint-visitor-keys "^1.3.0"
esprima@^4.0.0:
version "4.0.1"
resolved "https://registry.yarnpkg.com/esprima/-/esprima-4.0.1.tgz#13b04cdb3e6c5d19df91ab6987a8695619b0aa71"
+ integrity sha512-eGuFFw7Upda+g4p+QHvnW0RyTX/SVeJBDM/gCtMARO0cLuT2HcEKnTPvhjV6aGeqrCB/sbNop0Kszm0jsaWU4A==
-esquery@^1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/esquery/-/esquery-1.0.1.tgz#406c51658b1f5991a5f9b62b1dc25b00e3e5c708"
+esquery@^1.4.0:
+ version "1.4.0"
+ resolved "https://registry.yarnpkg.com/esquery/-/esquery-1.4.0.tgz#2148ffc38b82e8c7057dfed48425b3e61f0f24a5"
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dependencies:
- estraverse "^4.0.0"
+ estraverse "^5.1.0"
-esrecurse@^4.1.0:
- version "4.2.1"
- resolved "https://registry.yarnpkg.com/esrecurse/-/esrecurse-4.2.1.tgz#007a3b9fdbc2b3bb87e4879ea19c92fdbd3942cf"
+esrecurse@^4.3.0:
+ version "4.3.0"
+ resolved "https://registry.yarnpkg.com/esrecurse/-/esrecurse-4.3.0.tgz#7ad7964d679abb28bee72cec63758b1c5d2c9921"
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dependencies:
- estraverse "^4.1.0"
+ estraverse "^5.2.0"
-estraverse@^4.0.0, estraverse@^4.1.0, estraverse@^4.1.1:
+estraverse@^4.1.1:
version "4.3.0"
resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-4.3.0.tgz#398ad3f3c5a24948be7725e83d11a7de28cdbd1d"
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+
+estraverse@^5.1.0, estraverse@^5.2.0:
+ version "5.2.0"
+ resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-5.2.0.tgz#307df42547e6cc7324d3cf03c155d5cdb8c53880"
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esutils@^2.0.2:
- version "2.0.2"
- resolved "https://registry.yarnpkg.com/esutils/-/esutils-2.0.2.tgz#0abf4f1caa5bcb1f7a9d8acc6dea4faaa04bac9b"
+ version "2.0.3"
+ resolved "https://registry.yarnpkg.com/esutils/-/esutils-2.0.3.tgz#74d2eb4de0b8da1293711910d50775b9b710ef64"
+ integrity sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g==
etag@~1.8.1:
version "1.8.1"
resolved "https://registry.yarnpkg.com/etag/-/etag-1.8.1.tgz#41ae2eeb65efa62268aebfea83ac7d79299b0887"
+ integrity sha1-Qa4u62XvpiJorr/qg6x9eSmbCIc=
execa@^0.7.0:
version "0.7.0"
resolved "https://registry.yarnpkg.com/execa/-/execa-0.7.0.tgz#944becd34cc41ee32a63a9faf27ad5a65fc59777"
+ integrity sha1-lEvs00zEHuMqY6n68nrVpl/Fl3c=
dependencies:
cross-spawn "^5.0.1"
get-stream "^3.0.0"
@@ -1737,137 +2084,154 @@ execa@^0.7.0:
signal-exit "^3.0.0"
strip-eof "^1.0.0"
-express-joi-validator@^2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/express-joi-validator/-/express-joi-validator-2.0.0.tgz#24e26e6a8327f69985ed72588f00e295dc3e3234"
+execa@^1.0.0:
+ version "1.0.0"
+ resolved "https://registry.yarnpkg.com/execa/-/execa-1.0.0.tgz#c6236a5bb4df6d6f15e88e7f017798216749ddd8"
+ integrity sha512-adbxcyWV46qiHyvSp50TKt05tB4tK3HcmF7/nxfAdhnox83seTDbwnaqKO4sXRy7roHAIFqJP/Rw/AuEbX61LA==
+ dependencies:
+ cross-spawn "^6.0.0"
+ get-stream "^4.0.0"
+ is-stream "^1.1.0"
+ npm-run-path "^2.0.0"
+ p-finally "^1.0.0"
+ signal-exit "^3.0.0"
+ strip-eof "^1.0.0"
+
+express-joi-validator@^2.0.1:
+ version "2.0.1"
+ resolved "https://registry.yarnpkg.com/express-joi-validator/-/express-joi-validator-2.0.1.tgz#4ac524f27a2afcd56fea973c318256b8993b82a8"
+ integrity sha512-oBrvkbmYutFnnT8neddWVa/duRrVHQujl2T3WBhF6/riEkeRE1oKvtdp/bfGEaOysdICyHkg5OCnrAlzkFB0aQ==
dependencies:
boom "2.6.x"
extend "2.0.x"
joi "6.x.x"
-express@^4.16.3:
- version "4.16.3"
- resolved "https://registry.yarnpkg.com/express/-/express-4.16.3.tgz#6af8a502350db3246ecc4becf6b5a34d22f7ed53"
+express@^4.17.1:
+ version "4.17.1"
+ resolved "https://registry.yarnpkg.com/express/-/express-4.17.1.tgz#4491fc38605cf51f8629d39c2b5d026f98a4c134"
+ integrity sha512-mHJ9O79RqluphRrcw2X/GTh3k9tVv8YcoyY4Kkh4WDMUYKRZUq0h1o0w2rrrxBqM7VoeUVqgb27xlEMXTnYt4g==
dependencies:
- accepts "~1.3.5"
+ accepts "~1.3.7"
array-flatten "1.1.1"
- body-parser "1.18.2"
- content-disposition "0.5.2"
+ body-parser "1.19.0"
+ content-disposition "0.5.3"
content-type "~1.0.4"
- cookie "0.3.1"
+ cookie "0.4.0"
cookie-signature "1.0.6"
debug "2.6.9"
depd "~1.1.2"
encodeurl "~1.0.2"
escape-html "~1.0.3"
etag "~1.8.1"
- finalhandler "1.1.1"
+ finalhandler "~1.1.2"
fresh "0.5.2"
merge-descriptors "1.0.1"
methods "~1.1.2"
on-finished "~2.3.0"
- parseurl "~1.3.2"
+ parseurl "~1.3.3"
path-to-regexp "0.1.7"
- proxy-addr "~2.0.3"
- qs "6.5.1"
- range-parser "~1.2.0"
- safe-buffer "5.1.1"
- send "0.16.2"
- serve-static "1.13.2"
- setprototypeof "1.1.0"
- statuses "~1.4.0"
- type-is "~1.6.16"
+ proxy-addr "~2.0.5"
+ qs "6.7.0"
+ range-parser "~1.2.1"
+ safe-buffer "5.1.2"
+ send "0.17.1"
+ serve-static "1.14.1"
+ setprototypeof "1.1.1"
+ statuses "~1.5.0"
+ type-is "~1.6.18"
utils-merge "1.0.1"
vary "~1.1.2"
extend@2.0.x:
version "2.0.2"
resolved "https://registry.yarnpkg.com/extend/-/extend-2.0.2.tgz#1b74985400171b85554894459c978de6ef453ab7"
+ integrity sha512-AgFD4VU+lVLP6vjnlNfF7OeInLTyeyckCNPEsuxz1vi786UuK/nk6ynPuhn/h+Ju9++TQyr5EpLRI14fc1QtTQ==
-extend@^3.0.0, extend@^3.0.2, extend@~3.0.1, extend@~3.0.2:
+extend@^3.0.2, extend@~3.0.2:
version "3.0.2"
resolved "https://registry.yarnpkg.com/extend/-/extend-3.0.2.tgz#f8b1136b4071fbd8eb140aff858b1019ec2915fa"
-
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- version "3.1.0"
- resolved "https://registry.yarnpkg.com/external-editor/-/external-editor-3.1.0.tgz#cb03f740befae03ea4d283caed2741a83f335495"
- dependencies:
- chardet "^0.7.0"
- iconv-lite "^0.4.24"
- tmp "^0.0.33"
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extsprintf@1.3.0:
version "1.3.0"
resolved "https://registry.yarnpkg.com/extsprintf/-/extsprintf-1.3.0.tgz#96918440e3041a7a414f8c52e3c574eb3c3e1e05"
+ integrity sha1-lpGEQOMEGnpBT4xS48V06zw+HgU=
extsprintf@^1.2.0:
version "1.4.0"
resolved "https://registry.yarnpkg.com/extsprintf/-/extsprintf-1.4.0.tgz#e2689f8f356fad62cca65a3a91c5df5f9551692f"
+ integrity sha1-4mifjzVvrWLMplo6kcXfX5VRaS8=
-fast-deep-equal@^1.0.0:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/fast-deep-equal/-/fast-deep-equal-1.1.0.tgz#c053477817c86b51daa853c81e059b733d023614"
-
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- version "2.0.1"
- resolved "https://registry.yarnpkg.com/fast-deep-equal/-/fast-deep-equal-2.0.1.tgz#7b05218ddf9667bf7f370bf7fdb2cb15fdd0aa49"
+fast-deep-equal@^3.1.1, fast-deep-equal@^3.1.3:
+ version "3.1.3"
+ resolved "https://registry.yarnpkg.com/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz#3a7d56b559d6cbc3eb512325244e619a65c6c525"
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+
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+ version "3.2.5"
+ resolved "https://registry.yarnpkg.com/fast-glob/-/fast-glob-3.2.5.tgz#7939af2a656de79a4f1901903ee8adcaa7cb9661"
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+ dependencies:
+ "@nodelib/fs.stat" "^2.0.2"
+ "@nodelib/fs.walk" "^1.2.3"
+ glob-parent "^5.1.0"
+ merge2 "^1.3.0"
+ micromatch "^4.0.2"
+ picomatch "^2.2.1"
fast-json-stable-stringify@^2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.0.0.tgz#d5142c0caee6b1189f87d3a76111064f86c8bbf2"
+ version "2.1.0"
+ resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz#874bf69c6f404c2b5d99c481341399fd55892633"
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-fast-levenshtein@~2.0.6:
+fast-levenshtein@^2.0.6:
version "2.0.6"
resolved "https://registry.yarnpkg.com/fast-levenshtein/-/fast-levenshtein-2.0.6.tgz#3d8a5c66883a16a30ca8643e851f19baa7797917"
+ integrity sha1-PYpcZog6FqMMqGQ+hR8Zuqd5eRc=
-figgy-pudding@^3.4.1, figgy-pudding@^3.5.1:
- version "3.5.2"
- resolved "https://registry.yarnpkg.com/figgy-pudding/-/figgy-pudding-3.5.2.tgz#b4eee8148abb01dcf1d1ac34367d59e12fa61d6e"
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-
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- version "3.1.0"
- resolved "https://registry.yarnpkg.com/figures/-/figures-3.1.0.tgz#4b198dd07d8d71530642864af2d45dd9e459c4ec"
+fastq@^1.6.0:
+ version "1.11.0"
+ resolved "https://registry.yarnpkg.com/fastq/-/fastq-1.11.0.tgz#bb9fb955a07130a918eb63c1f5161cc32a5d0858"
+ integrity sha512-7Eczs8gIPDrVzT+EksYBcupqMyxSHXXrHOLRRxU2/DicV8789MRBRR8+Hc2uWzUupOs4YS4JzBmBxjjCVBxD/g==
dependencies:
- escape-string-regexp "^1.0.5"
+ reusify "^1.0.4"
-file-entry-cache@^5.0.1:
- version "5.0.1"
- resolved "https://registry.yarnpkg.com/file-entry-cache/-/file-entry-cache-5.0.1.tgz#ca0f6efa6dd3d561333fb14515065c2fafdf439c"
+file-entry-cache@^6.0.1:
+ version "6.0.1"
+ resolved "https://registry.yarnpkg.com/file-entry-cache/-/file-entry-cache-6.0.1.tgz#211b2dd9659cb0394b073e7323ac3c933d522027"
+ integrity sha512-7Gps/XWymbLk2QLYK4NzpMOrYjMhdIxXuIvy2QBsLE6ljuodKvdkWs/cpyJJ3CVIVpH0Oi1Hvg1ovbMzLdFBBg==
dependencies:
- flat-cache "^2.0.1"
+ flat-cache "^3.0.4"
fill-range@^7.0.1:
version "7.0.1"
resolved "https://registry.yarnpkg.com/fill-range/-/fill-range-7.0.1.tgz#1919a6a7c75fe38b2c7c77e5198535da9acdda40"
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dependencies:
to-regex-range "^5.0.1"
-finalhandler@1.1.1:
- version "1.1.1"
- resolved "https://registry.yarnpkg.com/finalhandler/-/finalhandler-1.1.1.tgz#eebf4ed840079c83f4249038c9d703008301b105"
+finalhandler@~1.1.2:
+ version "1.1.2"
+ resolved "https://registry.yarnpkg.com/finalhandler/-/finalhandler-1.1.2.tgz#b7e7d000ffd11938d0fdb053506f6ebabe9f587d"
+ integrity sha512-aAWcW57uxVNrQZqFXjITpW3sIUQmHGG3qSb9mUah9MgMC4NeWhNOlNjXEYq3HjRAvL6arUviZGGJsBg6z0zsWA==
dependencies:
debug "2.6.9"
encodeurl "~1.0.2"
escape-html "~1.0.3"
on-finished "~2.3.0"
- parseurl "~1.3.2"
- statuses "~1.4.0"
+ parseurl "~1.3.3"
+ statuses "~1.5.0"
unpipe "~1.0.0"
find-cache-dir@^3.2.0:
version "3.3.1"
resolved "https://registry.yarnpkg.com/find-cache-dir/-/find-cache-dir-3.3.1.tgz#89b33fad4a4670daa94f855f7fbe31d6d84fe880"
+ integrity sha512-t2GDMt3oGC/v+BMwzmllWDuJF/xcDtE5j/fCGbqDD7OLuJkj0cfh1YSA5VKPvwMeLFLNDBkwOKZ2X85jGLVftQ==
dependencies:
commondir "^1.0.1"
make-dir "^3.0.2"
pkg-dir "^4.1.0"
-find-npm-prefix@^1.0.2:
- version "1.0.2"
- resolved "https://registry.yarnpkg.com/find-npm-prefix/-/find-npm-prefix-1.0.2.tgz#8d8ce2c78b3b4b9e66c8acc6a37c231eb841cfdf"
- integrity sha512-KEftzJ+H90x6pcKtdXZEPsQse8/y/UnvzRKrOSQFprnrGaFuJ62fVkP34Iu2IYuMvyauCyoLTNkJZgrrGA2wkA==
-
find-up@5.0.0:
version "5.0.0"
resolved "https://registry.yarnpkg.com/find-up/-/find-up-5.0.0.tgz#4c92819ecb7083561e4f4a240a86be5198f536fc"
@@ -1876,54 +2240,60 @@ find-up@5.0.0:
locate-path "^6.0.0"
path-exists "^4.0.0"
-find-up@^3.0.0:
- version "3.0.0"
- resolved "https://registry.yarnpkg.com/find-up/-/find-up-3.0.0.tgz#49169f1d7993430646da61ecc5ae355c21c97b73"
+find-up@^2.1.0:
+ version "2.1.0"
+ resolved "https://registry.yarnpkg.com/find-up/-/find-up-2.1.0.tgz#45d1b7e506c717ddd482775a2b77920a3c0c57a7"
+ integrity sha1-RdG35QbHF93UgndaK3eSCjwMV6c=
dependencies:
- locate-path "^3.0.0"
+ locate-path "^2.0.0"
find-up@^4.0.0, find-up@^4.1.0:
version "4.1.0"
resolved "https://registry.yarnpkg.com/find-up/-/find-up-4.1.0.tgz#97afe7d6cdc0bc5928584b7c8d7b16e8a9aa5d19"
+ integrity sha512-PpOwAdQ/YlXQ2vj8a3h8IipDuYRi3wceVQQGYWxNINccq40Anw7BlsEXCMbt1Zt+OLA6Fq9suIpIWD0OsnISlw==
dependencies:
locate-path "^5.0.0"
path-exists "^4.0.0"
-flat-cache@^2.0.1:
- version "2.0.1"
- resolved "https://registry.yarnpkg.com/flat-cache/-/flat-cache-2.0.1.tgz#5d296d6f04bda44a4630a301413bdbc2ec085ec0"
+flat-cache@^3.0.4:
+ version "3.0.4"
+ resolved "https://registry.yarnpkg.com/flat-cache/-/flat-cache-3.0.4.tgz#61b0338302b2fe9f957dcc32fc2a87f1c3048b11"
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dependencies:
- flatted "^2.0.0"
- rimraf "2.6.3"
- write "1.0.3"
+ flatted "^3.1.0"
+ rimraf "^3.0.2"
-flat@^4.1.0:
- version "4.1.0"
- resolved "https://registry.yarnpkg.com/flat/-/flat-4.1.0.tgz#090bec8b05e39cba309747f1d588f04dbaf98db2"
- dependencies:
- is-buffer "~2.0.3"
+flat@^5.0.2:
+ version "5.0.2"
+ resolved "https://registry.yarnpkg.com/flat/-/flat-5.0.2.tgz#8ca6fe332069ffa9d324c327198c598259ceb241"
+ integrity sha512-b6suED+5/3rTpUBdG1gupIl8MPFCAMA0QXwmljLhvCUKcUvdE4gWky9zpuGCcXHOsz4J9wPGNWq6OKpmIzz3hQ==
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- resolved "https://registry.yarnpkg.com/flatted/-/flatted-2.0.1.tgz#69e57caa8f0eacbc281d2e2cb458d46fdb449e08"
+flatted@^3.1.0:
+ version "3.1.1"
+ resolved "https://registry.yarnpkg.com/flatted/-/flatted-3.1.1.tgz#c4b489e80096d9df1dfc97c79871aea7c617c469"
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- version "1.0.0"
- resolved "https://registry.yarnpkg.com/fluent-openapi/-/fluent-openapi-1.0.0.tgz#57ba662c70cfcee3fcc2f62e0e0672001981bc5c"
- dependencies:
- lodash.merge "^4.6.1"
+for-in@^0.1.3:
+ version "0.1.8"
+ resolved "https://registry.yarnpkg.com/for-in/-/for-in-0.1.8.tgz#d8773908e31256109952b1fdb9b3fa867d2775e1"
+ integrity sha1-2Hc5COMSVhCZUrH9ubP6hn0ndeE=
-flush-write-stream@^1.0.0:
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- resolved "https://registry.yarnpkg.com/flush-write-stream/-/flush-write-stream-1.1.1.tgz#8dd7d873a1babc207d94ead0c2e0e44276ebf2e8"
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+for-in@^1.0.1:
+ version "1.0.2"
+ resolved "https://registry.yarnpkg.com/for-in/-/for-in-1.0.2.tgz#81068d295a8142ec0ac726c6e2200c30fb6d5e80"
+ integrity sha1-gQaNKVqBQuwKxybG4iAMMPttXoA=
+
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+ version "0.1.5"
+ resolved "https://registry.yarnpkg.com/for-own/-/for-own-0.1.5.tgz#5265c681a4f294dabbf17c9509b6763aa84510ce"
+ integrity sha1-UmXGgaTylNq78XyVCbZ2OqhFEM4=
dependencies:
- inherits "^2.0.3"
- readable-stream "^2.3.6"
+ for-in "^1.0.1"
foreground-child@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/foreground-child/-/foreground-child-2.0.0.tgz#71b32800c9f15aa8f2f83f4a6bd9bff35d861a53"
+ integrity sha512-dCIq9FpEcyQyXKCkyzmlPTFNgrCzPudOe+mhvJU5zAtlBnGVy2yKxtfsxK2tQBThwq225jcvBjpw1Gr40uzZCA==
dependencies:
cross-spawn "^7.0.0"
signal-exit "^3.0.2"
@@ -1931,31 +2301,37 @@ foreground-child@^2.0.0:
forever-agent@~0.6.1:
version "0.6.1"
resolved "https://registry.yarnpkg.com/forever-agent/-/forever-agent-0.6.1.tgz#fbc71f0c41adeb37f96c577ad1ed42d8fdacca91"
+ integrity sha1-+8cfDEGt6zf5bFd60e1C2P2sypE=
-form-data@~2.3.1, form-data@~2.3.2:
- version "2.3.2"
- resolved "https://registry.yarnpkg.com/form-data/-/form-data-2.3.2.tgz#4970498be604c20c005d4f5c23aecd21d6b49099"
+form-data@^2.5.0:
+ version "2.5.1"
+ resolved "https://registry.yarnpkg.com/form-data/-/form-data-2.5.1.tgz#f2cbec57b5e59e23716e128fe44d4e5dd23895f4"
+ integrity sha512-m21N3WOmEEURgk6B9GLOE4RuWOFf28Lhh9qGYeNlGq4VDXUlJy2th2slBNU8Gp8EzloYZOibZJ7t5ecIrFSjVA==
dependencies:
asynckit "^0.4.0"
- combined-stream "1.0.6"
+ combined-stream "^1.0.6"
mime-types "^2.1.12"
-forwarded@~0.1.2:
- version "0.1.2"
- resolved "https://registry.yarnpkg.com/forwarded/-/forwarded-0.1.2.tgz#98c23dab1175657b8c0573e8ceccd91b0ff18c84"
+form-data@~2.3.2:
+ version "2.3.3"
+ resolved "https://registry.yarnpkg.com/form-data/-/form-data-2.3.3.tgz#dcce52c05f644f298c6a7ab936bd724ceffbf3a6"
+ integrity sha512-1lLKB2Mu3aGP1Q/2eCOx0fNbRMe7XdwktwOruhfqqd0rIJWwN4Dh+E3hrPSlDCXnSR7UtZ1N38rVXm+6+MEhJQ==
+ dependencies:
+ asynckit "^0.4.0"
+ combined-stream "^1.0.6"
+ mime-types "^2.1.12"
+
+forwarded@0.2.0:
+ version "0.2.0"
+ resolved "https://registry.yarnpkg.com/forwarded/-/forwarded-0.2.0.tgz#2269936428aad4c15c7ebe9779a84bf0b2a81811"
+ integrity sha512-buRG0fpBtRHSTCOASe6hD258tEubFoRLb4ZNA6NxMVHNw2gOcwHo9wyablzMzOA5z9xA9L1KNjk/Nt6MT9aYow==
fresh@0.5.2:
version "0.5.2"
resolved "https://registry.yarnpkg.com/fresh/-/fresh-0.5.2.tgz#3d8cadd90d976569fa835ab1f8e4b23a105605a7"
+ integrity sha1-PYyt2Q2XZWn6g1qx+OSyOhBWBac=
-from2@^1.3.0:
- version "1.3.0"
- resolved "https://registry.yarnpkg.com/from2/-/from2-1.3.0.tgz#88413baaa5f9a597cfde9221d86986cd3c061dfd"
- dependencies:
- inherits "~2.0.1"
- readable-stream "~1.1.10"
-
-from2@^2.1.0, from2@^2.1.1:
+from2@^2.1.1:
version "2.3.0"
resolved "https://registry.yarnpkg.com/from2/-/from2-2.3.0.tgz#8bfb5502bde4a4d36cfdeea007fcca21d7e382af"
integrity sha1-i/tVAr3kpNNs/e6gB/zKIdfjgq8=
@@ -1964,47 +2340,33 @@ from2@^2.1.0, from2@^2.1.1:
readable-stream "^2.0.0"
fromentries@^1.2.0:
- version "1.2.0"
- resolved "https://registry.yarnpkg.com/fromentries/-/fromentries-1.2.0.tgz#e6aa06f240d6267f913cea422075ef88b63e7897"
+ version "1.3.2"
+ resolved "https://registry.yarnpkg.com/fromentries/-/fromentries-1.3.2.tgz#e4bca6808816bf8f93b52750f1127f5a6fd86e3a"
+ integrity sha512-cHEpEQHUg0f8XdtZCc2ZAhrHzKzT0MrFUTcvx+hfxYu7rGMDc5SKoXFh+n4YigxsHXRzc6OrCshdR1bWH6HHyg==
fs-minipass@^1.2.5:
- version "1.2.5"
- resolved "https://registry.yarnpkg.com/fs-minipass/-/fs-minipass-1.2.5.tgz#06c277218454ec288df77ada54a03b8702aacb9d"
+ version "1.2.7"
+ resolved "https://registry.yarnpkg.com/fs-minipass/-/fs-minipass-1.2.7.tgz#ccff8570841e7fe4265693da88936c55aed7f7c7"
+ integrity sha512-GWSSJGFy4e9GUeCcbIkED+bgAoFyj7XF1mV8rma3QW4NIqX9Kyx79N/PF61H5udOV3aY1IaMLs6pGbH71nlCTA==
dependencies:
- minipass "^2.2.1"
+ minipass "^2.6.0"
-fs-minipass@^2.0.0:
+fs-minipass@^2.0.0, fs-minipass@^2.1.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/fs-minipass/-/fs-minipass-2.1.0.tgz#7f5036fdbf12c63c169190cbe4199c852271f9fb"
+ integrity sha512-V/JgOLFCS+R6Vcq0slCuaeWEdNC3ouDlJMNIsacH2VtALiu9mV4LPrHc5cDl8k5aw6J8jwgWWpiTo5RYhmIzvg==
dependencies:
minipass "^3.0.0"
-fs-vacuum@^1.2.10, fs-vacuum@~1.2.10:
- version "1.2.10"
- resolved "https://registry.yarnpkg.com/fs-vacuum/-/fs-vacuum-1.2.10.tgz#b7629bec07a4031a2548fdf99f5ecf1cc8b31e36"
- dependencies:
- graceful-fs "^4.1.2"
- path-is-inside "^1.0.1"
- rimraf "^2.5.2"
-
-fs-write-stream-atomic@^1.0.8, fs-write-stream-atomic@~1.0.10:
- version "1.0.10"
- resolved "https://registry.yarnpkg.com/fs-write-stream-atomic/-/fs-write-stream-atomic-1.0.10.tgz#b47df53493ef911df75731e70a9ded0189db40c9"
- dependencies:
- graceful-fs "^4.1.2"
- iferr "^0.1.5"
- imurmurhash "^0.1.4"
- readable-stream "1 || 2"
-
fs.realpath@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
integrity sha1-FQStJSMVjKpA20onh8sBQRmU6k8=
-fsevents@~2.1.2:
- version "2.1.3"
- resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.1.3.tgz#fb738703ae8d2f9fe900c33836ddebee8b97f23e"
- integrity sha512-Auw9a4AxqWpa9GUfj370BMPzzyncfBABW8Mab7BGWBYDj4Isgq+cDKtx0i6u9jcX9pQDnswsaaOTgTmA5pEjuQ==
+fsevents@~2.3.1:
+ version "2.3.2"
+ resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.2.tgz#8a526f78b8fdf4623b709e0b975c52c24c02fd1a"
+ integrity sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==
fstream@^1.0.0, fstream@^1.0.12:
version "1.0.12"
@@ -2019,14 +2381,17 @@ fstream@^1.0.0, fstream@^1.0.12:
function-bind@^1.1.1:
version "1.1.1"
resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.1.tgz#a56899d3ea3c9bab874bb9773b7c5ede92f4895d"
+ integrity sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A==
functional-red-black-tree@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/functional-red-black-tree/-/functional-red-black-tree-1.0.1.tgz#1b0ab3bd553b2a0d6399d29c0e3ea0b252078327"
+ integrity sha1-GwqzvVU7Kg1jmdKcDj6gslIHgyc=
gauge@~2.7.3:
version "2.7.4"
resolved "https://registry.yarnpkg.com/gauge/-/gauge-2.7.4.tgz#2c03405c7538c39d7eb37b317022e325fb018bf7"
+ integrity sha1-LANAXHU4w51+s3sxcCLjJfsBi/c=
dependencies:
aproba "^1.0.3"
console-control-strings "^1.0.0"
@@ -2037,33 +2402,17 @@ gauge@~2.7.3:
strip-ansi "^3.0.1"
wide-align "^1.1.0"
-genfun@^5.0.0:
- version "5.0.0"
- resolved "https://registry.yarnpkg.com/genfun/-/genfun-5.0.0.tgz#9dd9710a06900a5c4a5bf57aca5da4e52fe76537"
- integrity sha512-KGDOARWVga7+rnB3z9Sd2Letx515owfk0hSxHGuqjANb1M+x2bGZGqHLiozPsYMdM2OubeMni/Hpwmjq6qIUhA==
-
-gensync@^1.0.0-beta.1:
- version "1.0.0-beta.1"
- resolved "https://registry.yarnpkg.com/gensync/-/gensync-1.0.0-beta.1.tgz#58f4361ff987e5ff6e1e7a210827aa371eaac269"
+gensync@^1.0.0-beta.2:
+ version "1.0.0-beta.2"
+ resolved "https://registry.yarnpkg.com/gensync/-/gensync-1.0.0-beta.2.tgz#32a6ee76c3d7f52d46b2b1ae5d93fea8580a25e0"
+ integrity sha512-3hN7NaskYvMDLQY55gnW3NQ+mesEAepTqlg+VEbj7zzqEMBVNhzcGYYeqFo/TlYz6eQiFcp1HcsCZO+nGgS8zg==
-gentle-fs@^2.3.0, gentle-fs@^2.3.1:
- version "2.3.1"
- resolved "https://registry.yarnpkg.com/gentle-fs/-/gentle-fs-2.3.1.tgz#11201bf66c18f930ddca72cf69460bdfa05727b1"
- integrity sha512-OlwBBwqCFPcjm33rF2BjW+Pr6/ll2741l+xooiwTCeaX2CA1ZuclavyMBe0/KlR21/XGsgY6hzEQZ15BdNa13Q==
- dependencies:
- aproba "^1.1.2"
- chownr "^1.1.2"
- cmd-shim "^3.0.3"
- fs-vacuum "^1.2.10"
- graceful-fs "^4.1.11"
- iferr "^0.1.5"
- infer-owner "^1.0.4"
- mkdirp "^0.5.1"
- path-is-inside "^1.0.2"
- read-cmd-shim "^1.0.1"
- slide "^1.1.6"
+get-caller-file@^1.0.1:
+ version "1.0.3"
+ resolved "https://registry.yarnpkg.com/get-caller-file/-/get-caller-file-1.0.3.tgz#f978fa4c90d1dfe7ff2d6beda2a515e713bdcf4a"
+ integrity sha512-3t6rVToeoZfYSGd8YoLFR2DJkiQrIiUrGcjvFX2mDw3bn6k2OtwHN0TNCLbBO+w8qTvimhDkv+LSscbJY1vE6w==
-get-caller-file@^2.0.5:
+get-caller-file@^2.0.1, get-caller-file@^2.0.5:
version "2.0.5"
resolved "https://registry.yarnpkg.com/get-caller-file/-/get-caller-file-2.0.5.tgz#4f94412a82db32f36e3b0b9741f8a97feb031f7e"
integrity sha512-DyFP3BM/3YHTQOCUL/w0OZHR0lpKeGrxotcHWcqNEdnltqFwXVfhEBQ94eIo34AfQpo0rGki4cyIiftY06h2Fg==
@@ -2071,43 +2420,57 @@ get-caller-file@^2.0.5:
get-func-name@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/get-func-name/-/get-func-name-2.0.0.tgz#ead774abee72e20409433a066366023dd6887a41"
+ integrity sha1-6td0q+5y4gQJQzoGY2YCPdaIekE=
+
+get-intrinsic@^1.0.2:
+ version "1.1.1"
+ resolved "https://registry.yarnpkg.com/get-intrinsic/-/get-intrinsic-1.1.1.tgz#15f59f376f855c446963948f0d24cd3637b4abc6"
+ integrity sha512-kWZrnVM42QCiEA2Ig1bG8zjoIMOgxWwYCEeNdwY6Tv/cOSeGpcoX4pXHfKUxNKVoArnrEr2e9srnAxxGIraS9Q==
+ dependencies:
+ function-bind "^1.1.1"
+ has "^1.0.3"
+ has-symbols "^1.0.1"
+
+get-package-type@^0.1.0:
+ version "0.1.0"
+ resolved "https://registry.yarnpkg.com/get-package-type/-/get-package-type-0.1.0.tgz#8de2d803cff44df3bc6c456e6668b36c3926e11a"
+ integrity sha512-pjzuKtY64GYfWizNAJ0fr9VqttZkNiK2iS430LtIHzjBEr6bX8Am2zm4sW4Ro5wjWW5cAlRL1qAMTcXbjNAO2Q==
get-stream@3.0.0, get-stream@^3.0.0:
version "3.0.0"
- resolved "https://registry.npmjs.org/get-stream/-/get-stream-3.0.0.tgz#8e943d1358dc37555054ecbe2edb05aa174ede14"
+ resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-3.0.0.tgz#8e943d1358dc37555054ecbe2edb05aa174ede14"
+ integrity sha1-jpQ9E1jcN1VQVOy+LtsFqhdO3hQ=
-get-stream@^4.0.0, get-stream@^4.1.0:
+get-stream@^4.0.0:
version "4.1.0"
resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-4.1.0.tgz#c1b255575f3dc21d59bfc79cd3d2b46b1c3a54b5"
+ integrity sha512-GMat4EJ5161kIy2HevLlr4luNjBgvmj413KaQA7jt4V8B4RDsfpHk7WQ9GVqfYyyx8OS/L66Kox+rJRNklLK7w==
dependencies:
pump "^3.0.0"
getpass@^0.1.1:
version "0.1.7"
resolved "https://registry.yarnpkg.com/getpass/-/getpass-0.1.7.tgz#5eff8e3e684d569ae4cb2b1282604e8ba62149fa"
+ integrity sha1-Xv+OPmhNVprkyysSgmBOi6YhSfo=
dependencies:
assert-plus "^1.0.0"
glob-parent@^3.0.0:
version "3.1.0"
resolved "https://registry.yarnpkg.com/glob-parent/-/glob-parent-3.1.0.tgz#9e6af6299d8d3bd2bd40430832bd113df906c5ae"
+ integrity sha1-nmr2KZ2NO9K9QEMIMr0RPfkGxa4=
dependencies:
is-glob "^3.1.0"
path-dirname "^1.0.0"
-glob-parent@^5.0.0:
- version "5.1.0"
- resolved "https://registry.yarnpkg.com/glob-parent/-/glob-parent-5.1.0.tgz#5f4c1d1e748d30cd73ad2944b3577a81b081e8c2"
- dependencies:
- is-glob "^4.0.1"
-
-glob-parent@~5.1.0:
- version "5.1.1"
- resolved "https://registry.yarnpkg.com/glob-parent/-/glob-parent-5.1.1.tgz#b6c1ef417c4e5663ea498f1c45afac6916bbc229"
+glob-parent@^5.1.0, glob-parent@^5.1.2, glob-parent@~5.1.0:
+ version "5.1.2"
+ resolved "https://registry.yarnpkg.com/glob-parent/-/glob-parent-5.1.2.tgz#869832c58034fe68a4093c17dc15e8340d8401c4"
+ integrity sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==
dependencies:
is-glob "^4.0.1"
-glob@7.1.6, glob@^7.1.3, glob@^7.1.4, glob@^7.1.6:
+glob@7.1.6:
version "7.1.6"
resolved "https://registry.yarnpkg.com/glob/-/glob-7.1.6.tgz#141f33b81a7c2492e125594307480c46679278a6"
integrity sha512-LwaxwyZ72Lk7vZINtNNrywX0ZuLyStrdDtabefZKAY5ZGJhVtgdznluResxNmPitE0SAO+O26sWTHeKSI2wMBA==
@@ -2119,9 +2482,10 @@ glob@7.1.6, glob@^7.1.3, glob@^7.1.4, glob@^7.1.6:
once "^1.3.0"
path-is-absolute "^1.0.0"
-glob@^7.0.3, glob@^7.0.5, glob@^7.1.1:
- version "7.1.2"
- resolved "https://registry.yarnpkg.com/glob/-/glob-7.1.2.tgz#c19c9df9a028702d678612384a6552404c636d15"
+glob@^7.0.3, glob@^7.1.1, glob@^7.1.3, glob@^7.1.4, glob@^7.1.6, glob@^7.1.7:
+ version "7.1.7"
+ resolved "https://registry.yarnpkg.com/glob/-/glob-7.1.7.tgz#3b193e9233f01d42d0b3f78294bbeeb418f94a90"
+ integrity sha512-OvD9ENzPLbegENnYP5UUfJIirTg4+XwMWGaQfQTY0JenxNvvIKP3U3/tAQSPIu/lHxXYSZmpXlUHeqAIdKzBLQ==
dependencies:
fs.realpath "^1.0.0"
inflight "^1.0.4"
@@ -2133,22 +2497,38 @@ glob@^7.0.3, glob@^7.0.5, glob@^7.1.1:
global-dirs@^0.1.0:
version "0.1.1"
resolved "https://registry.yarnpkg.com/global-dirs/-/global-dirs-0.1.1.tgz#b319c0dd4607f353f3be9cca4c72fc148c49f445"
+ integrity sha1-sxnA3UYH81PzvpzKTHL8FIxJ9EU=
dependencies:
ini "^1.3.4"
globals@^11.1.0:
- version "11.9.0"
- resolved "https://registry.yarnpkg.com/globals/-/globals-11.9.0.tgz#bde236808e987f290768a93d065060d78e6ab249"
+ version "11.12.0"
+ resolved "https://registry.yarnpkg.com/globals/-/globals-11.12.0.tgz#ab8795338868a0babd8525758018c2a7eb95c42e"
+ integrity sha512-WOBp/EEGUiIsJSp7wcv/y6MO+lV9UoncWqxuFfm8eBwzWNgyfBd6Gz+IeKQ9jCmyhoH99g15M3T+QaVHFjizVA==
+
+globals@^13.6.0, globals@^13.9.0:
+ version "13.9.0"
+ resolved "https://registry.yarnpkg.com/globals/-/globals-13.9.0.tgz#4bf2bf635b334a173fb1daf7c5e6b218ecdc06cb"
+ integrity sha512-74/FduwI/JaIrr1H8e71UbDE+5x7pIPs1C2rrwC52SszOo043CsWOZEMW7o2Y58xwm9b+0RBKDxY5n2sUpEFxA==
+ dependencies:
+ type-fest "^0.20.2"
-globals@^12.1.0:
- version "12.3.0"
- resolved "https://registry.yarnpkg.com/globals/-/globals-12.3.0.tgz#1e564ee5c4dded2ab098b0f88f24702a3c56be13"
+globby@^11.0.3:
+ version "11.0.3"
+ resolved "https://registry.yarnpkg.com/globby/-/globby-11.0.3.tgz#9b1f0cb523e171dd1ad8c7b2a9fb4b644b9593cb"
+ integrity sha512-ffdmosjA807y7+lA1NM0jELARVmYul/715xiILEjo3hBLPTcirgQNnXECn5g3mtR8TOLCVbkfua1Hpen25/Xcg==
dependencies:
- type-fest "^0.8.1"
+ array-union "^2.1.0"
+ dir-glob "^3.0.1"
+ fast-glob "^3.1.1"
+ ignore "^5.1.4"
+ merge2 "^1.3.0"
+ slash "^3.0.0"
globby@^6.0.0:
version "6.1.0"
resolved "https://registry.yarnpkg.com/globby/-/globby-6.1.0.tgz#f5a6d70e8395e21c858fb0489d64df02424d506c"
+ integrity sha1-9abXDoOV4hyFj7BInWTfAkJNUGw=
dependencies:
array-union "^1.0.1"
glob "^7.0.3"
@@ -2159,6 +2539,7 @@ globby@^6.0.0:
got@^6.7.1:
version "6.7.1"
resolved "https://registry.yarnpkg.com/got/-/got-6.7.1.tgz#240cd05785a9a18e561dc1b44b41c763ef1e8db0"
+ integrity sha1-JAzQV4WpoY5WHcG0S0HHY+8ejbA=
dependencies:
create-error-class "^3.0.0"
duplexer3 "^0.1.4"
@@ -2175,6 +2556,7 @@ got@^6.7.1:
got@^8.3.2:
version "8.3.2"
resolved "https://registry.yarnpkg.com/got/-/got-8.3.2.tgz#1d23f64390e97f776cac52e5b936e5f514d2e937"
+ integrity sha512-qjUJ5U/hawxosMryILofZCkm3C84PLJS/0grRIpjAwu+Lkxxj5cxeCU25BG0/3mDSpXKTyZr8oh8wIgLaH0QCw==
dependencies:
"@sindresorhus/is" "^0.7.0"
cacheable-request "^2.1.1"
@@ -2194,79 +2576,81 @@ got@^8.3.2:
url-parse-lax "^3.0.0"
url-to-options "^1.0.1"
-graceful-fs@^4.1.11:
- version "4.1.15"
- resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.1.15.tgz#ffb703e1066e8a0eeaa4c8b80ba9253eeefbfb00"
-
-graceful-fs@^4.1.15, graceful-fs@^4.1.2, graceful-fs@^4.2.2, graceful-fs@^4.2.4:
- version "4.2.4"
- resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.4.tgz#2256bde14d3632958c465ebc96dc467ca07a29fb"
- integrity sha512-WjKPNJF79dtJAVniUlGGWHYGz2jWxT6VhN/4m1NdkbZ2nOsEF+cI1Edgql5zCRhs/VsQYRvrXctxktVXZUkixw==
+graceful-fs@^4.1.11, graceful-fs@^4.1.15, graceful-fs@^4.1.2, graceful-fs@^4.2.3, graceful-fs@^4.2.6:
+ version "4.2.6"
+ resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.6.tgz#ff040b2b0853b23c3d31027523706f1885d76bee"
+ integrity sha512-nTnJ528pbqxYanhpDYsi4Rd8MAeaBA67+RZ10CM1m3bTAVFEDcd5AuA4a6W5YkGZ1iNXHzZz8T6TBKLeBuNriQ==
growl@1.10.5:
version "1.10.5"
resolved "https://registry.yarnpkg.com/growl/-/growl-1.10.5.tgz#f2735dc2283674fa67478b10181059355c369e5e"
+ integrity sha512-qBr4OuELkhPenW6goKVXiv47US3clb3/IbuWF9KNKEijAy9oeHxU9IgzjvJhHkUzhaj7rOUD7+YGWqUjLp5oSA==
har-schema@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/har-schema/-/har-schema-2.0.0.tgz#a94c2224ebcac04782a0d9035521f24735b7ec92"
+ integrity sha1-qUwiJOvKwEeCoNkDVSHyRzW37JI=
-har-validator@~5.0.3:
- version "5.0.3"
- resolved "https://registry.yarnpkg.com/har-validator/-/har-validator-5.0.3.tgz#ba402c266194f15956ef15e0fcf242993f6a7dfd"
- dependencies:
- ajv "^5.1.0"
- har-schema "^2.0.0"
-
-har-validator@~5.1.0:
- version "5.1.0"
- resolved "https://registry.yarnpkg.com/har-validator/-/har-validator-5.1.0.tgz#44657f5688a22cfd4b72486e81b3a3fb11742c29"
+har-validator@~5.1.3:
+ version "5.1.5"
+ resolved "https://registry.yarnpkg.com/har-validator/-/har-validator-5.1.5.tgz#1f0803b9f8cb20c0fa13822df1ecddb36bde1efd"
+ integrity sha512-nmT2T0lljbxdQZfspsno9hgrG3Uir6Ks5afism62poxqBM6sDnMEuPmzTq8XN0OEwqKLLdh1jQI3qyE66Nzb3w==
