SparkSnail · SparkSnail · Jun 23, 2021 · May 26, 2021 · May 27, 2021 · May 27, 2021
diff --git a/Dockerfile b/Dockerfile
@@ -1,7 +1,7 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-FROM nvidia/cuda:9.2-cudnn7-runtime-ubuntu18.04
+FROM nvidia/cuda:10.2-cudnn8-runtime-ubuntu18.04
 
 ARG NNI_RELEASE
 
@@ -43,24 +43,23 @@ RUN ln -s python3 /usr/bin/python
 #
 RUN python3 -m pip install --upgrade pip==20.2.4 setuptools==50.3.2
 
-# numpy 1.14.3  scipy 1.1.0
-RUN python3 -m pip --no-cache-dir install numpy==1.14.3 scipy==1.1.0
+# numpy 1.19.5  scipy 1.5.4
+RUN python3 -m pip --no-cache-dir install numpy==1.19.5 scipy==1.5.4
 
 #
 # TensorFlow
 #
 RUN python3 -m pip --no-cache-dir install tensorflow==2.3.1
 
 #
-# Keras 2.1.6
+# Keras
 #
-RUN python3 -m pip --no-cache-dir install Keras==2.1.6
+RUN python3 -m pip --no-cache-dir install Keras==2.4.3
 
 #
 # PyTorch
 #
-RUN python3 -m pip --no-cache-dir install torch==1.6.0
-RUN python3 -m pip install torchvision==0.7.0
+RUN python3 -m pip --no-cache-dir install torch==1.7.1 torchvision==0.8.2 pytorch-lightning==1.3.3
 
 #
 # sklearn 0.24.1
@@ -70,7 +69,7 @@ RUN python3 -m pip --no-cache-dir install scikit-learn==0.24.1
 #
 # pandas==0.23.4 lightgbm==2.2.2
 #
-RUN python3 -m pip --no-cache-dir install pandas==0.23.4 lightgbm==2.2.2
+RUN python3 -m pip --no-cache-dir install pandas==1.1 lightgbm==2.2.2
 
 #
 # Install NNI

diff --git a/README.md b/README.md
@@ -26,10 +26,11 @@ The tool manages automated machine learning (AutoML) experiments, **dispatches a
 * ML Platform owners who want to **support AutoML in their platform**.
 
 ## **What's NEW!** &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
-* **New release**: [v2.2 is available](https://github.com/microsoft/nni/releases) - _released on April-26-2021_
-* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on Apr-21-2021_
 
-* **New use case sharing**: [Cost-effective Hyper-parameter Tuning using AdaptDL with NNI](https://medium.com/casl-project/cost-effective-hyper-parameter-tuning-using-adaptdl-with-nni-e55642888761) - _posted on Feb-23-2021_
+* **New release**: [v2.3 is available](https://github.com/microsoft/nni/releases) - _released on June-15-2021_
+* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on May-26-2021_
+* **New webinar**: [Introducing Retiarii: A deep learning exploratory-training framework on NNI](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html) - _scheduled on June-24-2021_
+* **New community channel**: [Discussions](https://github.com/microsoft/nni/discussions)
 
 ## **NNI capabilities in a glance**
 
@@ -122,25 +123,19 @@ Within the following table, we summarized the current NNI capabilities, we are g
             <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
             <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
             <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a></li>
+            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#DNGOTuner">DNGO Tuner</a></li>
             </ul>
-          <b>RL Based</b>
-          <ul>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#PPOTuner">PPO Tuner</a> </li>
-          </ul>
         </ul>
-          <a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search</a>
+          <a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search (Retiarii)</a>
           <ul>
-            <ul>
-              <li><a href="https://nni.readthedocs.io/en/stable/NAS/ENAS.html">ENAS</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/NAS/DARTS.html">DARTS</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/NAS/PDARTS.html">P-DARTS</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/NAS/CDARTS.html">CDARTS</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/NAS/SPOS.html">SPOS</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#NetworkMorphism">Network Morphism</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/NAS/TextNAS.html">TextNAS</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/NAS/Cream.html">Cream</a></li>
-            </ul>
+            <li><a href="https://nni.readthedocs.io/en/stable/NAS/ENAS.html">ENAS</a></li>
+            <li><a href="https://nni.readthedocs.io/en/stable/NAS/DARTS.html">DARTS</a></li>
+            <li><a href="https://nni.readthedocs.io/en/stable/NAS/SPOS.html">SPOS</a></li>
+            <li><a href="https://nni.readthedocs.io/en/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
+            <li><a href="https://nni.readthedocs.io/en/stable/NAS/FBNet.html">FBNet</a></li>
+            <li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Reinforcement Learning</a></li>
+            <li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Regularized Evolution</a></li>
+            <li><a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">More...</a></li>
           </ul>
           <a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">Model Compression</a>
           <ul>
@@ -153,11 +148,13 @@ Within the following table, we summarized the current NNI capabilities, we are g
               <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
               <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
               <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
+              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">More...</a></li>
             </ul>
             <b>Quantization</b>
             <ul>
               <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#qat-quantizer">QAT Quantizer</a></li>
               <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
+              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
             </ul>
           </ul>
           <a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/Overview.html">Feature Engineering (Beta)</a>
@@ -207,6 +204,8 @@ Within the following table, we summarized the current NNI capabilities, we are g
         <li><a href="https://nni.readthedocs.io/en/stable/Tuner/CustomizeTuner.html">CustomizeTuner</a></li>
         <li><a href="https://nni.readthedocs.io/en/stable/Assessor/CustomizeAssessor.html">CustomizeAssessor</a></li>
         <li><a href="https://nni.readthedocs.io/en/stable/Tutorial/InstallCustomizedAlgos.html">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
+        <li><a href="https://nni.readthedocs.io/en/stable/NAS/QuickStart.html#define-your-model-space">Define NAS Model Space</a></li>
+        <li><a href="https://nni.readthedocs.io/en/stable/NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
       </ul>
       </td>
         <td style="border-top:#FF0000 solid 0px;">
@@ -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.
 
-<table style="border: none">
-    <th><img src="./docs/img/webui-img/full-oview.png" alt="drawing" width="395" height="300"/></th>
-    <th><img src="./docs/img/webui-img/full-detail.png" alt="drawing" width="410" height="300"/></th>
-</table>
+<img src="docs/static/img/webui.gif" alt="webui" width="100%"/>
 
 ## **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
@@ -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
@@ -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
@@ -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 <tools/bash-completion>`.
+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 <tools/bash-completion>`.
 
 .. cannot find :githublink:`here <tools/bash-completion>`.
 

diff --git a/docs/en_US/Compression/AutoCompression.rst 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 <nni/algorithms/compression/pytorch/auto_compress/interface.py>`.
+An example of ``AutoCompressionModule`` implementation refers to :githublink:`auto_compress_module.py <examples/model_compress/auto_compress/torch/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
diff --git 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()
-Original file line number
+Diff line change
@@ Expand Up / @@ -11,3 +11,6 @@ statsmodels==0.12.0 @@
     # PPOTuner
     gym
+    # DNGO
+    pybnn