dependencies:
- ajv "^5.3.0"
+ ajv "^6.12.3"
har-schema "^2.0.0"
has-flag@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-3.0.0.tgz#b5d454dc2199ae225699f3467e5a07f3b955bafd"
+ integrity sha1-tdRU3CGZriJWmfNGfloH87lVuv0=
has-flag@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b"
+ integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
has-symbol-support-x@^1.4.1:
version "1.4.2"
resolved "https://registry.yarnpkg.com/has-symbol-support-x/-/has-symbol-support-x-1.4.2.tgz#1409f98bc00247da45da67cee0a36f282ff26455"
+ integrity sha512-3ToOva++HaW+eCpgqZrCfN51IPB+7bJNVT6CUATzueB5Heb8o6Nam0V3HG5dlDvZU1Gn5QLcbahiKw/XVk5JJw==
-has-symbols@^1.0.0, has-symbols@^1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.0.1.tgz#9f5214758a44196c406d9bd76cebf81ec2dd31e8"
+has-symbols@^1.0.1:
+ version "1.0.2"
+ resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.0.2.tgz#165d3070c00309752a1236a479331e3ac56f1423"
+ integrity sha512-chXa79rL/UC2KlX17jo3vRGz0azaWEx5tGqZg5pO3NUyEJVB17dMruQlzCCOfUvElghKcm5194+BCRvi2Rv/Gw==
has-to-string-tag-x@^1.2.0:
version "1.4.1"
resolved "https://registry.yarnpkg.com/has-to-string-tag-x/-/has-to-string-tag-x-1.4.1.tgz#a045ab383d7b4b2012a00148ab0aa5f290044d4d"
+ integrity sha512-vdbKfmw+3LoOYVr+mtxHaX5a96+0f3DljYd8JOqvOLsf5mw2Otda2qCDT9qRqLAhrjyQ0h7ual5nOiASpsGNFw==
dependencies:
has-symbol-support-x "^1.4.1"
-has-unicode@^2.0.0, has-unicode@~2.0.1:
+has-unicode@^2.0.0:
version "2.0.1"
resolved "https://registry.yarnpkg.com/has-unicode/-/has-unicode-2.0.1.tgz#e0e6fe6a28cf51138855e086d1691e771de2a8b9"
+ integrity sha1-4Ob+aijPUROIVeCG0Wkedx3iqLk=
has@^1.0.3:
version "1.0.3"
resolved "https://registry.yarnpkg.com/has/-/has-1.0.3.tgz#722d7cbfc1f6aa8241f16dd814e011e1f41e8796"
+ integrity sha512-f2dvO0VU6Oej7RkWJGrehjbzMAjFp5/VKPp5tTpWIV4JHHZK1/BxbFRtf/siA2SWTe09caDmVtYYzWEIbBS4zw==
dependencies:
function-bind "^1.1.1"
hash-base@^3.0.0:
- version "3.0.4"
- resolved "https://registry.yarnpkg.com/hash-base/-/hash-base-3.0.4.tgz#5fc8686847ecd73499403319a6b0a3f3f6ae4918"
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/hash-base/-/hash-base-3.1.0.tgz#55c381d9e06e1d2997a883b4a3fddfe7f0d3af33"
+ integrity sha512-1nmYp/rhMDiE7AYkDw+lLwlAzz0AntGIe51F3RfFfEqyQ3feY2eI/NcwC6umIQVOASPMsWJLJScWKSSvzL9IVA==
dependencies:
- inherits "^2.0.1"
- safe-buffer "^5.0.1"
+ inherits "^2.0.4"
+ readable-stream "^3.6.0"
+ safe-buffer "^5.2.0"
hasha@^5.0.0:
- version "5.2.0"
- resolved "https://registry.yarnpkg.com/hasha/-/hasha-5.2.0.tgz#33094d1f69c40a4a6ac7be53d5fe3ff95a269e0c"
+ version "5.2.2"
+ resolved "https://registry.yarnpkg.com/hasha/-/hasha-5.2.2.tgz#a48477989b3b327aea3c04f53096d816d97522a1"
+ integrity sha512-Hrp5vIK/xr5SkeN2onO32H0MgNZ0f17HRNH39WfL0SYUNOTZ5Lz1TJ8Pajo/87dYGEFlLMm7mIc/k/s6Bvz9HQ==
dependencies:
is-stream "^2.0.0"
type-fest "^0.8.0"
@@ -2274,66 +2658,87 @@ hasha@^5.0.0:
he@1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/he/-/he-1.2.0.tgz#84ae65fa7eafb165fddb61566ae14baf05664f0f"
+ integrity sha512-F/1DnUGPopORZi0ni+CvrCgHQ5FyEAHRLSApuYWMmrbSwoN2Mn/7k+Gl38gJnR7yyDZk6WLXwiGod1JOWNDKGw==
-hoek@2.x.x, hoek@^4.2.1:
- version "4.2.1"
- resolved "https://registry.yarnpkg.com/hoek/-/hoek-4.2.1.tgz#9634502aa12c445dd5a7c5734b572bb8738aacbb"
+hoek@2.x.x, hoek@>=6.1.3:
+ version "6.1.3"
+ resolved "https://registry.yarnpkg.com/hoek/-/hoek-6.1.3.tgz#73b7d33952e01fe27a38b0457294b79dd8da242c"
+ integrity sha512-YXXAAhmF9zpQbC7LEcREFtXfGq5K1fmd+4PHkBq8NUqmzW3G+Dq10bI/i0KucLRwss3YYFQ0fSfoxBZYiGUqtQ==
-hosted-git-info@^2.1.4, hosted-git-info@^2.7.1, hosted-git-info@^2.8.8:
+hosted-git-info@^2.7.1:
version "2.8.9"
resolved "https://registry.yarnpkg.com/hosted-git-info/-/hosted-git-info-2.8.9.tgz#dffc0bf9a21c02209090f2aa69429e1414daf3f9"
integrity sha512-mxIDAb9Lsm6DoOJ7xH+5+X4y1LU/4Hi50L9C5sIswK3JzULS4bwk1FvjdBgvYR4bzT4tuUQiC15FE2f5HbLvYw==
+hosted-git-info@^4.0.1, hosted-git-info@^4.0.2:
+ version "4.0.2"
+ resolved "https://registry.yarnpkg.com/hosted-git-info/-/hosted-git-info-4.0.2.tgz#5e425507eede4fea846b7262f0838456c4209961"
+ integrity sha512-c9OGXbZ3guC/xOlCg1Ci/VgWlwsqDv1yMQL1CWqXDL0hDjXuNcq0zuR4xqPSuasI3kqFDhqSyTjREz5gzq0fXg==
+ dependencies:
+ lru-cache "^6.0.0"
+
html-escaper@^2.0.0:
version "2.0.2"
resolved "https://registry.yarnpkg.com/html-escaper/-/html-escaper-2.0.2.tgz#dfd60027da36a36dfcbe236262c00a5822681453"
+ integrity sha512-H2iMtd0I4Mt5eYiapRdIDjp+XzelXQ0tFE4JS7YFwFevXXMmOp9myNrUvCg0D6ws8iqkRPBfKHgbwig1SmlLfg==
-http-cache-semantics@3.8.1, http-cache-semantics@^3.8.1:
+http-cache-semantics@3.8.1:
version "3.8.1"
resolved "https://registry.yarnpkg.com/http-cache-semantics/-/http-cache-semantics-3.8.1.tgz#39b0e16add9b605bf0a9ef3d9daaf4843b4cacd2"
integrity sha512-5ai2iksyV8ZXmnZhHH4rWPoxxistEexSi5936zIQ1bnNTW5VnA85B6P/VpXiRM017IgRvb2kKo1a//y+0wSp3w==
-http-errors@1.6.2:
- version "1.6.2"
- resolved "https://registry.yarnpkg.com/http-errors/-/http-errors-1.6.2.tgz#0a002cc85707192a7e7946ceedc11155f60ec736"
+http-cache-semantics@^4.1.0:
+ version "4.1.0"
+ resolved "https://registry.yarnpkg.com/http-cache-semantics/-/http-cache-semantics-4.1.0.tgz#49e91c5cbf36c9b94bcfcd71c23d5249ec74e390"
+ integrity sha512-carPklcUh7ROWRK7Cv27RPtdhYhUsela/ue5/jKzjegVvXDqM2ILE9Q2BGn9JZJh1g87cp56su/FgQSzcWS8cQ==
+
+http-errors@1.7.2:
+ version "1.7.2"
+ resolved "https://registry.yarnpkg.com/http-errors/-/http-errors-1.7.2.tgz#4f5029cf13239f31036e5b2e55292bcfbcc85c8f"
+ integrity sha512-uUQBt3H/cSIVfch6i1EuPNy/YsRSOUBXTVfZ+yR7Zjez3qjBz6i9+i4zjNaoqcoFVI4lQJ5plg63TvGfRSDCRg==
dependencies:
- depd "1.1.1"
+ depd "~1.1.2"
inherits "2.0.3"
- setprototypeof "1.0.3"
- statuses ">= 1.3.1 < 2"
+ setprototypeof "1.1.1"
+ statuses ">= 1.5.0 < 2"
+ toidentifier "1.0.0"
-http-errors@~1.6.2:
- version "1.6.3"
- resolved "https://registry.yarnpkg.com/http-errors/-/http-errors-1.6.3.tgz#8b55680bb4be283a0b5bf4ea2e38580be1d9320d"
+http-errors@~1.7.2:
+ version "1.7.3"
+ resolved "https://registry.yarnpkg.com/http-errors/-/http-errors-1.7.3.tgz#6c619e4f9c60308c38519498c14fbb10aacebb06"
+ integrity sha512-ZTTX0MWrsQ2ZAhA1cejAwDLycFsd7I7nVtnkT3Ol0aqodaKW+0CTZDQ1uBv5whptCnc8e8HeRRJxRs0kmm/Qfw==
dependencies:
depd "~1.1.2"
- inherits "2.0.3"
- setprototypeof "1.1.0"
- statuses ">= 1.4.0 < 2"
+ inherits "2.0.4"
+ setprototypeof "1.1.1"
+ statuses ">= 1.5.0 < 2"
+ toidentifier "1.0.0"
-http-proxy-agent@^2.1.0:
- version "2.1.0"
- resolved "https://registry.yarnpkg.com/http-proxy-agent/-/http-proxy-agent-2.1.0.tgz#e4821beef5b2142a2026bd73926fe537631c5405"
- integrity sha512-qwHbBLV7WviBl0rQsOzH6o5lwyOIvwp/BdFnvVxXORldu5TmjFfjzBcWUWS5kWAZhmv+JtiDhSuQCp4sBfbIgg==
+http-proxy-agent@^4.0.1:
+ version "4.0.1"
+ resolved "https://registry.yarnpkg.com/http-proxy-agent/-/http-proxy-agent-4.0.1.tgz#8a8c8ef7f5932ccf953c296ca8291b95aa74aa3a"
+ integrity sha512-k0zdNgqWTGA6aeIRVpvfVob4fL52dTfaehylg0Y4UvSySvOq/Y+BOyPrgpUrA7HylqvU8vIZGsRuXmspskV0Tg==
dependencies:
- agent-base "4"
- debug "3.1.0"
+ "@tootallnate/once" "1"
+ agent-base "6"
+ debug "4"
http-signature@~1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/http-signature/-/http-signature-1.2.0.tgz#9aecd925114772f3d95b65a60abb8f7c18fbace1"
+ integrity sha1-muzZJRFHcvPZW2WmCruPfBj7rOE=
dependencies:
assert-plus "^1.0.0"
jsprim "^1.2.2"
sshpk "^1.7.0"
-https-proxy-agent@^2.2.3:
- version "2.2.4"
- resolved "https://registry.yarnpkg.com/https-proxy-agent/-/https-proxy-agent-2.2.4.tgz#4ee7a737abd92678a293d9b34a1af4d0d08c787b"
- integrity sha512-OmvfoQ53WLjtA9HeYP9RNrWMJzzAz1JGaSFr1nijg0PVR1JaD/xbJq1mdEIIlxGpXp9eSe/O2LgU9DJmTPd0Eg==
+https-proxy-agent@^5.0.0:
+ version "5.0.0"
+ resolved "https://registry.yarnpkg.com/https-proxy-agent/-/https-proxy-agent-5.0.0.tgz#e2a90542abb68a762e0a0850f6c9edadfd8506b2"
+ integrity sha512-EkYm5BcKUGiduxzSt3Eppko+PiNWNEpa4ySk9vTC6wDsQJW9rHSa+UhGNJoRYp7bz6Ht1eaRIa6QaJqO5rCFbA==
dependencies:
- agent-base "^4.3.0"
- debug "^3.1.0"
+ agent-base "6"
+ debug "4"
humanize-ms@^1.2.1:
version "1.2.1"
@@ -2342,50 +2747,46 @@ humanize-ms@^1.2.1:
dependencies:
ms "^2.0.0"
-iconv-lite@0.4.19:
- version "0.4.19"
- resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.4.19.tgz#f7468f60135f5e5dad3399c0a81be9a1603a082b"
-
-iconv-lite@^0.4.24, iconv-lite@~0.4.13:
+iconv-lite@0.4.24, iconv-lite@^0.4.4:
version "0.4.24"
resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.4.24.tgz#2022b4b25fbddc21d2f524974a474aafe733908b"
integrity sha512-v3MXnZAcvnywkTUEZomIActle7RXXeedOR31wwl7VlyoXO4Qi9arvSenNQWne1TcRwhCL1HwLI21bEqdpj8/rA==
dependencies:
safer-buffer ">= 2.1.2 < 3"
-iconv-lite@^0.4.4:
- version "0.4.23"
- resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.4.23.tgz#297871f63be507adcfbfca715d0cd0eed84e9a63"
+iconv-lite@^0.6.2:
+ version "0.6.3"
+ resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.6.3.tgz#a52f80bf38da1952eb5c681790719871a1a72501"
+ integrity sha512-4fCk79wshMdzMp2rH06qWrJE4iolqLhCUH+OiuIgU++RB0+94NlDL81atO7GX55uUKueo0txHNtvEyI6D7WdMw==
dependencies:
- safer-buffer ">= 2.1.2 < 3"
-
-iferr@^0.1.5:
- version "0.1.5"
- resolved "https://registry.yarnpkg.com/iferr/-/iferr-0.1.5.tgz#c60eed69e6d8fdb6b3104a1fcbca1c192dc5b501"
- integrity sha1-xg7taebY/bazEEofy8ocGS3FtQE=
+ safer-buffer ">= 2.1.2 < 3.0.0"
-iferr@^1.0.2:
- version "1.0.2"
- resolved "https://registry.yarnpkg.com/iferr/-/iferr-1.0.2.tgz#e9fde49a9da06dc4a4194c6c9ed6d08305037a6d"
- integrity sha512-9AfeLfji44r5TKInjhz3W9DyZI1zR1JAf2hVBMGhddAKPqBsupb89jGfbCTHIGZd6fGZl9WlHdn4AObygyMKwg==
+ieee754@^1.1.13:
+ version "1.2.1"
+ resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.2.1.tgz#8eb7a10a63fff25d15a57b001586d177d1b0d352"
+ integrity sha512-dcyqhDvX1C46lXZcVqCpK+FtMRQVdIMN6/Df5js2zouUsqG7I6sFxitIC+7KYK29KdXOLHdu9zL4sFnoVQnqaA==
-ignore-walk@^3.0.1:
- version "3.0.1"
- resolved "https://registry.yarnpkg.com/ignore-walk/-/ignore-walk-3.0.1.tgz#a83e62e7d272ac0e3b551aaa82831a19b69f82f8"
+ignore-walk@^3.0.1, ignore-walk@^3.0.3:
+ version "3.0.4"
+ resolved "https://registry.yarnpkg.com/ignore-walk/-/ignore-walk-3.0.4.tgz#c9a09f69b7c7b479a5d74ac1a3c0d4236d2a6335"
+ integrity sha512-PY6Ii8o1jMRA1z4F2hRkH/xN59ox43DavKvD3oDpfurRlOJyAHpifIwpbdv1n4jt4ov0jSpw3kQ4GhJnpBL6WQ==
dependencies:
minimatch "^3.0.4"
ignore@^4.0.6:
version "4.0.6"
resolved "https://registry.yarnpkg.com/ignore/-/ignore-4.0.6.tgz#750e3db5862087b4737ebac8207ffd1ef27b25fc"
+ integrity sha512-cyFDKrqc/YdcWFniJhzI42+AzS+gNwmUzOSFcRCQYwySuBBBy/KjuxWLZ/FHEH6Moq1NizMOBWyTcv8O4OZIMg==
-ignore@^5.1.4:
- version "5.1.4"
- resolved "https://registry.yarnpkg.com/ignore/-/ignore-5.1.4.tgz#84b7b3dbe64552b6ef0eca99f6743dbec6d97adf"
+ignore@^5.1.4, ignore@^5.1.8:
+ version "5.1.8"
+ resolved "https://registry.yarnpkg.com/ignore/-/ignore-5.1.8.tgz#f150a8b50a34289b33e22f5889abd4d8016f0e57"
+ integrity sha512-BMpfD7PpiETpBl/A6S498BaIJ6Y/ABT93ETbby2fP00v4EbvPBXWEoaR1UBPKs3iR53pJY7EtZk5KACI57i1Uw==
-import-fresh@^3.0.0:
- version "3.2.1"
- resolved "https://registry.yarnpkg.com/import-fresh/-/import-fresh-3.2.1.tgz#633ff618506e793af5ac91bf48b72677e15cbe66"
+import-fresh@^3.0.0, import-fresh@^3.2.1:
+ version "3.3.0"
+ resolved "https://registry.yarnpkg.com/import-fresh/-/import-fresh-3.3.0.tgz#37162c25fcb9ebaa2e6e53d5b4d88ce17d9e0c2b"
+ integrity sha512-veYYhQa+D1QBKznvhUHxb8faxlrwUnxseDAbAp457E0wLNio2bOSKnjYDhMj+YiAq61xrMGhQk9iXVk5FzgQMw==
dependencies:
parent-module "^1.0.0"
resolve-from "^4.0.0"
@@ -2393,6 +2794,7 @@ import-fresh@^3.0.0:
import-lazy@^2.1.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/import-lazy/-/import-lazy-2.1.0.tgz#05698e3d45c88e8d7e9d92cb0584e77f096f3e43"
+ integrity sha1-BWmOPUXIjo1+nZLLBYTnfwlvPkM=
imurmurhash@^0.1.4:
version "0.1.4"
@@ -2402,307 +2804,292 @@ imurmurhash@^0.1.4:
indent-string@^3.0.0:
version "3.2.0"
resolved "https://registry.yarnpkg.com/indent-string/-/indent-string-3.2.0.tgz#4a5fd6d27cc332f37e5419a504dbb837105c9289"
+ integrity sha1-Sl/W0nzDMvN+VBmlBNu4NxBckok=
indent-string@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/indent-string/-/indent-string-4.0.0.tgz#624f8f4497d619b2d9768531d58f4122854d7251"
+ integrity sha512-EdDDZu4A2OyIK7Lr/2zG+w5jmbuk1DVBnEwREQvBzspBJkCEbRa8GxU1lghYcaGJCnRWibjDXlq779X1/y5xwg==
-infer-owner@^1.0.3, infer-owner@^1.0.4:
+infer-owner@^1.0.4:
version "1.0.4"
resolved "https://registry.yarnpkg.com/infer-owner/-/infer-owner-1.0.4.tgz#c4cefcaa8e51051c2a40ba2ce8a3d27295af9467"
integrity sha512-IClj+Xz94+d7irH5qRyfJonOdfTzuDaifE6ZPWfx0N0+/ATZCbuTPq2prFl526urkQd90WyUKIh1DfBQ2hMz9A==
-inflight@^1.0.4, inflight@~1.0.6:
+inflight@^1.0.4:
version "1.0.6"
resolved "https://registry.yarnpkg.com/inflight/-/inflight-1.0.6.tgz#49bd6331d7d02d0c09bc910a1075ba8165b56df9"
+ integrity sha1-Sb1jMdfQLQwJvJEKEHW6gWW1bfk=
dependencies:
once "^1.3.0"
wrappy "1"
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resolved "https://registry.yarnpkg.com/is-typedarray/-/is-typedarray-1.0.0.tgz#e479c80858df0c1b11ddda6940f96011fcda4a9a"
+ integrity sha1-5HnICFjfDBsR3dppQPlgEfzaSpo=
is-windows@^1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/is-windows/-/is-windows-1.0.2.tgz#d1850eb9791ecd18e6182ce12a30f396634bb19d"
+ integrity sha512-eXK1UInq2bPmjyX6e3VHIzMLobc4J94i4AWn+Hpq3OU5KkrRC96OAcR3PRJ/pGu6m8TRnBHP9dkXQVsT/COVIA==
is@^3.2.1:
version "3.3.0"
resolved "https://registry.yarnpkg.com/is/-/is-3.3.0.tgz#61cff6dd3c4193db94a3d62582072b44e5645d79"
-
-isarray@0.0.1:
- version "0.0.1"
- resolved "https://registry.yarnpkg.com/isarray/-/isarray-0.0.1.tgz#8a18acfca9a8f4177e09abfc6038939b05d1eedf"
-
-isarray@^2.0.5:
- version "2.0.5"
- resolved "https://registry.yarnpkg.com/isarray/-/isarray-2.0.5.tgz#8af1e4c1221244cc62459faf38940d4e644a5723"
- integrity sha512-xHjhDr3cNBK0BzdUJSPXZntQUx/mwMS5Rw4A7lPJ90XGAO6ISP/ePDNuo0vhqOZU+UD5JoodwCAAoZQd3FeAKw==
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isarray@~1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/isarray/-/isarray-1.0.0.tgz#bb935d48582cba168c06834957a54a3e07124f11"
integrity sha1-u5NdSFgsuhaMBoNJV6VKPgcSTxE=
-isemail@1.x.x:
- version "1.2.0"
- resolved "https://registry.yarnpkg.com/isemail/-/isemail-1.2.0.tgz#be03df8cc3e29de4d2c5df6501263f1fa4595e9a"
-
isexe@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/isexe/-/isexe-2.0.0.tgz#e8fbf374dc556ff8947a10dcb0572d633f2cfa10"
+ integrity sha1-6PvzdNxVb/iUehDcsFctYz8s+hA=
+
+isobject@^3.0.1:
+ version "3.0.1"
+ resolved "https://registry.yarnpkg.com/isobject/-/isobject-3.0.1.tgz#4e431e92b11a9731636aa1f9c8d1ccbcfdab78df"
+ integrity sha1-TkMekrEalzFjaqH5yNHMvP2reN8=
isstream@~0.1.2:
version "0.1.2"
resolved "https://registry.yarnpkg.com/isstream/-/isstream-0.1.2.tgz#47e63f7af55afa6f92e1500e690eb8b8529c099a"
+ integrity sha1-R+Y/evVa+m+S4VAOaQ64uFKcCZo=
istanbul-lib-coverage@^3.0.0, istanbul-lib-coverage@^3.0.0-alpha.1:
version "3.0.0"
resolved "https://registry.yarnpkg.com/istanbul-lib-coverage/-/istanbul-lib-coverage-3.0.0.tgz#f5944a37c70b550b02a78a5c3b2055b280cec8ec"
+ integrity sha512-UiUIqxMgRDET6eR+o5HbfRYP1l0hqkWOs7vNxC/mggutCMUIhWMm8gAHb8tHlyfD3/l6rlgNA5cKdDzEAf6hEg==
istanbul-lib-hook@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/istanbul-lib-hook/-/istanbul-lib-hook-3.0.0.tgz#8f84c9434888cc6b1d0a9d7092a76d239ebf0cc6"
+ integrity sha512-Pt/uge1Q9s+5VAZ+pCo16TYMWPBIl+oaNIjgLQxcX0itS6ueeaA+pEfThZpH8WxhFgCiEb8sAJY6MdUKgiIWaQ==
dependencies:
append-transform "^2.0.0"
istanbul-lib-instrument@^4.0.0:
- version "4.0.1"
- resolved "https://registry.yarnpkg.com/istanbul-lib-instrument/-/istanbul-lib-instrument-4.0.1.tgz#61f13ac2c96cfefb076fe7131156cc05907874e6"
+ version "4.0.3"
+ resolved "https://registry.yarnpkg.com/istanbul-lib-instrument/-/istanbul-lib-instrument-4.0.3.tgz#873c6fff897450118222774696a3f28902d77c1d"
+ integrity sha512-BXgQl9kf4WTCPCCpmFGoJkz/+uhvm7h7PFKUYxh7qarQd3ER33vHG//qaE8eN25l07YqZPpHXU9I09l/RD5aGQ==
dependencies:
"@babel/core" "^7.7.5"
- "@babel/parser" "^7.7.5"
- "@babel/template" "^7.7.4"
- "@babel/traverse" "^7.7.4"
"@istanbuljs/schema" "^0.1.2"
istanbul-lib-coverage "^3.0.0"
semver "^6.3.0"
@@ -2710,6 +3097,7 @@ istanbul-lib-instrument@^4.0.0:
istanbul-lib-processinfo@^2.0.2:
version "2.0.2"
resolved "https://registry.yarnpkg.com/istanbul-lib-processinfo/-/istanbul-lib-processinfo-2.0.2.tgz#e1426514662244b2f25df728e8fd1ba35fe53b9c"
+ integrity sha512-kOwpa7z9hme+IBPZMzQ5vdQj8srYgAtaRqeI48NGmAQ+/5yKiHLV0QbYqQpxsdEF0+w14SoB8YbnHKcXE2KnYw==
dependencies:
archy "^1.0.0"
cross-spawn "^7.0.0"
@@ -2722,6 +3110,7 @@ istanbul-lib-processinfo@^2.0.2:
istanbul-lib-report@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/istanbul-lib-report/-/istanbul-lib-report-3.0.0.tgz#7518fe52ea44de372f460a76b5ecda9ffb73d8a6"
+ integrity sha512-wcdi+uAKzfiGT2abPpKZ0hSU1rGQjUQnLvtY5MpQ7QCTahD3VODhcu4wcfY1YtkGaDD5yuydOLINXsfbus9ROw==
dependencies:
istanbul-lib-coverage "^3.0.0"
make-dir "^3.0.0"
@@ -2730,6 +3119,7 @@ istanbul-lib-report@^3.0.0:
istanbul-lib-source-maps@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/istanbul-lib-source-maps/-/istanbul-lib-source-maps-4.0.0.tgz#75743ce6d96bb86dc7ee4352cf6366a23f0b1ad9"
+ integrity sha512-c16LpFRkR8vQXyHZ5nLpY35JZtzj1PQY1iZmesUbf1FZHbIupcWfjgOXBY9YHkLEQ6puz1u4Dgj6qmU/DisrZg==
dependencies:
debug "^4.1.1"
istanbul-lib-coverage "^3.0.0"
@@ -2738,6 +3128,7 @@ istanbul-lib-source-maps@^4.0.0:
istanbul-reports@^3.0.2:
version "3.0.2"
resolved "https://registry.yarnpkg.com/istanbul-reports/-/istanbul-reports-3.0.2.tgz#d593210e5000683750cb09fc0644e4b6e27fd53b"
+ integrity sha512-9tZvz7AiR3PEDNGiV9vIouQ/EAcqMXFmkcA1CDFTwOB98OZVDL0PH9glHotf5Ugp6GCOTypfzGWI/OqjWNCRUw==
dependencies:
html-escaper "^2.0.0"
istanbul-lib-report "^3.0.0"
@@ -2745,43 +3136,43 @@ istanbul-reports@^3.0.2:
isurl@^1.0.0-alpha5:
version "1.0.0"
resolved "https://registry.yarnpkg.com/isurl/-/isurl-1.0.0.tgz#b27f4f49f3cdaa3ea44a0a5b7f3462e6edc39d67"
+ integrity sha512-1P/yWsxPlDtn7QeRD+ULKQPaIaN6yF368GZ2vDfv0AL0NwpStafjWCDDdn0k8wgFMWpVAqG7oJhxHnlud42i9w==
dependencies:
has-to-string-tag-x "^1.2.0"
is-object "^1.0.1"
-iterate-iterator@^1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/iterate-iterator/-/iterate-iterator-1.0.1.tgz#1693a768c1ddd79c969051459453f082fe82e9f6"
- integrity sha512-3Q6tudGN05kbkDQDI4CqjaBf4qf85w6W6GnuZDtUVYwKgtC1q8yxYX7CZed7N+tLzQqS6roujWvszf13T+n9aw==
-
-iterate-value@^1.0.0:
- version "1.0.2"
- resolved "https://registry.yarnpkg.com/iterate-value/-/iterate-value-1.0.2.tgz#935115bd37d006a52046535ebc8d07e9c9337f57"
- integrity sha512-A6fMAio4D2ot2r/TYzr4yUWrmwNdsN5xL7+HUiyACE4DXm+q8HtPcnFTp+NnW3k4N05tZ7FVYFFb2CR13NxyHQ==
+joi@6.x.x, joi@>=17.4.0:
+ version "17.4.0"
+ resolved "https://registry.yarnpkg.com/joi/-/joi-17.4.0.tgz#b5c2277c8519e016316e49ababd41a1908d9ef20"
+ integrity sha512-F4WiW2xaV6wc1jxete70Rw4V/VuMd6IN+a5ilZsxG4uYtUXWu2kq9W5P2dz30e7Gmw8RCbY/u/uk+dMPma9tAg==
dependencies:
- es-get-iterator "^1.0.2"
- iterate-iterator "^1.0.1"
+ "@hapi/hoek" "^9.0.0"
+ "@hapi/topo" "^5.0.0"
+ "@sideway/address" "^4.1.0"
+ "@sideway/formula" "^3.0.0"
+ "@sideway/pinpoint" "^2.0.0"
-joi@6.x.x:
- version "6.10.1"
- resolved "https://registry.yarnpkg.com/joi/-/joi-6.10.1.tgz#4d50c318079122000fe5f16af1ff8e1917b77e06"
- dependencies:
- hoek "2.x.x"
- isemail "1.x.x"
- moment "2.x.x"
- topo "1.x.x"
-
-js-base64@^2.4.9:
- version "2.5.0"
- resolved "https://registry.yarnpkg.com/js-base64/-/js-base64-2.5.0.tgz#42255ba183ab67ce59a0dee640afdc00ab5ae93e"
+js-base64@*, js-base64@^3.6.1:
+ version "3.6.1"
+ resolved "https://registry.yarnpkg.com/js-base64/-/js-base64-3.6.1.tgz#555aae398b74694b4037af1f8a5a6209d170efbe"
+ integrity sha512-Frdq2+tRRGLQUIQOgsIGSCd1VePCS2fsddTG5dTCqR0JHgltXWfsxnY0gIXPoMeRmdom6Oyq+UMOFg5suduOjQ==
js-tokens@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/js-tokens/-/js-tokens-4.0.0.tgz#19203fb59991df98e3a287050d4647cdeaf32499"
+ integrity sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==
-js-yaml@3.14.0, js-yaml@^3.10.0, js-yaml@^3.13.1:
- version "3.13.1"
- resolved "https://registry.yarnpkg.com/js-yaml/-/js-yaml-3.13.1.tgz#aff151b30bfdfa8e49e05da22e7415e9dfa37847"
+js-yaml@4.0.0:
+ version "4.0.0"
+ resolved "https://registry.yarnpkg.com/js-yaml/-/js-yaml-4.0.0.tgz#f426bc0ff4b4051926cd588c71113183409a121f"
+ integrity sha512-pqon0s+4ScYUvX30wxQi3PogGFAlUyH0awepWvwkj4jD4v+ova3RiYw8bmA6x2rDrEaj8i/oWKoRxpVNW+Re8Q==
+ dependencies:
+ argparse "^2.0.1"
+
+js-yaml@^3.13.0, js-yaml@^3.13.1:
+ version "3.14.1"
+ resolved "https://registry.yarnpkg.com/js-yaml/-/js-yaml-3.14.1.tgz#dae812fdb3825fa306609a8717383c50c36a0537"
+ integrity sha512-okMH7OXXJ7YrN9Ok3/SXrnu4iX9yOk+25nqX4imS2npuvTYDmo/QEZoqwZkYaIDk3jVvBOTOIEgEhaLOynBS9g==
dependencies:
argparse "^1.0.7"
esprima "^4.0.0"
@@ -2789,57 +3180,68 @@ js-yaml@3.14.0, js-yaml@^3.10.0, js-yaml@^3.13.1:
jsbn@~0.1.0:
version "0.1.1"
resolved "https://registry.yarnpkg.com/jsbn/-/jsbn-0.1.1.tgz#a5e654c2e5a2deb5f201d96cefbca80c0ef2f513"
+ integrity sha1-peZUwuWi3rXyAdls77yoDA7y9RM=
jsesc@^2.5.1:
version "2.5.2"
resolved "https://registry.yarnpkg.com/jsesc/-/jsesc-2.5.2.tgz#80564d2e483dacf6e8ef209650a67df3f0c283a4"
+ integrity sha512-OYu7XEzjkCQ3C5Ps3QIZsQfNpqoJyZZA99wd9aWd05NCtC5pWOkShK2mkL6HXQR6/Cy2lbNdPlZBpuQHXE63gA==
json-buffer@3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/json-buffer/-/json-buffer-3.0.0.tgz#5b1f397afc75d677bde8bcfc0e47e1f9a3d9a898"
+ integrity sha1-Wx85evx11ne96Lz8Dkfh+aPZqJg=
json-edm-parser@0.1.2:
version "0.1.2"
resolved "https://registry.yarnpkg.com/json-edm-parser/-/json-edm-parser-0.1.2.tgz#1e60b0fef1bc0af67bc0d146dfdde5486cd615b4"
+ integrity sha1-HmCw/vG8CvZ7wNFG393lSGzWFbQ=
dependencies:
jsonparse "~1.2.0"
-json-parse-better-errors@^1.0.0, json-parse-better-errors@^1.0.1, json-parse-better-errors@^1.0.2:
- version "1.0.2"
- resolved "https://registry.yarnpkg.com/json-parse-better-errors/-/json-parse-better-errors-1.0.2.tgz#bb867cfb3450e69107c131d1c514bab3dc8bcaa9"
-
-json-parse-even-better-errors@^2.3.0:
+json-parse-even-better-errors@^2.3.0, json-parse-even-better-errors@^2.3.1:
version "2.3.1"
resolved "https://registry.yarnpkg.com/json-parse-even-better-errors/-/json-parse-even-better-errors-2.3.1.tgz#7c47805a94319928e05777405dc12e1f7a4ee02d"
integrity sha512-xyFwyhro/JEof6Ghe2iz2NcXoj2sloNsWr/XsERDK/oiPCfaNhl5ONfp+jQdAZRQQ0IJWNzH9zIZF7li91kh2w==
-json-schema-traverse@^0.3.0:
- version "0.3.1"
- resolved "https://registry.yarnpkg.com/json-schema-traverse/-/json-schema-traverse-0.3.1.tgz#349a6d44c53a51de89b40805c5d5e59b417d3340"
-
json-schema-traverse@^0.4.1:
version "0.4.1"
resolved "https://registry.yarnpkg.com/json-schema-traverse/-/json-schema-traverse-0.4.1.tgz#69f6a87d9513ab8bb8fe63bdb0979c448e684660"
+ integrity sha512-xbbCH5dCYU5T8LcEhhuh7HJ88HXuW3qsI3Y0zOZFKfZEHcpWiHU/Jxzk629Brsab/mMiHQti9wMP+845RPe3Vg==
+
+json-schema-traverse@^1.0.0:
+ version "1.0.0"
+ resolved "https://registry.yarnpkg.com/json-schema-traverse/-/json-schema-traverse-1.0.0.tgz#ae7bcb3656ab77a73ba5c49bf654f38e6b6860e2"
+ integrity sha512-NM8/P9n3XjXhIZn1lLhkFaACTOURQXjWhV4BA/RnOv8xvgqtqpAX9IO4mRQxSx1Rlo4tqzeqb0sOlruaOy3dug==
json-schema@0.2.3:
version "0.2.3"
resolved "https://registry.yarnpkg.com/json-schema/-/json-schema-0.2.3.tgz#b480c892e59a2f05954ce727bd3f2a4e882f9e13"
+ integrity sha1-tIDIkuWaLwWVTOcnvT8qTogvnhM=
json-stable-stringify-without-jsonify@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/json-stable-stringify-without-jsonify/-/json-stable-stringify-without-jsonify-1.0.1.tgz#9db7b59496ad3f3cfef30a75142d2d930ad72651"
+ integrity sha1-nbe1lJatPzz+8wp1FC0tkwrXJlE=
+
+json-stringify-nice@^1.1.4:
+ version "1.1.4"
+ resolved "https://registry.yarnpkg.com/json-stringify-nice/-/json-stringify-nice-1.1.4.tgz#2c937962b80181d3f317dd39aa323e14f5a60a67"
+ integrity sha512-5Z5RFW63yxReJ7vANgW6eZFGWaQvnPE3WNmZoOJrSkGju2etKA2L5rrOa1sm877TVTFt57A80BH1bArcmlLfPw==
json-stringify-safe@~5.0.1:
version "5.0.1"
resolved "https://registry.yarnpkg.com/json-stringify-safe/-/json-stringify-safe-5.0.1.tgz#1296a2d58fd45f19a0f6ce01d65701e2c735b6eb"
+ integrity sha1-Epai1Y/UXxmg9s4B1lcB4sc1tus=
json5@^2.1.2:
- version "2.1.2"
- resolved "https://registry.yarnpkg.com/json5/-/json5-2.1.2.tgz#43ef1f0af9835dd624751a6b7fa48874fb2d608e"
+ version "2.2.0"
+ resolved "https://registry.yarnpkg.com/json5/-/json5-2.2.0.tgz#2dfefe720c6ba525d9ebd909950f0515316c89a3"
+ integrity sha512-f+8cldu7X/y7RAJurMEJmdoKXGB/X550w2Nr3tTbezL6RwEE/iMcm+tZnXeoZtKuOq6ft8+CqzEkrIgx1fPoQA==
dependencies:
minimist "^1.2.5"
-jsonparse@^1.2.0:
+jsonparse@^1.3.1:
version "1.3.1"
resolved "https://registry.yarnpkg.com/jsonparse/-/jsonparse-1.3.1.tgz#3f4dae4a91fac315f71062f8521cc239f1366280"
integrity sha1-P02uSpH6wxX3EGL4UhzCOfE2YoA=
@@ -2847,170 +3249,209 @@ jsonparse@^1.2.0:
jsonparse@~1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/jsonparse/-/jsonparse-1.2.0.tgz#5c0c5685107160e72fe7489bddea0b44c2bc67bd"
+ integrity sha1-XAxWhRBxYOcv50ib3eoLRMK8Z70=
jsprim@^1.2.2:
version "1.4.1"
resolved "https://registry.yarnpkg.com/jsprim/-/jsprim-1.4.1.tgz#313e66bc1e5cc06e438bc1b7499c2e5c56acb6a2"
+ integrity sha1-MT5mvB5cwG5Di8G3SZwuXFastqI=
dependencies:
assert-plus "1.0.0"
extsprintf "1.3.0"
json-schema "0.2.3"
verror "1.10.0"
+just-diff-apply@^3.0.0:
+ version "3.0.0"
+ resolved "https://registry.yarnpkg.com/just-diff-apply/-/just-diff-apply-3.0.0.tgz#a77348d24f0694e378b57293dceb65bdf5a91c4f"
+ integrity sha512-K2MLc+ZC2DVxX4V61bIKPeMUUfj1YYZ3h0myhchDXOW1cKoPZMnjIoNCqv9bF2n5Oob1PFxuR2gVJxkxz4e58w==
+
+just-diff@^3.0.1:
+ version "3.1.1"
+ resolved "https://registry.yarnpkg.com/just-diff/-/just-diff-3.1.1.tgz#d50c597c6fd4776495308c63bdee1b6839082647"
+ integrity sha512-sdMWKjRq8qWZEjDcVA6llnUT8RDEBIfOiGpYFPYa9u+2c39JCsejktSP7mj5eRid5EIvTzIpQ2kDOCw1Nq9BjQ==
+
keyv@3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/keyv/-/keyv-3.0.0.tgz#44923ba39e68b12a7cec7df6c3268c031f2ef373"
+ integrity sha512-eguHnq22OE3uVoSYG0LVWNP+4ppamWr9+zWBe1bsNcovIMy6huUJFPgy4mGwCd/rnl3vOLGW1MTlu4c57CT1xA==
dependencies:
json-buffer "3.0.0"
-kubernetes-client@^6.5.0:
- version "6.5.1"
- resolved "https://registry.yarnpkg.com/kubernetes-client/-/kubernetes-client-6.5.1.tgz#40dc285b7d30d8ddd083b16a6d06068521f1d017"
+kind-of@^2.0.1:
+ version "2.0.1"
+ resolved "https://registry.yarnpkg.com/kind-of/-/kind-of-2.0.1.tgz#018ec7a4ce7e3a86cb9141be519d24c8faa981b5"
+ integrity sha1-AY7HpM5+OobLkUG+UZ0kyPqpgbU=
+ dependencies:
+ is-buffer "^1.0.2"
+
+kind-of@^3.0.2:
+ version "3.2.2"
+ resolved "https://registry.yarnpkg.com/kind-of/-/kind-of-3.2.2.tgz#31ea21a734bab9bbb0f32466d893aea51e4a3c64"
+ integrity sha1-MeohpzS6ubuw8yRm2JOupR5KPGQ=
+ dependencies:
+ is-buffer "^1.1.5"
+
+kubernetes-client@^6.12.1:
+ version "6.12.1"
+ resolved "https://registry.yarnpkg.com/kubernetes-client/-/kubernetes-client-6.12.1.tgz#236c4ed5c0417035a4b5c71bbe81549b3bef7cf2"
+ integrity sha512-NA5e2zaL+BWJsboPDL/+Uv8AF60inOQjKvzHwRhQWJHlg2deaxguyMU3MKi9NEptltkv23Pm0871wuRcDMYrzA==
dependencies:
- deepmerge "^2.1.1"
- fluent-openapi "^1.0.0"
- js-yaml "^3.10.0"
+ deepmerge "^3.2.0"
+ js-yaml "^3.13.0"
openid-client "^2.4.4"
- qs "^6.5.2"
+ qs "^6.7.0"
request "^2.88.0"
- ws "^6.0.0"
+ swagger-fluent "^3.1.2"
+ url-join "^4.0.0"
+ ws "^6.2.1"
latest-version@^3.0.0:
version "3.1.0"
resolved "https://registry.yarnpkg.com/latest-version/-/latest-version-3.1.0.tgz#a205383fea322b33b5ae3b18abee0dc2f356ee15"
+ integrity sha1-ogU4P+oyKzO1rjsYq+4NwvNW7hU=
dependencies:
package-json "^4.0.0"
-lazy-property@~1.0.0:
- version "1.0.0"
- resolved "https://registry.yarnpkg.com/lazy-property/-/lazy-property-1.0.0.tgz#84ddc4b370679ba8bd4cdcfa4c06b43d57111147"
+lazy-cache@^0.2.3:
+ version "0.2.7"
+ resolved "https://registry.yarnpkg.com/lazy-cache/-/lazy-cache-0.2.7.tgz#7feddf2dcb6edb77d11ef1d117ab5ffdf0ab1b65"
+ integrity sha1-f+3fLctu23fRHvHRF6tf/fCrG2U=
-levn@^0.3.0, levn@~0.3.0:
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- resolved "https://registry.yarnpkg.com/levn/-/levn-0.3.0.tgz#3b09924edf9f083c0490fdd4c0bc4421e04764ee"
- dependencies:
- prelude-ls "~1.1.2"
- type-check "~0.3.2"
+lazy-cache@^1.0.3:
+ version "1.0.4"
+ resolved "https://registry.yarnpkg.com/lazy-cache/-/lazy-cache-1.0.4.tgz#a1d78fc3a50474cb80845d3b3b6e1da49a446e8e"
+ integrity sha1-odePw6UEdMuAhF07O24dpJpEbo4=
-libcipm@^4.0.8:
- version "4.0.8"
- resolved "https://registry.yarnpkg.com/libcipm/-/libcipm-4.0.8.tgz#dcea4919e10dfbce420327e63901613b9141bc89"
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+lcid@^2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/lcid/-/lcid-2.0.0.tgz#6ef5d2df60e52f82eb228a4c373e8d1f397253cf"
+ integrity sha512-avPEb8P8EGnwXKClwsNUgryVjllcRqtMYa49NTsbQagYuT1DcXnl1915oxWjoyGrXR6zH/Y0Zc96xWsPcoDKeA==
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- bluebird "^3.5.1"
- figgy-pudding "^3.5.1"
- find-npm-prefix "^1.0.2"
- graceful-fs "^4.1.11"
- ini "^1.3.5"
- lock-verify "^2.1.0"
- mkdirp "^0.5.1"
- npm-lifecycle "^3.0.0"
- npm-logical-tree "^1.2.1"
- npm-package-arg "^6.1.0"
- pacote "^9.1.0"
- read-package-json "^2.0.13"
- rimraf "^2.6.2"
- worker-farm "^1.6.0"
+ invert-kv "^2.0.0"
-libnpm@^3.0.1:
- version "3.0.1"
- resolved "https://registry.yarnpkg.com/libnpm/-/libnpm-3.0.1.tgz#0be11b4c9dd4d1ffd7d95c786e92e55d65be77a2"
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- dependencies:
- bin-links "^1.1.2"
- bluebird "^3.5.3"
- find-npm-prefix "^1.0.2"
- libnpmaccess "^3.0.2"
- libnpmconfig "^1.2.1"
- libnpmhook "^5.0.3"
- libnpmorg "^1.0.1"
- libnpmpublish "^1.1.2"
- libnpmsearch "^2.0.2"
- libnpmteam "^1.0.2"
- lock-verify "^2.0.2"
- npm-lifecycle "^3.0.0"
- npm-logical-tree "^1.2.1"
- npm-package-arg "^6.1.0"
- npm-profile "^4.0.2"
- npm-registry-fetch "^4.0.0"
- npmlog "^4.1.2"
- pacote "^9.5.3"
- read-package-json "^2.0.13"
- stringify-package "^1.0.0"
+leven@^3.1.0:
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/leven/-/leven-3.1.0.tgz#77891de834064cccba82ae7842bb6b14a13ed7f2"
+ integrity sha512-qsda+H8jTaUaN/x5vzW2rzc+8Rw4TAQ/4KjB46IwK5VH+IlVeeeje/EoZRpiXvIqjFgK84QffqPztGI3VBLG1A==
-libnpmaccess@^3.0.2:
- version "3.0.2"
- resolved "https://registry.yarnpkg.com/libnpmaccess/-/libnpmaccess-3.0.2.tgz#8b2d72345ba3bef90d3b4f694edd5c0417f58923"
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+levn@^0.4.1:
+ version "0.4.1"
+ resolved "https://registry.yarnpkg.com/levn/-/levn-0.4.1.tgz#ae4562c007473b932a6200d403268dd2fffc6ade"
+ integrity sha512-+bT2uH4E5LGE7h/n3evcS/sQlJXCpIp6ym8OWJ5eV6+67Dsql/LaaT7qJBAt2rzfoa/5QBGBhxDix1dMt2kQKQ==
+ dependencies:
+ prelude-ls "^1.2.1"
+ type-check "~0.4.0"
+
+libnpmaccess@^4.0.2:
+ version "4.0.3"
+ resolved "https://registry.yarnpkg.com/libnpmaccess/-/libnpmaccess-4.0.3.tgz#dfb0e5b0a53c315a2610d300e46b4ddeb66e7eec"
+ integrity sha512-sPeTSNImksm8O2b6/pf3ikv4N567ERYEpeKRPSmqlNt1dTZbvgpJIzg5vAhXHpw2ISBsELFRelk0jEahj1c6nQ==
dependencies:
aproba "^2.0.0"
- get-stream "^4.0.0"
- npm-package-arg "^6.1.0"
- npm-registry-fetch "^4.0.0"
+ minipass "^3.1.1"
+ npm-package-arg "^8.1.2"
+ npm-registry-fetch "^11.0.0"
-libnpmconfig@^1.2.1:
- version "1.2.1"
- resolved "https://registry.yarnpkg.com/libnpmconfig/-/libnpmconfig-1.2.1.tgz#c0c2f793a74e67d4825e5039e7a02a0044dfcbc0"
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+libnpmdiff@^2.0.4:
+ version "2.0.4"
+ resolved "https://registry.yarnpkg.com/libnpmdiff/-/libnpmdiff-2.0.4.tgz#bb1687992b1a97a8ea4a32f58ad7c7f92de53b74"
+ integrity sha512-q3zWePOJLHwsLEUjZw3Kyu/MJMYfl4tWCg78Vl6QGSfm4aXBUSVzMzjJ6jGiyarsT4d+1NH4B1gxfs62/+y9iQ==
dependencies:
- figgy-pudding "^3.5.1"
- find-up "^3.0.0"
- ini "^1.3.5"
+ "@npmcli/disparity-colors" "^1.0.1"
+ "@npmcli/installed-package-contents" "^1.0.7"
+ binary-extensions "^2.2.0"
+ diff "^5.0.0"
+ minimatch "^3.0.4"
+ npm-package-arg "^8.1.1"
+ pacote "^11.3.0"
+ tar "^6.1.0"
-libnpmhook@^5.0.3:
- version "5.0.3"
- resolved "https://registry.yarnpkg.com/libnpmhook/-/libnpmhook-5.0.3.tgz#4020c0f5edbf08ebe395325caa5ea01885b928f7"
- integrity sha512-UdNLMuefVZra/wbnBXECZPefHMGsVDTq5zaM/LgKNE9Keyl5YXQTnGAzEo+nFOpdRqTWI9LYi4ApqF9uVCCtuA==
+libnpmexec@^1.2.0:
+ version "1.2.0"
+ resolved "https://registry.yarnpkg.com/libnpmexec/-/libnpmexec-1.2.0.tgz#ab0294ab63bb599b3ac6921129b7a706d4d56da2"
+ integrity sha512-LkxnH2wsMUI4thsgUK0r+EFZ5iCjKlp21J68dFY7AzD5uaaIPqO3lqVvYbyl1Umz1R4rY9t3vFa1fF3hzo6Y2Q==
+ dependencies:
+ "@npmcli/arborist" "^2.3.0"
+ "@npmcli/ci-detect" "^1.3.0"
+ "@npmcli/run-script" "^1.8.4"
+ chalk "^4.1.0"
+ mkdirp-infer-owner "^2.0.0"
+ npm-package-arg "^8.1.2"
+ pacote "^11.3.1"
+ proc-log "^1.0.0"
+ read "^1.0.7"
+ read-package-json-fast "^2.0.2"
+ walk-up-path "^1.0.0"
+
+libnpmfund@^1.1.0:
+ version "1.1.0"
+ resolved "https://registry.yarnpkg.com/libnpmfund/-/libnpmfund-1.1.0.tgz#ee91313905b3194b900530efa339bc3f9fc4e5c4"
+ integrity sha512-Kfmh3pLS5/RGKG5WXEig8mjahPVOxkik6lsbH4iX0si1xxNi6eeUh/+nF1MD+2cgalsQif3O5qyr6mNz2ryJrQ==
dependencies:
- aproba "^2.0.0"
- figgy-pudding "^3.4.1"
- get-stream "^4.0.0"
- npm-registry-fetch "^4.0.0"
+ "@npmcli/arborist" "^2.5.0"
-libnpmorg@^1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/libnpmorg/-/libnpmorg-1.0.1.tgz#5d2503f6ceb57f33dbdcc718e6698fea6d5ad087"
- integrity sha512-0sRUXLh+PLBgZmARvthhYXQAWn0fOsa6T5l3JSe2n9vKG/lCVK4nuG7pDsa7uMq+uTt2epdPK+a2g6btcY11Ww==
+libnpmhook@^6.0.2:
+ version "6.0.3"
+ resolved "https://registry.yarnpkg.com/libnpmhook/-/libnpmhook-6.0.3.tgz#1d7f0d7e6a7932fbf7ce0881fdb0ed8bf8748a30"
+ integrity sha512-3fmkZJibIybzmAvxJ65PeV3NzRc0m4xmYt6scui5msocThbEp4sKFT80FhgrCERYDjlUuFahU6zFNbJDHbQ++g==
dependencies:
aproba "^2.0.0"
- figgy-pudding "^3.4.1"
- get-stream "^4.0.0"
- npm-registry-fetch "^4.0.0"
+ npm-registry-fetch "^11.0.0"
-libnpmpublish@^1.1.2:
- version "1.1.3"
- resolved "https://registry.yarnpkg.com/libnpmpublish/-/libnpmpublish-1.1.3.tgz#e3782796722d79eef1a0a22944c117e0c4ca4280"
- integrity sha512-/3LsYqVc52cHXBmu26+J8Ed7sLs/hgGVFMH1mwYpL7Qaynb9RenpKqIKu0sJ130FB9PMkpMlWjlbtU8A4m7CQw==
+libnpmorg@^2.0.2:
+ version "2.0.3"
+ resolved "https://registry.yarnpkg.com/libnpmorg/-/libnpmorg-2.0.3.tgz#4e605d4113dfa16792d75343824a0625c76703bc"
+ integrity sha512-JSGl3HFeiRFUZOUlGdiNcUZOsUqkSYrg6KMzvPZ1WVZ478i47OnKSS0vkPmX45Pai5mTKuwIqBMcGWG7O8HfdA==
dependencies:
aproba "^2.0.0"
- figgy-pudding "^3.5.1"
- get-stream "^4.0.0"
- lodash.clonedeep "^4.5.0"
- normalize-package-data "^2.4.0"
- npm-package-arg "^6.1.0"
- npm-registry-fetch "^4.0.0"
- semver "^5.5.1"
- ssri "^6.0.1"
+ npm-registry-fetch "^11.0.0"
-libnpmsearch@^2.0.2:
- version "2.0.2"
- resolved "https://registry.yarnpkg.com/libnpmsearch/-/libnpmsearch-2.0.2.tgz#9a4f059102d38e3dd44085bdbfe5095f2a5044cf"
- integrity sha512-VTBbV55Q6fRzTdzziYCr64+f8AopQ1YZ+BdPOv16UegIEaE8C0Kch01wo4s3kRTFV64P121WZJwgmBwrq68zYg==
+libnpmpack@^2.0.1:
+ version "2.0.1"
+ resolved "https://registry.yarnpkg.com/libnpmpack/-/libnpmpack-2.0.1.tgz#d3eac25cc8612f4e7cdeed4730eee339ba51c643"
+ integrity sha512-He4/jxOwlaQ7YG7sIC1+yNeXeUDQt8RLBvpI68R3RzPMZPa4/VpxhlDo8GtBOBDYoU8eq6v1wKL38sq58u4ibQ==
dependencies:
- figgy-pudding "^3.5.1"
- get-stream "^4.0.0"
- npm-registry-fetch "^4.0.0"
+ "@npmcli/run-script" "^1.8.3"
+ npm-package-arg "^8.1.0"
+ pacote "^11.2.6"
-libnpmteam@^1.0.2:
- version "1.0.2"
- resolved "https://registry.yarnpkg.com/libnpmteam/-/libnpmteam-1.0.2.tgz#8b48bcbb6ce70dd8150c950fcbdbf3feb6eec820"
- integrity sha512-p420vM28Us04NAcg1rzgGW63LMM6rwe+6rtZpfDxCcXxM0zUTLl7nPFEnRF3JfFBF5skF/yuZDUthTsHgde8QA==
+libnpmpublish@^4.0.1:
+ version "4.0.2"
+ resolved "https://registry.yarnpkg.com/libnpmpublish/-/libnpmpublish-4.0.2.tgz#be77e8bf5956131bcb45e3caa6b96a842dec0794"
+ integrity sha512-+AD7A2zbVeGRCFI2aO//oUmapCwy7GHqPXFJh3qpToSRNU+tXKJ2YFUgjt04LPPAf2dlEH95s6EhIHM1J7bmOw==
+ dependencies:
+ normalize-package-data "^3.0.2"
+ npm-package-arg "^8.1.2"
+ npm-registry-fetch "^11.0.0"
+ semver "^7.1.3"
+ ssri "^8.0.1"
+
+libnpmsearch@^3.1.1:
+ version "3.1.2"
+ resolved "https://registry.yarnpkg.com/libnpmsearch/-/libnpmsearch-3.1.2.tgz#aee81b9e4768750d842b627a3051abc89fdc15f3"
+ integrity sha512-BaQHBjMNnsPYk3Bl6AiOeVuFgp72jviShNBw5aHaHNKWqZxNi38iVNoXbo6bG/Ccc/m1To8s0GtMdtn6xZ1HAw==
+ dependencies:
+ npm-registry-fetch "^11.0.0"
+
+libnpmteam@^2.0.3:
+ version "2.0.4"
+ resolved "https://registry.yarnpkg.com/libnpmteam/-/libnpmteam-2.0.4.tgz#9dbe2e18ae3cb97551ec07d2a2daf9944f3edc4c"
+ integrity sha512-FPrVJWv820FZFXaflAEVTLRWZrerCvfe7ZHSMzJ/62EBlho2KFlYKjyNEsPW3JiV7TLSXi3vo8u0gMwIkXSMTw==
dependencies:
aproba "^2.0.0"
- figgy-pudding "^3.4.1"
- get-stream "^4.0.0"
- npm-registry-fetch "^4.0.0"
+ npm-registry-fetch "^11.0.0"
+
+libnpmversion@^1.2.0:
+ version "1.2.0"
+ resolved "https://registry.yarnpkg.com/libnpmversion/-/libnpmversion-1.2.0.tgz#e181eb7ab750003b7fd29a578c31e84bb91a67b9"
+ integrity sha512-0pfmobLZbOvq1cLIONZk8ISvEM1k3JdkNXWhMDZvUeH+ijBNvMVdPu/CPUr1eDFbNINS3b6R/0PbTIZDVz7thg==
+ dependencies:
+ "@npmcli/git" "^2.0.7"
+ "@npmcli/run-script" "^1.8.4"
+ json-parse-even-better-errors "^2.3.1"
+ semver "^7.3.5"
+ stringify-package "^1.0.1"
libnpx@10.2.2:
version "10.2.2"
@@ -3026,30 +3467,18 @@ libnpx@10.2.2:
y18n "^4.0.0"
yargs "^11.0.0"
-libnpx@^10.2.4:
- version "10.2.4"
- resolved "https://registry.yarnpkg.com/libnpx/-/libnpx-10.2.4.tgz#ef0e3258e29aef2ec7ee3276115e20e67f67d4ee"
- integrity sha512-BPc0D1cOjBeS8VIBKUu5F80s6njm0wbVt7CsGMrIcJ+SI7pi7V0uVPGpEMH9H5L8csOcclTxAXFE2VAsJXUhfA==
- dependencies:
- dotenv "^5.0.1"
- npm-package-arg "^6.0.0"
- rimraf "^2.6.2"
- safe-buffer "^5.1.0"
- update-notifier "^2.3.0"
- which "^1.3.0"
- y18n "^4.0.0"
- yargs "^14.2.3"
-
-locate-path@^3.0.0:
- version "3.0.0"
- resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-3.0.0.tgz#dbec3b3ab759758071b58fe59fc41871af21400e"
+locate-path@^2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-2.0.0.tgz#2b568b265eec944c6d9c0de9c3dbbbca0354cd8e"
+ integrity sha1-K1aLJl7slExtnA3pw9u7ygNUzY4=
dependencies:
- p-locate "^3.0.0"
+ p-locate "^2.0.0"
path-exists "^3.0.0"
locate-path@^5.0.0:
version "5.0.0"
resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-5.0.0.tgz#1afba396afd676a6d42504d0a67a3a7eb9f62aa0"
+ integrity sha512-t7hw9pI+WvuwNJXwk5zVHpyhIqzg2qTlklJOf0mVxGSbe3Fp2VieZcduNYjaLDoy6p9uGpQEGWG87WpMKlNq8g==
dependencies:
p-locate "^4.1.0"
@@ -3060,15 +3489,6 @@ locate-path@^6.0.0:
dependencies:
p-locate "^5.0.0"
-lock-verify@^2.0.2, lock-verify@^2.1.0:
- version "2.2.1"
- resolved "https://registry.yarnpkg.com/lock-verify/-/lock-verify-2.2.1.tgz#81107948c51ed16f97b96ff8b60675affb243fc1"
- integrity sha512-n0Zw2DVupKfZMazy/HIFVNohJ1z8fIoZ77WBnyyBGG6ixw83uJNyrbiJvvHWe1QKkGiBCjj8RCPlymltliqEww==
- dependencies:
- "@iarna/cli" "^1.2.0"
- npm-package-arg "^6.1.0"
- semver "^5.4.1"
-
lockfile@^1.0.4:
version "1.0.4"
resolved "https://registry.yarnpkg.com/lockfile/-/lockfile-1.0.4.tgz#07f819d25ae48f87e538e6578b6964a4981a5609"
@@ -3076,83 +3496,27 @@ lockfile@^1.0.4:
dependencies:
signal-exit "^3.0.2"
-lodash._baseuniq@~4.6.0:
- version "4.6.0"
- resolved "https://registry.yarnpkg.com/lodash._baseuniq/-/lodash._baseuniq-4.6.0.tgz#0ebb44e456814af7905c6212fa2c9b2d51b841e8"
- dependencies:
- lodash._createset "~4.0.0"
- lodash._root "~3.0.0"
-
-lodash._createset@~4.0.0:
- version "4.0.3"
- resolved "https://registry.yarnpkg.com/lodash._createset/-/lodash._createset-4.0.3.tgz#0f4659fbb09d75194fa9e2b88a6644d363c9fe26"
-
-lodash._root@~3.0.0:
- version "3.0.1"
- resolved "https://registry.yarnpkg.com/lodash._root/-/lodash._root-3.0.1.tgz#fba1c4524c19ee9a5f8136b4609f017cf4ded692"
-
-lodash.assign@^4.2.0:
- version "4.2.0"
- resolved "https://registry.yarnpkg.com/lodash.assign/-/lodash.assign-4.2.0.tgz#0d99f3ccd7a6d261d19bdaeb9245005d285808e7"
-
-lodash.clone@^4.5.0:
- version "4.5.0"
- resolved "https://registry.yarnpkg.com/lodash.clone/-/lodash.clone-4.5.0.tgz#195870450f5a13192478df4bc3d23d2dea1907b6"
-
-lodash.clonedeep@^4.5.0, lodash.clonedeep@~4.5.0:
+lodash.clonedeep@^4.5.0:
version "4.5.0"
resolved "https://registry.yarnpkg.com/lodash.clonedeep/-/lodash.clonedeep-4.5.0.tgz#e23f3f9c4f8fbdde872529c1071857a086e5ccef"
-
-lodash.fill@^3.4.0:
- version "3.4.0"
- resolved "https://registry.yarnpkg.com/lodash.fill/-/lodash.fill-3.4.0.tgz#a3c74ae640d053adf0dc2079f8720788e8bfef85"
-
-lodash.flatten@^4.4.0:
- version "4.4.0"
- resolved "https://registry.yarnpkg.com/lodash.flatten/-/lodash.flatten-4.4.0.tgz#f31c22225a9632d2bbf8e4addbef240aa765a61f"
+ integrity sha1-4j8/nE+Pvd6HJSnBBxhXoIblzO8=
lodash.flattendeep@^4.4.0:
version "4.4.0"
resolved "https://registry.yarnpkg.com/lodash.flattendeep/-/lodash.flattendeep-4.4.0.tgz#fb030917f86a3134e5bc9bec0d69e0013ddfedb2"
+ integrity sha1-+wMJF/hqMTTlvJvsDWngAT3f7bI=
-lodash.intersection@^4.4.0:
- version "4.4.0"
- resolved "https://registry.yarnpkg.com/lodash.intersection/-/lodash.intersection-4.4.0.tgz#0a11ba631d0e95c23c7f2f4cbb9a692ed178e705"
-
-lodash.merge@>=4.6.2, lodash.merge@^4.6.1:
+lodash.merge@^4.6.2:
version "4.6.2"
resolved "https://registry.yarnpkg.com/lodash.merge/-/lodash.merge-4.6.2.tgz#558aa53b43b661e1925a0afdfa36a9a1085fe57a"
integrity sha512-0KpjqXRVvrYyCsX1swR/XTK0va6VQkQM6MNo7PqW77ByjAhoARA8EfrP1N4+KlKj8YS0ZUCtRT/YUuhyYDujIQ==
-lodash.omit@^4.5.0:
- version "4.5.0"
- resolved "https://registry.yarnpkg.com/lodash.omit/-/lodash.omit-4.5.0.tgz#6eb19ae5a1ee1dd9df0b969e66ce0b7fa30b5e60"
-
-lodash.partialright@^4.2.1:
- version "4.2.1"
- resolved "https://registry.yarnpkg.com/lodash.partialright/-/lodash.partialright-4.2.1.tgz#0130d80e83363264d40074f329b8a3e7a8a1cc4b"
-
-lodash.pick@^4.4.0:
- version "4.4.0"
- resolved "https://registry.yarnpkg.com/lodash.pick/-/lodash.pick-4.4.0.tgz#52f05610fff9ded422611441ed1fc123a03001b3"
-
-lodash.unescape@4.0.1:
- version "4.0.1"
- resolved "https://registry.yarnpkg.com/lodash.unescape/-/lodash.unescape-4.0.1.tgz#bf2249886ce514cda112fae9218cdc065211fc9c"
-
-lodash.union@~4.6.0:
- version "4.6.0"
- resolved "https://registry.yarnpkg.com/lodash.union/-/lodash.union-4.6.0.tgz#48bb5088409f16f1821666641c44dd1aaae3cd88"
-
-lodash.uniq@^4.5.0, lodash.uniq@~4.5.0:
- version "4.5.0"
- resolved "https://registry.yarnpkg.com/lodash.uniq/-/lodash.uniq-4.5.0.tgz#d0225373aeb652adc1bc82e4945339a842754773"
+lodash.truncate@^4.4.2:
+ version "4.4.2"
+ resolved "https://registry.yarnpkg.com/lodash.truncate/-/lodash.truncate-4.4.2.tgz#5a350da0b1113b837ecfffd5812cbe58d6eae193"
+ integrity sha1-WjUNoLERO4N+z//VgSy+WNbq4ZM=
-lodash.without@~4.4.0:
- version "4.4.0"
- resolved "https://registry.yarnpkg.com/lodash.without/-/lodash.without-4.4.0.tgz#3cd4574a00b67bae373a94b748772640507b7aac"
-
-lodash@>=4.17.13, lodash@^4.17.11, lodash@^4.17.13, lodash@^4.17.14, lodash@^4.17.15:
+lodash@^4.17.11, lodash@^4.17.15:
version "4.17.21"
resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.21.tgz#679591c564c3bffaae8454cf0b3df370c3d6911c"
integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==
@@ -3167,18 +3531,22 @@ log-symbols@4.0.0:
long@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/long/-/long-4.0.0.tgz#9a7b71cfb7d361a194ea555241c92f7468d5bf28"
+ integrity sha512-XsP+KhQif4bjX1kbuSiySJFNAehNxgLb6hPRGJ9QsUr8ajHkuXGdrHmFUTUUXhDwVX2R5bY4JNZEwbUiMhV+MA==
lowercase-keys@1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/lowercase-keys/-/lowercase-keys-1.0.0.tgz#4e3366b39e7f5457e35f1324bdf6f88d0bfc7306"
+ integrity sha1-TjNms55/VFfjXxMkvfb4jQv8cwY=
lowercase-keys@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/lowercase-keys/-/lowercase-keys-1.0.1.tgz#6f9e30b47084d971a7c820ff15a6c5167b74c26f"
+ integrity sha512-G2Lj61tXDnVFFOi8VZds+SoQjtQC3dgokKdDG2mTm1tx4m50NUHBOZSBwQQHyy0V12A0JTG4icfZQH+xPyh8VA==
-lru-cache@^4.0.1, lru-cache@^4.1.3:
+lru-cache@^4.0.1:
version "4.1.5"
resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-4.1.5.tgz#8bbe50ea85bed59bc9e33dcab8235ee9bcf443cd"
+ integrity sha512-sWZlbEP2OsHNkXrMl5GYk/jKk70MBng6UU4YI/qGDYbgf6YbP4EvmqISbXCoJiRKs+1bSpFHVgQxvJ17F2li5g==
dependencies:
pseudomap "^1.0.2"
yallist "^2.1.2"
@@ -3190,108 +3558,141 @@ lru-cache@^5.1.1:
dependencies:
yallist "^3.0.2"
+lru-cache@^6.0.0:
+ version "6.0.0"
+ resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-6.0.0.tgz#6d6fe6570ebd96aaf90fcad1dafa3b2566db3a94"
+ integrity sha512-Jo6dJ04CmSjuznwJSS3pUeWmd/H0ffTlkXXgwZi+eq1UCmqQwCh+eLsYOYCwY991i2Fah4h1BEMCx4qThGbsiA==
+ dependencies:
+ yallist "^4.0.0"
+
make-dir@^1.0.0:
version "1.3.0"
resolved "https://registry.yarnpkg.com/make-dir/-/make-dir-1.3.0.tgz#79c1033b80515bd6d24ec9933e860ca75ee27f0c"
+ integrity sha512-2w31R7SJtieJJnQtGc7RVL2StM2vGYVfqUOvUDxH6bC6aJTxPxTF0GnIgCyu7tjockiUWAYQRbxa7vKn34s5sQ==
dependencies:
pify "^3.0.0"
make-dir@^3.0.0, make-dir@^3.0.2:
- version "3.0.2"
- resolved "https://registry.yarnpkg.com/make-dir/-/make-dir-3.0.2.tgz#04a1acbf22221e1d6ef43559f43e05a90dbb4392"
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/make-dir/-/make-dir-3.1.0.tgz#415e967046b3a7f1d185277d84aa58203726a13f"
+ integrity sha512-g3FeP20LNwhALb/6Cz6Dd4F2ngze0jz7tbzrD2wAV+o9FeNHe4rL+yK2md0J/fiSf1sa1ADhXqi5+oVwOM/eGw==
dependencies:
semver "^6.0.0"
make-error@^1.1.1:
- version "1.3.4"
- resolved "https://registry.yarnpkg.com/make-error/-/make-error-1.3.4.tgz#19978ed575f9e9545d2ff8c13e33b5d18a67d535"
-
-make-fetch-happen@^5.0.0:
- version "5.0.2"
- resolved "https://registry.yarnpkg.com/make-fetch-happen/-/make-fetch-happen-5.0.2.tgz#aa8387104f2687edca01c8687ee45013d02d19bd"
- integrity sha512-07JHC0r1ykIoruKO8ifMXu+xEU8qOXDFETylktdug6vJDACnP+HKevOu3PXyNPzFyTSlz8vrBYlBO1JZRe8Cag==
- dependencies:
- agentkeepalive "^3.4.1"
- cacache "^12.0.0"
- http-cache-semantics "^3.8.1"
- http-proxy-agent "^2.1.0"
- https-proxy-agent "^2.2.3"
- lru-cache "^5.1.1"
- mississippi "^3.0.0"
- node-fetch-npm "^2.0.2"
- promise-retry "^1.1.1"
- socks-proxy-agent "^4.0.0"
- ssri "^6.0.0"
+ version "1.3.6"
+ resolved "https://registry.yarnpkg.com/make-error/-/make-error-1.3.6.tgz#2eb2e37ea9b67c4891f684a1394799af484cf7a2"
+ integrity sha512-s8UhlNe7vPKomQhC1qFelMokr/Sc3AgNbso3n74mVPA5LTZwkB9NlXf4XPamLxJE8h0gh73rM94xvwRT2CVInw==
+
+make-fetch-happen@^9.0.1:
+ version "9.0.2"
+ resolved "https://registry.yarnpkg.com/make-fetch-happen/-/make-fetch-happen-9.0.2.tgz#aa8c0e4a5e3a5f2be86c54d3abed44fe5a32ad5d"
+ integrity sha512-UkAWAuXPXSSlVviTjH2We20mtj1NnZW2Qq/oTY2dyMbRQ5CR3Xed3akCDMnM7j6axrMY80lhgM7loNE132PfAw==
+ dependencies:
+ agentkeepalive "^4.1.3"
+ cacache "^15.2.0"
+ http-cache-semantics "^4.1.0"
+ http-proxy-agent "^4.0.1"
+ https-proxy-agent "^5.0.0"
+ is-lambda "^1.0.1"
+ lru-cache "^6.0.0"
+ minipass "^3.1.3"
+ minipass-collect "^1.0.2"
+ minipass-fetch "^1.3.2"
+ minipass-flush "^1.0.5"
+ minipass-pipeline "^1.2.4"
+ negotiator "^0.6.2"
+ promise-retry "^2.0.1"
+ socks-proxy-agent "^5.0.0"
+ ssri "^8.0.0"
map-age-cleaner@^0.1.1:
version "0.1.3"
resolved "https://registry.yarnpkg.com/map-age-cleaner/-/map-age-cleaner-0.1.3.tgz#7d583a7306434c055fe474b0f45078e6e1b4b92a"
+ integrity sha512-bJzx6nMoP6PDLPBFmg7+xRKeFZvFboMrGlxmNj9ClvX53KrmvM5bXFXEWjbz4cz1AFn+jWJ9z/DJSz7hrs0w3w==
dependencies:
p-defer "^1.0.0"
md5.js@1.3.4:
version "1.3.4"
- resolved "https://registry.npmjs.org/md5.js/-/md5.js-1.3.4.tgz#e9bdbde94a20a5ac18b04340fc5764d5b09d901d"
+ resolved "https://registry.yarnpkg.com/md5.js/-/md5.js-1.3.4.tgz#e9bdbde94a20a5ac18b04340fc5764d5b09d901d"
+ integrity sha1-6b296UogpawYsENA/Fdk1bCdkB0=
dependencies:
hash-base "^3.0.0"
inherits "^2.0.1"
-meant@^1.0.2:
- version "1.0.2"
- resolved "https://registry.yarnpkg.com/meant/-/meant-1.0.2.tgz#5d0c78310a3d8ae1408a16be0fe0bd42a969f560"
- integrity sha512-KN+1uowN/NK+sT/Lzx7WSGIj2u+3xe5n2LbwObfjOhPZiA+cCfCm6idVl0RkEfjThkw5XJ96CyRcanq6GmKtUg==
-
media-typer@0.3.0:
version "0.3.0"
resolved "https://registry.yarnpkg.com/media-typer/-/media-typer-0.3.0.tgz#8710d7af0aa626f8fffa1ce00168545263255748"
+ integrity sha1-hxDXrwqmJvj/+hzgAWhUUmMlV0g=
mem@^4.0.0:
version "4.3.0"
resolved "https://registry.yarnpkg.com/mem/-/mem-4.3.0.tgz#461af497bc4ae09608cdb2e60eefb69bff744178"
+ integrity sha512-qX2bG48pTqYRVmDB37rn/6PT7LcR8T7oAX3bf99u1Tt1nzxYfxkgqDwUwolPlXweM0XzBOBFzSx4kfp7KP1s/w==
dependencies:
map-age-cleaner "^0.1.1"
mimic-fn "^2.0.0"
p-is-promise "^2.0.0"
+merge-deep@^3.0.2:
+ version "3.0.3"
+ resolved "https://registry.yarnpkg.com/merge-deep/-/merge-deep-3.0.3.tgz#1a2b2ae926da8b2ae93a0ac15d90cd1922766003"
+ integrity sha512-qtmzAS6t6grwEkNrunqTBdn0qKwFgNWvlxUbAV8es9M7Ot1EbyApytCnvE0jALPa46ZpKDUo527kKiaWplmlFA==
+ dependencies:
+ arr-union "^3.1.0"
+ clone-deep "^0.2.4"
+ kind-of "^3.0.2"
+
merge-descriptors@1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/merge-descriptors/-/merge-descriptors-1.0.1.tgz#b00aaa556dd8b44568150ec9d1b953f3f90cbb61"
+ integrity sha1-sAqqVW3YtEVoFQ7J0blT8/kMu2E=
+
+merge2@^1.3.0:
+ version "1.4.1"
+ resolved "https://registry.yarnpkg.com/merge2/-/merge2-1.4.1.tgz#4368892f885e907455a6fd7dc55c0c9d404990ae"
+ integrity sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==
methods@~1.1.2:
version "1.1.2"
resolved "https://registry.yarnpkg.com/methods/-/methods-1.1.2.tgz#5529a4d67654134edcc5266656835b0f851afcee"
+ integrity sha1-VSmk1nZUE07cxSZmVoNbD4Ua/O4=
-mime-db@~1.35.0:
- version "1.35.0"
- resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.35.0.tgz#0569d657466491283709663ad379a99b90d9ab47"
-
-mime-db@~1.36.0:
- version "1.36.0"
- resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.36.0.tgz#5020478db3c7fe93aad7bbcc4dcf869c43363397"
-
-mime-types@^2.1.12, mime-types@~2.1.17, mime-types@~2.1.18:
- version "2.1.19"
- resolved "https://registry.yarnpkg.com/mime-types/-/mime-types-2.1.19.tgz#71e464537a7ef81c15f2db9d97e913fc0ff606f0"
+micromatch@^4.0.2:
+ version "4.0.4"
+ resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-4.0.4.tgz#896d519dfe9db25fce94ceb7a500919bf881ebf9"
+ integrity sha512-pRmzw/XUcwXGpD9aI9q/0XOwLNygjETJ8y0ao0wdqprrzDa4YnxLcz7fQRZr8voh8V10kGhABbNcHVk5wHgWwg==
dependencies:
- mime-db "~1.35.0"
+ braces "^3.0.1"
+ picomatch "^2.2.3"
+
+mime-db@1.48.0:
+ version "1.48.0"
+ resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.48.0.tgz#e35b31045dd7eada3aaad537ed88a33afbef2d1d"
+ integrity sha512-FM3QwxV+TnZYQ2aRqhlKBMHxk10lTbMt3bBkMAp54ddrNeVSfcQYOOKuGuy3Ddrm38I04If834fOUSq1yzslJQ==
-mime-types@~2.1.19:
- version "2.1.20"
- resolved "https://registry.yarnpkg.com/mime-types/-/mime-types-2.1.20.tgz#930cb719d571e903738520f8470911548ca2cc19"
+mime-types@^2.1.12, mime-types@~2.1.19, mime-types@~2.1.24:
+ version "2.1.31"
+ resolved "https://registry.yarnpkg.com/mime-types/-/mime-types-2.1.31.tgz#a00d76b74317c61f9c2db2218b8e9f8e9c5c9e6b"
+ integrity sha512-XGZnNzm3QvgKxa8dpzyhFTHmpP3l5YNusmne07VUOXxou9CqUqYa/HBy124RqtVh/O2pECas/MOcsDgpilPOPg==
dependencies:
- mime-db "~1.36.0"
+ mime-db "1.48.0"
-mime@1.4.1:
- version "1.4.1"
- resolved "https://registry.yarnpkg.com/mime/-/mime-1.4.1.tgz#121f9ebc49e3766f311a76e1fa1c8003c4b03aa6"
+mime@1.6.0:
+ version "1.6.0"
+ resolved "https://registry.yarnpkg.com/mime/-/mime-1.6.0.tgz#32cd9e5c64553bd58d19a568af452acff04981b1"
+ integrity sha512-x0Vn8spI+wuJ1O6S7gnbaQg8Pxh4NNHb7KSINmEWKiPE4RKOplvijn+NkmYmmRgP68mc70j2EbeTFRsrswaQeg==
-mimic-fn@^2.0.0, mimic-fn@^2.1.0:
+mimic-fn@^2.0.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/mimic-fn/-/mimic-fn-2.1.0.tgz#7ed2c2ccccaf84d3ffcb7a69b57711fc2083401b"
+ integrity sha512-OqbOk5oEQeAZ8WXWydlu9HJjz9WVdEIvamMCcXmuqUYjTknH/sqsWvhQ3vgwKFRR1HpjvNBKQ37nbJgYzGqGcg==
mimic-response@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/mimic-response/-/mimic-response-1.0.1.tgz#4923538878eef42063cb8a3e3b0798781487ab1b"
+ integrity sha512-j5EctnkH7amfV/q5Hgmoal1g2QHFJRraOtmx0JpIqkxhBhI/lJSl1nMpQ45hVarwNETOoWEimndZ4QK0RHxuxQ==
minimatch@3.0.4, minimatch@^3.0.4:
version "3.0.4"
@@ -3305,14 +3706,54 @@ minimist@^1.2.0, minimist@^1.2.5:
resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.5.tgz#67d66014b66a6a8aaa0c083c5fd58df4e4e97602"
integrity sha512-FM9nNUYrRBAELZQT3xeZQ7fmMOBg6nWNmJKTcgsJeaLstP/UODVpGsr5OhXhhXg6f+qtJ8uiZ+PUxkDWcgIXLw==
-minipass@^2.2.1, minipass@^2.3.3:
- version "2.3.3"
- resolved "https://registry.yarnpkg.com/minipass/-/minipass-2.3.3.tgz#a7dcc8b7b833f5d368759cce544dccb55f50f233"
+minipass-collect@^1.0.2:
+ version "1.0.2"
+ resolved "https://registry.yarnpkg.com/minipass-collect/-/minipass-collect-1.0.2.tgz#22b813bf745dc6edba2576b940022ad6edc8c617"
+ integrity sha512-6T6lH0H8OG9kITm/Jm6tdooIbogG9e0tLgpY6mphXSm/A9u8Nq1ryBG+Qspiub9LjWlBPsPS3tWQ/Botq4FdxA==
dependencies:
- safe-buffer "^5.1.2"
- yallist "^3.0.0"
+ minipass "^3.0.0"
+
+minipass-fetch@^1.3.0, minipass-fetch@^1.3.2:
+ version "1.3.3"
+ resolved "https://registry.yarnpkg.com/minipass-fetch/-/minipass-fetch-1.3.3.tgz#34c7cea038c817a8658461bf35174551dce17a0a"
+ integrity sha512-akCrLDWfbdAWkMLBxJEeWTdNsjML+dt5YgOI4gJ53vuO0vrmYQkUPxa6j6V65s9CcePIr2SSWqjT2EcrNseryQ==
+ dependencies:
+ minipass "^3.1.0"
+ minipass-sized "^1.0.3"
+ minizlib "^2.0.0"
+ optionalDependencies:
+ encoding "^0.1.12"
+
+minipass-flush@^1.0.5:
+ version "1.0.5"
+ resolved "https://registry.yarnpkg.com/minipass-flush/-/minipass-flush-1.0.5.tgz#82e7135d7e89a50ffe64610a787953c4c4cbb373"
+ integrity sha512-JmQSYYpPUqX5Jyn1mXaRwOda1uQ8HP5KAT/oDSLCzt1BYRhQU0/hDtsB1ufZfEEzMZ9aAVmsBw8+FWsIXlClWw==
+ dependencies:
+ minipass "^3.0.0"
+
+minipass-json-stream@^1.0.1:
+ version "1.0.1"
+ resolved "https://registry.yarnpkg.com/minipass-json-stream/-/minipass-json-stream-1.0.1.tgz#7edbb92588fbfc2ff1db2fc10397acb7b6b44aa7"
+ integrity sha512-ODqY18UZt/I8k+b7rl2AENgbWE8IDYam+undIJONvigAz8KR5GWblsFTEfQs0WODsjbSXWlm+JHEv8Gr6Tfdbg==
+ dependencies:
+ jsonparse "^1.3.1"
+ minipass "^3.0.0"
+
+minipass-pipeline@^1.2.2, minipass-pipeline@^1.2.4:
+ version "1.2.4"
+ resolved "https://registry.yarnpkg.com/minipass-pipeline/-/minipass-pipeline-1.2.4.tgz#68472f79711c084657c067c5c6ad93cddea8214c"
+ integrity sha512-xuIq7cIOt09RPRJ19gdi4b+RiNvDFYe5JH+ggNvBqGqpQXcru3PcRmOZuHBKWK1Txf9+cQ+HMVN4d6z46LZP7A==
+ dependencies:
+ minipass "^3.0.0"
+
+minipass-sized@^1.0.3:
+ version "1.0.3"
+ resolved "https://registry.yarnpkg.com/minipass-sized/-/minipass-sized-1.0.3.tgz#70ee5a7c5052070afacfbc22977ea79def353b70"
+ integrity sha512-MbkQQ2CTiBMlA2Dm/5cY+9SWFEN8pzzOXi6rlM5Xxq0Yqbda5ZQy9sU75a673FE9ZK0Zsbr6Y5iP6u9nktfg2g==
+ dependencies:
+ minipass "^3.0.0"
-minipass@^2.3.5, minipass@^2.8.6, minipass@^2.9.0:
+minipass@^2.6.0, minipass@^2.8.6, minipass@^2.9.0:
version "2.9.0"
resolved "https://registry.yarnpkg.com/minipass/-/minipass-2.9.0.tgz#e713762e7d3e32fed803115cf93e04bca9fcc9a6"
integrity sha512-wxfUjg9WebH+CUDX/CdbRlh5SmfZiy/hpkxaRI16Y9W56Pa75sWgd/rvFilSgrauD9NyFymP/+JFV3KwzIsJeg==
@@ -3320,18 +3761,13 @@ minipass@^2.3.5, minipass@^2.8.6, minipass@^2.9.0:
safe-buffer "^5.1.2"
yallist "^3.0.0"
-minipass@^3.0.0:
+minipass@^3.0.0, minipass@^3.1.0, minipass@^3.1.1, minipass@^3.1.3:
version "3.1.3"
resolved "https://registry.yarnpkg.com/minipass/-/minipass-3.1.3.tgz#7d42ff1f39635482e15f9cdb53184deebd5815fd"
+ integrity sha512-Mgd2GdMVzY+x3IJ+oHnVM+KG3lA5c8tnabyJKmHSaG2kAGpudxuOf8ToDkhumF7UzME7DecbQE9uOZhNm7PuJg==
dependencies:
yallist "^4.0.0"
-minizlib@^1.1.0:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/minizlib/-/minizlib-1.1.0.tgz#11e13658ce46bc3a70a267aac58359d1e0c29ceb"
- dependencies:
- minipass "^2.2.1"
-
minizlib@^1.2.1:
version "1.3.3"
resolved "https://registry.yarnpkg.com/minizlib/-/minizlib-1.3.3.tgz#2290de96818a34c29551c8a8d301216bd65a861d"
@@ -3339,145 +3775,142 @@ minizlib@^1.2.1:
dependencies:
minipass "^2.9.0"
-minizlib@^2.1.0:
- version "2.1.0"
- resolved "https://registry.yarnpkg.com/minizlib/-/minizlib-2.1.0.tgz#fd52c645301ef09a63a2c209697c294c6ce02cf3"
+minizlib@^2.0.0, minizlib@^2.1.1:
+ version "2.1.2"
+ resolved "https://registry.yarnpkg.com/minizlib/-/minizlib-2.1.2.tgz#e90d3466ba209b932451508a11ce3d3632145931"
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minipass "^3.0.0"
yallist "^4.0.0"
-mississippi@^3.0.0:
- version "3.0.0"
- resolved "https://registry.yarnpkg.com/mississippi/-/mississippi-3.0.0.tgz#ea0a3291f97e0b5e8776b363d5f0a12d94c67022"
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+mixin-object@^2.0.1:
+ version "2.0.1"
+ resolved "https://registry.yarnpkg.com/mixin-object/-/mixin-object-2.0.1.tgz#4fb949441dab182540f1fe035ba60e1947a5e57e"
+ integrity sha1-T7lJRB2rGCVA8f4DW6YOGUel5X4=
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- duplexify "^3.4.2"
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- flush-write-stream "^1.0.0"
- from2 "^2.1.0"
- parallel-transform "^1.1.0"
- pump "^3.0.0"
- pumpify "^1.3.3"
- stream-each "^1.1.0"
- through2 "^2.0.0"
+ for-in "^0.1.3"
+ is-extendable "^0.1.1"
+
+mkdirp-infer-owner@^2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/mkdirp-infer-owner/-/mkdirp-infer-owner-2.0.0.tgz#55d3b368e7d89065c38f32fd38e638f0ab61d316"
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+ dependencies:
+ chownr "^2.0.0"
+ infer-owner "^1.0.4"
+ mkdirp "^1.0.3"
-"mkdirp@>=0.5 0", mkdirp@^0.5.1, mkdirp@^0.5.5:
+"mkdirp@>=0.5 0", mkdirp@^0.5.0, mkdirp@^0.5.1:
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resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.5.tgz#d91cefd62d1436ca0f41620e251288d420099def"
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minimist "^1.2.5"
-mkdirp@^0.5.0, mkdirp@~0.5.0:
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- resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.3.tgz#5a514b7179259287952881e94410ec5465659f8c"
- dependencies:
- minimist "^1.2.5"
-
-mkdirp@^1.0.3:
+mkdirp@^1.0.3, mkdirp@^1.0.4:
version "1.0.4"
resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-1.0.4.tgz#3eb5ed62622756d79a5f0e2a221dfebad75c2f7e"
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-mocha@^8.1.3:
- version "8.1.3"
- resolved "https://registry.yarnpkg.com/mocha/-/mocha-8.1.3.tgz#5e93f873e35dfdd69617ea75f9c68c2ca61c2ac5"
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+mocha@^8.4.0:
+ version "8.4.0"
+ resolved "https://registry.yarnpkg.com/mocha/-/mocha-8.4.0.tgz#677be88bf15980a3cae03a73e10a0fc3997f0cff"
+ integrity sha512-hJaO0mwDXmZS4ghXsvPVriOhsxQ7ofcpQdm8dE+jISUOKopitvnXFQmpRR7jd2K6VBG6E26gU3IAbXXGIbu4sQ==
dependencies:
+ "@ungap/promise-all-settled" "1.1.2"
ansi-colors "4.1.1"
browser-stdout "1.3.1"
- chokidar "3.4.2"
- debug "4.1.1"
- diff "4.0.2"
+ chokidar "3.5.1"
+ debug "4.3.1"
+ diff "5.0.0"
escape-string-regexp "4.0.0"
find-up "5.0.0"
glob "7.1.6"
growl "1.10.5"
he "1.2.0"
- js-yaml "3.14.0"
+ js-yaml "4.0.0"
log-symbols "4.0.0"
minimatch "3.0.4"
- ms "2.1.2"
- object.assign "4.1.0"
- promise.allsettled "1.0.2"
- serialize-javascript "4.0.0"
- strip-json-comments "3.0.1"
- supports-color "7.1.0"
+ ms "2.1.3"
+ nanoid "3.1.20"
+ serialize-javascript "5.0.1"
+ strip-json-comments "3.1.1"
+ supports-color "8.1.1"
which "2.0.2"
wide-align "1.1.3"
- workerpool "6.0.0"
- yargs "13.3.2"
- yargs-parser "13.1.2"
- yargs-unparser "1.6.1"
-
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- version "2.22.2"
- resolved "https://registry.yarnpkg.com/moment/-/moment-2.22.2.tgz#3c257f9839fc0e93ff53149632239eb90783ff66"
-
-move-concurrently@^1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/move-concurrently/-/move-concurrently-1.0.1.tgz#be2c005fda32e0b29af1f05d7c4b33214c701f92"
- integrity sha1-viwAX9oy4LKa8fBdfEszIUxwH5I=
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- copy-concurrently "^1.0.0"
- fs-write-stream-atomic "^1.0.8"
- mkdirp "^0.5.1"
- rimraf "^2.5.4"
- run-queue "^1.0.3"
+ workerpool "6.1.0"
+ yargs "16.2.0"
+ yargs-parser "20.2.4"
+ yargs-unparser "2.0.0"
ms@2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/ms/-/ms-2.0.0.tgz#5608aeadfc00be6c2901df5f9861788de0d597c8"
integrity sha1-VgiurfwAvmwpAd9fmGF4jeDVl8g=
-ms@2.1.2, ms@^2.0.0, ms@^2.1.1:
+ms@2.1.1:
+ version "2.1.1"
+ resolved "https://registry.yarnpkg.com/ms/-/ms-2.1.1.tgz#30a5864eb3ebb0a66f2ebe6d727af06a09d86e0a"
+ integrity sha512-tgp+dl5cGk28utYktBsrFqA7HKgrhgPsg6Z/EfhWI4gl1Hwq8B/GmY/0oXZ6nF8hDVesS/FpnYaD/kOWhYQvyg==
+
+ms@2.1.2:
version "2.1.2"
resolved "https://registry.yarnpkg.com/ms/-/ms-2.1.2.tgz#d09d1f357b443f493382a8eb3ccd183872ae6009"
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-mute-stream@0.0.8, mute-stream@~0.0.4:
+ms@2.1.3, ms@^2.0.0, ms@^2.1.1, ms@^2.1.2:
+ version "2.1.3"
+ resolved "https://registry.yarnpkg.com/ms/-/ms-2.1.3.tgz#574c8138ce1d2b5861f0b44579dbadd60c6615b2"
+ integrity sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==
+
+mute-stream@~0.0.4:
version "0.0.8"
resolved "https://registry.yarnpkg.com/mute-stream/-/mute-stream-0.0.8.tgz#1630c42b2251ff81e2a283de96a5497ea92e5e0d"
+ integrity sha512-nnbWWOkoWyUsTjKrhgD0dcz22mdkSnpYqbEjIm2nhwhuxlSkpywJmBo8h0ZqJdkp73mb90SssHkN4rsRaBAfAA==
+
+nan@^2.14.1, nan@^2.14.2:
+ version "2.14.2"
+ resolved "https://registry.yarnpkg.com/nan/-/nan-2.14.2.tgz#f5376400695168f4cc694ac9393d0c9585eeea19"
+ integrity sha512-M2ufzIiINKCuDfBSAUr1vWQ+vuVcA9kqx8JJUsbQi6yf1uGRyb7HfpdfUr5qLXf3B/t8dPvcjhKMmlfnP47EzQ==
+
+nanoid@3.1.20:
+ version "3.1.20"
+ resolved "https://registry.yarnpkg.com/nanoid/-/nanoid-3.1.20.tgz#badc263c6b1dcf14b71efaa85f6ab4c1d6cfc788"
+ integrity sha512-a1cQNyczgKbLX9jwbS/+d7W8fX/RfgYR7lVWwWOGIPNgK2m0MWvrGF6/m4kk6U3QcFMnZf3RIhL0v2Jgh/0Uxw==
natural-compare@^1.4.0:
version "1.4.0"
resolved "https://registry.yarnpkg.com/natural-compare/-/natural-compare-1.4.0.tgz#4abebfeed7541f2c27acfb29bdbbd15c8d5ba4f7"
+ integrity sha1-Sr6/7tdUHywnrPspvbvRXI1bpPc=
needle@^2.2.1:
- version "2.2.1"
- resolved "https://registry.yarnpkg.com/needle/-/needle-2.2.1.tgz#b5e325bd3aae8c2678902fa296f729455d1d3a7d"
+ version "2.6.0"
+ resolved "https://registry.yarnpkg.com/needle/-/needle-2.6.0.tgz#24dbb55f2509e2324b4a99d61f413982013ccdbe"
+ integrity sha512-KKYdza4heMsEfSWD7VPUIz3zX2XDwOyX2d+geb4vrERZMT5RMU6ujjaD+I5Yr54uZxQ2w6XRTAhHBbSCyovZBg==
dependencies:
- debug "^2.1.2"
+ debug "^3.2.6"
iconv-lite "^0.4.4"
sax "^1.2.4"
-negotiator@0.6.1:
- version "0.6.1"
- resolved "https://registry.yarnpkg.com/negotiator/-/negotiator-0.6.1.tgz#2b327184e8992101177b28563fb5e7102acd0ca9"
+negotiator@0.6.2, negotiator@^0.6.2:
+ version "0.6.2"
+ resolved "https://registry.yarnpkg.com/negotiator/-/negotiator-0.6.2.tgz#feacf7ccf525a77ae9634436a64883ffeca346fb"
+ integrity sha512-hZXc7K2e+PgeI1eDBe/10Ard4ekbfrrqG8Ep+8Jmf4JID2bNg7NvCPOZN+kfF574pFQI7mum2AUqDidoKqcTOw==
nice-try@^1.0.4:
version "1.0.5"
resolved "https://registry.yarnpkg.com/nice-try/-/nice-try-1.0.5.tgz#a3378a7696ce7d223e88fc9b764bd7ef1089e366"
+ integrity sha512-1nh45deeb5olNY7eX82BkPO7SSxR5SSYJiPTrTdFUVYwAl8CKMA5N9PjTYkHiRjisVcxcQ1HXdLhx2qxxJzLNQ==
-node-addon-api@2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/node-addon-api/-/node-addon-api-2.0.0.tgz#f9afb8d777a91525244b01775ea0ddbe1125483b"
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-
-node-fetch-npm@^2.0.2:
- version "2.0.4"
- resolved "https://registry.yarnpkg.com/node-fetch-npm/-/node-fetch-npm-2.0.4.tgz#6507d0e17a9ec0be3bec516958a497cec54bf5a4"
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- dependencies:
- encoding "^0.1.11"
- json-parse-better-errors "^1.0.0"
- safe-buffer "^5.1.1"
+node-addon-api@^3.0.0:
+ version "3.2.1"
+ resolved "https://registry.yarnpkg.com/node-addon-api/-/node-addon-api-3.2.1.tgz#81325e0a2117789c0128dab65e7e38f07ceba161"
+ integrity sha512-mmcei9JghVNDYydghQmeDX8KoAm0FAiYyIcUt/N4nhyAipB17pllZQDOJD2fotxABnt4Mdz+dKTO7eftLg4d0A==
-node-forge@>=0.10.0, node-forge@^0.7.6:
- version "0.10.0"
- resolved "https://registry.yarnpkg.com/node-forge/-/node-forge-0.10.0.tgz#32dea2afb3e9926f02ee5ce8794902691a676bf3"
- integrity sha512-PPmu8eEeG9saEUvI97fm4OYxXVB6bFvyNTyiUOBichBpFG8A1Ljw3bY62+5oOjDEMHRnd0Y7HQ+x7uzxOzC6JA==
+node-forge@^0.8.5:
+ version "0.8.5"
+ resolved "https://registry.yarnpkg.com/node-forge/-/node-forge-0.8.5.tgz#57906f07614dc72762c84cef442f427c0e1b86ee"
+ integrity sha512-vFMQIWt+J/7FLNyKouZ9TazT74PRV3wgv9UT4cRjC8BffxFbKXkgIWR42URCPSnHm/QDz6BOlb2Q0U4+VQT67Q==
node-gyp@3.x:
version "3.8.0"
@@ -3497,42 +3930,37 @@ node-gyp@3.x:
tar "^2.0.0"
which "1"
-node-gyp@^5.0.2, node-gyp@^5.1.0:
- version "5.1.1"
- resolved "https://registry.yarnpkg.com/node-gyp/-/node-gyp-5.1.1.tgz#eb915f7b631c937d282e33aed44cb7a025f62a3e"
- integrity sha512-WH0WKGi+a4i4DUt2mHnvocex/xPLp9pYt5R6M2JdFB7pJ7Z34hveZ4nDTGTiLXCkitA9T8HFZjhinBCiVHYcWw==
+node-gyp@^7.1.0, node-gyp@^7.1.2:
+ version "7.1.2"
+ resolved "https://registry.yarnpkg.com/node-gyp/-/node-gyp-7.1.2.tgz#21a810aebb187120251c3bcec979af1587b188ae"
+ integrity sha512-CbpcIo7C3eMu3dL1c3d0xw449fHIGALIJsRP4DDPHpyiW8vcriNY7ubh9TE4zEKfSxscY7PjeFnshE7h75ynjQ==
dependencies:
env-paths "^2.2.0"
glob "^7.1.4"
- graceful-fs "^4.2.2"
- mkdirp "^0.5.1"
- nopt "^4.0.1"
+ graceful-fs "^4.2.3"
+ nopt "^5.0.0"
npmlog "^4.1.2"
- request "^2.88.0"
- rimraf "^2.6.3"
- semver "^5.7.1"
- tar "^4.4.12"
- which "^1.3.1"
+ request "^2.88.2"
+ rimraf "^3.0.2"
+ semver "^7.3.2"
+ tar "^6.0.2"
+ which "^2.0.2"
node-jose@^1.1.0:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/node-jose/-/node-jose-1.1.0.tgz#15b0808ed7cf40cc6114c95c1a4d7394051c472a"
+ version "1.1.4"
+ resolved "https://registry.yarnpkg.com/node-jose/-/node-jose-1.1.4.tgz#af3f44a392e586d26b123b0e12dc09bef1e9863b"
+ integrity sha512-L31IFwL3pWWcMHxxidCY51ezqrDXMkvlT/5pLTfNw5sXmmOLJuN6ug7txzF/iuZN55cRpyOmoJrotwBQIoo5Lw==
dependencies:
- base64url "^3.0.0"
- es6-promise "^4.2.5"
- lodash.assign "^4.2.0"
- lodash.clone "^4.5.0"
- lodash.fill "^3.4.0"
- lodash.flatten "^4.4.0"
- lodash.intersection "^4.4.0"
- lodash.merge "^4.6.1"
- lodash.omit "^4.5.0"
- lodash.partialright "^4.2.1"
- lodash.pick "^4.4.0"
- lodash.uniq "^4.5.0"
+ base64url "^3.0.1"
+ browserify-zlib "^0.2.0"
+ buffer "^5.5.0"
+ es6-promise "^4.2.8"
+ lodash "^4.17.15"
long "^4.0.0"
- node-forge "^0.7.6"
- uuid "^3.3.2"
+ node-forge "^0.8.5"
+ process "^0.11.10"
+ react-zlib-js "^1.0.4"
+ uuid "^3.3.3"
node-pre-gyp@^0.11.0:
version "0.11.0"
@@ -3553,16 +3981,24 @@ node-pre-gyp@^0.11.0:
node-preload@^0.2.1:
version "0.2.1"
resolved "https://registry.yarnpkg.com/node-preload/-/node-preload-0.2.1.tgz#c03043bb327f417a18fee7ab7ee57b408a144301"
+ integrity sha512-RM5oyBy45cLEoHqCeh+MNuFAxO0vTFBLskvQbOKnEE7YTTSN4tbN8QWDIPQ6L+WvKsB/qLEGpYe2ZZ9d4W9OIQ==
dependencies:
process-on-spawn "^1.0.0"
+node-releases@^1.1.71:
+ version "1.1.72"
+ resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-1.1.72.tgz#14802ab6b1039a79a0c7d662b610a5bbd76eacbe"
+ integrity sha512-LLUo+PpH3dU6XizX3iVoubUNheF/owjXCZZ5yACDxNnPtgFuludV1ZL3ayK1kVep42Rmm0+R9/Y60NQbZ2bifw==
+
node-version@^1.0.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/node-version/-/node-version-1.2.0.tgz#34fde3ffa8e1149bd323983479dda620e1b5060d"
+ integrity sha512-ma6oU4Sk0qOoKEAymVoTvk8EdXEobdS7m/mAGhDJ8Rouugho48crHBORAmy5BoOcv8wraPM6xumapQp5hl4iIQ==
-node.extend@1.0.8, node.extend@^1.1.7:
- version "1.1.8"
- resolved "https://registry.yarnpkg.com/node.extend/-/node.extend-1.1.8.tgz#0aab3e63789f4e6d68b42bc00073ad1881243cf0"
+node.extend@1.0.8, node.extend@>=1.1.8:
+ version "2.0.2"
+ resolved "https://registry.yarnpkg.com/node.extend/-/node.extend-2.0.2.tgz#b4404525494acc99740f3703c496b7d5182cc6cc"
+ integrity sha512-pDT4Dchl94/+kkgdwyS2PauDFjZG0Hk0IcHIB+LkW27HLDtdoeMxHTxZh39DYbPP8UflWXWj9JcdDozF+YDOpQ==
dependencies:
has "^1.0.3"
is "^3.2.1"
@@ -3570,6 +4006,7 @@ node.extend@1.0.8, node.extend@^1.1.7:
node.flow@1.2.3:
version "1.2.3"
resolved "https://registry.yarnpkg.com/node.flow/-/node.flow-1.2.3.tgz#e1c44a82aeca8d78b458a77fb3dc642f2eba2649"
+ integrity sha1-4cRKgq7KjXi0WKd/s9xkLy66Jkk=
dependencies:
node.extend "1.0.8"
@@ -3581,13 +4018,6 @@ node.flow@1.2.3:
abbrev "1"
nopt@^4.0.1:
- version "4.0.1"
- resolved "https://registry.yarnpkg.com/nopt/-/nopt-4.0.1.tgz#d0d4685afd5415193c8c7505602d0d17cd64474d"
- dependencies:
- abbrev "1"
- osenv "^0.1.4"
-
-nopt@^4.0.3:
version "4.0.3"
resolved "https://registry.yarnpkg.com/nopt/-/nopt-4.0.3.tgz#a375cad9d02fd921278d954c2254d5aa57e15e48"
integrity sha512-CvaGwVMztSMJLOeXPrez7fyfObdZqNUK1cPAEzLHrTybIua9pMdmmPR5YwtfNftIOMv3DPUhFaxsZMNTQO20Kg==
@@ -3595,84 +4025,83 @@ nopt@^4.0.3:
abbrev "1"
osenv "^0.1.4"
-normalize-package-data@^2.0.0, normalize-package-data@^2.4.0, normalize-package-data@^2.5.0:
- version "2.5.0"
- resolved "https://registry.yarnpkg.com/normalize-package-data/-/normalize-package-data-2.5.0.tgz#e66db1838b200c1dfc233225d12cb36520e234a8"
+nopt@^5.0.0:
+ version "5.0.0"
+ resolved "https://registry.yarnpkg.com/nopt/-/nopt-5.0.0.tgz#530942bb58a512fccafe53fe210f13a25355dc88"
+ integrity sha512-Tbj67rffqceeLpcRXrT7vKAN8CwfPeIBgM7E6iBkmKLV7bEMwpGgYLGv0jACUsECaa/vuxP0IjEont6umdMgtQ==
+ dependencies:
+ abbrev "1"
+
+normalize-package-data@^3.0.0, normalize-package-data@^3.0.2:
+ version "3.0.2"
+ resolved "https://registry.yarnpkg.com/normalize-package-data/-/normalize-package-data-3.0.2.tgz#cae5c410ae2434f9a6c1baa65d5bc3b9366c8699"
+ integrity sha512-6CdZocmfGaKnIHPVFhJJZ3GuR8SsLKvDANFp47Jmy51aKIr8akjAWTSxtpI+MBgBFdSMRyo4hMpDlT6dTffgZg==
dependencies:
- hosted-git-info "^2.1.4"
- resolve "^1.10.0"
- semver "2 || 3 || 4 || 5"
+ hosted-git-info "^4.0.1"
+ resolve "^1.20.0"
+ semver "^7.3.4"
validate-npm-package-license "^3.0.1"
normalize-path@^3.0.0, normalize-path@~3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/normalize-path/-/normalize-path-3.0.0.tgz#0dcd69ff23a1c9b11fd0978316644a0388216a65"
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normalize-url@2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/normalize-url/-/normalize-url-2.0.1.tgz#835a9da1551fa26f70e92329069a23aa6574d7e6"
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query-string "^5.0.1"
sort-keys "^2.0.0"
-npm-audit-report@^1.3.3:
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+npm-audit-report@^2.1.5:
+ version "2.1.5"
+ resolved "https://registry.yarnpkg.com/npm-audit-report/-/npm-audit-report-2.1.5.tgz#a5b8850abe2e8452fce976c8960dd432981737b5"
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- console-control-strings "^1.1.0"
-
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- resolved "https://registry.yarnpkg.com/npm-bundled/-/npm-bundled-1.0.3.tgz#7e71703d973af3370a9591bafe3a63aca0be2308"
-
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- resolved "https://registry.yarnpkg.com/npm-cache-filename/-/npm-cache-filename-1.0.2.tgz#ded306c5b0bfc870a9e9faf823bc5f283e05ae11"
+ chalk "^4.0.0"
-npm-install-checks@^3.0.2:
- version "3.0.2"
- resolved "https://registry.yarnpkg.com/npm-install-checks/-/npm-install-checks-3.0.2.tgz#ab2e32ad27baa46720706908e5b14c1852de44d9"
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+npm-bundled@^1.0.1, npm-bundled@^1.1.1:
+ version "1.1.2"
+ resolved "https://registry.yarnpkg.com/npm-bundled/-/npm-bundled-1.1.2.tgz#944c78789bd739035b70baa2ca5cc32b8d860bc1"
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dependencies:
- semver "^2.3.0 || 3.x || 4 || 5"
+ npm-normalize-package-bin "^1.0.1"
-npm-lifecycle@^3.0.0, npm-lifecycle@^3.1.5:
- version "3.1.5"
- resolved "https://registry.yarnpkg.com/npm-lifecycle/-/npm-lifecycle-3.1.5.tgz#9882d3642b8c82c815782a12e6a1bfeed0026309"
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+npm-install-checks@^4.0.0:
+ version "4.0.0"
+ resolved "https://registry.yarnpkg.com/npm-install-checks/-/npm-install-checks-4.0.0.tgz#a37facc763a2fde0497ef2c6d0ac7c3fbe00d7b4"
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-
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+ semver "^7.1.1"
npm-normalize-package-bin@^1.0.0, npm-normalize-package-bin@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/npm-normalize-package-bin/-/npm-normalize-package-bin-1.0.1.tgz#6e79a41f23fd235c0623218228da7d9c23b8f6e2"
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+npm-package-arg@^6.0.0:
version "6.1.1"
resolved "https://registry.yarnpkg.com/npm-package-arg/-/npm-package-arg-6.1.1.tgz#02168cb0a49a2b75bf988a28698de7b529df5cb7"
+ integrity sha512-qBpssaL3IOZWi5vEKUKW0cO7kzLeT+EQO9W8RsLOZf76KF9E/K9+wH0C7t06HXPpaH8WH5xF1MExLuCwbTqRUg==
dependencies:
hosted-git-info "^2.7.1"
osenv "^0.1.5"
semver "^5.6.0"
validate-npm-package-name "^3.0.0"
-npm-packlist@^1.1.12, npm-packlist@^1.4.8:
+npm-package-arg@^8.0.0, npm-package-arg@^8.0.1, npm-package-arg@^8.1.0, npm-package-arg@^8.1.1, npm-package-arg@^8.1.2, npm-package-arg@^8.1.4:
+ version "8.1.4"
+ resolved "https://registry.yarnpkg.com/npm-package-arg/-/npm-package-arg-8.1.4.tgz#8001cdbc4363997b8ef6c6cf7aaf543c5805879d"
+ integrity sha512-xLokoCFqj/rPdr3LvcdDL6Kj6ipXGEDHD/QGpzwU6/pibYUOXmp5DBmg76yukFyx4ZDbrXNOTn+BPyd8TD4Jlw==
+ dependencies:
+ hosted-git-info "^4.0.1"
+ semver "^7.3.4"
+ validate-npm-package-name "^3.0.0"
+
+npm-packlist@^1.1.6:
version "1.4.8"
resolved "https://registry.yarnpkg.com/npm-packlist/-/npm-packlist-1.4.8.tgz#56ee6cc135b9f98ad3d51c1c95da22bbb9b2ef3e"
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@@ -3681,179 +4110,134 @@ npm-packlist@^1.1.12, npm-packlist@^1.4.8:
npm-bundled "^1.0.1"
npm-normalize-package-bin "^1.0.1"
-npm-packlist@^1.1.6:
- version "1.1.11"
- resolved "https://registry.yarnpkg.com/npm-packlist/-/npm-packlist-1.1.11.tgz#84e8c683cbe7867d34b1d357d893ce29e28a02de"
+npm-packlist@^2.1.4:
+ version "2.2.2"
+ resolved "https://registry.yarnpkg.com/npm-packlist/-/npm-packlist-2.2.2.tgz#076b97293fa620f632833186a7a8f65aaa6148c8"
+ integrity sha512-Jt01acDvJRhJGthnUJVF/w6gumWOZxO7IkpY/lsX9//zqQgnF7OJaxgQXcerd4uQOLu7W5bkb4mChL9mdfm+Zg==
dependencies:
- ignore-walk "^3.0.1"
- npm-bundled "^1.0.1"
+ glob "^7.1.6"
+ ignore-walk "^3.0.3"
+ npm-bundled "^1.1.1"
+ npm-normalize-package-bin "^1.0.1"
-npm-pick-manifest@^3.0.0, npm-pick-manifest@^3.0.2:
- version "3.0.2"
- resolved "https://registry.yarnpkg.com/npm-pick-manifest/-/npm-pick-manifest-3.0.2.tgz#f4d9e5fd4be2153e5f4e5f9b7be8dc419a99abb7"
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+npm-pick-manifest@^6.0.0, npm-pick-manifest@^6.1.0, npm-pick-manifest@^6.1.1:
+ version "6.1.1"
+ resolved "https://registry.yarnpkg.com/npm-pick-manifest/-/npm-pick-manifest-6.1.1.tgz#7b5484ca2c908565f43b7f27644f36bb816f5148"
+ integrity sha512-dBsdBtORT84S8V8UTad1WlUyKIY9iMsAmqxHbLdeEeBNMLQDlDWWra3wYUx9EBEIiG/YwAy0XyNHDd2goAsfuA==
dependencies:
- figgy-pudding "^3.5.1"
- npm-package-arg "^6.0.0"
- semver "^5.4.1"
+ npm-install-checks "^4.0.0"
+ npm-normalize-package-bin "^1.0.1"
+ npm-package-arg "^8.1.2"
+ semver "^7.3.4"
-npm-profile@^4.0.2, npm-profile@^4.0.4:
- version "4.0.4"
- resolved "https://registry.yarnpkg.com/npm-profile/-/npm-profile-4.0.4.tgz#28ee94390e936df6d084263ee2061336a6a1581b"
- integrity sha512-Ta8xq8TLMpqssF0H60BXS1A90iMoM6GeKwsmravJ6wYjWwSzcYBTdyWa3DZCYqPutacBMEm7cxiOkiIeCUAHDQ==
+npm-profile@^5.0.3:
+ version "5.0.4"
+ resolved "https://registry.yarnpkg.com/npm-profile/-/npm-profile-5.0.4.tgz#73e5bd1d808edc2c382d7139049cc367ac43161b"
+ integrity sha512-OKtU7yoAEBOnc8zJ+/uo5E4ugPp09sopo+6y1njPp+W99P8DvQon3BJYmpvyK2Bf1+3YV5LN1bvgXRoZ1LUJBA==
dependencies:
- aproba "^1.1.2 || 2"
- figgy-pudding "^3.4.1"
- npm-registry-fetch "^4.0.0"
+ npm-registry-fetch "^11.0.0"
-npm-registry-fetch@^4.0.0, npm-registry-fetch@^4.0.7:
- version "4.0.7"
- resolved "https://registry.yarnpkg.com/npm-registry-fetch/-/npm-registry-fetch-4.0.7.tgz#57951bf6541e0246b34c9f9a38ab73607c9449d7"
- integrity sha512-cny9v0+Mq6Tjz+e0erFAB+RYJ/AVGzkjnISiobqP8OWj9c9FLoZZu8/SPSKJWE17F1tk4018wfjV+ZbIbqC7fQ==
+npm-registry-fetch@^11.0.0:
+ version "11.0.0"
+ resolved "https://registry.yarnpkg.com/npm-registry-fetch/-/npm-registry-fetch-11.0.0.tgz#68c1bb810c46542760d62a6a965f85a702d43a76"
+ integrity sha512-jmlgSxoDNuhAtxUIG6pVwwtz840i994dL14FoNVZisrmZW5kWd63IUTNv1m/hyRSGSqWjCUp/YZlS1BJyNp9XA==
dependencies:
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- bluebird "^3.5.1"
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- lru-cache "^5.1.1"
- make-fetch-happen "^5.0.0"
- npm-package-arg "^6.1.0"
- safe-buffer "^5.2.0"
+ make-fetch-happen "^9.0.1"
+ minipass "^3.1.3"
+ minipass-fetch "^1.3.0"
+ minipass-json-stream "^1.0.1"
+ minizlib "^2.0.0"
+ npm-package-arg "^8.0.0"
npm-run-path@^2.0.0:
version "2.0.2"
resolved "https://registry.yarnpkg.com/npm-run-path/-/npm-run-path-2.0.2.tgz#35a9232dfa35d7067b4cb2ddf2357b1871536c5f"
+ integrity sha1-NakjLfo11wZ7TLLd8jV7GHFTbF8=
dependencies:
path-key "^2.0.0"
-npm-user-validate@~1.0.0:
+npm-user-validate@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/npm-user-validate/-/npm-user-validate-1.0.1.tgz#31428fc5475fe8416023f178c0ab47935ad8c561"
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-npm@5.1.0, npm@>=6.14.8:
- version "6.14.8"
- resolved "https://registry.yarnpkg.com/npm/-/npm-6.14.8.tgz#64ef754345639bc035982ec3f609353c8539033c"
- integrity sha512-HBZVBMYs5blsj94GTeQZel7s9odVuuSUHy1+AlZh7rPVux1os2ashvEGLy/STNK7vUjbrCg5Kq9/GXisJgdf6A==
+npm@5.1.0, npm@>=7.16.0:
+ version "7.16.0"
+ resolved "https://registry.yarnpkg.com/npm/-/npm-7.16.0.tgz#2325af134d221059ebc245a4ae5b39d24216a2ba"
+ integrity sha512-UUnKcjS7qFhZT90iZY/ZWz/jwF+rS0fIohDf41T6/SRXEqut0aav+1NkL6g6GqQGpIVBzpZc75BDfpq4PhfXBg==
dependencies:
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+ "@npmcli/arborist" "^2.6.1"
+ "@npmcli/ci-detect" "^1.2.0"
+ "@npmcli/config" "^2.2.0"
+ "@npmcli/run-script" "^1.8.5"
abbrev "~1.1.1"
ansicolors "~0.3.2"
ansistyles "~0.1.3"
- aproba "^2.0.0"
archy "~1.0.0"
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- cacache "^12.0.3"
- call-limit "^1.1.1"
- chownr "^1.1.4"
- ci-info "^2.0.0"
+ byte-size "^7.0.1"
+ cacache "^15.2.0"
+ chalk "^4.1.0"
+ chownr "^2.0.0"
cli-columns "^3.1.2"
- cli-table3 "^0.5.1"
- cmd-shim "^3.0.3"
+ cli-table3 "^0.6.0"
columnify "~1.5.4"
- config-chain "^1.1.12"
- detect-indent "~5.0.0"
- detect-newline "^2.1.0"
- dezalgo "~1.0.3"
- editor "~1.0.0"
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- iferr "^1.0.2"
- infer-owner "^1.0.4"
- inflight "~1.0.6"
- inherits "^2.0.4"
- ini "^1.3.5"
- init-package-json "^1.10.3"
- is-cidr "^3.0.0"
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- lazy-property "~1.0.0"
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- libnpmsearch "^2.0.2"
- libnpmteam "^1.0.2"
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- lodash.clonedeep "~4.5.0"
- lodash.union "~4.6.0"
- lodash.uniq "~4.5.0"
- lodash.without "~4.4.0"
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- meant "^1.0.2"
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- nopt "^4.0.3"
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- npm-cache-filename "~1.0.2"
- npm-install-checks "^3.0.2"
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- npm-package-arg "^6.1.1"
- npm-packlist "^1.4.8"
- npm-pick-manifest "^3.0.2"
- npm-profile "^4.0.4"
- npm-registry-fetch "^4.0.7"
- npm-user-validate "~1.0.0"
+ glob "^7.1.7"
+ graceful-fs "^4.2.6"
+ hosted-git-info "^4.0.2"
+ ini "^2.0.0"
+ init-package-json "^2.0.3"
+ is-cidr "^4.0.2"
+ json-parse-even-better-errors "^2.3.1"
+ leven "^3.1.0"
+ libnpmaccess "^4.0.2"
+ libnpmdiff "^2.0.4"
+ libnpmexec "^1.2.0"
+ libnpmfund "^1.1.0"
+ libnpmhook "^6.0.2"
+ libnpmorg "^2.0.2"
+ libnpmpack "^2.0.1"
+ libnpmpublish "^4.0.1"
+ libnpmsearch "^3.1.1"
+ libnpmteam "^2.0.3"
+ libnpmversion "^1.2.0"
+ make-fetch-happen "^9.0.1"
+ minipass "^3.1.3"
+ minipass-pipeline "^1.2.4"
+ mkdirp "^1.0.4"
+ mkdirp-infer-owner "^2.0.0"
+ ms "^2.1.2"
+ node-gyp "^7.1.2"
+ nopt "^5.0.0"
+ npm-audit-report "^2.1.5"
+ npm-package-arg "^8.1.4"
+ npm-pick-manifest "^6.1.1"
+ npm-profile "^5.0.3"
+ npm-registry-fetch "^11.0.0"
+ npm-user-validate "^1.0.1"
npmlog "~4.1.2"
- once "~1.4.0"
- opener "^1.5.1"
- osenv "^0.1.5"
- pacote "^9.5.12"
- path-is-inside "~1.0.2"
- promise-inflight "~1.0.1"
+ opener "^1.5.2"
+ pacote "^11.3.3"
+ parse-conflict-json "^1.1.1"
qrcode-terminal "^0.12.0"
- query-string "^6.8.2"
- qw "~1.0.1"
read "~1.0.7"
- read-cmd-shim "^1.0.5"
- read-installed "~4.0.3"
- read-package-json "^2.1.1"
- read-package-tree "^5.3.1"
- readable-stream "^3.6.0"
+ read-package-json "^3.0.1"
+ read-package-json-fast "^2.0.2"
readdir-scoped-modules "^1.1.0"
- request "^2.88.0"
- retry "^0.12.0"
- rimraf "^2.7.1"
- safe-buffer "^5.1.2"
- semver "^5.7.1"
- sha "^3.0.0"
- slide "~1.1.6"
- sorted-object "~2.0.1"
- sorted-union-stream "~2.1.3"
- ssri "^6.0.1"
- stringify-package "^1.0.1"
- tar "^4.4.13"
+ rimraf "^3.0.2"
+ semver "^7.3.5"
+ ssri "^8.0.1"
+ tar "^6.1.0"
text-table "~0.2.0"
tiny-relative-date "^1.3.0"
- uid-number "0.0.6"
- umask "~1.1.0"
- unique-filename "^1.1.1"
- unpipe "~1.0.0"
- update-notifier "^2.5.0"
- uuid "^3.3.3"
- validate-npm-package-license "^3.0.4"
+ treeverse "^1.0.4"
validate-npm-package-name "~3.0.0"
- which "^1.3.1"
- worker-farm "^1.7.0"
- write-file-atomic "^2.4.3"
+ which "^2.0.2"
+ write-file-atomic "^3.0.3"
"npmlog@0 || 1 || 2 || 3 || 4", npmlog@^4.0.2, npmlog@^4.1.2, npmlog@~4.1.2:
version "4.1.2"
resolved "https://registry.yarnpkg.com/npmlog/-/npmlog-4.1.2.tgz#08a7f2a8bf734604779a9efa4ad5cc717abb954b"
+ integrity sha512-2uUqazuKlTaSI/dC8AzicUck7+IrEaOnN/e0jd3Xtt1KcGpwx30v50mL7oPyr/h9bL3E4aZccVwpwP+5W9Vjkg==
dependencies:
are-we-there-yet "~1.1.2"
console-control-strings "~1.1.0"
@@ -3871,10 +4255,12 @@ npx@^10.2.2:
number-is-nan@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/number-is-nan/-/number-is-nan-1.0.1.tgz#097b602b53422a522c1afb8790318336941a011d"
+ integrity sha1-CXtgK1NCKlIsGvuHkDGDNpQaAR0=
-nyc@^15.0.0:
- version "15.0.1"
- resolved "https://registry.yarnpkg.com/nyc/-/nyc-15.0.1.tgz#bd4d5c2b17f2ec04370365a5ca1fc0ed26f9f93d"
+nyc@^15.1.0:
+ version "15.1.0"
+ resolved "https://registry.yarnpkg.com/nyc/-/nyc-15.1.0.tgz#1335dae12ddc87b6e249d5a1994ca4bdaea75f02"
+ integrity sha512-jMW04n9SxKdKi1ZMGhvUTHBN0EICCRkHemEoE5jm6mTYcqcdas0ATzgUgejlQUHMvpnOZqGB5Xxsv9KxJW1j8A==
dependencies:
"@istanbuljs/load-nyc-config" "^1.0.0"
"@istanbuljs/schema" "^0.1.2"
@@ -3884,6 +4270,7 @@ nyc@^15.0.0:
find-cache-dir "^3.2.0"
find-up "^4.1.0"
foreground-child "^2.0.0"
+ get-package-type "^0.1.0"
glob "^7.1.6"
istanbul-lib-coverage "^3.0.0"
istanbul-lib-hook "^3.0.0"
@@ -3903,100 +4290,91 @@ nyc@^15.0.0:
test-exclude "^6.0.0"
yargs "^15.0.2"
-oauth-sign@~0.8.2:
- version "0.8.2"
- resolved "https://registry.yarnpkg.com/oauth-sign/-/oauth-sign-0.8.2.tgz#46a6ab7f0aead8deae9ec0565780b7d4efeb9d43"
-
oauth-sign@~0.9.0:
version "0.9.0"
resolved "https://registry.yarnpkg.com/oauth-sign/-/oauth-sign-0.9.0.tgz#47a7b016baa68b5fa0ecf3dee08a85c679ac6455"
+ integrity sha512-fexhUFFPTGV8ybAtSIGbV6gOkSv8UtRbDBnAyLQw4QPKkgNlsH2ByPGtMUqdWkos6YCRmAqViwgZrJc/mRDzZQ==
object-assign@^4.0.1, object-assign@^4.1.0:
version "4.1.1"
resolved "https://registry.yarnpkg.com/object-assign/-/object-assign-4.1.1.tgz#2109adc7965887cfc05cbbd442cac8bfbb360863"
+ integrity sha1-IQmtx5ZYh8/AXLvUQsrIv7s2CGM=
-object-inspect@^1.7.0:
- version "1.7.0"
- resolved "https://registry.yarnpkg.com/object-inspect/-/object-inspect-1.7.0.tgz#f4f6bd181ad77f006b5ece60bd0b6f398ff74a67"
-
-object-keys@^1.0.11, object-keys@^1.0.12, object-keys@^1.1.1:
- version "1.1.1"
- resolved "https://registry.yarnpkg.com/object-keys/-/object-keys-1.1.1.tgz#1c47f272df277f3b1daf061677d9c82e2322c60e"
-
-object.assign@4.1.0, object.assign@^4.1.0:
- version "4.1.0"
- resolved "https://registry.yarnpkg.com/object.assign/-/object.assign-4.1.0.tgz#968bf1100d7956bb3ca086f006f846b3bc4008da"
- dependencies:
- define-properties "^1.1.2"
- function-bind "^1.1.1"
- has-symbols "^1.0.0"
- object-keys "^1.0.11"
+object-hash@^1.3.1:
+ version "1.3.1"
+ resolved "https://registry.yarnpkg.com/object-hash/-/object-hash-1.3.1.tgz#fde452098a951cb145f039bb7d455449ddc126df"
+ integrity sha512-OSuu/pU4ENM9kmREg0BdNrUDIl1heYa4mBZacJc+vVWz4GtAwu7jO8s4AIt2aGRUTqxykpWzI3Oqnsm13tTMDA==
-object.getownpropertydescriptors@^2.0.3:
- version "2.1.0"
- resolved "https://registry.yarnpkg.com/object.getownpropertydescriptors/-/object.getownpropertydescriptors-2.1.0.tgz#369bf1f9592d8ab89d712dced5cb81c7c5352649"
- dependencies:
- define-properties "^1.1.3"
- es-abstract "^1.17.0-next.1"
+object-inspect@^1.9.0:
+ version "1.10.3"
+ resolved "https://registry.yarnpkg.com/object-inspect/-/object-inspect-1.10.3.tgz#c2aa7d2d09f50c99375704f7a0adf24c5782d369"
+ integrity sha512-e5mCJlSH7poANfC8z8S9s9S2IN5/4Zb3aZ33f5s8YqoazCFzNLloLU8r5VCG+G7WoqLvAAZoVMcy3tp/3X0Plw==
oidc-token-hash@^3.0.1:
- version "3.0.1"
- resolved "https://registry.yarnpkg.com/oidc-token-hash/-/oidc-token-hash-3.0.1.tgz#f9e2496a3eea5f755671be54a97f57170a74081d"
- dependencies:
- base64url "^3.0.0"
+ version "3.0.2"
+ resolved "https://registry.yarnpkg.com/oidc-token-hash/-/oidc-token-hash-3.0.2.tgz#5bd4716cc48ad433f4e4e99276811019b165697e"
+ integrity sha512-dTzp80/y/da+um+i+sOucNqiPpwRL7M/xPwj7pH1TFA2/bqQ+OK2sJahSXbemEoLtPkHcFLyhLhLWZa9yW5+RA==
on-finished@~2.3.0:
version "2.3.0"
resolved "https://registry.yarnpkg.com/on-finished/-/on-finished-2.3.0.tgz#20f1336481b083cd75337992a16971aa2d906947"
+ integrity sha1-IPEzZIGwg811M3mSoWlxqi2QaUc=
dependencies:
ee-first "1.1.1"
-once@^1.3.0, once@^1.3.1, once@^1.4.0, once@~1.4.0:
+once@^1.3.0, once@^1.3.1, once@^1.4.0:
version "1.4.0"
resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
+ integrity sha1-WDsap3WWHUsROsF9nFC6753Xa9E=
dependencies:
wrappy "1"
-onetime@^5.1.0:
- version "5.1.0"
- resolved "https://registry.yarnpkg.com/onetime/-/onetime-5.1.0.tgz#fff0f3c91617fe62bb50189636e99ac8a6df7be5"
- dependencies:
- mimic-fn "^2.1.0"
-
-opener@^1.5.1:
+opener@^1.5.2:
version "1.5.2"
resolved "https://registry.yarnpkg.com/opener/-/opener-1.5.2.tgz#5d37e1f35077b9dcac4301372271afdeb2a13598"
integrity sha512-ur5UIdyw5Y7yEj9wLzhqXiy6GZ3Mwx0yGI+5sMn2r0N0v3cKJvUmFH5yPP+WXh9e0xfyzyJX95D8l088DNFj7A==
openid-client@^2.4.4:
- version "2.4.5"
- resolved "https://registry.yarnpkg.com/openid-client/-/openid-client-2.4.5.tgz#675e42dc50ab25bc20a625533f5da7e0f573b8cb"
+ version "2.5.0"
+ resolved "https://registry.yarnpkg.com/openid-client/-/openid-client-2.5.0.tgz#7d4cf552b30dbad26917d7e2722422eda057ea93"
+ integrity sha512-t3hFD7xEoW1U25RyBcRFaL19fGGs6hNVTysq9pgmiltH0IVUPzH/bQV9w24pM5Q7MunnGv2/5XjIru6BQcWdxg==
dependencies:
base64url "^3.0.0"
got "^8.3.2"
lodash "^4.17.11"
- lru-cache "^4.1.3"
+ lru-cache "^5.1.1"
node-jose "^1.1.0"
+ object-hash "^1.3.1"
oidc-token-hash "^3.0.1"
p-any "^1.1.0"
-optionator@^0.8.3:
- version "0.8.3"
- resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.8.3.tgz#84fa1d036fe9d3c7e21d99884b601167ec8fb495"
+optionator@^0.9.1:
+ version "0.9.1"
+ resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.9.1.tgz#4f236a6373dae0566a6d43e1326674f50c291499"
+ integrity sha512-74RlY5FCnhq4jRxVUPKDaRwrVNXMqsGsiW6AJw4XK8hmtm10wC0ypZBLw5IIp85NZMr91+qd1RvvENwg7jjRFw==
dependencies:
- deep-is "~0.1.3"
- fast-levenshtein "~2.0.6"
- levn "~0.3.0"
- prelude-ls "~1.1.2"
- type-check "~0.3.2"
- word-wrap "~1.2.3"
+ deep-is "^0.1.3"
+ fast-levenshtein "^2.0.6"
+ levn "^0.4.1"
+ prelude-ls "^1.2.1"
+ type-check "^0.4.0"
+ word-wrap "^1.2.3"
os-homedir@^1.0.0:
version "1.0.2"
resolved "https://registry.yarnpkg.com/os-homedir/-/os-homedir-1.0.2.tgz#ffbc4988336e0e833de0c168c7ef152121aa7fb3"
integrity sha1-/7xJiDNuDoM94MFox+8VISGqf7M=
-os-tmpdir@^1.0.0, os-tmpdir@~1.0.2:
+os-locale@^3.1.0:
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/os-locale/-/os-locale-3.1.0.tgz#a802a6ee17f24c10483ab9935719cef4ed16bf1a"
+ integrity sha512-Z8l3R4wYWM40/52Z+S265okfFj8Kt2cC2MKY+xNi3kFs+XGI7WXu/I309QQQYbRW4ijiZ+yxs9pqEhJh0DqW3Q==
+ dependencies:
+ execa "^1.0.0"
+ lcid "^2.0.0"
+ mem "^4.0.0"
+
+os-tmpdir@^1.0.0:
version "1.0.2"
resolved "https://registry.yarnpkg.com/os-tmpdir/-/os-tmpdir-1.0.2.tgz#bbe67406c79aa85c5cfec766fe5734555dfa1274"
integrity sha1-u+Z0BseaqFxc/sdm/lc0VV36EnQ=
@@ -4012,57 +4390,67 @@ osenv@0, osenv@^0.1.4, osenv@^0.1.5:
p-any@^1.1.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/p-any/-/p-any-1.1.0.tgz#1d03835c7eed1e34b8e539c47b7b60d0d015d4e1"
+ integrity sha512-Ef0tVa4CZ5pTAmKn+Cg3w8ABBXh+hHO1aV8281dKOoUHfX+3tjG2EaFcC+aZyagg9b4EYGsHEjz21DnEE8Og2g==
dependencies:
p-some "^2.0.0"
p-cancelable@^0.4.0:
version "0.4.1"
- resolved "https://registry.npmjs.org/p-cancelable/-/p-cancelable-0.4.1.tgz#35f363d67d52081c8d9585e37bcceb7e0bbcb2a0"
+ resolved "https://registry.yarnpkg.com/p-cancelable/-/p-cancelable-0.4.1.tgz#35f363d67d52081c8d9585e37bcceb7e0bbcb2a0"
+ integrity sha512-HNa1A8LvB1kie7cERyy21VNeHb2CWJJYqyyC2o3klWFfMGlFmWv2Z7sFgZH8ZiaYL95ydToKTFVXgMV/Os0bBQ==
p-defer@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/p-defer/-/p-defer-1.0.0.tgz#9f6eb182f6c9aa8cd743004a7d4f96b196b0fb0c"
+ integrity sha1-n26xgvbJqozXQwBKfU+WsZaw+ww=
p-finally@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/p-finally/-/p-finally-1.0.0.tgz#3fbcfb15b899a44123b34b6dcc18b724336a2cae"
+ integrity sha1-P7z7FbiZpEEjs0ttzBi3JDNqLK4=
p-is-promise@^1.1.0:
version "1.1.0"
- resolved "https://registry.npmjs.org/p-is-promise/-/p-is-promise-1.1.0.tgz#9c9456989e9f6588017b0434d56097675c3da05e"
+ resolved "https://registry.yarnpkg.com/p-is-promise/-/p-is-promise-1.1.0.tgz#9c9456989e9f6588017b0434d56097675c3da05e"
+ integrity sha1-nJRWmJ6fZYgBewQ01WCXZ1w9oF4=
p-is-promise@^2.0.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/p-is-promise/-/p-is-promise-2.1.0.tgz#918cebaea248a62cf7ffab8e3bca8c5f882fc42e"
+ integrity sha512-Y3W0wlRPK8ZMRbNq97l4M5otioeA5lm1z7bkNkxCka8HSPjR0xRWmpCmc9utiaLP9Jb1eD8BgeIxTW4AIF45Pg==
-p-limit@^2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-2.0.0.tgz#e624ed54ee8c460a778b3c9f3670496ff8a57aec"
+p-limit@^1.1.0:
+ version "1.3.0"
+ resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-1.3.0.tgz#b86bd5f0c25690911c7590fcbfc2010d54b3ccb8"
+ integrity sha512-vvcXsLAJ9Dr5rQOPk7toZQZJApBl2K4J6dANSsEuh6QI41JYcsS/qhTGa9ErIUUgK3WNQoJYvylxvjqmiqEA9Q==
dependencies:
- p-try "^2.0.0"
+ p-try "^1.0.0"
p-limit@^2.2.0:
- version "2.2.2"
- resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-2.2.2.tgz#61279b67721f5287aa1c13a9a7fbbc48c9291b1e"
+ version "2.3.0"
+ resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-2.3.0.tgz#3dd33c647a214fdfffd835933eb086da0dc21db1"
+ integrity sha512-//88mFWSJx8lxCzwdAABTJL2MyWB12+eIY7MDL2SqLmAkeKU9qxRvWuSyTjm3FUmpBEMuFfckAIqEaVGUDxb6w==
dependencies:
p-try "^2.0.0"
p-limit@^3.0.2:
- version "3.0.2"
- resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-3.0.2.tgz#1664e010af3cadc681baafd3e2a437be7b0fb5fe"
- integrity sha512-iwqZSOoWIW+Ew4kAGUlN16J4M7OB3ysMLSZtnhmqx7njIHFPlxWBX8xo3lVTyFVq6mI/lL9qt2IsN1sHwaxJkg==
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-3.1.0.tgz#e1daccbe78d0d1388ca18c64fea38e3e57e3706b"
+ integrity sha512-TYOanM3wGwNGsZN2cVTYPArw454xnXj5qmWF1bEoAc4+cU/ol7GVh7odevjp1FNHduHc3KZMcFduxU5Xc6uJRQ==
dependencies:
- p-try "^2.0.0"
+ yocto-queue "^0.1.0"
-p-locate@^3.0.0:
- version "3.0.0"
- resolved "https://registry.yarnpkg.com/p-locate/-/p-locate-3.0.0.tgz#322d69a05c0264b25997d9f40cd8a891ab0064a4"
+p-locate@^2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/p-locate/-/p-locate-2.0.0.tgz#20a0103b222a70c8fd39cc2e580680f3dde5ec43"
+ integrity sha1-IKAQOyIqcMj9OcwuWAaA893l7EM=
dependencies:
- p-limit "^2.0.0"
+ p-limit "^1.1.0"
p-locate@^4.1.0:
version "4.1.0"
resolved "https://registry.yarnpkg.com/p-locate/-/p-locate-4.1.0.tgz#a3428bb7088b3a60292f66919278b7c297ad4f07"
+ integrity sha512-R79ZZ/0wAxKGu3oYMlz8jy/kbhsNrS7SKZ7PxEHBgJ5+F2mtFW2fK2cOtBh1cHYkQsbzFV7I+EoRKe6Yt0oK7A==
dependencies:
p-limit "^2.2.0"
@@ -4076,28 +4464,45 @@ p-locate@^5.0.0:
p-map@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/p-map/-/p-map-3.0.0.tgz#d704d9af8a2ba684e2600d9a215983d4141a979d"
+ integrity sha512-d3qXVTF/s+W+CdJ5A29wywV2n8CQQYahlgz2bFiA+4eVNJbHJodPZ+/gXwPGh0bOqA+j8S+6+ckmvLGPk1QpxQ==
+ dependencies:
+ aggregate-error "^3.0.0"
+
+p-map@^4.0.0:
+ version "4.0.0"
+ resolved "https://registry.yarnpkg.com/p-map/-/p-map-4.0.0.tgz#bb2f95a5eda2ec168ec9274e06a747c3e2904d2b"
+ integrity sha512-/bjOqmgETBYB5BoEeGVea8dmvHb2m9GLy1E9W43yeyfP6QQCZGFNa+XRceJEuDB6zqr+gKpIAmlLebMpykw/MQ==
dependencies:
aggregate-error "^3.0.0"
p-some@^2.0.0:
version "2.0.1"
resolved "https://registry.yarnpkg.com/p-some/-/p-some-2.0.1.tgz#65d87c8b154edbcf5221d167778b6d2e150f6f06"
+ integrity sha1-Zdh8ixVO289SIdFnd4ttLhUPbwY=
dependencies:
aggregate-error "^1.0.0"
p-timeout@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/p-timeout/-/p-timeout-2.0.1.tgz#d8dd1979595d2dc0139e1fe46b8b646cb3cdf038"
+ integrity sha512-88em58dDVB/KzPEx1X0N3LwFfYZPyDc4B6eF38M1rk9VTZMbxXXgjugz8mmwpS9Ox4BDZ+t6t3QP5+/gazweIA==
dependencies:
p-finally "^1.0.0"
+p-try@^1.0.0:
+ version "1.0.0"
+ resolved "https://registry.yarnpkg.com/p-try/-/p-try-1.0.0.tgz#cbc79cdbaf8fd4228e13f621f2b1a237c1b207b3"
+ integrity sha1-y8ec26+P1CKOE/Yh8rGiN8GyB7M=
+
p-try@^2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/p-try/-/p-try-2.0.0.tgz#85080bb87c64688fa47996fe8f7dfbe8211760b1"
+ version "2.2.0"
+ resolved "https://registry.yarnpkg.com/p-try/-/p-try-2.2.0.tgz#cb2868540e313d61de58fafbe35ce9004d5540e6"
+ integrity sha512-R4nPAVTAU0B9D35/Gk3uJf/7XYbQcyohSKdvAxIRSNghFl4e71hVoGnBNQz9cWaXxO2I10KTC+3jMdvvoKw6dQ==
package-hash@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/package-hash/-/package-hash-4.0.0.tgz#3537f654665ec3cc38827387fc904c163c54f506"
+ integrity sha512-whdkPIooSu/bASggZ96BWVvZTRMOFxnyUG5PnTSGKoJE2gd5mbVNmR2Nj20QFzxYYgAXpoqC+AiXzl+UMRh7zQ==
dependencies:
graceful-fs "^4.1.15"
hasha "^5.0.0"
@@ -4107,161 +4512,189 @@ package-hash@^4.0.0:
package-json@^4.0.0:
version "4.0.1"
resolved "https://registry.yarnpkg.com/package-json/-/package-json-4.0.1.tgz#8869a0401253661c4c4ca3da6c2121ed555f5eed"
+ integrity sha1-iGmgQBJTZhxMTKPabCEh7VVfXu0=
dependencies:
got "^6.7.1"
registry-auth-token "^3.0.1"
registry-url "^3.0.3"
semver "^5.1.0"
-pacote@^9.1.0, pacote@^9.5.12, pacote@^9.5.3:
- version "9.5.12"
- resolved "https://registry.yarnpkg.com/pacote/-/pacote-9.5.12.tgz#1e11dd7a8d736bcc36b375a9804d41bb0377bf66"
- integrity sha512-BUIj/4kKbwWg4RtnBncXPJd15piFSVNpTzY0rysSr3VnMowTYgkGKcaHrbReepAkjTr8lH2CVWRi58Spg2CicQ==
+pacote@^11.1.11, pacote@^11.2.6, pacote@^11.3.0, pacote@^11.3.1, pacote@^11.3.3:
+ version "11.3.4"
+ resolved "https://registry.yarnpkg.com/pacote/-/pacote-11.3.4.tgz#c290b790a5cee3082bb8fa223f3f3e2fdf3d0bfc"
+ integrity sha512-RfahPCunM9GI7ryJV/zY0bWQiokZyLqaSNHXtbNSoLb7bwTvBbJBEyCJ01KWs4j1Gj7GmX8crYXQ1sNX6P2VKA==
dependencies:
- bluebird "^3.5.3"
- cacache "^12.0.2"
- chownr "^1.1.2"
- figgy-pudding "^3.5.1"
- get-stream "^4.1.0"
- glob "^7.1.3"
+ "@npmcli/git" "^2.0.1"
+ "@npmcli/installed-package-contents" "^1.0.6"
+ "@npmcli/promise-spawn" "^1.2.0"
+ "@npmcli/run-script" "^1.8.2"
+ cacache "^15.0.5"
+ chownr "^2.0.0"
+ fs-minipass "^2.1.0"
infer-owner "^1.0.4"
- lru-cache "^5.1.1"
- make-fetch-happen "^5.0.0"
- minimatch "^3.0.4"
- minipass "^2.3.5"
- mississippi "^3.0.0"
- mkdirp "^0.5.1"
- normalize-package-data "^2.4.0"
- npm-normalize-package-bin "^1.0.0"
- npm-package-arg "^6.1.0"
- npm-packlist "^1.1.12"
- npm-pick-manifest "^3.0.0"
- npm-registry-fetch "^4.0.0"
- osenv "^0.1.5"
- promise-inflight "^1.0.1"
- promise-retry "^1.1.1"
- protoduck "^5.0.1"
- rimraf "^2.6.2"
- safe-buffer "^5.1.2"
- semver "^5.6.0"
- ssri "^6.0.1"
- tar "^4.4.10"
- unique-filename "^1.1.1"
- which "^1.3.1"
-
-parallel-transform@^1.1.0:
- version "1.2.0"
- resolved "https://registry.yarnpkg.com/parallel-transform/-/parallel-transform-1.2.0.tgz#9049ca37d6cb2182c3b1d2c720be94d14a5814fc"
- integrity sha512-P2vSmIu38uIlvdcU7fDkyrxj33gTUy/ABO5ZUbGowxNCopBq/OoD42bP4UmMrJoPyk4Uqf0mu3mtWBhHCZD8yg==
- dependencies:
- cyclist "^1.0.1"
- inherits "^2.0.3"
- readable-stream "^2.1.5"
+ minipass "^3.1.3"
+ mkdirp "^1.0.3"
+ npm-package-arg "^8.0.1"
+ npm-packlist "^2.1.4"
+ npm-pick-manifest "^6.0.0"
+ npm-registry-fetch "^11.0.0"
+ promise-retry "^2.0.1"
+ read-package-json-fast "^2.0.1"
+ rimraf "^3.0.2"
+ ssri "^8.0.1"
+ tar "^6.1.0"
+
+pako@~1.0.5:
+ version "1.0.11"
+ resolved "https://registry.yarnpkg.com/pako/-/pako-1.0.11.tgz#6c9599d340d54dfd3946380252a35705a6b992bf"
+ integrity sha512-4hLB8Py4zZce5s4yd9XzopqwVv/yGNhV1Bl8NTmCq1763HeK2+EwVTv+leGeL13Dnh2wfbqowVPXCIO0z4taYw==
parent-module@^0.1.0:
version "0.1.0"
resolved "https://registry.yarnpkg.com/parent-module/-/parent-module-0.1.0.tgz#b5292863a1e8c476ecf857e7d75c98920b24b8a6"
+ integrity sha1-tSkoY6HoxHbs+Ffn11yYkgskuKY=
dependencies:
callsites "^1.0.0"
parent-module@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/parent-module/-/parent-module-1.0.1.tgz#691d2709e78c79fae3a156622452d00762caaaa2"
+ integrity sha512-GQ2EWRpQV8/o+Aw8YqtfZZPfNRWZYkbidE9k5rpl/hC3vtHHBfGm2Ifi6qWV+coDGkrUKZAxE3Lot5kcsRlh+g==
dependencies:
callsites "^3.0.0"
-parseurl@~1.3.2:
- version "1.3.2"
- resolved "https://registry.yarnpkg.com/parseurl/-/parseurl-1.3.2.tgz#fc289d4ed8993119460c156253262cdc8de65bf3"
+parse-conflict-json@^1.1.1:
+ version "1.1.1"
+ resolved "https://registry.yarnpkg.com/parse-conflict-json/-/parse-conflict-json-1.1.1.tgz#54ec175bde0f2d70abf6be79e0e042290b86701b"
+ integrity sha512-4gySviBiW5TRl7XHvp1agcS7SOe0KZOjC//71dzZVWJrY9hCrgtvl5v3SyIxCZ4fZF47TxD9nfzmxcx76xmbUw==
+ dependencies:
+ json-parse-even-better-errors "^2.3.0"
+ just-diff "^3.0.1"
+ just-diff-apply "^3.0.0"
+
+parseurl@~1.3.3:
+ version "1.3.3"
+ resolved "https://registry.yarnpkg.com/parseurl/-/parseurl-1.3.3.tgz#9da19e7bee8d12dff0513ed5b76957793bc2e8d4"
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path-dirname@^1.0.0:
version "1.0.2"
resolved "https://registry.yarnpkg.com/path-dirname/-/path-dirname-1.0.2.tgz#cc33d24d525e099a5388c0336c6e32b9160609e0"
+ integrity sha1-zDPSTVJeCZpTiMAzbG4yuRYGCeA=
path-exists@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/path-exists/-/path-exists-3.0.0.tgz#ce0ebeaa5f78cb18925ea7d810d7b59b010fd515"
+ integrity sha1-zg6+ql94yxiSXqfYENe1mwEP1RU=
path-exists@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/path-exists/-/path-exists-4.0.0.tgz#513bdbe2d3b95d7762e8c1137efa195c6c61b5b3"
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path-is-absolute@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/path-is-absolute/-/path-is-absolute-1.0.1.tgz#174b9268735534ffbc7ace6bf53a5a9e1b5c5f5f"
integrity sha1-F0uSaHNVNP+8es5r9TpanhtcX18=
-path-is-inside@^1.0.1, path-is-inside@^1.0.2, path-is-inside@~1.0.2:
+path-is-inside@^1.0.1:
version "1.0.2"
resolved "https://registry.yarnpkg.com/path-is-inside/-/path-is-inside-1.0.2.tgz#365417dede44430d1c11af61027facf074bdfc53"
+ integrity sha1-NlQX3t5EQw0cEa9hAn+s8HS9/FM=
path-key@^2.0.0, path-key@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/path-key/-/path-key-2.0.1.tgz#411cadb574c5a140d3a4b1910d40d80cc9f40b40"
+ integrity sha1-QRyttXTFoUDTpLGRDUDYDMn0C0A=
path-key@^3.1.0:
version "3.1.1"
resolved "https://registry.yarnpkg.com/path-key/-/path-key-3.1.1.tgz#581f6ade658cbba65a0d3380de7753295054f375"
+ integrity sha512-ojmeN0qd+y0jszEtoY48r0Peq5dwMEkIlCOu6Q5f41lfkswXuKtYrhgoTpLnyIcHm24Uhqx+5Tqm2InSwLhE6Q==
path-parse@^1.0.6:
- version "1.0.6"
- resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.6.tgz#d62dbb5679405d72c4737ec58600e9ddcf06d24c"
+ version "1.0.7"
+ resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
+ integrity sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==
path-to-regexp@0.1.7:
version "0.1.7"
resolved "https://registry.yarnpkg.com/path-to-regexp/-/path-to-regexp-0.1.7.tgz#df604178005f522f15eb4490e7247a1bfaa67f8c"
+ integrity sha1-32BBeABfUi8V60SQ5yR6G/qmf4w=
-pathval@^1.0.0:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/pathval/-/pathval-1.1.0.tgz#b942e6d4bde653005ef6b71361def8727d0645e0"
+path-type@^4.0.0:
+ version "4.0.0"
+ resolved "https://registry.yarnpkg.com/path-type/-/path-type-4.0.0.tgz#84ed01c0a7ba380afe09d90a8c180dcd9d03043b"
+ integrity sha512-gDKb8aZMDeD/tZWs9P6+q0J9Mwkdl6xMV8TjnGP3qJVJ06bdMgkbBlLU8IdfOsIsFz2BW1rNVT3XuNEl8zPAvw==
+
+pathval@^1.1.1:
+ version "1.1.1"
+ resolved "https://registry.yarnpkg.com/pathval/-/pathval-1.1.1.tgz#8534e77a77ce7ac5a2512ea21e0fdb8fcf6c3d8d"
+ integrity sha512-Dp6zGqpTdETdR63lehJYPeIOqpiNBNtc7BpWSLrOje7UaIsE5aY92r/AunQA7rsXvet3lrJ3JnZX29UPTKXyKQ==
performance-now@^2.1.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/performance-now/-/performance-now-2.1.0.tgz#6309f4e0e5fa913ec1c69307ae364b4b377c9e7b"
+ integrity sha1-Ywn04OX6kT7BxpMHrjZLSzd8nns=
-picomatch@^2.0.4, picomatch@^2.2.1:
- version "2.2.2"
- resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.2.2.tgz#21f333e9b6b8eaff02468f5146ea406d345f4dad"
+picomatch@^2.0.4, picomatch@^2.2.1, picomatch@^2.2.3:
+ version "2.3.0"
+ resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.0.tgz#f1f061de8f6a4bf022892e2d128234fb98302972"
+ integrity sha512-lY1Q/PiJGC2zOv/z391WOTD+Z02bCgsFfvxoXXf6h7kv9o+WmsmzYqrAwY63sNgOxE4xEdq0WyUnXfKeBrSvYw==
pify@^2.0.0:
version "2.3.0"
resolved "https://registry.yarnpkg.com/pify/-/pify-2.3.0.tgz#ed141a6ac043a849ea588498e7dca8b15330e90c"
+ integrity sha1-7RQaasBDqEnqWISY59yosVMw6Qw=
pify@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/pify/-/pify-3.0.0.tgz#e5a4acd2c101fdf3d9a4d07f0dbc4db49dd28176"
+ integrity sha1-5aSs0sEB/fPZpNB/DbxNtJ3SgXY=
pinkie-promise@^2.0.0:
version "2.0.1"
resolved "https://registry.yarnpkg.com/pinkie-promise/-/pinkie-promise-2.0.1.tgz#2135d6dfa7a358c069ac9b178776288228450ffa"
+ integrity sha1-ITXW36ejWMBprJsXh3YogihFD/o=
dependencies:
pinkie "^2.0.0"
pinkie@^2.0.0:
version "2.0.4"
resolved "https://registry.yarnpkg.com/pinkie/-/pinkie-2.0.4.tgz#72556b80cfa0d48a974e80e77248e80ed4f7f870"
+ integrity sha1-clVrgM+g1IqXToDnckjoDtT3+HA=
pkg-dir@^4.1.0:
version "4.2.0"
resolved "https://registry.yarnpkg.com/pkg-dir/-/pkg-dir-4.2.0.tgz#f099133df7ede422e81d1d8448270eeb3e4261f3"
+ integrity sha512-HRDzbaKjC+AOWVXxAU/x54COGeIv9eb+6CkDSQoNTt4XyWoIJvuPsXizxu/Fr23EiekbtZwmh1IcIG/l/a10GQ==
dependencies:
find-up "^4.0.0"
-prelude-ls@~1.1.2:
- version "1.1.2"
- resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.1.2.tgz#21932a549f5e52ffd9a827f570e04be62a97da54"
+prelude-ls@^1.2.1:
+ version "1.2.1"
+ resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.2.1.tgz#debc6489d7a6e6b0e7611888cec880337d316396"
+ integrity sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g==
prepend-http@^1.0.1:
version "1.0.4"
resolved "https://registry.yarnpkg.com/prepend-http/-/prepend-http-1.0.4.tgz#d4f4562b0ce3696e41ac52d0e002e57a635dc6dc"
+ integrity sha1-1PRWKwzjaW5BrFLQ4ALlemNdxtw=
prepend-http@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/prepend-http/-/prepend-http-2.0.0.tgz#e92434bfa5ea8c19f41cdfd401d741a3c819d897"
+ integrity sha1-6SQ0v6XqjBn0HN/UAddBo8gZ2Jc=
+
+proc-log@^1.0.0:
+ version "1.0.0"
+ resolved "https://registry.yarnpkg.com/proc-log/-/proc-log-1.0.0.tgz#0d927307401f69ed79341e83a0b2c9a13395eb77"
+ integrity sha512-aCk8AO51s+4JyuYGg3Q/a6gnrlDO09NpVWePtjp7xwphcoQ04x5WAfCyugcsbLooWcMJ87CLkD4+604IckEdhg==
process-nextick-args@~1.0.6:
version "1.0.7"
resolved "https://registry.yarnpkg.com/process-nextick-args/-/process-nextick-args-1.0.7.tgz#150e20b756590ad3f91093f25a4f2ad8bff30ba3"
+ integrity sha1-FQ4gt1ZZCtP5EJPyWk8q2L/zC6M=
process-nextick-args@~2.0.0:
version "2.0.1"
@@ -4271,83 +4704,72 @@ process-nextick-args@~2.0.0:
process-on-spawn@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/process-on-spawn/-/process-on-spawn-1.0.0.tgz#95b05a23073d30a17acfdc92a440efd2baefdc93"
+ integrity sha512-1WsPDsUSMmZH5LeMLegqkPDrsGgsWwk1Exipy2hvB0o/F0ASzbpIctSCcZIK1ykJvtTJULEH+20WOFjMvGnCTg==
dependencies:
fromentries "^1.2.0"
+process@^0.11.10:
+ version "0.11.10"
+ resolved "https://registry.yarnpkg.com/process/-/process-0.11.10.tgz#7332300e840161bda3e69a1d1d91a7d4bc16f182"
+ integrity sha1-czIwDoQBYb2j5podHZGn1LwW8YI=
+
progress@^2.0.0:
version "2.0.3"
resolved "https://registry.yarnpkg.com/progress/-/progress-2.0.3.tgz#7e8cf8d8f5b8f239c1bc68beb4eb78567d572ef8"
+ integrity sha512-7PiHtLll5LdnKIMw100I+8xJXR5gW2QwWYkT6iJva0bXitZKa/XMrSbdmg3r2Xnaidz9Qumd0VPaMrZlF9V9sA==
+
+promise-all-reject-late@^1.0.0:
+ version "1.0.1"
+ resolved "https://registry.yarnpkg.com/promise-all-reject-late/-/promise-all-reject-late-1.0.1.tgz#f8ebf13483e5ca91ad809ccc2fcf25f26f8643c2"
+ integrity sha512-vuf0Lf0lOxyQREH7GDIOUMLS7kz+gs8i6B+Yi8dC68a2sychGrHTJYghMBD6k7eUcH0H5P73EckCA48xijWqXw==
-promise-inflight@^1.0.1, promise-inflight@~1.0.1:
+promise-call-limit@^1.0.1:
+ version "1.0.1"
+ resolved "https://registry.yarnpkg.com/promise-call-limit/-/promise-call-limit-1.0.1.tgz#4bdee03aeb85674385ca934da7114e9bcd3c6e24"
+ integrity sha512-3+hgaa19jzCGLuSCbieeRsu5C2joKfYn8pY6JAuXFRVfF4IO+L7UPpFWNTeWT9pM7uhskvbPPd/oEOktCn317Q==
+
+promise-inflight@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/promise-inflight/-/promise-inflight-1.0.1.tgz#98472870bf228132fcbdd868129bad12c3c029e3"
+ integrity sha1-mEcocL8igTL8vdhoEputEsPAKeM=
promise-polyfill@^6.0.1:
version "6.1.0"
resolved "https://registry.yarnpkg.com/promise-polyfill/-/promise-polyfill-6.1.0.tgz#dfa96943ea9c121fca4de9b5868cb39d3472e057"
+ integrity sha1-36lpQ+qcEh/KTem1hoyznTRy4Fc=
-promise-retry@^1.1.1:
- version "1.1.1"
- resolved "https://registry.yarnpkg.com/promise-retry/-/promise-retry-1.1.1.tgz#6739e968e3051da20ce6497fb2b50f6911df3d6d"
- integrity sha1-ZznpaOMFHaIM5kl/srUPaRHfPW0=
- dependencies:
- err-code "^1.0.0"
- retry "^0.10.0"
-
-promise.allsettled@1.0.2:
- version "1.0.2"
- resolved "https://registry.yarnpkg.com/promise.allsettled/-/promise.allsettled-1.0.2.tgz#d66f78fbb600e83e863d893e98b3d4376a9c47c9"
- integrity sha512-UpcYW5S1RaNKT6pd+s9jp9K9rlQge1UXKskec0j6Mmuq7UJCvlS2J2/s/yuPN8ehftf9HXMxWlKiPbGGUzpoRg==
+promise-retry@^2.0.1:
+ version "2.0.1"
+ resolved "https://registry.yarnpkg.com/promise-retry/-/promise-retry-2.0.1.tgz#ff747a13620ab57ba688f5fc67855410c370da22"
+ integrity sha512-y+WKFlBR8BGXnsNlIHFGPZmyDf3DFMoLhaflAnyZgV6rG6xu+JwesTo2Q9R6XwYmtmwAFCkAk3e35jEdoeh/3g==
dependencies:
- array.prototype.map "^1.0.1"
- define-properties "^1.1.3"
- es-abstract "^1.17.0-next.1"
- function-bind "^1.1.1"
- iterate-value "^1.0.0"
+ err-code "^2.0.2"
+ retry "^0.12.0"
promzard@^0.3.0:
version "0.3.0"
resolved "https://registry.yarnpkg.com/promzard/-/promzard-0.3.0.tgz#26a5d6ee8c7dee4cb12208305acfb93ba382a9ee"
+ integrity sha1-JqXW7ox97kyxIggwWs+5O6OCqe4=
dependencies:
read "1"
-proto-list@~1.2.1:
- version "1.2.4"
- resolved "https://registry.yarnpkg.com/proto-list/-/proto-list-1.2.4.tgz#212d5bfe1318306a420f6402b8e26ff39647a849"
-
-protoduck@^5.0.1:
- version "5.0.1"
- resolved "https://registry.yarnpkg.com/protoduck/-/protoduck-5.0.1.tgz#03c3659ca18007b69a50fd82a7ebcc516261151f"
- integrity sha512-WxoCeDCoCBY55BMvj4cAEjdVUFGRWed9ZxPlqTKYyw1nDDTQ4pqmnIMAGfJlg7Dx35uB/M+PHJPTmGOvaCaPTg==
- dependencies:
- genfun "^5.0.0"
-
-proxy-addr@~2.0.3:
- version "2.0.3"
- resolved "https://registry.yarnpkg.com/proxy-addr/-/proxy-addr-2.0.3.tgz#355f262505a621646b3130a728eb647e22055341"
+proxy-addr@~2.0.5:
+ version "2.0.7"
+ resolved "https://registry.yarnpkg.com/proxy-addr/-/proxy-addr-2.0.7.tgz#f19fe69ceab311eeb94b42e70e8c2070f9ba1025"
+ integrity sha512-llQsMLSUDUPT44jdrU/O37qlnifitDP+ZwrmmZcoSKyLKvtZxpyV0n2/bD/N4tBAAZ/gJEdZU7KMraoK1+XYAg==
dependencies:
- forwarded "~0.1.2"
- ipaddr.js "1.6.0"
-
-prr@~1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/prr/-/prr-1.0.1.tgz#d3fc114ba06995a45ec6893f484ceb1d78f5f476"
+ forwarded "0.2.0"
+ ipaddr.js "1.9.1"
pseudomap@^1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/pseudomap/-/pseudomap-1.0.2.tgz#f052a28da70e618917ef0a8ac34c1ae5a68286b3"
+ integrity sha1-8FKijacOYYkX7wqKw0wa5aaChrM=
-psl@^1.1.24:
- version "1.1.29"
- resolved "https://registry.yarnpkg.com/psl/-/psl-1.1.29.tgz#60f580d360170bb722a797cc704411e6da850c67"
-
-pump@^2.0.0:
- version "2.0.1"
- resolved "https://registry.yarnpkg.com/pump/-/pump-2.0.1.tgz#12399add6e4cf7526d973cbc8b5ce2e2908b3909"
- integrity sha512-ruPMNRkN3MHP1cWJc9OWr+T/xDP0jhXYCLfJcBuX54hhfIBnaQmAUMfDcG4DM5UMWByBbJY69QSphm3jtDKIkA==
- dependencies:
- end-of-stream "^1.1.0"
- once "^1.3.1"
+psl@^1.1.28:
+ version "1.8.0"
+ resolved "https://registry.yarnpkg.com/psl/-/psl-1.8.0.tgz#9326f8bcfb013adcc005fdff056acce020e51c24"
+ integrity sha512-RIdOzyoavK+hA18OGGWDqUTsCLhtA7IcZ/6NCs4fFJaHBDab+pDDmDIByWFRQJq2Cd7r1OoQxBGKOaztq+hjIQ==
pump@^3.0.0:
version "3.0.0"
@@ -4357,66 +4779,51 @@ pump@^3.0.0:
end-of-stream "^1.1.0"
once "^1.3.1"
-pumpify@^1.3.3:
- version "1.5.1"
- resolved "https://registry.yarnpkg.com/pumpify/-/pumpify-1.5.1.tgz#36513be246ab27570b1a374a5ce278bfd74370ce"
- integrity sha512-oClZI37HvuUJJxSKKrC17bZ9Cu0ZYhEAGPsPUy9KlMUmv9dKX2o77RUmq7f3XjIxbwyGwYzbzQ1L2Ks8sIradQ==
- dependencies:
- duplexify "^3.6.0"
- inherits "^2.0.3"
- pump "^2.0.0"
-
-punycode@^1.4.1:
- version "1.4.1"
- resolved "https://registry.yarnpkg.com/punycode/-/punycode-1.4.1.tgz#c0d5a63b2718800ad8e1eb0fa5269c84dd41845e"
-
-punycode@^2.1.0:
+punycode@^2.1.0, punycode@^2.1.1:
version "2.1.1"
resolved "https://registry.yarnpkg.com/punycode/-/punycode-2.1.1.tgz#b58b010ac40c22c5657616c8d2c2c02c7bf479ec"
+ integrity sha512-XRsRjdf+j5ml+y/6GKHPZbrF/8p2Yga0JPtdqTIY2Xe5ohJPD9saDJJLPvp9+NSBprVvevdXZybnj2cv8OEd0A==
-python-shell@^2.0.1:
- version "2.0.1"
- resolved "https://registry.yarnpkg.com/python-shell/-/python-shell-2.0.1.tgz#314eccd523bd6f41cbcc1c399eadab70f76e6e92"
- integrity sha512-Ys+SiCinY9JrldIJxGWd2AMQSQZLU7PFzrCWY7HTawZ73tIthFdlLLU1Y6Y40Hwdutc+TmfMe5TXNU73s07Xyg==
+python-shell@^3.0.0:
+ version "3.0.0"
+ resolved "https://registry.yarnpkg.com/python-shell/-/python-shell-3.0.0.tgz#4eb04b6e7e8878e715b9ccd782b15194555dd074"
+ integrity sha512-vlIkpJBwkhtG8d2rBbPEweg+3UXdkoduRZ0jLbIX3efYutBjTdmdmMrEQCQy9tkabH36yUjOhwTPFkH3BvoYZQ==
qrcode-terminal@^0.12.0:
version "0.12.0"
resolved "https://registry.yarnpkg.com/qrcode-terminal/-/qrcode-terminal-0.12.0.tgz#bb5b699ef7f9f0505092a3748be4464fe71b5819"
integrity sha512-EXtzRZmC+YGmGlDFbXKxQiMZNwCLEO6BANKXG4iCtSIM0yqc/pappSx3RIKr4r0uh5JsBckOXeKrB3Iz7mdQpQ==
-qs@6.5.1:
- version "6.5.1"
- resolved "https://registry.yarnpkg.com/qs/-/qs-6.5.1.tgz#349cdf6eef89ec45c12d7d5eb3fc0c870343a6d8"
+qs@6.7.0:
+ version "6.7.0"
+ resolved "https://registry.yarnpkg.com/qs/-/qs-6.7.0.tgz#41dc1a015e3d581f1621776be31afb2876a9b1bc"
+ integrity sha512-VCdBRNFTX1fyE7Nb6FYoURo/SPe62QCaAyzJvUjwRaIsc+NePBEniHlvxFmmX56+HZphIGtV0XeCirBtpDrTyQ==
-qs@^6.5.2:
- version "6.6.0"
- resolved "https://registry.yarnpkg.com/qs/-/qs-6.6.0.tgz#a99c0f69a8d26bf7ef012f871cdabb0aee4424c2"
+qs@^6.7.0:
+ version "6.10.1"
+ resolved "https://registry.yarnpkg.com/qs/-/qs-6.10.1.tgz#4931482fa8d647a5aab799c5271d2133b981fb6a"
+ integrity sha512-M528Hph6wsSVOBiYUnGf+K/7w0hNshs/duGsNXPUCLH5XAqjEtiPGwNONLV0tBH8NoGb0mvD5JubnUTrujKDTg==
+ dependencies:
+ side-channel "^1.0.4"
-qs@~6.5.1, qs@~6.5.2:
+qs@~6.5.2:
version "6.5.2"
resolved "https://registry.yarnpkg.com/qs/-/qs-6.5.2.tgz#cb3ae806e8740444584ef154ce8ee98d403f3e36"
+ integrity sha512-N5ZAX4/LxJmF+7wN74pUD6qAh9/wnvdQcjq9TZjevvXzSUo7bfmw91saqMjzGS2xq91/odN2dW/WOl7qQHNDGA==
query-string@^5.0.1:
version "5.1.1"
- resolved "https://registry.npmjs.org/query-string/-/query-string-5.1.1.tgz#a78c012b71c17e05f2e3fa2319dd330682efb3cb"
+ resolved "https://registry.yarnpkg.com/query-string/-/query-string-5.1.1.tgz#a78c012b71c17e05f2e3fa2319dd330682efb3cb"
+ integrity sha512-gjWOsm2SoGlgLEdAGt7a6slVOk9mGiXmPFMqrEhLQ68rhQuBnpfs3+EmlvqKyxnCo9/PPlF+9MtY02S1aFg+Jw==
dependencies:
decode-uri-component "^0.2.0"
object-assign "^4.1.0"
strict-uri-encode "^1.0.0"
-query-string@^6.8.2:
- version "6.13.4"
- resolved "https://registry.yarnpkg.com/query-string/-/query-string-6.13.4.tgz#b35a9a3bd4955bce55f94feb0e819b3d0be6f66f"
- integrity sha512-E2NPIeJoBEJGQNy3ib1k/Z/OkDBUKIo8IV2ZVwbKfoa65IS9unqWWUlLcbfU70Da0qNoxUZZA8CfKUjKLE641Q==
- dependencies:
- decode-uri-component "^0.2.0"
- split-on-first "^1.0.0"
- strict-uri-encode "^2.0.0"
-
-qw@~1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/qw/-/qw-1.0.1.tgz#efbfdc740f9ad054304426acb183412cc8b996d4"
- integrity sha1-77/cdA+a0FQwRCassYNBLMi5ltQ=
+queue-microtask@^1.2.2:
+ version "1.2.3"
+ resolved "https://registry.yarnpkg.com/queue-microtask/-/queue-microtask-1.2.3.tgz#4929228bbc724dfac43e0efb058caf7b6cfb6243"
+ integrity sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==
randombytes@^2.1.0:
version "2.1.0"
@@ -4425,85 +4832,67 @@ randombytes@^2.1.0:
dependencies:
safe-buffer "^5.1.0"
-range-parser@~1.2.0:
- version "1.2.0"
- resolved "https://registry.yarnpkg.com/range-parser/-/range-parser-1.2.0.tgz#f49be6b487894ddc40dcc94a322f611092e00d5e"
+range-parser@~1.2.1:
+ version "1.2.1"
+ resolved "https://registry.yarnpkg.com/range-parser/-/range-parser-1.2.1.tgz#3cf37023d199e1c24d1a55b84800c2f3e6468031"
+ integrity sha512-Hrgsx+orqoygnmhFbKaHE6c296J+HTAQXoxEF6gNupROmmGJRoyzfG3ccAveqCBrwr/2yxQ5BVd/GTl5agOwSg==
-raw-body@2.3.2:
- version "2.3.2"
- resolved "https://registry.yarnpkg.com/raw-body/-/raw-body-2.3.2.tgz#bcd60c77d3eb93cde0050295c3f379389bc88f89"
+raw-body@2.4.0:
+ version "2.4.0"
+ resolved "https://registry.yarnpkg.com/raw-body/-/raw-body-2.4.0.tgz#a1ce6fb9c9bc356ca52e89256ab59059e13d0332"
+ integrity sha512-4Oz8DUIwdvoa5qMJelxipzi/iJIi40O5cGV1wNYp5hvZP8ZN0T+jiNkL0QepXs+EsQ9XJ8ipEDoiH70ySUJP3Q==
dependencies:
- bytes "3.0.0"
- http-errors "1.6.2"
- iconv-lite "0.4.19"
+ bytes "3.1.0"
+ http-errors "1.7.2"
+ iconv-lite "0.4.24"
unpipe "1.0.0"
rc@^1.0.1, rc@^1.1.6, rc@^1.2.7:
version "1.2.8"
resolved "https://registry.yarnpkg.com/rc/-/rc-1.2.8.tgz#cd924bf5200a075b83c188cd6b9e211b7fc0d3ed"
+ integrity sha512-y3bGgqKj3QBdxLbLkomlohkvsA8gdAiUQlSBJnBhfn+BPxg4bc62d8TcBW15wavDfgexCgccckhcZvywyQYPOw==
dependencies:
deep-extend "^0.6.0"
ini "~1.3.0"
minimist "^1.2.0"
strip-json-comments "~2.0.1"
-read-cmd-shim@^1.0.1, read-cmd-shim@^1.0.5:
+react-zlib-js@^1.0.4:
version "1.0.5"
- resolved "https://registry.yarnpkg.com/read-cmd-shim/-/read-cmd-shim-1.0.5.tgz#87e43eba50098ba5a32d0ceb583ab8e43b961c16"
- integrity sha512-v5yCqQ/7okKoZZkBQUAfTsQ3sVJtXdNfbPnI5cceppoxEVLYA3k+VtV2omkeo8MS94JCy4fSiUwlRBAwCVRPUA==
- dependencies:
- graceful-fs "^4.1.2"
+ resolved "https://registry.yarnpkg.com/react-zlib-js/-/react-zlib-js-1.0.5.tgz#7bb433e1a4ae53a8e6f361b3d36166baf5bbc60f"
+ integrity sha512-TLcPdmqhIl+ylwOwlfm1WUuI7NVvhAv3L74d1AabhjyaAbmLOROTA/Q4EQ/UMCFCOjIkVim9fT3UZOQSFk/mlA==
-read-installed@~4.0.3:
- version "4.0.3"
- resolved "https://registry.yarnpkg.com/read-installed/-/read-installed-4.0.3.tgz#ff9b8b67f187d1e4c29b9feb31f6b223acd19067"
- dependencies:
- debuglog "^1.0.1"
- read-package-json "^2.0.0"
- readdir-scoped-modules "^1.0.0"
- semver "2 || 3 || 4 || 5"
- slide "~1.1.3"
- util-extend "^1.0.1"
- optionalDependencies:
- graceful-fs "^4.1.2"
+read-cmd-shim@^2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/read-cmd-shim/-/read-cmd-shim-2.0.0.tgz#4a50a71d6f0965364938e9038476f7eede3928d9"
+ integrity sha512-HJpV9bQpkl6KwjxlJcBoqu9Ba0PQg8TqSNIOrulGt54a0uup0HtevreFHzYzkm0lpnleRdNBzXznKrgxglEHQw==
-"read-package-json@1 || 2", read-package-json@^2.0.0:
- version "2.1.0"
- resolved "https://registry.yarnpkg.com/read-package-json/-/read-package-json-2.1.0.tgz#e3d42e6c35ea5ae820d9a03ab0c7291217fc51d5"
+read-package-json-fast@^2.0.1, read-package-json-fast@^2.0.2:
+ version "2.0.2"
+ resolved "https://registry.yarnpkg.com/read-package-json-fast/-/read-package-json-fast-2.0.2.tgz#2dcb24d9e8dd50fb322042c8c35a954e6cc7ac9e"
+ integrity sha512-5fyFUyO9B799foVk4n6ylcoAktG/FbE3jwRKxvwaeSrIunaoMc0u81dzXxjeAFKOce7O5KncdfwpGvvs6r5PsQ==
dependencies:
- glob "^7.1.1"
- json-parse-better-errors "^1.0.1"
- normalize-package-data "^2.0.0"
- slash "^1.0.0"
- optionalDependencies:
- graceful-fs "^4.1.2"
+ json-parse-even-better-errors "^2.3.0"
+ npm-normalize-package-bin "^1.0.1"
-read-package-json@^2.0.13, read-package-json@^2.1.1:
- version "2.1.2"
- resolved "https://registry.yarnpkg.com/read-package-json/-/read-package-json-2.1.2.tgz#6992b2b66c7177259feb8eaac73c3acd28b9222a"
- integrity sha512-D1KmuLQr6ZSJS0tW8hf3WGpRlwszJOXZ3E8Yd/DNRaM5d+1wVRZdHlpGBLAuovjr28LbWvjpWkBHMxpRGGjzNA==
+read-package-json@^3.0.1:
+ version "3.0.1"
+ resolved "https://registry.yarnpkg.com/read-package-json/-/read-package-json-3.0.1.tgz#c7108f0b9390257b08c21e3004d2404c806744b9"
+ integrity sha512-aLcPqxovhJTVJcsnROuuzQvv6oziQx4zd3JvG0vGCL5MjTONUc4uJ90zCBC6R7W7oUKBNoR/F8pkyfVwlbxqng==
dependencies:
glob "^7.1.1"
json-parse-even-better-errors "^2.3.0"
- normalize-package-data "^2.0.0"
+ normalize-package-data "^3.0.0"
npm-normalize-package-bin "^1.0.0"
-read-package-tree@^5.3.1:
- version "5.3.1"
- resolved "https://registry.yarnpkg.com/read-package-tree/-/read-package-tree-5.3.1.tgz#a32cb64c7f31eb8a6f31ef06f9cedf74068fe636"
- integrity sha512-mLUDsD5JVtlZxjSlPPx1RETkNjjvQYuweKwNVt1Sn8kP5Jh44pvYuUHCp6xSVDZWbNxVxG5lyZJ921aJH61sTw==
- dependencies:
- read-package-json "^2.0.0"
- readdir-scoped-modules "^1.0.0"
- util-promisify "^2.1.0"
-
-read@1, read@~1.0.1, read@~1.0.7:
+read@1, read@^1.0.7, read@~1.0.1, read@~1.0.7:
version "1.0.7"
resolved "https://registry.yarnpkg.com/read/-/read-1.0.7.tgz#b3da19bd052431a97671d44a42634adf710b40c4"
+ integrity sha1-s9oZvQUkMal2cdRKQmNK33ELQMQ=
dependencies:
mute-stream "~0.0.4"
-"readable-stream@1 || 2", readable-stream@^2.0.0, readable-stream@^2.1.5, readable-stream@^2.2.2, readable-stream@^2.3.6, readable-stream@~2.3.6:
+readable-stream@^2.0.0, readable-stream@^2.0.6:
version "2.3.7"
resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.7.tgz#1eca1cf711aef814c04f62252a36a62f6cb23b57"
integrity sha512-Ebho8K4jIbHAxnuxi7o42OrZgF/ZTNcsZj6nRKyUmkhLFq8CHItp/fy6hQZuZmP/n3yZ9VBUbp4zz/mX8hmYPw==
@@ -4516,18 +4905,6 @@ read@1, read@~1.0.1, read@~1.0.7:
string_decoder "~1.1.1"
util-deprecate "~1.0.1"
-readable-stream@^2.0.6:
- version "2.3.6"
- resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.6.tgz#b11c27d88b8ff1fbe070643cf94b0c79ae1b0aaf"
- dependencies:
- core-util-is "~1.0.0"
- inherits "~2.0.3"
- isarray "~1.0.0"
- process-nextick-args "~2.0.0"
- safe-buffer "~5.1.1"
- string_decoder "~1.1.1"
- util-deprecate "~1.0.1"
-
readable-stream@^3.6.0:
version "3.6.0"
resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-3.6.0.tgz#337bbda3adc0706bd3e024426a286d4b4b2c9198"
@@ -4537,18 +4914,10 @@ readable-stream@^3.6.0:
string_decoder "^1.1.1"
util-deprecate "^1.0.1"
-readable-stream@~1.1.10:
- version "1.1.14"
- resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-1.1.14.tgz#7cf4c54ef648e3813084c636dd2079e166c081d9"
- dependencies:
- core-util-is "~1.0.0"
- inherits "~2.0.1"
- isarray "0.0.1"
- string_decoder "~0.10.x"
-
readable-stream@~2.0.0:
version "2.0.6"
- resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.0.6.tgz#8f90341e68a53ccc928788dacfcd11b36eb9b78e"
+ resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.0.6.tgz#8f90341e68a53ccc928788dacfcd11b36eb9b78e"
+ integrity sha1-j5A0HmilPMySh4jaz80Rs265t44=
dependencies:
core-util-is "~1.0.0"
inherits "~2.0.1"
@@ -4557,37 +4926,37 @@ readable-stream@~2.0.0:
string_decoder "~0.10.x"
util-deprecate "~1.0.1"
-readdir-scoped-modules@^1.0.0, readdir-scoped-modules@^1.1.0:
+readdir-scoped-modules@^1.1.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/readdir-scoped-modules/-/readdir-scoped-modules-1.1.0.tgz#8d45407b4f870a0dcaebc0e28670d18e74514309"
+ integrity sha512-asaikDeqAQg7JifRsZn1NJZXo9E+VwlyCfbkZhwyISinqk5zNS6266HS5kah6P0SaQKGF6SkNnZVHUzHFYxYDw==
dependencies:
debuglog "^1.0.1"
dezalgo "^1.0.0"
graceful-fs "^4.1.2"
once "^1.3.0"
-readdirp@~3.4.0:
- version "3.4.0"
- resolved "https://registry.yarnpkg.com/readdirp/-/readdirp-3.4.0.tgz#9fdccdf9e9155805449221ac645e8303ab5b9ada"
- integrity sha512-0xe001vZBnJEK+uKcj8qOhyAKPzIT+gStxWr3LCB0DwcXR5NZJ3IaC+yGnHCYzB/S7ov3m3EEbZI2zeNvX+hGQ==
+readdirp@~3.5.0:
+ version "3.5.0"
+ resolved "https://registry.yarnpkg.com/readdirp/-/readdirp-3.5.0.tgz#9ba74c019b15d365278d2e91bb8c48d7b4d42c9e"
+ integrity sha512-cMhu7c/8rdhkHXWsY+osBhfSy0JikwpHK/5+imo+LpeasTF8ouErHrlYkwT0++njiyuDvc7OFY5T3ukvZ8qmFQ==
dependencies:
picomatch "^2.2.1"
-reflect-metadata@^0.1.10:
- version "0.1.12"
- resolved "https://registry.yarnpkg.com/reflect-metadata/-/reflect-metadata-0.1.12.tgz#311bf0c6b63cd782f228a81abe146a2bfa9c56f2"
-
-regexpp@^2.0.1:
- version "2.0.1"
- resolved "https://registry.yarnpkg.com/regexpp/-/regexpp-2.0.1.tgz#8d19d31cf632482b589049f8281f93dbcba4d07f"
+reflect-metadata@^0.1.13:
+ version "0.1.13"
+ resolved "https://registry.yarnpkg.com/reflect-metadata/-/reflect-metadata-0.1.13.tgz#67ae3ca57c972a2aa1642b10fe363fe32d49dc08"
+ integrity sha512-Ts1Y/anZELhSsjMcU605fU9RE4Oi3p5ORujwbIKXfWa+0Zxs510Qrmrce5/Jowq3cHSZSJqBjypxmHarc+vEWg==
-regexpp@^3.0.0:
- version "3.0.0"
- resolved "https://registry.yarnpkg.com/regexpp/-/regexpp-3.0.0.tgz#dd63982ee3300e67b41c1956f850aa680d9d330e"
+regexpp@^3.0.0, regexpp@^3.1.0:
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/regexpp/-/regexpp-3.1.0.tgz#206d0ad0a5648cffbdb8ae46438f3dc51c9f78e2"
+ integrity sha512-ZOIzd8yVsQQA7j8GCSlPGXwg5PfmA1mrq0JP4nGhh54LaKN3xdai/vHUDu74pKwV8OxseMS65u2NImosQcSD0Q==
registry-auth-token@^3.0.1:
version "3.4.0"
resolved "https://registry.yarnpkg.com/registry-auth-token/-/registry-auth-token-3.4.0.tgz#d7446815433f5d5ed6431cd5dca21048f66b397e"
+ integrity sha512-4LM6Fw8eBQdwMYcES4yTnn2TqIasbXuwDx3um+QRs7S55aMKCBKBxvPXl2RiUjHwuJLTyYfxSpmfSAjQpcuP+A==
dependencies:
rc "^1.1.6"
safe-buffer "^5.0.1"
@@ -4595,18 +4964,21 @@ registry-auth-token@^3.0.1:
registry-url@^3.0.3:
version "3.1.0"
resolved "https://registry.yarnpkg.com/registry-url/-/registry-url-3.1.0.tgz#3d4ef870f73dde1d77f0cf9a381432444e174942"
+ integrity sha1-PU74cPc93h138M+aOBQyRE4XSUI=
dependencies:
rc "^1.0.1"
release-zalgo@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/release-zalgo/-/release-zalgo-1.0.0.tgz#09700b7e5074329739330e535c5a90fb67851730"
+ integrity sha1-CXALflB0Mpc5Mw5TXFqQ+2eFFzA=
dependencies:
es6-error "^4.0.1"
-request@^2.74.0, request@^2.86.0, request@^2.88.0:
- version "2.88.0"
- resolved "https://registry.yarnpkg.com/request/-/request-2.88.0.tgz#9c2fca4f7d35b592efe57c7f0a55e81052124fef"
+request@^2.86.0, request@^2.87.0, request@^2.88.0, request@^2.88.2:
+ version "2.88.2"
+ resolved "https://registry.yarnpkg.com/request/-/request-2.88.2.tgz#d73c918731cb5a87da047e207234146f664d12b3"
+ integrity sha512-MsvtOrfG9ZcrOwAW+Qi+F6HbD0CWXEh9ou77uOb7FM2WPhwT7smM833PzanhJLsgXjN89Ir6V2PczXNnMpwKhw==
dependencies:
aws-sign2 "~0.7.0"
aws4 "^1.8.0"
@@ -4615,7 +4987,7 @@ request@^2.74.0, request@^2.86.0, request@^2.88.0:
extend "~3.0.2"
forever-agent "~0.6.1"
form-data "~2.3.2"
- har-validator "~5.1.0"
+ har-validator "~5.1.3"
http-signature "~1.2.0"
is-typedarray "~1.0.0"
isstream "~0.1.2"
@@ -4623,202 +4995,167 @@ request@^2.74.0, request@^2.86.0, request@^2.88.0:
mime-types "~2.1.19"
oauth-sign "~0.9.0"
performance-now "^2.1.0"
- qs "~6.5.2"
- safe-buffer "^5.1.2"
- tough-cookie "~2.4.3"
- tunnel-agent "^0.6.0"
- uuid "^3.3.2"
-
-request@^2.87.0:
- version "2.87.0"
- resolved "https://registry.yarnpkg.com/request/-/request-2.87.0.tgz#32f00235cd08d482b4d0d68db93a829c0ed5756e"
- dependencies:
- aws-sign2 "~0.7.0"
- aws4 "^1.6.0"
- caseless "~0.12.0"
- combined-stream "~1.0.5"
- extend "~3.0.1"
- forever-agent "~0.6.1"
- form-data "~2.3.1"
- har-validator "~5.0.3"
- http-signature "~1.2.0"
- is-typedarray "~1.0.0"
- isstream "~0.1.2"
- json-stringify-safe "~5.0.1"
- mime-types "~2.1.17"
- oauth-sign "~0.8.2"
- performance-now "^2.1.0"
- qs "~6.5.1"
- safe-buffer "^5.1.1"
- tough-cookie "~2.3.3"
+ qs "~6.5.2"
+ safe-buffer "^5.1.2"
+ tough-cookie "~2.5.0"
tunnel-agent "^0.6.0"
- uuid "^3.1.0"
+ uuid "^3.3.2"
require-directory@^2.1.1:
version "2.1.1"
resolved "https://registry.yarnpkg.com/require-directory/-/require-directory-2.1.1.tgz#8c64ad5fd30dab1c976e2344ffe7f792a6a6df42"
+ integrity sha1-jGStX9MNqxyXbiNE/+f3kqam30I=
+
+require-from-string@^2.0.2:
+ version "2.0.2"
+ resolved "https://registry.yarnpkg.com/require-from-string/-/require-from-string-2.0.2.tgz#89a7fdd938261267318eafe14f9c32e598c36909"
+ integrity sha512-Xf0nWe6RseziFMu+Ap9biiUbmplq6S9/p+7w7YXP/JBHhrUDDUhwa+vANyubuqfZWTveU//DYVGsDG7RKL/vEw==
require-glob@^3.2.0:
version "3.2.0"
resolved "https://registry.yarnpkg.com/require-glob/-/require-glob-3.2.0.tgz#90bfe2c8efb4b9f972eb9a3f5e580832e04f64d3"
+ integrity sha1-kL/iyO+0ufly65o/XlgIMuBPZNM=
dependencies:
glob-parent "^3.0.0"
globby "^6.0.0"
parent-module "^0.1.0"
+require-main-filename@^1.0.1:
+ version "1.0.1"
+ resolved "https://registry.yarnpkg.com/require-main-filename/-/require-main-filename-1.0.1.tgz#97f717b69d48784f5f526a6c5aa8ffdda055a4d1"
+ integrity sha1-l/cXtp1IeE9fUmpsWqj/3aBVpNE=
+
+require-main-filename@^2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/require-main-filename/-/require-main-filename-2.0.0.tgz#d0b329ecc7cc0f61649f62215be69af54aa8989b"
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version "4.0.0"
resolved "https://registry.yarnpkg.com/resolve-from/-/resolve-from-4.0.0.tgz#4abcd852ad32dd7baabfe9b40e00a36db5f392e6"
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version "5.0.0"
resolved "https://registry.yarnpkg.com/resolve-from/-/resolve-from-5.0.0.tgz#c35225843df8f776df21c57557bc087e9dfdfc69"
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- resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.15.1.tgz#27bdcdeffeaf2d6244b95bb0f9f4b4653451f3e8"
+resolve@^1.20.0:
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dependencies:
+ is-core-module "^2.2.0"
path-parse "^1.0.6"
responselike@1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/responselike/-/responselike-1.0.2.tgz#918720ef3b631c5642be068f15ade5a46f4ba1e7"
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resolved "https://registry.yarnpkg.com/rimraf/-/rimraf-3.0.2.tgz#f1a5402ba6220ad52cc1282bac1ae3aa49fd061a"
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version "1.2.0"
resolved "https://registry.yarnpkg.com/rmdir/-/rmdir-1.2.0.tgz#4fe0357cb06168c258e73e968093dc4e8a0f3253"
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node.flow "1.2.3"
-run-async@^2.2.0:
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- integrity sha1-6Eg5bwV9Ij8kOGkkYY4laUFh7Ec=
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dependencies:
- aproba "^1.1.1"
+ queue-microtask "^1.2.2"
rx@^4.1.0:
version "4.1.0"
resolved "https://registry.yarnpkg.com/rx/-/rx-4.1.0.tgz#a5f13ff79ef3b740fe30aa803fb09f98805d4782"
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- resolved "https://registry.yarnpkg.com/rxjs/-/rxjs-6.5.3.tgz#510e26317f4db91a7eb1de77d9dd9ba0a4899a3a"
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- resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.1.1.tgz#893312af69b2123def71f57889001671eeb2c853"
+safe-buffer@5.1.2, safe-buffer@~5.1.0, safe-buffer@~5.1.1:
+ version "5.1.2"
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resolved "https://registry.yarnpkg.com/safer-buffer/-/safer-buffer-2.1.2.tgz#44fa161b0187b9549dd84bb91802f9bd8385cd6a"
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version "0.5.8"
- resolved "https://registry.npmjs.org/sax/-/sax-0.5.8.tgz#d472db228eb331c2506b0e8c15524adb939d12c1"
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+ integrity sha1-1HLbIo6zMcJQaw6MFVJK25OdEsE=
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version "1.2.4"
resolved "https://registry.yarnpkg.com/sax/-/sax-1.2.4.tgz#2816234e2378bddc4e5354fab5caa895df7100d9"
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semver-diff@^2.0.0:
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resolved "https://registry.yarnpkg.com/semver-diff/-/semver-diff-2.1.0.tgz#4bbb8437c8d37e4b0cf1a68fd726ec6d645d6d36"
+ integrity sha1-S7uEN8jTfksM8aaP1ybsbWRdbTY=
dependencies:
semver "^5.0.3"
-"semver@2 || 3 || 4 || 5", semver@^5.5.0:
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version "5.7.1"
resolved "https://registry.yarnpkg.com/semver/-/semver-5.7.1.tgz#a954f931aeba508d307bbf069eff0c01c96116f7"
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-
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+semver@^6.0.0, semver@^6.3.0:
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resolved "https://registry.yarnpkg.com/semver/-/semver-6.3.0.tgz#ee0a64c8af5e8ceea67687b133761e1becbd1d3d"
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+ dependencies:
+ lru-cache "^6.0.0"
semver@~5.3.0:
version "5.3.0"
resolved "https://registry.yarnpkg.com/semver/-/semver-5.3.0.tgz#9b2ce5d3de02d17c6012ad326aa6b4d0cf54f94f"
integrity sha1-myzl094C0XxgEq0yaqa00M9U+U8=
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- version "0.16.2"
- resolved "https://registry.yarnpkg.com/send/-/send-0.16.2.tgz#6ecca1e0f8c156d141597559848df64730a6bbc1"
+send@0.17.1:
+ version "0.17.1"
+ resolved "https://registry.yarnpkg.com/send/-/send-0.17.1.tgz#c1d8b059f7900f7466dd4938bdc44e11ddb376c8"
+ integrity sha512-BsVKsiGcQMFwT8UxypobUKyv7irCNRHk1T0G680vk88yf6LBByGcZJOTJCrTP2xVN6yI+XjPJcNuE3V4fT9sAg==
dependencies:
debug "2.6.9"
depd "~1.1.2"
@@ -4827,129 +5164,135 @@ send@0.16.2:
escape-html "~1.0.3"
etag "~1.8.1"
fresh "0.5.2"
- http-errors "~1.6.2"
- mime "1.4.1"
- ms "2.0.0"
+ http-errors "~1.7.2"
+ mime "1.6.0"
+ ms "2.1.1"
on-finished "~2.3.0"
- range-parser "~1.2.0"
- statuses "~1.4.0"
+ range-parser "~1.2.1"
+ statuses "~1.5.0"
-serialize-javascript@4.0.0, serialize-javascript@>=5.0.1:
+serialize-javascript@5.0.1:
version "5.0.1"
resolved "https://registry.yarnpkg.com/serialize-javascript/-/serialize-javascript-5.0.1.tgz#7886ec848049a462467a97d3d918ebb2aaf934f4"
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dependencies:
randombytes "^2.1.0"
-serve-static@1.13.2:
- version "1.13.2"
- resolved "https://registry.yarnpkg.com/serve-static/-/serve-static-1.13.2.tgz#095e8472fd5b46237db50ce486a43f4b86c6cec1"
+serve-static@1.14.1:
+ version "1.14.1"
+ resolved "https://registry.yarnpkg.com/serve-static/-/serve-static-1.14.1.tgz#666e636dc4f010f7ef29970a88a674320898b2f9"
+ integrity sha512-JMrvUwE54emCYWlTI+hGrGv5I8dEwmco/00EvkzIIsR7MqrHonbD9pO2MOfFnpFntl7ecpZs+3mW+XbQZu9QCg==
dependencies:
encodeurl "~1.0.2"
escape-html "~1.0.3"
- parseurl "~1.3.2"
- send "0.16.2"
+ parseurl "~1.3.3"
+ send "0.17.1"
-set-blocking@~2.0.0:
+set-blocking@^2.0.0, set-blocking@~2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/set-blocking/-/set-blocking-2.0.0.tgz#045f9782d011ae9a6803ddd382b24392b3d890f7"
+ integrity sha1-BF+XgtARrppoA93TgrJDkrPYkPc=
-setprototypeof@1.0.3:
- version "1.0.3"
- resolved "https://registry.yarnpkg.com/setprototypeof/-/setprototypeof-1.0.3.tgz#66567e37043eeb4f04d91bd658c0cbefb55b8e04"
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- resolved "https://registry.yarnpkg.com/setprototypeof/-/setprototypeof-1.1.0.tgz#d0bd85536887b6fe7c0d818cb962d9d91c54e656"
+setprototypeof@1.1.1:
+ version "1.1.1"
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+shallow-clone@^0.1.2:
+ version "0.1.2"
+ resolved "https://registry.yarnpkg.com/shallow-clone/-/shallow-clone-0.1.2.tgz#5909e874ba77106d73ac414cfec1ffca87d97060"
+ integrity sha1-WQnodLp3EG1zrEFM/sH/yofZcGA=
dependencies:
- graceful-fs "^4.1.2"
+ is-extendable "^0.1.1"
+ kind-of "^2.0.1"
+ lazy-cache "^0.2.3"
+ mixin-object "^2.0.1"
shebang-command@^1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/shebang-command/-/shebang-command-1.2.0.tgz#44aac65b695b03398968c39f363fee5deafdf1ea"
+ integrity sha1-RKrGW2lbAzmJaMOfNj/uXer98eo=
dependencies:
shebang-regex "^1.0.0"
shebang-command@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/shebang-command/-/shebang-command-2.0.0.tgz#ccd0af4f8835fbdc265b82461aaf0c36663f34ea"
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shebang-regex "^3.0.0"
shebang-regex@^1.0.0:
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resolved "https://registry.yarnpkg.com/shebang-regex/-/shebang-regex-1.0.0.tgz#da42f49740c0b42db2ca9728571cb190c98efea3"
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shebang-regex@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/shebang-regex/-/shebang-regex-3.0.0.tgz#ae16f1644d873ecad843b0307b143362d4c42172"
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+
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+ version "1.0.4"
+ resolved "https://registry.yarnpkg.com/side-channel/-/side-channel-1.0.4.tgz#efce5c8fdc104ee751b25c58d4290011fa5ea2cf"
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+ dependencies:
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+ get-intrinsic "^1.0.2"
+ object-inspect "^1.9.0"
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- resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-3.0.2.tgz#b5fdc08f1287ea1178628e415e25132b73646c6d"
+ version "3.0.3"
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-slash@^1.0.0:
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- resolved "https://registry.yarnpkg.com/slash/-/slash-1.0.0.tgz#c41f2f6c39fc16d1cd17ad4b5d896114ae470d55"
+slash@^3.0.0:
+ version "3.0.0"
+ resolved "https://registry.yarnpkg.com/slash/-/slash-3.0.0.tgz#6539be870c165adbd5240220dbe361f1bc4d4634"
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- resolved "https://registry.yarnpkg.com/slice-ansi/-/slice-ansi-2.1.0.tgz#cacd7693461a637a5788d92a7dd4fba068e81636"
+slice-ansi@^4.0.0:
+ version "4.0.0"
+ resolved "https://registry.yarnpkg.com/slice-ansi/-/slice-ansi-4.0.0.tgz#500e8dd0fd55b05815086255b3195adf2a45fe6b"
+ integrity sha512-qMCMfhY040cVHT43K9BFygqYbUPFZKHOg7K73mtTWJRb8pyP3fzf4Ixd5SzdEJQ6MRUg/WBnOLxghZtKKurENQ==
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- ansi-styles "^3.2.0"
- astral-regex "^1.0.0"
- is-fullwidth-code-point "^2.0.0"
-
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- version "1.1.6"
- resolved "https://registry.yarnpkg.com/slide/-/slide-1.1.6.tgz#56eb027d65b4d2dce6cb2e2d32c4d4afc9e1d707"
+ ansi-styles "^4.0.0"
+ astral-regex "^2.0.0"
+ is-fullwidth-code-point "^3.0.0"
smart-buffer@^4.1.0:
version "4.1.0"
resolved "https://registry.yarnpkg.com/smart-buffer/-/smart-buffer-4.1.0.tgz#91605c25d91652f4661ea69ccf45f1b331ca21ba"
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-socks-proxy-agent@^4.0.0:
- version "4.0.2"
- resolved "https://registry.yarnpkg.com/socks-proxy-agent/-/socks-proxy-agent-4.0.2.tgz#3c8991f3145b2799e70e11bd5fbc8b1963116386"
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+socks-proxy-agent@^5.0.0:
+ version "5.0.0"
+ resolved "https://registry.yarnpkg.com/socks-proxy-agent/-/socks-proxy-agent-5.0.0.tgz#7c0f364e7b1cf4a7a437e71253bed72e9004be60"
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dependencies:
- agent-base "~4.2.1"
- socks "~2.3.2"
+ agent-base "6"
+ debug "4"
+ socks "^2.3.3"
-socks@~2.3.2:
- version "2.3.3"
- resolved "https://registry.yarnpkg.com/socks/-/socks-2.3.3.tgz#01129f0a5d534d2b897712ed8aceab7ee65d78e3"
- integrity sha512-o5t52PCNtVdiOvzMry7wU4aOqYWL0PeCXRWBEiJow4/i/wr+wpsJQ9awEu1EonLIqsfGd5qSgDdxEOvCdmBEpA==
+socks@^2.3.3:
+ version "2.6.1"
+ resolved "https://registry.yarnpkg.com/socks/-/socks-2.6.1.tgz#989e6534a07cf337deb1b1c94aaa44296520d30e"
+ integrity sha512-kLQ9N5ucj8uIcxrDwjm0Jsqk06xdpBjGNQtpXy4Q8/QY2k+fY7nZH8CARy+hkbG+SGAovmzzuauCpBlb8FrnBA==
dependencies:
- ip "1.1.5"
+ ip "^1.1.5"
smart-buffer "^4.1.0"
sort-keys@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/sort-keys/-/sort-keys-2.0.0.tgz#658535584861ec97d730d6cf41822e1f56684128"
+ integrity sha1-ZYU1WEhh7JfXMNbPQYIuH1ZoQSg=
dependencies:
is-plain-obj "^1.0.0"
-sorted-object@~2.0.1:
- version "2.0.1"
- resolved "https://registry.yarnpkg.com/sorted-object/-/sorted-object-2.0.1.tgz#7d631f4bd3a798a24af1dffcfbfe83337a5df5fc"
-
-sorted-union-stream@~2.1.3:
- version "2.1.3"
- resolved "https://registry.yarnpkg.com/sorted-union-stream/-/sorted-union-stream-2.1.3.tgz#c7794c7e077880052ff71a8d4a2dbb4a9a638ac7"
- dependencies:
- from2 "^1.3.0"
- stream-iterate "^1.1.0"
-
-source-map-support@^0.5.6:
- version "0.5.6"
- resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.6.tgz#4435cee46b1aab62b8e8610ce60f788091c51c13"
+source-map-support@^0.5.17:
+ version "0.5.19"
+ resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.19.tgz#a98b62f86dcaf4f67399648c085291ab9e8fed61"
+ integrity sha512-Wonm7zOCIJzBGQdB+thsPar0kYuCIzYvxZwlBa87yi/Mdjv7Tip2cyVbLj5o0cFPN4EVkuTwb3GDDyUx2DGnGw==
dependencies:
buffer-from "^1.0.0"
source-map "^0.6.0"
@@ -4957,14 +5300,17 @@ source-map-support@^0.5.6:
source-map@^0.5.0:
version "0.5.7"
resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.5.7.tgz#8a039d2d1021d22d1ea14c80d8ea468ba2ef3fcc"
+ integrity sha1-igOdLRAh0i0eoUyA2OpGi6LvP8w=
source-map@^0.6.0, source-map@^0.6.1:
version "0.6.1"
resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
+ integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
spawn-wrap@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/spawn-wrap/-/spawn-wrap-2.0.0.tgz#103685b8b8f9b79771318827aa78650a610d457e"
+ integrity sha512-EeajNjfN9zMnULLwhZZQU3GWBoFNkbngTUPfaawT4RkMiviTxcX0qfhVbGey39mfctfDHkWtuecgQ8NJcyQWHg==
dependencies:
foreground-child "^2.0.0"
is-windows "^1.0.2"
@@ -4974,132 +5320,98 @@ spawn-wrap@^2.0.0:
which "^2.0.1"
spdx-correct@^3.0.0:
- version "3.1.0"
- resolved "https://registry.yarnpkg.com/spdx-correct/-/spdx-correct-3.1.0.tgz#fb83e504445268f154b074e218c87c003cd31df4"
+ version "3.1.1"
+ resolved "https://registry.yarnpkg.com/spdx-correct/-/spdx-correct-3.1.1.tgz#dece81ac9c1e6713e5f7d1b6f17d468fa53d89a9"
+ integrity sha512-cOYcUWwhCuHCXi49RhFRCyJEK3iPj1Ziz9DpViV3tbZOwXD49QzIN3MpOLJNxh2qwq2lJJZaKMVw9qNi4jTC0w==
dependencies:
spdx-expression-parse "^3.0.0"
spdx-license-ids "^3.0.0"
spdx-exceptions@^2.1.0:
- version "2.2.0"
- resolved "https://registry.yarnpkg.com/spdx-exceptions/-/spdx-exceptions-2.2.0.tgz#2ea450aee74f2a89bfb94519c07fcd6f41322977"
+ version "2.3.0"
+ resolved "https://registry.yarnpkg.com/spdx-exceptions/-/spdx-exceptions-2.3.0.tgz#3f28ce1a77a00372683eade4a433183527a2163d"
+ integrity sha512-/tTrYOC7PPI1nUAgx34hUpqXuyJG+DTHJTnIULG4rDygi4xu/tfgmq1e1cIRwRzwZgo4NLySi+ricLkZkw4i5A==
spdx-expression-parse@^3.0.0:
- version "3.0.0"
- resolved "https://registry.yarnpkg.com/spdx-expression-parse/-/spdx-expression-parse-3.0.0.tgz#99e119b7a5da00e05491c9fa338b7904823b41d0"
+ version "3.0.1"
+ resolved "https://registry.yarnpkg.com/spdx-expression-parse/-/spdx-expression-parse-3.0.1.tgz#cf70f50482eefdc98e3ce0a6833e4a53ceeba679"
+ integrity sha512-cbqHunsQWnJNE6KhVSMsMeH5H/L9EpymbzqTQ3uLwNCLZ1Q481oWaofqH7nO6V07xlXwY6PhQdQ2IedWx/ZK4Q==
dependencies:
spdx-exceptions "^2.1.0"
spdx-license-ids "^3.0.0"
spdx-license-ids@^3.0.0:
- version "3.0.3"
- resolved "https://registry.yarnpkg.com/spdx-license-ids/-/spdx-license-ids-3.0.3.tgz#81c0ce8f21474756148bbb5f3bfc0f36bf15d76e"
-
-split-on-first@^1.0.0:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/split-on-first/-/split-on-first-1.1.0.tgz#f610afeee3b12bce1d0c30425e76398b78249a5f"
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+ version "3.0.9"
+ resolved "https://registry.yarnpkg.com/spdx-license-ids/-/spdx-license-ids-3.0.9.tgz#8a595135def9592bda69709474f1cbeea7c2467f"
+ integrity sha512-Ki212dKK4ogX+xDo4CtOZBVIwhsKBEfsEEcwmJfLQzirgc2jIWdzg40Unxz/HzEUqM1WFzVlQSMF9kZZ2HboLQ==
sprintf-js@~1.0.2:
version "1.0.3"
resolved "https://registry.yarnpkg.com/sprintf-js/-/sprintf-js-1.0.3.tgz#04e6926f662895354f3dd015203633b857297e2c"
+ integrity sha1-BOaSb2YolTVPPdAVIDYzuFcpfiw=
-sqlite3@5.0.0:
- version "5.0.0"
- resolved "https://registry.yarnpkg.com/sqlite3/-/sqlite3-5.0.0.tgz#1bfef2151c6bc48a3ab1a6c126088bb8dd233566"
- integrity sha512-rjvqHFUaSGnzxDy2AHCwhHy6Zp6MNJzCPGYju4kD8yi6bze4d1/zMTg6C7JI49b7/EM7jKMTvyfN/4ylBKdwfw==
+sqlite3@5.0.2:
+ version "5.0.2"
+ resolved "https://registry.yarnpkg.com/sqlite3/-/sqlite3-5.0.2.tgz#00924adcc001c17686e0a6643b6cbbc2d3965083"
+ integrity sha512-1SdTNo+BVU211Xj1csWa8lV6KM0CtucDwRyA0VHl91wEH1Mgh7RxUpI4rVvG7OhHrzCSGaVyW5g8vKvlrk9DJA==
dependencies:
- node-addon-api "2.0.0"
+ node-addon-api "^3.0.0"
node-pre-gyp "^0.11.0"
optionalDependencies:
node-gyp "3.x"
-ssh2-streams@~0.4.10:
- version "0.4.10"
- resolved "https://registry.yarnpkg.com/ssh2-streams/-/ssh2-streams-0.4.10.tgz#48ef7e8a0e39d8f2921c30521d56dacb31d23a34"
- integrity sha512-8pnlMjvnIZJvmTzUIIA5nT4jr2ZWNNVHwyXfMGdRJbug9TpI3kd99ffglgfSWqujVv/0gxwMsDn9j9RVst8yhQ==
+ssh2@^1.1.0:
+ version "1.1.0"
+ resolved "https://registry.yarnpkg.com/ssh2/-/ssh2-1.1.0.tgz#43dd24930e15e317687f519d6b40270d9cd00d00"
+ integrity sha512-CidQLG2ZacoT0Z7O6dOyisj4JdrOrLVJ4KbHjVNz9yI1vO08FAYQPcnkXY9BP8zeYo+J/nBgY6Gg4R7w4WFWtg==
dependencies:
- asn1 "~0.2.0"
+ asn1 "^0.2.4"
bcrypt-pbkdf "^1.0.2"
- streamsearch "~0.1.2"
-
-ssh2@^0.8.9:
- version "0.8.9"
- resolved "https://registry.yarnpkg.com/ssh2/-/ssh2-0.8.9.tgz#54da3a6c4ba3daf0d8477a538a481326091815f3"
- integrity sha512-GmoNPxWDMkVpMFa9LVVzQZHF6EW3WKmBwL+4/GeILf2hFmix5Isxm7Amamo8o7bHiU0tC+wXsGcUXOxp8ChPaw==
- dependencies:
- ssh2-streams "~0.4.10"
+ optionalDependencies:
+ cpu-features "0.0.2"
+ nan "^2.14.2"
sshpk@^1.7.0:
- version "1.14.2"
- resolved "https://registry.yarnpkg.com/sshpk/-/sshpk-1.14.2.tgz#c6fc61648a3d9c4e764fd3fcdf4ea105e492ba98"
+ version "1.16.1"
+ resolved "https://registry.yarnpkg.com/sshpk/-/sshpk-1.16.1.tgz#fb661c0bef29b39db40769ee39fa70093d6f6877"
+ integrity sha512-HXXqVUq7+pcKeLqqZj6mHFUMvXtOJt1uoUx09pFW6011inTMxqI8BA8PM95myrIyyKwdnzjdFjLiE6KBPVtJIg==
dependencies:
asn1 "~0.2.3"
assert-plus "^1.0.0"
- dashdash "^1.12.0"
- getpass "^0.1.1"
- safer-buffer "^2.0.2"
- optionalDependencies:
bcrypt-pbkdf "^1.0.0"
+ dashdash "^1.12.0"
ecc-jsbn "~0.1.1"
+ getpass "^0.1.1"
jsbn "~0.1.0"
+ safer-buffer "^2.0.2"
tweetnacl "~0.14.0"
-ssri@^6.0.0, ssri@^6.0.1:
- version "6.0.2"
- resolved "https://registry.yarnpkg.com/ssri/-/ssri-6.0.2.tgz#157939134f20464e7301ddba3e90ffa8f7728ac5"
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+ssri@^8.0.0, ssri@^8.0.1:
+ version "8.0.1"
+ resolved "https://registry.yarnpkg.com/ssri/-/ssri-8.0.1.tgz#638e4e439e2ffbd2cd289776d5ca457c4f51a2af"
+ integrity sha512-97qShzy1AiyxvPNIkLWoGua7xoQzzPjQ0HAH4B0rWKo7SZ6USuPcrUiAFrws0UH8RrbWmgq3LMTObhPIHbbBeQ==
dependencies:
- figgy-pudding "^3.5.1"
+ minipass "^3.1.1"
-"statuses@>= 1.3.1 < 2", "statuses@>= 1.4.0 < 2":
+"statuses@>= 1.5.0 < 2", statuses@~1.5.0:
version "1.5.0"
resolved "https://registry.yarnpkg.com/statuses/-/statuses-1.5.0.tgz#161c7dac177659fd9811f43771fa99381478628c"
-
-statuses@~1.4.0:
- version "1.4.0"
- resolved "https://registry.yarnpkg.com/statuses/-/statuses-1.4.0.tgz#bb73d446da2796106efcc1b601a253d6c46bd087"
+ integrity sha1-Fhx9rBd2Wf2YEfQ3cfqZOBR4Yow=
stream-buffers@^3.0.2:
version "3.0.2"
resolved "https://registry.yarnpkg.com/stream-buffers/-/stream-buffers-3.0.2.tgz#5249005a8d5c2d00b3a32e6e0a6ea209dc4f3521"
-
-stream-each@^1.1.0:
- version "1.2.3"
- resolved "https://registry.yarnpkg.com/stream-each/-/stream-each-1.2.3.tgz#ebe27a0c389b04fbcc233642952e10731afa9bae"
- integrity sha512-vlMC2f8I2u/bZGqkdfLQW/13Zihpej/7PmSiMQsbYddxuTsJp8vRe2x2FvVExZg7FaOds43ROAuFJwPR4MTZLw==
- dependencies:
- end-of-stream "^1.1.0"
- stream-shift "^1.0.0"
-
-stream-iterate@^1.1.0:
- version "1.2.0"
- resolved "https://registry.yarnpkg.com/stream-iterate/-/stream-iterate-1.2.0.tgz#2bd7c77296c1702a46488b8ad41f79865eecd4e1"
- dependencies:
- readable-stream "^2.1.5"
- stream-shift "^1.0.0"
-
-stream-shift@^1.0.0:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/stream-shift/-/stream-shift-1.0.1.tgz#d7088281559ab2778424279b0877da3c392d5a3d"
- integrity sha512-AiisoFqQ0vbGcZgQPY1cdP2I76glaVA/RauYR4G4thNFgkTqr90yXTo4LYX60Jl+sIlPNHHdGSwo01AvbKUSVQ==
-
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- version "0.1.2"
- resolved "https://registry.yarnpkg.com/streamsearch/-/streamsearch-0.1.2.tgz#808b9d0e56fc273d809ba57338e929919a1a9f1a"
+ integrity sha512-DQi1h8VEBA/lURbSwFtEHnSTb9s2/pwLEaFuNhXwy1Dx3Sa0lOuYT2yNUr4/j2fs8oCAMANtrZ5OrPZtyVs3MQ==
strict-uri-encode@^1.0.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/strict-uri-encode/-/strict-uri-encode-1.1.0.tgz#279b225df1d582b1f54e65addd4352e18faa0713"
-
-strict-uri-encode@^2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/strict-uri-encode/-/strict-uri-encode-2.0.0.tgz#b9c7330c7042862f6b142dc274bbcc5866ce3546"
- integrity sha1-ucczDHBChi9rFC3CdLvMWGbONUY=
+ integrity sha1-J5siXfHVgrH1TmWt3UNS4Y+qBxM=
string-width@^1.0.1:
version "1.0.2"
resolved "https://registry.yarnpkg.com/string-width/-/string-width-1.0.2.tgz#118bdf5b8cdc51a2a7e70d211e07e2b0b9b107d3"
+ integrity sha1-EYvfW4zcUaKn5w0hHgfisLmxB9M=
dependencies:
code-point-at "^1.0.0"
is-fullwidth-code-point "^1.0.0"
@@ -5108,56 +5420,20 @@ string-width@^1.0.1:
"string-width@^1.0.2 || 2", string-width@^2.0.0, string-width@^2.1.1:
version "2.1.1"
resolved "https://registry.yarnpkg.com/string-width/-/string-width-2.1.1.tgz#ab93f27a8dc13d28cac815c462143a6d9012ae9e"
+ integrity sha512-nOqH59deCq9SRHlxq1Aw85Jnt4w6KvLKqWVik6oA9ZklXLNIOlqg4F2yrT1MVaTjAqvVwdfeZ7w7aCvJD7ugkw==
dependencies:
is-fullwidth-code-point "^2.0.0"
strip-ansi "^4.0.0"
-string-width@^3.0.0:
- version "3.1.0"
- resolved "https://registry.yarnpkg.com/string-width/-/string-width-3.1.0.tgz#22767be21b62af1081574306f69ac51b62203961"
- dependencies:
- emoji-regex "^7.0.1"
- is-fullwidth-code-point "^2.0.0"
- strip-ansi "^5.1.0"
-
string-width@^4.1.0, string-width@^4.2.0:
- version "4.2.0"
- resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.0.tgz#952182c46cc7b2c313d1596e623992bd163b72b5"
+ version "4.2.2"
+ resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.2.tgz#dafd4f9559a7585cfba529c6a0a4f73488ebd4c5"
+ integrity sha512-XBJbT3N4JhVumXE0eoLU9DCjcaF92KLNqTmFCnG1pf8duUxFGwtP6AD6nkjw9a3IdiRtL3E2w3JDiE/xi3vOeA==
dependencies:
emoji-regex "^8.0.0"
is-fullwidth-code-point "^3.0.0"
strip-ansi "^6.0.0"
-string.prototype.trimend@^1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/string.prototype.trimend/-/string.prototype.trimend-1.0.1.tgz#85812a6b847ac002270f5808146064c995fb6913"
- integrity sha512-LRPxFUaTtpqYsTeNKaFOw3R4bxIzWOnbQ837QfBylo8jIxtcbK/A/sMV7Q+OAV/vWo+7s25pOE10KYSjaSO06g==
- dependencies:
- define-properties "^1.1.3"
- es-abstract "^1.17.5"
-
-string.prototype.trimleft@^2.1.0:
- version "2.1.0"
- resolved "https://registry.yarnpkg.com/string.prototype.trimleft/-/string.prototype.trimleft-2.1.0.tgz#6cc47f0d7eb8d62b0f3701611715a3954591d634"
- dependencies:
- define-properties "^1.1.3"
- function-bind "^1.1.1"
-
-string.prototype.trimright@^2.1.0:
- version "2.1.0"
- resolved "https://registry.yarnpkg.com/string.prototype.trimright/-/string.prototype.trimright-2.1.0.tgz#669d164be9df9b6f7559fa8e89945b168a5a6c58"
- dependencies:
- define-properties "^1.1.3"
- function-bind "^1.1.1"
-
-string.prototype.trimstart@^1.0.1:
- version "1.0.1"
- resolved "https://registry.yarnpkg.com/string.prototype.trimstart/-/string.prototype.trimstart-1.0.1.tgz#14af6d9f34b053f7cfc89b72f8f2ee14b9039a54"
- integrity sha512-XxZn+QpvrBI1FOcg6dIpxUPgWCPuNXvMD72aaRaUQv1eD4e/Qy8i/hFTe0BUmD60p/QA6bh1avmuPTfNjqVWRw==
- dependencies:
- define-properties "^1.1.3"
- es-abstract "^1.17.5"
-
string_decoder@^1.1.1:
version "1.3.0"
resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-1.3.0.tgz#42f114594a46cf1a8e30b0a84f56c78c3edac21e"
@@ -5167,7 +5443,8 @@ string_decoder@^1.1.1:
string_decoder@~0.10.x:
version "0.10.31"
- resolved "https://registry.npmjs.org/string_decoder/-/string_decoder-0.10.31.tgz#62e203bc41766c6c28c9fc84301dab1c5310fa94"
+ resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-0.10.31.tgz#62e203bc41766c6c28c9fc84301dab1c5310fa94"
+ integrity sha1-YuIDvEF2bGwoyfyEMB2rHFMQ+pQ=
string_decoder@~1.1.1:
version "1.1.1"
@@ -5176,7 +5453,7 @@ string_decoder@~1.1.1:
dependencies:
safe-buffer "~5.1.0"
-stringify-package@^1.0.0, stringify-package@^1.0.1:
+stringify-package@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/stringify-package/-/stringify-package-1.0.1.tgz#e5aa3643e7f74d0f28628b72f3dad5cecfc3ba85"
integrity sha512-sa4DUQsYciMP1xhKWGuFM04fB0LG/9DlluZoSVywUMRNvzid6XucHK0/90xGxRoHrAaROrcHK1aPKaijCtSrhg==
@@ -5184,67 +5461,89 @@ stringify-package@^1.0.0, stringify-package@^1.0.1:
strip-ansi@^3.0.0, strip-ansi@^3.0.1:
version "3.0.1"
resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-3.0.1.tgz#6a385fb8853d952d5ff05d0e8aaf94278dc63dcf"
+ integrity sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=
dependencies:
ansi-regex "^2.0.0"
strip-ansi@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-4.0.0.tgz#a8479022eb1ac368a871389b635262c505ee368f"
+ integrity sha1-qEeQIusaw2iocTibY1JixQXuNo8=
dependencies:
ansi-regex "^3.0.0"
-strip-ansi@^5.1.0, strip-ansi@^5.2.0:
- version "5.2.0"
- resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-5.2.0.tgz#8c9a536feb6afc962bdfa5b104a5091c1ad9c0ae"
- dependencies:
- ansi-regex "^4.1.0"
-
strip-ansi@^6.0.0:
version "6.0.0"
resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-6.0.0.tgz#0b1571dd7669ccd4f3e06e14ef1eed26225ae532"
+ integrity sha512-AuvKTrTfQNYNIctbR1K/YGTR1756GycPsg7b9bdV9Duqur4gv6aKqHXah67Z8ImS7WEz5QVcOtlfW2rZEugt6w==
dependencies:
ansi-regex "^5.0.0"
strip-bom@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/strip-bom/-/strip-bom-4.0.0.tgz#9c3505c1db45bcedca3d9cf7a16f5c5aa3901878"
+ integrity sha512-3xurFv5tEgii33Zi8Jtp55wEIILR9eh34FAW00PZf+JnSsTmV/ioewSgQl97JHvgjoRGwPShsWm+IdrxB35d0w==
strip-eof@^1.0.0:
version "1.0.0"
- resolved "https://registry.npmjs.org/strip-eof/-/strip-eof-1.0.0.tgz#bb43ff5598a6eb05d89b59fcd129c983313606bf"
+ resolved "https://registry.yarnpkg.com/strip-eof/-/strip-eof-1.0.0.tgz#bb43ff5598a6eb05d89b59fcd129c983313606bf"
+ integrity sha1-u0P/VZim6wXYm1n80SnJgzE2Br8=
-strip-json-comments@3.0.1, strip-json-comments@^3.0.1:
- version "3.0.1"
- resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.0.1.tgz#85713975a91fb87bf1b305cca77395e40d2a64a7"
+strip-json-comments@3.1.1, strip-json-comments@^3.1.0, strip-json-comments@^3.1.1:
+ version "3.1.1"
+ resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.1.1.tgz#31f1281b3832630434831c310c01cccda8cbe006"
+ integrity sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==
strip-json-comments@~2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-2.0.1.tgz#3c531942e908c2697c0ec344858c286c7ca0a60a"
+ integrity sha1-PFMZQukIwml8DsNEhYwobHygpgo=
-supports-color@7.1.0, supports-color@^7.1.0:
- version "7.1.0"
- resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.1.0.tgz#68e32591df73e25ad1c4b49108a2ec507962bfd1"
+supports-color@8.1.1:
+ version "8.1.1"
+ resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-8.1.1.tgz#cd6fc17e28500cff56c1b86c0a7fd4a54a73005c"
+ integrity sha512-MpUEN2OodtUzxvKQl72cUF7RQ5EiHsGvSsVG0ia9c5RbWGL2CI4C7EpPS8UTBIplnlzZiNuV56w+FuNxy3ty2Q==
dependencies:
has-flag "^4.0.0"
supports-color@^5.3.0:
- version "5.4.0"
- resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.4.0.tgz#1c6b337402c2137605efe19f10fec390f6faab54"
+ version "5.5.0"
+ resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.5.0.tgz#e2e69a44ac8772f78a1ec0b35b689df6530efc8f"
+ integrity sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==
dependencies:
has-flag "^3.0.0"
-table@^5.2.3:
- version "5.4.6"
- resolved "https://registry.yarnpkg.com/table/-/table-5.4.6.tgz#1292d19500ce3f86053b05f0e8e7e4a3bb21079e"
+supports-color@^7.1.0:
+ version "7.2.0"
+ resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.2.0.tgz#1b7dcdcb32b8138801b3e478ba6a51caa89648da"
+ integrity sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==
+ dependencies:
+ has-flag "^4.0.0"
+
+swagger-fluent@^3.1.2:
+ version "3.2.1"
+ resolved "https://registry.yarnpkg.com/swagger-fluent/-/swagger-fluent-3.2.1.tgz#755d4617cd74f4c30c25013c725269bdcbb0c599"
+ integrity sha512-Ol4wwbc9kKNdOGPS3aoKLEkCFPiEioA9gMUeeRzlugHHu0HYVvftLpK9v4X8ZUm8l2n2yGiLUBp+gKqhQzHspw==
+ dependencies:
+ merge-deep "^3.0.2"
+ request "^2.88.0"
+
+table@^6.0.9:
+ version "6.7.1"
+ resolved "https://registry.yarnpkg.com/table/-/table-6.7.1.tgz#ee05592b7143831a8c94f3cee6aae4c1ccef33e2"
+ integrity sha512-ZGum47Yi6KOOFDE8m223td53ath2enHcYLgOCjGr5ngu8bdIARQk6mN/wRMv4yMRcHnCSnHbCEha4sobQx5yWg==
dependencies:
- ajv "^6.10.2"
- lodash "^4.17.14"
- slice-ansi "^2.1.0"
- string-width "^3.0.0"
+ ajv "^8.0.1"
+ lodash.clonedeep "^4.5.0"
+ lodash.truncate "^4.4.2"
+ slice-ansi "^4.0.0"
+ string-width "^4.2.0"
+ strip-ansi "^6.0.0"
tail-stream@^0.3.4:
version "0.3.4"
resolved "https://registry.yarnpkg.com/tail-stream/-/tail-stream-0.3.4.tgz#bc675a20e92732b1a6a7cc65d6be66f7817fd5c1"
+ integrity sha1-vGdaIOknMrGmp8xl1r5m94F/1cE=
tar@^2.0.0:
version "2.2.2"
@@ -5256,18 +5555,6 @@ tar@^2.0.0:
inherits "2"
tar@^4:
- version "4.4.4"
- resolved "https://registry.yarnpkg.com/tar/-/tar-4.4.4.tgz#ec8409fae9f665a4355cc3b4087d0820232bb8cd"
- dependencies:
- chownr "^1.0.1"
- fs-minipass "^1.2.5"
- minipass "^2.3.3"
- minizlib "^1.1.0"
- mkdirp "^0.5.0"
- safe-buffer "^5.1.2"
- yallist "^3.0.2"
-
-tar@^4.4.10, tar@^4.4.12, tar@^4.4.13:
version "4.4.13"
resolved "https://registry.yarnpkg.com/tar/-/tar-4.4.13.tgz#43b364bc52888d555298637b10d60790254ab525"
integrity sha512-w2VwSrBoHa5BsSyH+KxEqeQBAllHhccyMFVHtGtdMpF4W7IRWfZjFiQceJPChOeTsSDVUpER2T8FA93pr0L+QA==
@@ -5280,26 +5567,29 @@ tar@^4.4.10, tar@^4.4.12, tar@^4.4.13:
safe-buffer "^5.1.2"
yallist "^3.0.3"
-tar@^6.0.2:
- version "6.0.2"
- resolved "https://registry.yarnpkg.com/tar/-/tar-6.0.2.tgz#5df17813468a6264ff14f766886c622b84ae2f39"
+tar@^6.0.2, tar@^6.1.0:
+ version "6.1.0"
+ resolved "https://registry.yarnpkg.com/tar/-/tar-6.1.0.tgz#d1724e9bcc04b977b18d5c573b333a2207229a83"
+ integrity sha512-DUCttfhsnLCjwoDoFcI+B2iJgYa93vBnDUATYEeRx6sntCTdN01VnqsIuTlALXla/LWooNg0yEGeB+Y8WdFxGA==
dependencies:
chownr "^2.0.0"
fs-minipass "^2.0.0"
minipass "^3.0.0"
- minizlib "^2.1.0"
+ minizlib "^2.1.1"
mkdirp "^1.0.3"
yallist "^4.0.0"
term-size@^1.2.0:
version "1.2.0"
resolved "https://registry.yarnpkg.com/term-size/-/term-size-1.2.0.tgz#458b83887f288fc56d6fffbfad262e26638efa69"
+ integrity sha1-RYuDiH8oj8Vtb/+/rSYuJmOO+mk=
dependencies:
execa "^0.7.0"
test-exclude@^6.0.0:
version "6.0.0"
resolved "https://registry.yarnpkg.com/test-exclude/-/test-exclude-6.0.0.tgz#04a8698661d805ea6fa293b6cb9e63ac044ef15e"
+ integrity sha512-cAGWPIyOHU6zlmg88jwm7VRyXnMN7iV68OGAbYDk/Mh/xC/pzVPlQtY6ngoIH/5/tciuhGfvESU8GrHrcxD56w==
dependencies:
"@istanbuljs/schema" "^0.1.2"
glob "^7.1.4"
@@ -5308,168 +5598,164 @@ test-exclude@^6.0.0:
text-table@^0.2.0, text-table@~0.2.0:
version "0.2.0"
resolved "https://registry.yarnpkg.com/text-table/-/text-table-0.2.0.tgz#7f5ee823ae805207c00af2df4a84ec3fcfa570b4"
-
-through2@^2.0.0:
- version "2.0.5"
- resolved "https://registry.yarnpkg.com/through2/-/through2-2.0.5.tgz#01c1e39eb31d07cb7d03a96a70823260b23132cd"
- integrity sha512-/mrRod8xqpA+IHSLyGCQ2s8SPHiCDEeQJSep1jqLYeEUClOFG2Qsh+4FU6G9VeqpZnGW/Su8LQGc4YKni5rYSQ==
- dependencies:
- readable-stream "~2.3.6"
- xtend "~4.0.1"
-
-"through@>=2.2.7 <3", through@^2.3.6:
- version "2.3.8"
- resolved "https://registry.yarnpkg.com/through/-/through-2.3.8.tgz#0dd4c9ffaabc357960b1b724115d7e0e86a2e1f5"
- integrity sha1-DdTJ/6q8NXlgsbckEV1+Doai4fU=
+ integrity sha1-f17oI66AUgfACvLfSoTsP8+lcLQ=
timed-out@^4.0.0, timed-out@^4.0.1:
version "4.0.1"
resolved "https://registry.yarnpkg.com/timed-out/-/timed-out-4.0.1.tgz#f32eacac5a175bea25d7fab565ab3ed8741ef56f"
+ integrity sha1-8y6srFoXW+ol1/q1Zas+2HQe9W8=
tiny-relative-date@^1.3.0:
version "1.3.0"
resolved "https://registry.yarnpkg.com/tiny-relative-date/-/tiny-relative-date-1.3.0.tgz#fa08aad501ed730f31cc043181d995c39a935e07"
integrity sha512-MOQHpzllWxDCHHaDno30hhLfbouoYlOI8YlMNtvKe1zXbjEVhbcEovQxvZrPvtiYW630GQDoMMarCnjfyfHA+A==
-tmp@^0.0.33:
- version "0.0.33"
- resolved "https://registry.yarnpkg.com/tmp/-/tmp-0.0.33.tgz#6d34335889768d21b2bcda0aa277ced3b1bfadf9"
+tmp@^0.2.1:
+ version "0.2.1"
+ resolved "https://registry.yarnpkg.com/tmp/-/tmp-0.2.1.tgz#8457fc3037dcf4719c251367a1af6500ee1ccf14"
+ integrity sha512-76SUhtfqR2Ijn+xllcI5P1oyannHNHByD80W1q447gU3mp9G9PSpGdWmjUOHRDPiHYacIk66W7ubDTuPF3BEtQ==
dependencies:
- os-tmpdir "~1.0.2"
+ rimraf "^3.0.0"
to-fast-properties@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/to-fast-properties/-/to-fast-properties-2.0.0.tgz#dc5e698cbd079265bc73e0377681a4e4e83f616e"
+ integrity sha1-3F5pjL0HkmW8c+A3doGk5Og/YW4=
to-regex-range@^5.0.1:
version "5.0.1"
resolved "https://registry.yarnpkg.com/to-regex-range/-/to-regex-range-5.0.1.tgz#1648c44aae7c8d988a326018ed72f5b4dd0392e4"
+ integrity sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ==
dependencies:
is-number "^7.0.0"
-topo@1.x.x:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/topo/-/topo-1.1.0.tgz#e9d751615d1bb87dc865db182fa1ca0a5ef536d5"
- dependencies:
- hoek "2.x.x"
-
-tough-cookie@~2.3.3:
- version "2.3.4"
- resolved "https://registry.yarnpkg.com/tough-cookie/-/tough-cookie-2.3.4.tgz#ec60cee38ac675063ffc97a5c18970578ee83655"
- dependencies:
- punycode "^1.4.1"
+toidentifier@1.0.0:
+ version "1.0.0"
+ resolved "https://registry.yarnpkg.com/toidentifier/-/toidentifier-1.0.0.tgz#7e1be3470f1e77948bc43d94a3c8f4d7752ba553"
+ integrity sha512-yaOH/Pk/VEhBWWTlhI+qXxDFXlejDGcQipMlyxda9nthulaxLZUNcUqFxokp0vcYnvteJln5FNQDRrxj3YcbVw==
-tough-cookie@~2.4.3:
- version "2.4.3"
- resolved "https://registry.yarnpkg.com/tough-cookie/-/tough-cookie-2.4.3.tgz#53f36da3f47783b0925afa06ff9f3b165280f781"
+tough-cookie@~2.5.0:
+ version "2.5.0"
+ resolved "https://registry.yarnpkg.com/tough-cookie/-/tough-cookie-2.5.0.tgz#cd9fb2a0aa1d5a12b473bd9fb96fa3dcff65ade2"
+ integrity sha512-nlLsUzgm1kfLXSXfRZMc1KLAugd4hqJHDTvc2hDIwS3mZAfMEuMbc03SujMF+GEcpaX/qboeycw6iO8JwVv2+g==
dependencies:
- psl "^1.1.24"
- punycode "^1.4.1"
+ psl "^1.1.28"
+ punycode "^2.1.1"
tree-kill@^1.2.2:
version "1.2.2"
resolved "https://registry.yarnpkg.com/tree-kill/-/tree-kill-1.2.2.tgz#4ca09a9092c88b73a7cdc5e8a01b507b0790a0cc"
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+treeverse@^1.0.4:
+ version "1.0.4"
+ resolved "https://registry.yarnpkg.com/treeverse/-/treeverse-1.0.4.tgz#a6b0ebf98a1bca6846ddc7ecbc900df08cb9cd5f"
+ integrity sha512-whw60l7r+8ZU8Tu/Uc2yxtc4ZTZbR/PF3u1IPNKGQ6p8EICLb3Z2lAgoqw9bqYd8IkgnsaOcLzYHFckjqNsf0g==
+
ts-deferred@^1.0.4:
version "1.0.4"
resolved "https://registry.yarnpkg.com/ts-deferred/-/ts-deferred-1.0.4.tgz#58145ebaeef5b8f2a290b8cec3d060839f9489c7"
-
-ts-node@^7.0.0:
- version "7.0.0"
- resolved "https://registry.yarnpkg.com/ts-node/-/ts-node-7.0.0.tgz#a94a13c75e5e1aa6b82814b84c68deb339ba7bff"
- dependencies:
- arrify "^1.0.0"
- buffer-from "^1.1.0"
- diff "^3.1.0"
+ integrity sha1-WBReuu71uPKikLjOw9Bgg5+Uicc=
+
+ts-node@^10.0.0:
+ version "10.0.0"
+ resolved "https://registry.yarnpkg.com/ts-node/-/ts-node-10.0.0.tgz#05f10b9a716b0b624129ad44f0ea05dac84ba3be"
+ integrity sha512-ROWeOIUvfFbPZkoDis0L/55Fk+6gFQNZwwKPLinacRl6tsxstTF1DbAcLKkovwnpKMVvOMHP1TIbnwXwtLg1gg==
+ dependencies:
+ "@tsconfig/node10" "^1.0.7"
+ "@tsconfig/node12" "^1.0.7"
+ "@tsconfig/node14" "^1.0.0"
+ "@tsconfig/node16" "^1.0.1"
+ arg "^4.1.0"
+ create-require "^1.1.0"
+ diff "^4.0.1"
make-error "^1.1.1"
- minimist "^1.2.0"
- mkdirp "^0.5.1"
- source-map-support "^0.5.6"
- yn "^2.0.0"
+ source-map-support "^0.5.17"
+ yn "3.1.1"
tslib@^1.8.1:
- version "1.9.3"
- resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.9.3.tgz#d7e4dd79245d85428c4d7e4822a79917954ca286"
-
-tslib@^1.9.0:
- version "1.10.0"
- resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.10.0.tgz#c3c19f95973fb0a62973fb09d90d961ee43e5c8a"
+ version "1.14.1"
+ resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.14.1.tgz#cf2d38bdc34a134bcaf1091c41f6619e2f672d00"
+ integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
-tsutils@^3.17.1:
- version "3.17.1"
- resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-3.17.1.tgz#ed719917f11ca0dee586272b2ac49e015a2dd759"
+tsutils@^3.17.1, tsutils@^3.21.0:
+ version "3.21.0"
+ resolved "https://registry.yarnpkg.com/tsutils/-/tsutils-3.21.0.tgz#b48717d394cea6c1e096983eed58e9d61715b623"
+ integrity sha512-mHKK3iUXL+3UF6xL5k0PEhKRUBKPBCv/+RkEOpjRWxxx27KKRBmmA60A9pgOUvMi8GKhRMPEmjBRPzs2W7O1OA==
dependencies:
tslib "^1.8.1"
tunnel-agent@^0.6.0:
version "0.6.0"
resolved "https://registry.yarnpkg.com/tunnel-agent/-/tunnel-agent-0.6.0.tgz#27a5dea06b36b04a0a9966774b290868f0fc40fd"
+ integrity sha1-J6XeoGs2sEoKmWZ3SykIaPD8QP0=
dependencies:
safe-buffer "^5.0.1"
tweetnacl@^0.14.3, tweetnacl@~0.14.0:
version "0.14.5"
resolved "https://registry.yarnpkg.com/tweetnacl/-/tweetnacl-0.14.5.tgz#5ae68177f192d4456269d108afa93ff8743f4f64"
+ integrity sha1-WuaBd/GS1EViadEIr6k/+HQ/T2Q=
-type-check@~0.3.2:
- version "0.3.2"
- resolved "https://registry.yarnpkg.com/type-check/-/type-check-0.3.2.tgz#5884cab512cf1d355e3fb784f30804b2b520db72"
+type-check@^0.4.0, type-check@~0.4.0:
+ version "0.4.0"
+ resolved "https://registry.yarnpkg.com/type-check/-/type-check-0.4.0.tgz#07b8203bfa7056c0657050e3ccd2c37730bab8f1"
+ integrity sha512-XleUoc9uwGXqjWwXaUTZAmzMcFZ5858QA2vvx1Ur5xIcixXIP+8LnFDgRplU30us6teqdlskFfu+ae4K79Ooew==
dependencies:
- prelude-ls "~1.1.2"
+ prelude-ls "^1.2.1"
-type-detect@^4.0.0:
+type-detect@^4.0.0, type-detect@^4.0.5:
version "4.0.8"
resolved "https://registry.yarnpkg.com/type-detect/-/type-detect-4.0.8.tgz#7646fb5f18871cfbb7749e69bd39a6388eb7450c"
+ integrity sha512-0fr/mIH1dlO+x7TlcMy+bIDqKPsw/70tVyeHW787goQjhmqaZe10uwLujubK9q9Lg6Fiho1KUKDYz0Z7k7g5/g==
+
+type-fest@^0.20.2:
+ version "0.20.2"
+ resolved "https://registry.yarnpkg.com/type-fest/-/type-fest-0.20.2.tgz#1bf207f4b28f91583666cb5fbd327887301cd5f4"
+ integrity sha512-Ne+eE4r0/iWnpAxD852z3A+N0Bt5RN//NjJwRd2VFHEmrywxf5vsZlh4R6lixl6B+wz/8d+maTSAkN1FIkI3LQ==
-type-fest@^0.8.0, type-fest@^0.8.1:
+type-fest@^0.8.0:
version "0.8.1"
resolved "https://registry.yarnpkg.com/type-fest/-/type-fest-0.8.1.tgz#09e249ebde851d3b1e48d27c105444667f17b83d"
+ integrity sha512-4dbzIzqvjtgiM5rw1k5rEHtBANKmdudhGyBEajN01fEyhaAIhsoKNy6y7+IN93IfpFtwY9iqi7kD+xwKhQsNJA==
-type-is@~1.6.15, type-is@~1.6.16:
- version "1.6.16"
- resolved "https://registry.yarnpkg.com/type-is/-/type-is-1.6.16.tgz#f89ce341541c672b25ee7ae3c73dee3b2be50194"
+type-is@~1.6.17, type-is@~1.6.18:
+ version "1.6.18"
+ resolved "https://registry.yarnpkg.com/type-is/-/type-is-1.6.18.tgz#4e552cd05df09467dcbc4ef739de89f2cf37c131"
+ integrity sha512-TkRKr9sUTxEH8MdfuCSP7VizJyzRNMjj2J2do2Jr3Kym598JVdEksuzPQCnlFPW4ky9Q+iA+ma9BGm06XQBy8g==
dependencies:
media-typer "0.3.0"
- mime-types "~2.1.18"
+ mime-types "~2.1.24"
typedarray-to-buffer@^3.1.5:
version "3.1.5"
resolved "https://registry.yarnpkg.com/typedarray-to-buffer/-/typedarray-to-buffer-3.1.5.tgz#a97ee7a9ff42691b9f783ff1bc5112fe3fca9080"
+ integrity sha512-zdu8XMNEDepKKR+XYOXAVPtWui0ly0NtohUscw+UmaHiAWT8hrV1rr//H6V+0DvJ3OQ19S979M0laLfX8rm82Q==
dependencies:
is-typedarray "^1.0.0"
-typedarray@^0.0.6:
- version "0.0.6"
- resolved "https://registry.yarnpkg.com/typedarray/-/typedarray-0.0.6.tgz#867ac74e3864187b1d3d47d996a78ec5c8830777"
- integrity sha1-hnrHTjhkGHsdPUfZlqeOxciDB3c=
-
-typescript-ioc@^1.2.4:
- version "1.2.4"
- resolved "https://registry.yarnpkg.com/typescript-ioc/-/typescript-ioc-1.2.4.tgz#21290097b163632de58a3abba7553daef8651f49"
+typescript-ioc@^1.2.6:
+ version "1.2.6"
+ resolved "https://registry.yarnpkg.com/typescript-ioc/-/typescript-ioc-1.2.6.tgz#7b535d81f674b2dd8f3b0f33dfc30f1f02906753"
+ integrity sha512-ksyRctgYtHsjmKBceEgeifV3Zq3tnqLh6/q9HlWC08lnng9ZHA3IwXw8oQlv77TpHbs2J3GVUbxTuhmLLSWCTg==
dependencies:
- reflect-metadata "^0.1.10"
+ reflect-metadata "^0.1.13"
require-glob "^3.2.0"
-typescript-string-operations@^1.3.1:
- version "1.3.1"
- resolved "https://registry.yarnpkg.com/typescript-string-operations/-/typescript-string-operations-1.3.1.tgz#461b886cc9ccd4dd16810b1f248b2e6f6580956b"
-
-typescript@^3.2.2:
- version "3.5.2"
- resolved "https://registry.yarnpkg.com/typescript/-/typescript-3.5.2.tgz#a09e1dc69bc9551cadf17dba10ee42cf55e5d56c"
-
-uid-number@0.0.6:
- version "0.0.6"
- resolved "https://registry.yarnpkg.com/uid-number/-/uid-number-0.0.6.tgz#0ea10e8035e8eb5b8e4449f06da1c730663baa81"
+typescript-string-operations@^1.4.1:
+ version "1.4.1"
+ resolved "https://registry.yarnpkg.com/typescript-string-operations/-/typescript-string-operations-1.4.1.tgz#889bd1a9f234346691c00ea5ceaf97b2754d1894"
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-umask@^1.1.0, umask@~1.1.0:
- version "1.1.0"
- resolved "https://registry.yarnpkg.com/umask/-/umask-1.1.0.tgz#f29cebf01df517912bb58ff9c4e50fde8e33320d"
+typescript@^4.3.2:
+ version "4.3.2"
+ resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.3.2.tgz#399ab18aac45802d6f2498de5054fcbbe716a805"
+ integrity sha512-zZ4hShnmnoVnAHpVHWpTcxdv7dWP60S2FsydQLV8V5PbS3FifjWFFRiHSWpDJahly88PRyV5teTSLoq4eG7mKw==
-underscore@~1.8.3:
- version "1.8.3"
- resolved "https://registry.npmjs.org/underscore/-/underscore-1.8.3.tgz#4f3fb53b106e6097fcf9cb4109f2a5e9bdfa5022"
+underscore@^1.12.1:
+ version "1.13.1"
+ resolved "https://registry.yarnpkg.com/underscore/-/underscore-1.13.1.tgz#0c1c6bd2df54b6b69f2314066d65b6cde6fcf9d1"
+ integrity sha512-hzSoAVtJF+3ZtiFX0VgfFPHEDRm7Y/QPjGyNo4TVdnDTdft3tr8hEkD25a1jC+TjTuE7tkHGKkhwCgs9dgBB2g==
unique-filename@^1.1.1:
version "1.1.1"
@@ -5488,20 +5774,24 @@ unique-slug@^2.0.0:
unique-string@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/unique-string/-/unique-string-1.0.0.tgz#9e1057cca851abb93398f8b33ae187b99caec11a"
+ integrity sha1-nhBXzKhRq7kzmPizOuGHuZyuwRo=
dependencies:
crypto-random-string "^1.0.0"
unpipe@1.0.0, unpipe@~1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/unpipe/-/unpipe-1.0.0.tgz#b2bf4ee8514aae6165b4817829d21b2ef49904ec"
+ integrity sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw=
unzip-response@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/unzip-response/-/unzip-response-2.0.1.tgz#d2f0f737d16b0615e72a6935ed04214572d56f97"
+ integrity sha1-0vD3N9FrBhXnKmk17QQhRXLVb5c=
-update-notifier@^2.2.0, update-notifier@^2.3.0, update-notifier@^2.5.0:
+update-notifier@^2.3.0:
version "2.5.0"
resolved "https://registry.yarnpkg.com/update-notifier/-/update-notifier-2.5.0.tgz#d0744593e13f161e406acb1d9408b72cad08aff6"
+ integrity sha512-gwMdhgJHGuj/+wHJJs9e6PcCszpxR1b236igrOkUofGhqJuG+amlIKwApH1IW1WWl7ovZxsX49lMBWLxSdm5Dw==
dependencies:
boxen "^1.2.1"
chalk "^2.0.1"
@@ -5515,61 +5805,60 @@ update-notifier@^2.2.0, update-notifier@^2.3.0, update-notifier@^2.5.0:
xdg-basedir "^3.0.0"
uri-js@^4.2.2:
- version "4.2.2"
- resolved "https://registry.yarnpkg.com/uri-js/-/uri-js-4.2.2.tgz#94c540e1ff772956e2299507c010aea6c8838eb0"
+ version "4.4.1"
+ resolved "https://registry.yarnpkg.com/uri-js/-/uri-js-4.4.1.tgz#9b1a52595225859e55f669d928f88c6c57f2a77e"
+ integrity sha512-7rKUyy33Q1yc98pQ1DAmLtwX109F7TIfWlW1Ydo8Wl1ii1SeHieeh0HHfPeL2fMXK6z0s8ecKs9frCuLJvndBg==
dependencies:
punycode "^2.1.0"
+url-join@^4.0.0:
+ version "4.0.1"
+ resolved "https://registry.yarnpkg.com/url-join/-/url-join-4.0.1.tgz#b642e21a2646808ffa178c4c5fda39844e12cde7"
+ integrity sha512-jk1+QP6ZJqyOiuEI9AEWQfju/nB2Pw466kbA0LEZljHwKeMgd9WrAEgEGxjPDD2+TNbbb37rTyhEfrCXfuKXnA==
+
url-parse-lax@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/url-parse-lax/-/url-parse-lax-1.0.0.tgz#7af8f303645e9bd79a272e7a14ac68bc0609da73"
+ integrity sha1-evjzA2Rem9eaJy56FKxovAYJ2nM=
dependencies:
prepend-http "^1.0.1"
url-parse-lax@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/url-parse-lax/-/url-parse-lax-3.0.0.tgz#16b5cafc07dbe3676c1b1999177823d6503acb0c"
+ integrity sha1-FrXK/Afb42dsGxmZF3gj1lA6yww=
dependencies:
prepend-http "^2.0.0"
url-to-options@^1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/url-to-options/-/url-to-options-1.0.1.tgz#1505a03a289a48cbd7a434efbaeec5055f5633a9"
+ integrity sha1-FQWgOiiaSMvXpDTvuu7FBV9WM6k=
util-deprecate@^1.0.1, util-deprecate@~1.0.1:
version "1.0.2"
resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
-
-util-extend@^1.0.1:
- version "1.0.3"
- resolved "https://registry.yarnpkg.com/util-extend/-/util-extend-1.0.3.tgz#a7c216d267545169637b3b6edc6ca9119e2ff93f"
-
-util-promisify@^2.1.0:
- version "2.1.0"
- resolved "https://registry.yarnpkg.com/util-promisify/-/util-promisify-2.1.0.tgz#3c2236476c4d32c5ff3c47002add7c13b9a82a53"
- integrity sha1-PCI2R2xNMsX/PEcAKt18E7moKlM=
- dependencies:
- object.getownpropertydescriptors "^2.0.3"
+ integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
utils-merge@1.0.1:
version "1.0.1"
resolved "https://registry.yarnpkg.com/utils-merge/-/utils-merge-1.0.1.tgz#9f95710f50a267947b2ccc124741c1028427e713"
+ integrity sha1-n5VxD1CiZ5R7LMwSR0HBAoQn5xM=
-uuid@^3.0.0, uuid@^3.1.0, uuid@^3.3.2:
- version "3.3.2"
- resolved "https://registry.yarnpkg.com/uuid/-/uuid-3.3.2.tgz#1b4af4955eb3077c501c23872fc6513811587131"
-
-uuid@^3.3.3:
- version "3.3.3"
- resolved "https://registry.yarnpkg.com/uuid/-/uuid-3.3.3.tgz#4568f0216e78760ee1dbf3a4d2cf53e224112866"
+uuid@^3.0.0, uuid@^3.3.2, uuid@^3.3.3:
+ version "3.4.0"
+ resolved "https://registry.yarnpkg.com/uuid/-/uuid-3.4.0.tgz#b23e4358afa8a202fe7a100af1f5f883f02007ee"
+ integrity sha512-HjSDRw6gZE5JMggctHBcjVak08+KEVhSIiDzFnT9S9aegmp85S/bReBVTb4QTFaRNptJ9kuYaNhnbNEOkbKb/A==
v8-compile-cache@^2.0.3:
- version "2.1.0"
- resolved "https://registry.yarnpkg.com/v8-compile-cache/-/v8-compile-cache-2.1.0.tgz#e14de37b31a6d194f5690d67efc4e7f6fc6ab30e"
+ version "2.3.0"
+ resolved "https://registry.yarnpkg.com/v8-compile-cache/-/v8-compile-cache-2.3.0.tgz#2de19618c66dc247dcfb6f99338035d8245a2cee"
+ integrity sha512-l8lCEmLcLYZh4nbunNZvQCJc5pv7+RCwa8q/LdUx8u7lsWvPDKmpodJAJNwkAhJC//dFY48KuIEmjtd4RViDrA==
validate-npm-package-license@^3.0.1, validate-npm-package-license@^3.0.4:
version "3.0.4"
resolved "https://registry.yarnpkg.com/validate-npm-package-license/-/validate-npm-package-license-3.0.4.tgz#fc91f6b9c7ba15c857f4cb2c5defeec39d4f410a"
+ integrity sha512-DpKm2Ui/xN7/HQKCtpZxoRWBhZ9Z0kqtygG8XCgNQ8ZlDnxuQmWhj566j8fN4Cu3/JmbhsDo7fcAJq4s9h27Ew==
dependencies:
spdx-correct "^3.0.0"
spdx-expression-parse "^3.0.0"
@@ -5577,78 +5866,100 @@ validate-npm-package-license@^3.0.1, validate-npm-package-license@^3.0.4:
validate-npm-package-name@^3.0.0, validate-npm-package-name@~3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/validate-npm-package-name/-/validate-npm-package-name-3.0.0.tgz#5fa912d81eb7d0c74afc140de7317f0ca7df437e"
+ integrity sha1-X6kS2B630MdK/BQN5zF/DKffQ34=
dependencies:
builtins "^1.0.3"
validator@~9.4.1:
version "9.4.1"
- resolved "https://registry.npmjs.org/validator/-/validator-9.4.1.tgz#abf466d398b561cd243050112c6ff1de6cc12663"
+ resolved "https://registry.yarnpkg.com/validator/-/validator-9.4.1.tgz#abf466d398b561cd243050112c6ff1de6cc12663"
+ integrity sha512-YV5KjzvRmSyJ1ee/Dm5UED0G+1L4GZnLN3w6/T+zZm8scVua4sOhYKWTUrKa0H/tMiJyO9QLHMPN+9mB/aMunA==
vary@~1.1.2:
version "1.1.2"
resolved "https://registry.yarnpkg.com/vary/-/vary-1.1.2.tgz#2299f02c6ded30d4a5961b0b9f74524a18f634fc"
+ integrity sha1-IpnwLG3tMNSllhsLn3RSShj2NPw=
verror@1.10.0:
version "1.10.0"
resolved "https://registry.yarnpkg.com/verror/-/verror-1.10.0.tgz#3a105ca17053af55d6e270c1f8288682e18da400"
+ integrity sha1-OhBcoXBTr1XW4nDB+CiGguGNpAA=
dependencies:
assert-plus "^1.0.0"
core-util-is "1.0.2"
extsprintf "^1.2.0"
+walk-up-path@^1.0.0:
+ version "1.0.0"
+ resolved "https://registry.yarnpkg.com/walk-up-path/-/walk-up-path-1.0.0.tgz#d4745e893dd5fd0dbb58dd0a4c6a33d9c9fec53e"
+ integrity sha512-hwj/qMDUEjCU5h0xr90KGCf0tg0/LgJbmOWgrWKYlcJZM7XvquvUJZ0G/HMGr7F7OQMOUuPHWP9JpriinkAlkg==
+
wcwidth@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/wcwidth/-/wcwidth-1.0.1.tgz#f0b0dcf915bc5ff1528afadb2c0e17b532da2fe8"
+ integrity sha1-8LDc+RW8X/FSivrbLA4XtTLaL+g=
dependencies:
defaults "^1.0.3"
-webhdfs@^1.2.0:
- version "1.2.0"
- resolved "https://registry.yarnpkg.com/webhdfs/-/webhdfs-1.2.0.tgz#c41b08ae33944a0220863bfd4b6719b9aaec1d37"
- dependencies:
- buffer-stream-reader "^0.1.1"
- extend "^3.0.0"
- request "^2.74.0"
+which-module@^2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/which-module/-/which-module-2.0.0.tgz#d9ef07dce77b9902b8a3a8fa4b31c3e3f7e6e87a"
+ integrity sha1-2e8H3Od7mQK4o6j6SzHD4/fm6Ho=
-which@1, which@^1.2.9, which@^1.3.0, which@^1.3.1:
+which@1, which@^1.2.9, which@^1.3.0:
version "1.3.1"
resolved "https://registry.yarnpkg.com/which/-/which-1.3.1.tgz#a45043d54f5805316da8d62f9f50918d3da70b0a"
+ integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
dependencies:
isexe "^2.0.0"
-which@2.0.2, which@^2.0.1:
+which@2.0.2, which@^2.0.1, which@^2.0.2:
version "2.0.2"
resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1"
+ integrity sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==
dependencies:
isexe "^2.0.0"
wide-align@1.1.3, wide-align@^1.1.0:
version "1.1.3"
resolved "https://registry.yarnpkg.com/wide-align/-/wide-align-1.1.3.tgz#ae074e6bdc0c14a431e804e624549c633b000457"
+ integrity sha512-QGkOQc8XL6Bt5PwnsExKBPuMKBxnGxWWW3fU55Xt4feHozMUhdUMaBCk290qpm/wG5u/RSKzwdAC4i51YigihA==
dependencies:
string-width "^1.0.2 || 2"
widest-line@^2.0.0:
version "2.0.1"
resolved "https://registry.yarnpkg.com/widest-line/-/widest-line-2.0.1.tgz#7438764730ec7ef4381ce4df82fb98a53142a3fc"
+ integrity sha512-Ba5m9/Fa4Xt9eb2ELXt77JxVDV8w7qQrH0zS/TWSJdLyAwQjWoOzpzj5lwVftDz6n/EOu3tNACS84v509qwnJA==
dependencies:
string-width "^2.1.1"
-word-wrap@~1.2.3:
+word-wrap@^1.2.3:
version "1.2.3"
resolved "https://registry.yarnpkg.com/word-wrap/-/word-wrap-1.2.3.tgz#610636f6b1f703891bd34771ccb17fb93b47079c"
+ integrity sha512-Hz/mrNwitNRh/HUAtM/VT/5VH+ygD6DV7mYKZAtHOrbs8U7lvPS6xf7EJKMF0uW1KJCl0H701g3ZGus+muE5vQ==
-worker-farm@^1.6.0, worker-farm@^1.7.0:
- version "1.7.0"
- resolved "https://registry.yarnpkg.com/worker-farm/-/worker-farm-1.7.0.tgz#26a94c5391bbca926152002f69b84a4bf772e5a8"
- integrity sha512-rvw3QTZc8lAxyVrqcSGVm5yP/IJ2UcB3U0graE3LCFoZ0Yn2x4EoVSqJKdB/T5M+FLcRPjz4TDacRf3OCfNUzw==
+workerpool@6.1.0:
+ version "6.1.0"
+ resolved "https://registry.yarnpkg.com/workerpool/-/workerpool-6.1.0.tgz#a8e038b4c94569596852de7a8ea4228eefdeb37b"
+ integrity sha512-toV7q9rWNYha963Pl/qyeZ6wG+3nnsyvolaNUS8+R5Wtw6qJPTxIlOP1ZSvcGhEJw+l3HMMmtiNo9Gl61G4GVg==
+
+wrap-ansi@^2.0.0:
+ version "2.1.0"
+ resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-2.1.0.tgz#d8fc3d284dd05794fe84973caecdd1cf824fdd85"
+ integrity sha1-2Pw9KE3QV5T+hJc8rs3Rz4JP3YU=
dependencies:
- errno "~0.1.7"
+ string-width "^1.0.1"
+ strip-ansi "^3.0.1"
-workerpool@6.0.0:
- version "6.0.0"
- resolved "https://registry.yarnpkg.com/workerpool/-/workerpool-6.0.0.tgz#85aad67fa1a2c8ef9386a1b43539900f61d03d58"
- integrity sha512-fU2OcNA/GVAJLLyKUoHkAgIhKb0JoCpSjLC/G2vYKxUjVmQwGbRVeoPJ1a8U4pnVofz4AQV5Y/NEw8oKqxEBtA==
+wrap-ansi@^6.2.0:
+ version "6.2.0"
+ resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-6.2.0.tgz#e9393ba07102e6c91a3b221478f0257cd2856e53"
+ integrity sha512-r6lPcBGxZXlIcymEu7InxDMhdW0KDxpLgoFLcguasxCaJ/SOIZwINatK9KY/tf+ZrlywOKU0UDj3ATXUBfxJXA==
+ dependencies:
+ ansi-styles "^4.0.0"
+ string-width "^4.1.0"
+ strip-ansi "^6.0.0"
wrap-ansi@^7.0.0:
version "7.0.0"
@@ -5665,14 +5976,6 @@ wrappy@1:
integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
write-file-atomic@^2.0.0:
- version "2.3.0"
- resolved "https://registry.yarnpkg.com/write-file-atomic/-/write-file-atomic-2.3.0.tgz#1ff61575c2e2a4e8e510d6fa4e243cce183999ab"
- dependencies:
- graceful-fs "^4.1.11"
- imurmurhash "^0.1.4"
- signal-exit "^3.0.2"
-
-write-file-atomic@^2.3.0, write-file-atomic@^2.4.3:
version "2.4.3"
resolved "https://registry.yarnpkg.com/write-file-atomic/-/write-file-atomic-2.4.3.tgz#1fd2e9ae1df3e75b8d8c367443c692d4ca81f481"
integrity sha512-GaETH5wwsX+GcnzhPgKcKjJ6M2Cq3/iZp1WyY/X1CSqrW+jVNM9Y7D8EC2sM4ZG/V8wZlSniJnCKWPmBYAucRQ==
@@ -5681,64 +5984,56 @@ write-file-atomic@^2.3.0, write-file-atomic@^2.4.3:
imurmurhash "^0.1.4"
signal-exit "^3.0.2"
-write-file-atomic@^3.0.0:
+write-file-atomic@^3.0.0, write-file-atomic@^3.0.3:
version "3.0.3"
resolved "https://registry.yarnpkg.com/write-file-atomic/-/write-file-atomic-3.0.3.tgz#56bd5c5a5c70481cd19c571bd39ab965a5de56e8"
+ integrity sha512-AvHcyZ5JnSfq3ioSyjrBkH9yW4m7Ayk8/9My/DD9onKeu/94fwrMocemO2QAJFAlnnDN+ZDS+ZjAR5ua1/PV/Q==
dependencies:
imurmurhash "^0.1.4"
is-typedarray "^1.0.0"
signal-exit "^3.0.2"
typedarray-to-buffer "^3.1.5"
-write@1.0.3:
- version "1.0.3"
- resolved "https://registry.yarnpkg.com/write/-/write-1.0.3.tgz#0800e14523b923a387e415123c865616aae0f5c3"
- dependencies:
- mkdirp "^0.5.1"
-
-ws@^6.0.0:
- version "6.1.2"
- resolved "https://registry.yarnpkg.com/ws/-/ws-6.1.2.tgz#3cc7462e98792f0ac679424148903ded3b9c3ad8"
+ws@^6.2.1:
+ version "6.2.2"
+ resolved "https://registry.yarnpkg.com/ws/-/ws-6.2.2.tgz#dd5cdbd57a9979916097652d78f1cc5faea0c32e"
+ integrity sha512-zmhltoSR8u1cnDsD43TX59mzoMZsLKqUweyYBAIvTngR3shc0W6aOZylZmq/7hqyVxPdi+5Ud2QInblgyE72fw==
dependencies:
async-limiter "~1.0.0"
-ws@^7.3.0:
- version "7.3.0"
- resolved "https://registry.yarnpkg.com/ws/-/ws-7.3.0.tgz#4b2f7f219b3d3737bc1a2fbf145d825b94d38ffd"
+ws@^7.4.6:
+ version "7.4.6"
+ resolved "https://registry.yarnpkg.com/ws/-/ws-7.4.6.tgz#5654ca8ecdeee47c33a9a4bf6d28e2be2980377c"
+ integrity sha512-YmhHDO4MzaDLB+M9ym/mDA5z0naX8j7SIlT8f8z+I0VtzsRbekxEutHSme7NPS2qE8StCYQNUnfWdXta/Yu85A==
xdg-basedir@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/xdg-basedir/-/xdg-basedir-3.0.0.tgz#496b2cc109eca8dbacfe2dc72b603c17c5870ad4"
+ integrity sha1-SWsswQnsqNus/i3HK2A8F8WHCtQ=
xml2js@0.2.8:
version "0.2.8"
resolved "https://registry.yarnpkg.com/xml2js/-/xml2js-0.2.8.tgz#9b81690931631ff09d1957549faf54f4f980b3c2"
+ integrity sha1-m4FpCTFjH/CdGVdUn69U9PmAs8I=
dependencies:
sax "0.5.x"
xmlbuilder@^9.0.7:
version "9.0.7"
- resolved "https://registry.npmjs.org/xmlbuilder/-/xmlbuilder-9.0.7.tgz#132ee63d2ec5565c557e20f4c22df9aca686b10d"
+ resolved "https://registry.yarnpkg.com/xmlbuilder/-/xmlbuilder-9.0.7.tgz#132ee63d2ec5565c557e20f4c22df9aca686b10d"
+ integrity sha1-Ey7mPS7FVlxVfiD0wi35rKaGsQ0=
-xtend@~4.0.1:
- version "4.0.2"
- resolved "https://registry.yarnpkg.com/xtend/-/xtend-4.0.2.tgz#bb72779f5fa465186b1f438f674fa347fdb5db54"
- integrity sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==
-
-y18n@>=5.0.5, y18n@^4.0.0, y18n@^5.0.1:
- version "5.0.5"
- resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.5.tgz#8769ec08d03b1ea2df2500acef561743bbb9ab18"
- integrity sha512-hsRUr4FFrvhhRH12wOdfs38Gy7k2FFzB9qgN9v3aLykRq0dRcdcpz5C9FxdS2NuhOrI/628b/KSTJ3rwHysYSg==
+y18n@>=5.0.8, y18n@^3.2.1, y18n@^4.0.0, y18n@^5.0.5:
+ version "5.0.8"
+ resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
+ integrity sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==
yallist@^2.1.2:
version "2.1.2"
resolved "https://registry.yarnpkg.com/yallist/-/yallist-2.1.2.tgz#1c11f9218f076089a47dd512f93c6699a6a81d52"
+ integrity sha1-HBH5IY8HYImkfdUS+TxmmaaoHVI=
-yallist@^3.0.0:
- version "3.0.2"
- resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.0.2.tgz#8452b4bb7e83c7c188d8041c1a837c773d6d8bb9"
-
-yallist@^3.0.2, yallist@^3.0.3:
+yallist@^3.0.0, yallist@^3.0.2, yallist@^3.0.3:
version "3.1.1"
resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
integrity sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==
@@ -5746,36 +6041,77 @@ yallist@^3.0.2, yallist@^3.0.3:
yallist@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
+ integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
-yargs-parser@13.1.2, yargs-parser@>=20.2.0, yargs-parser@^20.0.0:
- version "20.2.3"
- resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.3.tgz#92419ba867b858c868acf8bae9bf74af0dd0ce26"
- integrity sha512-emOFRT9WVHw03QSvN5qor9QQT9+sw5vwxfYweivSMHTcAXPefwVae2FjO7JJjj8hCE4CzPOPeFM83VwT29HCww==
+yargs-parser@20.2.4, yargs-parser@>=20.2.7, yargs-parser@^18.1.2, yargs-parser@^20.2.2, yargs-parser@^9.0.2:
+ version "20.2.7"
+ resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.7.tgz#61df85c113edfb5a7a4e36eb8aa60ef423cbc90a"
+ integrity sha512-FiNkvbeHzB/syOjIUxFDCnhSfzAL8R5vs40MgLFBorXACCOAEaWu0gRZl14vG8MR9AOJIZbmkjhusqBYZ3HTHw==
-yargs-unparser@1.6.1:
- version "1.6.1"
- resolved "https://registry.yarnpkg.com/yargs-unparser/-/yargs-unparser-1.6.1.tgz#bd4b0ee05b4c94d058929c32cb09e3fce71d3c5f"
- integrity sha512-qZV14lK9MWsGCmcr7u5oXGH0dbGqZAIxTDrWXZDo5zUr6b6iUmelNKO6x6R1dQT24AH3LgRxJpr8meWy2unolA==
+yargs-unparser@2.0.0:
+ version "2.0.0"
+ resolved "https://registry.yarnpkg.com/yargs-unparser/-/yargs-unparser-2.0.0.tgz#f131f9226911ae5d9ad38c432fe809366c2325eb"
+ integrity sha512-7pRTIA9Qc1caZ0bZ6RYRGbHJthJWuakf+WmHK0rVeLkNrrGhfoabBNdue6kdINI6r4if7ocq9aD/n7xwKOdzOA==
dependencies:
- camelcase "^5.3.1"
- decamelize "^1.2.0"
- flat "^4.1.0"
- is-plain-obj "^1.1.0"
- yargs "^14.2.3"
+ camelcase "^6.0.0"
+ decamelize "^4.0.0"
+ flat "^5.0.2"
+ is-plain-obj "^2.1.0"
-yargs@13.3.2, yargs@^11.0.0, yargs@^14.2.3, yargs@^15.0.2, yargs@^8.0.2, yargs@~16.0.3:
- version "16.0.3"
- resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.0.3.tgz#7a919b9e43c90f80d4a142a89795e85399a7e54c"
- integrity sha512-6+nLw8xa9uK1BOEOykaiYAJVh6/CjxWXK/q9b5FpRgNslt8s22F2xMBqVIKgCRjNgGvGPBy8Vog7WN7yh4amtA==
+yargs@16.2.0:
+ version "16.2.0"
+ resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.2.0.tgz#1c82bf0f6b6a66eafce7ef30e376f49a12477f66"
+ integrity sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==
dependencies:
- cliui "^7.0.0"
- escalade "^3.0.2"
+ cliui "^7.0.2"
+ escalade "^3.1.1"
get-caller-file "^2.0.5"
require-directory "^2.1.1"
string-width "^4.2.0"
- y18n "^5.0.1"
- yargs-parser "^20.0.0"
+ y18n "^5.0.5"
+ yargs-parser "^20.2.2"
+
+yargs@^11.0.0:
+ version "11.1.1"
+ resolved "https://registry.yarnpkg.com/yargs/-/yargs-11.1.1.tgz#5052efe3446a4df5ed669c995886cc0f13702766"
+ integrity sha512-PRU7gJrJaXv3q3yQZ/+/X6KBswZiaQ+zOmdprZcouPYtQgvNU35i+68M4b1ZHLZtYFT5QObFLV+ZkmJYcwKdiw==
+ dependencies:
+ cliui "^4.0.0"
+ decamelize "^1.1.1"
+ find-up "^2.1.0"
+ get-caller-file "^1.0.1"
+ os-locale "^3.1.0"
+ require-directory "^2.1.1"
+ require-main-filename "^1.0.1"
+ set-blocking "^2.0.0"
+ string-width "^2.0.0"
+ which-module "^2.0.0"
+ y18n "^3.2.1"
+ yargs-parser "^9.0.2"
-yn@^2.0.0:
- version "2.0.0"
- resolved "https://registry.yarnpkg.com/yn/-/yn-2.0.0.tgz#e5adabc8acf408f6385fc76495684c88e6af689a"
+yargs@^15.0.2:
+ version "15.4.1"
+ resolved "https://registry.yarnpkg.com/yargs/-/yargs-15.4.1.tgz#0d87a16de01aee9d8bec2bfbf74f67851730f4f8"
+ integrity sha512-aePbxDmcYW++PaqBsJ+HYUFwCdv4LVvdnhBy78E57PIor8/OVvhMrADFFEDh8DHDFRv/O9i3lPhsENjO7QX0+A==
+ dependencies:
+ cliui "^6.0.0"
+ decamelize "^1.2.0"
+ find-up "^4.1.0"
+ get-caller-file "^2.0.1"
+ require-directory "^2.1.1"
+ require-main-filename "^2.0.0"
+ set-blocking "^2.0.0"
+ string-width "^4.2.0"
+ which-module "^2.0.0"
+ y18n "^4.0.0"
+ yargs-parser "^18.1.2"
+
+yn@3.1.1:
+ version "3.1.1"
+ resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
+ integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==
+
+yocto-queue@^0.1.0:
+ version "0.1.0"
+ resolved "https://registry.yarnpkg.com/yocto-queue/-/yocto-queue-0.1.0.tgz#0294eb3dee05028d31ee1a5fa2c556a6aaf10a1b"
+ integrity sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==
diff --git a/ts/webui/.eslintrc b/ts/webui/.eslintrc
index e220b1f541..cc3750e566 100644
--- a/ts/webui/.eslintrc
+++ b/ts/webui/.eslintrc
@@ -17,7 +17,6 @@
"plugin:react/recommended",
"plugin:@typescript-eslint/eslint-recommended",
"plugin:@typescript-eslint/recommended",
- "prettier/react",
"prettier"
],
"rules": {
@@ -29,6 +28,17 @@
"@typescript-eslint/no-use-before-define": [2, "nofunc"],
"@typescript-eslint/no-var-requires": 0,
"@typescript-eslint/no-unused-vars": [2, { "argsIgnorePattern": "^_" }],
+ "@typescript-eslint/ban-types": [
+ "error",
+ {
+ "extendDefaults": true,
+ "types": {
+ "{}": false,
+ "object": false,
+ "Function": false
+ }
+ }
+ ],
"arrow-parens": [2, "as-needed"],
"no-inner-declarations": 0,
"no-empty": 2,
@@ -45,4 +55,4 @@
"**/*.js"
]
}
-}
\ No newline at end of file
+}
diff --git a/ts/webui/config/webpack.config.js b/ts/webui/config/webpack.config.js
index d3a1161141..849de5a104 100644
--- a/ts/webui/config/webpack.config.js
+++ b/ts/webui/config/webpack.config.js
@@ -1,7 +1,6 @@
'use strict';
const fs = require('fs');
-const isWsl = require('is-wsl');
const path = require('path');
const webpack = require('webpack');
const resolve = require('resolve');
@@ -11,9 +10,9 @@ const CaseSensitivePathsPlugin = require('case-sensitive-paths-webpack-plugin');
const InlineChunkHtmlPlugin = require('react-dev-utils/InlineChunkHtmlPlugin');
const TerserPlugin = require('terser-webpack-plugin');
const MiniCssExtractPlugin = require('mini-css-extract-plugin');
-const OptimizeCSSAssetsPlugin = require('optimize-css-assets-webpack-plugin');
-const safePostCssParser = require('postcss-safe-parser');
-const ManifestPlugin = require('webpack-manifest-plugin');
+const CssMinimizerPlugin = require('css-minimizer-webpack-plugin');
+//const safePostCssParser = require('postcss-safe-parser');
+const { WebpackManifestPlugin } = require('webpack-manifest-plugin');
const InterpolateHtmlPlugin = require('react-dev-utils/InterpolateHtmlPlugin');
const WorkboxWebpackPlugin = require('workbox-webpack-plugin');
const WatchMissingNodeModulesPlugin = require('react-dev-utils/WatchMissingNodeModulesPlugin');
@@ -94,20 +93,22 @@ module.exports = function(webpackEnv) {
options: {
// Necessary for external CSS imports to work
// https://github.com/facebook/create-react-app/issues/2677
- ident: 'postcss',
- plugins: () => [
- require('postcss-flexbugs-fixes'),
- require('postcss-preset-env')({
- autoprefixer: {
- flexbox: 'no-2009',
- },
- stage: 3,
- }),
- // Adds PostCSS Normalize as the reset css with default options,
- // so that it honors browserslist config in package.json
- // which in turn let's users customize the target behavior as per their needs.
- postcssNormalize(),
- ],
+ //ident: 'postcss',
+ postcssOptions: {
+ plugins: () => [
+ require('postcss-flexbugs-fixes'),
+ require('postcss-preset-env')({
+ autoprefixer: {
+ flexbox: 'no-2009',
+ },
+ stage: 3,
+ }),
+ // Adds PostCSS Normalize as the reset css with default options,
+ // so that it honors browserslist config in package.json
+ // which in turn let's users customize the target behavior as per their needs.
+ postcssNormalize(),
+ ],
+ },
sourceMap: isEnvProduction && shouldUseSourceMap,
},
},
@@ -163,8 +164,6 @@ module.exports = function(webpackEnv) {
filename: isEnvProduction
? 'static/js/[name].[contenthash:8].js'
: isEnvDevelopment && 'static/js/bundle.js',
- // TODO: remove this when upgrading to webpack 5
- futureEmitAssets: true,
// There are also additional JS chunk files if you use code splitting.
chunkFilename: isEnvProduction
? 'static/js/[name].[contenthash:8].chunk.js'
@@ -220,29 +219,23 @@ module.exports = function(webpackEnv) {
ascii_only: true,
},
},
- // Use multi-process parallel running to improve the build speed
- // Default number of concurrent runs: os.cpus().length - 1
- // Disabled on WSL (Windows Subsystem for Linux) due to an issue with Terser
- // https://github.com/webpack-contrib/terser-webpack-plugin/issues/21
- parallel: !isWsl,
- // Enable file caching
- cache: true,
- sourceMap: shouldUseSourceMap,
}),
// This is only used in production mode
- new OptimizeCSSAssetsPlugin({
- cssProcessorOptions: {
- parser: safePostCssParser,
- map: shouldUseSourceMap
- ? {
- // `inline: false` forces the sourcemap to be output into a
- // separate file
- inline: false,
- // `annotation: true` appends the sourceMappingURL to the end of
- // the css file, helping the browser find the sourcemap
- annotation: true,
- }
- : false,
+ new CssMinimizerPlugin({
+ minimizerOptions: {
+ processorOptions: {
+ //parser: safePostCssParser,
+ map: shouldUseSourceMap
+ ? {
+ // `inline: false` forces the sourcemap to be output into a
+ // separate file
+ inline: false,
+ // `annotation: true` appends the sourceMappingURL to the end of
+ // the css file, helping the browser find the sourcemap
+ annotation: true,
+ }
+ : false,
+ },
},
}),
],
@@ -552,7 +545,7 @@ module.exports = function(webpackEnv) {
// Generate a manifest file which contains a mapping of all asset filenames
// to their corresponding output file so that tools can pick it up without
// having to parse `index.html`.
- new ManifestPlugin({
+ new WebpackManifestPlugin({
fileName: 'asset-manifest.json',
publicPath: publicPath,
generate: (seed, files) => {
@@ -578,9 +571,9 @@ module.exports = function(webpackEnv) {
new WorkboxWebpackPlugin.GenerateSW({
clientsClaim: true,
exclude: [/\.map$/, /asset-manifest\.json$/],
- importWorkboxFrom: 'cdn',
+ //importWorkboxFrom: 'cdn',
navigateFallback: publicUrl + '/index.html',
- navigateFallbackBlacklist: [
+ navigateFallbackDenylist: [
// Exclude URLs starting with /_, as they're likely an API call
new RegExp('^/_'),
// Exclude URLs containing a dot, as they're likely a resource in
@@ -614,16 +607,16 @@ module.exports = function(webpackEnv) {
].filter(Boolean),
// Some libraries import Node modules but don't use them in the browser.
// Tell Webpack to provide empty mocks for them so importing them works.
- node: {
- module: 'empty',
- dgram: 'empty',
- dns: 'mock',
- fs: 'empty',
- http2: 'empty',
- net: 'empty',
- tls: 'empty',
- child_process: 'empty',
- },
+ //node: {
+ // module: 'empty',
+ // dgram: 'empty',
+ // dns: 'mock',
+ // fs: 'empty',
+ // http2: 'empty',
+ // net: 'empty',
+ // tls: 'empty',
+ // child_process: 'empty',
+ //},
// Turn off performance processing because we utilize
// our own hints via the FileSizeReporter
performance: false,
diff --git a/ts/webui/package.json b/ts/webui/package.json
index 541948418e..56114b0f2f 100644
--- a/ts/webui/package.json
+++ b/ts/webui/package.json
@@ -4,44 +4,40 @@
"license": "MIT",
"dependencies": {
"axios": "^0.21.1",
- "babel-eslint": "10.0.1",
- "babel-jest": "24.7.1",
- "babel-loader": "8.0.5",
+ "babel-jest": "^27.0.2",
+ "babel-loader": "^8.2.2",
"babel-plugin-named-asset-import": "^0.3.2",
- "babel-preset-react-app": "^8.0.0",
- "case-sensitive-paths-webpack-plugin": "2.2.0",
+ "babel-preset-react-app": "^10.0.0",
+ "case-sensitive-paths-webpack-plugin": "^2.4.0",
"copy-to-clipboard": "^3.0.8",
- "css-loader": "2.1.1",
- "d3": "^5.16.0",
- "dotenv": "6.2.0",
- "dotenv-expand": "4.2.0",
+ "css-loader": "^5.2.6",
+ "css-minimizer-webpack-plugin": "^3.0.1",
+ "d3": "^6.7.0",
+ "dotenv": "^10.0.0",
+ "dotenv-expand": "^5.1.0",
"echarts": "^5.0",
- "echarts-for-react": "3.0.0",
- "file-loader": "3.0.1",
- "fs-extra": "7.0.1",
- "html-webpack-plugin": "4.0.0-beta.5",
- "identity-obj-proxy": "3.0.0",
- "is-wsl": "^1.1.0",
- "jest": "24.7.1",
- "jest-environment-jsdom-fourteen": "0.1.0",
- "jest-resolve": "24.7.1",
- "jest-watch-typeahead": "0.3.0",
+ "echarts-for-react": "^3.0.0",
+ "file-loader": "^6.2.0",
+ "fs-extra": "^10.0.0",
+ "html-webpack-plugin": "^5.3.1",
+ "jest": "^27.0.4",
+ "jest-environment-jsdom-fourteen": "^1.0.1",
+ "jest-resolve": "^27.0.4",
+ "jest-watch-typeahead": "^0.6.4",
"json5": "^2.1.1",
- "mini-css-extract-plugin": "0.5.0",
- "monaco-editor": "^0.19.0",
- "monaco-editor-webpack-plugin": "^1.8.1",
- "node-sass": "^4.13.0",
- "optimize-css-assets-webpack-plugin": "5.0.1",
+ "mini-css-extract-plugin": "^1.6.0",
+ "monaco-editor": "^0.25.0",
+ "monaco-editor-webpack-plugin": "^4.0.0",
+ "node-sass": "^6.0.0",
"parcoord-es": "^2.2.10",
- "pnp-webpack-plugin": "1.2.1",
- "postcss-flexbugs-fixes": "4.1.0",
- "postcss-loader": "3.0.0",
- "postcss-normalize": "7.0.1",
- "postcss-preset-env": "6.6.0",
- "postcss-safe-parser": "4.0.1",
+ "pnp-webpack-plugin": "^1.6.4",
+ "postcss-flexbugs-fixes": "^5.0.2",
+ "postcss-loader": "^6.1.0",
+ "postcss-normalize": "^10.0.0",
+ "postcss-preset-env": "^6.6.0",
"react": "^16.8.6",
"react-app-polyfill": "^1.0.0",
- "react-dev-utils": "^9.0.0",
+ "react-dev-utils": "^11.0.4",
"react-dom": "^16.8.6",
"react-json-tree": "^0.11.2",
"react-monaco-editor": "^0.32.1",
@@ -50,46 +46,47 @@
"react-router": "^5.2.0",
"react-router-dom": "^5.2.0",
"react-table": "^7.0.0-rc.15",
- "resolve": "1.10.0",
- "sass-loader": "7.1.0",
- "semver": "6.0.0",
- "style-loader": "0.23.1",
- "terser-webpack-plugin": "1.2.3",
- "url-loader": "1.1.2",
- "webpack": "4.29.6",
- "webpack-dev-server": "3.2.1",
- "webpack-manifest-plugin": "2.0.4",
- "workbox-webpack-plugin": "4.2.0"
+ "resolve": "^1.10.0",
+ "sass-loader": "^12.1.0",
+ "semver": "^7.3.5",
+ "style-loader": "^2.0.0",
+ "url-loader": "^4.1.1",
+ "webpack": "^5.39.0",
+ "webpack-dev-server": "^3.11.2",
+ "webpack-manifest-plugin": "^3.1.1",
+ "workbox-webpack-plugin": "^6.1.5"
},
"devDependencies": {
- "@babel/core": "7.4.3",
+ "@babel/core": "^7.14.6",
+ "@babel/eslint-parser": "^7.14.5",
+ "@babel/preset-react": "^7.14.5",
+ "@babel/preset-typescript": "^7.14.5",
+ "@babel/runtime": "^7.14.6",
"@fluentui/react": "^7.135.0",
- "@svgr/webpack": "4.1.0",
- "@types/d3": "^5.7.2",
- "@types/jest": "24.0.12",
- "@types/json5": "^0.0.30",
- "@types/node": "^10.14.2",
- "@types/react": "16.8.15",
- "@types/react-dom": "16.8.4",
- "@typescript-eslint/eslint-plugin": "^2.11.0",
- "@typescript-eslint/parser": "^2.11.0",
- "concurrently": "^5.2.0",
- "eslint": "^5.16.0",
- "eslint-config-prettier": "^6.1.0",
- "eslint-config-react-app": "^4.0.0",
- "eslint-loader": "2.1.2",
- "eslint-plugin-flowtype": "2.50.1",
- "eslint-plugin-import": "2.16.0",
- "eslint-plugin-jsx-a11y": "6.2.1",
+ "@svgr/webpack": "^5.5.0",
+ "@types/d3": "^6.7.0",
+ "@types/node": "^15.12.2",
+ "@types/react": "^16.8.15",
+ "@types/react-dom": "^16.8.4",
+ "@typescript-eslint/eslint-plugin": "^4.27.0",
+ "@typescript-eslint/parser": "^4.27.0",
+ "concurrently": "^6.2.0",
+ "eslint": "^7.28.0",
+ "eslint-config-prettier": "^8.3.0",
+ "eslint-config-react-app": "^6.0.0",
+ "eslint-loader": "^4.0.2",
+ "eslint-plugin-flowtype": "^5.7.2",
+ "eslint-plugin-import": "^2.23.4",
+ "eslint-plugin-jsx-a11y": "^6.4.1",
"eslint-plugin-prettier": "^3.1.0",
- "eslint-plugin-react": "7.12.4",
- "eslint-plugin-react-hooks": "^1.5.0",
+ "eslint-plugin-react": "^7.24.0",
+ "eslint-plugin-react-hooks": "^4.2.0",
"express": "^4.17.1",
"npx": "^10.2.0",
- "prettier": "^1.18.2",
+ "prettier": "^2.3.1",
"stylelint": "^13.7.0",
- "stylelint-config-standard": "^20.0.0",
- "typescript": "^3.8.0"
+ "stylelint-config-standard": "^22.0.0",
+ "typescript": "^4.3.2"
},
"proxy": "http://localhost:12138",
"scripts": {
@@ -109,15 +106,15 @@
],
"babel": {
"presets": [
- "react-app"
+ "@babel/preset-react",
+ "@babel/preset-typescript"
]
},
"resolutions": {
- "npm": ">=6.14.4",
- "yargs": "~16.0.3",
- "acorn": ">=8.0.4",
- "node-forge": ">=0.10.0",
- "y18n": ">=5.0.5",
+ "npm": ">=7.17.0",
+ "yargs": ">=17.0.1",
+ "acorn": ">=8.4.0",
+ "y18n": ">=5.0.8",
"serialize-javascript": ">=5.0.1"
},
"jest": {
diff --git a/ts/webui/prettier.config.js b/ts/webui/prettier.config.js
index 09b26e21a6..375d625e1a 100644
--- a/ts/webui/prettier.config.js
+++ b/ts/webui/prettier.config.js
@@ -8,5 +8,5 @@ module.exports = {
singleQuote: true,
eslintIntegration: true,
jsxSingleQuote: true,
- endOfline: 'lf'
+ endOfLine: process.platform === 'win32' ? 'auto' : 'lf'
};
diff --git a/ts/webui/src/static/img/loading.gif b/ts/webui/public/loading.gif
similarity index 100%
rename from ts/webui/src/static/img/loading.gif
rename to ts/webui/public/loading.gif
diff --git a/ts/webui/src/static/img/logo.png b/ts/webui/public/logo.png
similarity index 100%
rename from ts/webui/src/static/img/logo.png
rename to ts/webui/public/logo.png
diff --git a/ts/webui/src/logo.svg b/ts/webui/public/logo.svg
similarity index 100%
rename from ts/webui/src/logo.svg
rename to ts/webui/public/logo.svg
diff --git a/ts/webui/scripts/build.js b/ts/webui/scripts/build.js
index bdd5d82784..6569881237 100644
--- a/ts/webui/scripts/build.js
+++ b/ts/webui/scripts/build.js
@@ -22,7 +22,7 @@ const webpack = require('webpack');
const configFactory = require('../config/webpack.config');
const paths = require('../config/paths');
const checkRequiredFiles = require('react-dev-utils/checkRequiredFiles');
-const formatWebpackMessages = require('react-dev-utils/formatWebpackMessages');
+const formatWebpackMessages = require('./formatWebpackMessages');
const printHostingInstructions = require('react-dev-utils/printHostingInstructions');
const FileSizeReporter = require('react-dev-utils/FileSizeReporter');
const printBuildError = require('react-dev-utils/printBuildError');
diff --git a/ts/webui/scripts/formatWebpackMessages.js b/ts/webui/scripts/formatWebpackMessages.js
new file mode 100644
index 0000000000..4b2dd5c0a7
--- /dev/null
+++ b/ts/webui/scripts/formatWebpackMessages.js
@@ -0,0 +1,130 @@
+// FIXME: This file is copied from react-dev-utils master branch. Waiting for its next release.
+
+/**
+ * Copyright (c) 2015-present, Facebook, Inc.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+'use strict';
+
+const friendlySyntaxErrorLabel = 'Syntax error:';
+
+function isLikelyASyntaxError(message) {
+ return message.indexOf(friendlySyntaxErrorLabel) !== -1;
+}
+
+// Cleans up webpack error messages.
+function formatMessage(message) {
+ let lines = [];
+
+ if (typeof message === 'string') {
+ lines = message.split('\n');
+ } else if ('message' in message) {
+ lines = message['message'].split('\n');
+ } else if (Array.isArray(message)) {
+ message.forEach(message => {
+ if ('message' in message) {
+ lines = message['message'].split('\n');
+ }
+ });
+ }
+
+ // Strip webpack-added headers off errors/warnings
+ // https://github.com/webpack/webpack/blob/master/lib/ModuleError.js
+ lines = lines.filter(line => !/Module [A-z ]+\(from/.test(line));
+
+ // Transform parsing error into syntax error
+ // TODO: move this to our ESLint formatter?
+ lines = lines.map(line => {
+ const parsingError = /Line (\d+):(?:(\d+):)?\s*Parsing error: (.+)$/.exec(
+ line
+ );
+ if (!parsingError) {
+ return line;
+ }
+ const [, errorLine, errorColumn, errorMessage] = parsingError;
+ return `${friendlySyntaxErrorLabel} ${errorMessage} (${errorLine}:${errorColumn})`;
+ });
+
+ message = lines.join('\n');
+ // Smoosh syntax errors (commonly found in CSS)
+ message = message.replace(
+ /SyntaxError\s+\((\d+):(\d+)\)\s*(.+?)\n/g,
+ `${friendlySyntaxErrorLabel} $3 ($1:$2)\n`
+ );
+ // Clean up export errors
+ message = message.replace(
+ /^.*export '(.+?)' was not found in '(.+?)'.*$/gm,
+ `Attempted import error: '$1' is not exported from '$2'.`
+ );
+ message = message.replace(
+ /^.*export 'default' \(imported as '(.+?)'\) was not found in '(.+?)'.*$/gm,
+ `Attempted import error: '$2' does not contain a default export (imported as '$1').`
+ );
+ message = message.replace(
+ /^.*export '(.+?)' \(imported as '(.+?)'\) was not found in '(.+?)'.*$/gm,
+ `Attempted import error: '$1' is not exported from '$3' (imported as '$2').`
+ );
+ lines = message.split('\n');
+
+ // Remove leading newline
+ if (lines.length > 2 && lines[1].trim() === '') {
+ lines.splice(1, 1);
+ }
+ // Clean up file name
+ lines[0] = lines[0].replace(/^(.*) \d+:\d+-\d+$/, '$1');
+
+ // Cleans up verbose "module not found" messages for files and packages.
+ if (lines[1] && lines[1].indexOf('Module not found: ') === 0) {
+ lines = [
+ lines[0],
+ lines[1]
+ .replace('Error: ', '')
+ .replace('Module not found: Cannot find file:', 'Cannot find file:'),
+ ];
+ }
+
+ // Add helpful message for users trying to use Sass for the first time
+ if (lines[1] && lines[1].match(/Cannot find module.+sass/)) {
+ lines[1] = 'To import Sass files, you first need to install sass.\n';
+ lines[1] +=
+ 'Run `npm install sass` or `yarn add sass` inside your workspace.';
+ }
+
+ message = lines.join('\n');
+ // Internal stacks are generally useless so we strip them... with the
+ // exception of stacks containing `webpack:` because they're normally
+ // from user code generated by webpack. For more information see
+ // https://github.com/facebook/create-react-app/pull/1050
+ message = message.replace(
+ /^\s*at\s((?!webpack:).)*:\d+:\d+[\s)]*(\n|$)/gm,
+ ''
+ ); // at ... ...:x:y
+ message = message.replace(/^\s*at\s(\n|$)/gm, ''); // at
+ lines = message.split('\n');
+
+ // Remove duplicated newlines
+ lines = lines.filter(
+ (line, index, arr) =>
+ index === 0 || line.trim() !== '' || line.trim() !== arr[index - 1].trim()
+ );
+
+ // Reassemble the message
+ message = lines.join('\n');
+ return message.trim();
+}
+
+function formatWebpackMessages(json) {
+ const formattedErrors = json.errors.map(formatMessage);
+ const formattedWarnings = json.warnings.map(formatMessage);
+ const result = { errors: formattedErrors, warnings: formattedWarnings };
+ if (result.errors.some(isLikelyASyntaxError)) {
+ // If there are any syntax errors, show just them.
+ result.errors = result.errors.filter(isLikelyASyntaxError);
+ }
+ return result;
+}
+
+module.exports = formatWebpackMessages;
diff --git a/ts/webui/src/components/managementExp/ExperimentManager.tsx b/ts/webui/src/components/managementExp/ExperimentManager.tsx
index c6efc56bbc..faa7497eb8 100644
--- a/ts/webui/src/components/managementExp/ExperimentManager.tsx
+++ b/ts/webui/src/components/managementExp/ExperimentManager.tsx
@@ -362,14 +362,8 @@ class Experiment extends React.Component<{}, ExpListState> {
private getSelectedData(type: string, date: Date | null | undefined): void {
if (date !== null && date !== undefined) {
- const {
- selectedStatus,
- selectedPlatform,
- selectedStartDate,
- selectedEndDate,
- searchSource,
- sortInfo
- } = this.state;
+ const { selectedStatus, selectedPlatform, selectedStartDate, selectedEndDate, searchSource, sortInfo } =
+ this.state;
const hasPlatform = selectedPlatform === '' ? false : true;
// filter status, platform
diff --git a/ts/webui/src/components/managementExp/expFunction.ts b/ts/webui/src/components/managementExp/expFunction.ts
index 080e93118d..193c79f2ca 100644
--- a/ts/webui/src/components/managementExp/expFunction.ts
+++ b/ts/webui/src/components/managementExp/expFunction.ts
@@ -32,13 +32,7 @@ const filterByStatusOrPlatform = (
};
function fillOptions(arr: string[]): any {
- const list: Array
## **Releases and Contributing**
NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by [filling an issue](https://github.com/microsoft/nni/issues/new/choose).
@@ -320,6 +316,7 @@ We appreciate all contributions and thank all the contributors!
## **Feedback**
* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
+* Open or participate in a [discussion](https://github.com/microsoft/nni/discussions).
* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
Join IM discussion groups:
diff --git a/dependencies/required.txt b/dependencies/required.txt
index 493ccc95f4..917b808377 100644
--- a/dependencies/required.txt
+++ b/dependencies/required.txt
@@ -3,7 +3,7 @@ hyperopt == 0.1.2
json_tricks
netifaces
psutil
-ruamel.yaml
+pyyaml
requests
responses
schema
@@ -19,3 +19,5 @@ numpy < 1.20 ; sys_platform != "win32" and python_version < "3.7"
numpy ; sys.platform != "win32" and python_version >= "3.7"
scipy < 1.6 ; python_version < "3.7"
scipy ; python_version >= "3.7"
+pandas < 1.2 ; python_version < "3.7"
+matplotlib < 3.4 ; python_version < "3.7"
diff --git a/dependencies/required_extra.txt b/dependencies/required_extra.txt
index 65940c9194..9bf0cac29a 100644
--- a/dependencies/required_extra.txt
+++ b/dependencies/required_extra.txt
@@ -11,3 +11,6 @@ statsmodels==0.12.0
# PPOTuner
gym
+
+# DNGO
+pybnn
diff --git a/docs/en_US/CommunitySharings/AutoCompletion.rst b/docs/en_US/CommunitySharings/AutoCompletion.rst
index 8664aa733f..f9b97a1efb 100644
--- a/docs/en_US/CommunitySharings/AutoCompletion.rst
+++ b/docs/en_US/CommunitySharings/AutoCompletion.rst
@@ -25,7 +25,7 @@ Step 1. Download ``bash-completion``
cd ~
wget https://raw.githubusercontent.com/microsoft/nni/{nni-version}/tools/bash-completion
-Here, {nni-version} should by replaced by the version of NNI, e.g., ``master``, ``v2.2``. You can also check the latest ``bash-completion`` script :githublink:`here 
- 
- 