doctest for .rst files

.circleci/config.yml

@@ -64,10 +64,13 @@ references:
      name: Make Documentation
      command: |
        # sudo apt-get install pandoc
+       sudo apt-get update && sudo apt-get install -y cmake
        pip install -r requirements.txt --user
        sudo pip install -r docs/requirements.txt
+       pip install -r requirements-extra.txt --user  # for doctesting loggers etc.
        # sphinx-apidoc -o ./docs/source ./pytorch_lightning **/test_* --force --follow-links
-       cd docs; make clean ; make html --debug --jobs 2 SPHINXOPTS="-W"
+       cd docs; make clean; make html --debug --jobs 2 SPHINXOPTS="-W"
+       make doctest; make coverage
 
 jobs:
 

.drone.yml

@@ -35,9 +35,11 @@ steps:
     - apt-get update && apt-get install -y cmake
     - pip install -r requirements.txt --user -q
     - pip install -r ./tests/requirements-devel.txt --user -q
+    - pip install -r ./docs/requirements.txt --user -q
     - pip list
     - python -c "import torch ; print(' & '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())]) if torch.cuda.is_available() else 'only CPU')"
     - coverage run --source pytorch_lightning -m py.test pytorch_lightning tests benchmarks -v --doctest-modules #  --flake8
+    #- cd docs; make doctest; make coverage
     - coverage report
     - codecov --token $CODECOV_TOKEN  # --pr $DRONE_PULL_REQUEST --build $DRONE_BUILD_NUMBER --branch $DRONE_BRANCH --commit $DRONE_COMMIT --tag $DRONE_TAG
     - python tests/collect_env_details.py

docs/source/apex.rst

@@ -1,3 +1,8 @@
+.. testsetup:: *
+
+    from pytorch_lightning.trainer.trainer import Trainer
+
+
 16-bit training
 =================
 Lightning offers 16-bit training for CPUs, GPUs and TPUs.
@@ -38,7 +43,7 @@ Install apex
 Enable 16-bit
 ^^^^^^^^^^^^^
 
-.. code-block:: python
+.. testcode::
 
     # turn on 16-bit
     trainer = Trainer(amp_level='O1', precision=16)
@@ -50,7 +55,7 @@ TPU 16-bit
 ----------
 16-bit on TPus is much simpler. To use 16-bit with TPUs set precision to 16 when using the tpu flag
 
-.. code-block:: python
+.. testcode::
 
     # DEFAULT
     trainer = Trainer(num_tpu_cores=8, precision=32)

docs/source/callbacks.rst

@@ -1,3 +1,8 @@
+.. testsetup:: *
+
+    from pytorch_lightning.trainer.trainer import Trainer
+    from pytorch_lightning.callbacks.base import Callback
+
 .. role:: hidden
     :class: hidden-section
 
@@ -18,21 +23,23 @@ An overall Lightning system should have:
 
 Example:
 
-.. doctest::
-
-    >>> import pytorch_lightning as pl
-    >>> class MyPrintingCallback(pl.Callback):
-    ...
-    ...     def on_init_start(self, trainer):
-    ...         print('Starting to init trainer!')
-    ...
-    ...     def on_init_end(self, trainer):
-    ...         print('trainer is init now')
-    ...
-    ...     def on_train_end(self, trainer, pl_module):
-    ...         print('do something when training ends')
-    ...
-    >>> trainer = pl.Trainer(callbacks=[MyPrintingCallback()])
+.. testcode::
+
+    class MyPrintingCallback(Callback):
+
+        def on_init_start(self, trainer):
+            print('Starting to init trainer!')
+
+        def on_init_end(self, trainer):
+            print('trainer is init now')
+
+        def on_train_end(self, trainer, pl_module):
+            print('do something when training ends')
+
+    trainer = Trainer(callbacks=[MyPrintingCallback()])
+
+.. testoutput::
+
     Starting to init trainer!
     trainer is init now
 

docs/source/child_modules.rst

@@ -1,3 +1,22 @@
+.. testsetup:: *
+
+    import torch
+    from pytorch_lightning.trainer.trainer import Trainer
+    from pytorch_lightning.callbacks.base import Callback
+    from pytorch_lightning.core.lightning import LightningModule
+
+    class LitMNIST(LightningModule):
+
+        def __init__(self):
+            super().__init__()
+
+        def train_dataloader():
+            pass
+
+        def val_dataloader():
+            pass
+
+
 Child Modules
 -------------
 Research projects tend to test different approaches to the same dataset.
@@ -7,13 +26,18 @@ For example, imagine we now want to train an Autoencoder to use as a feature ext
 Recall that `LitMNIST` already defines all the dataloading etc... The only things
 that change in the `Autoencoder` model are the init, forward, training, validation and test step.
 
-.. code-block:: python
+.. testcode::
 
     class Encoder(torch.nn.Module):
-        ...
+        pass
+
+    class Decoder(torch.nn.Module):
+        pass
 
     class AutoEncoder(LitMNIST):
+
         def __init__(self):
+            super().__init__()
             self.encoder = Encoder()
             self.decoder = Decoder()
 
@@ -30,10 +54,10 @@ that change in the `Autoencoder` model are the init, forward, training, validati
             return loss
 
         def validation_step(self, batch, batch_idx):
-            return self._shared_eval(batch, batch_idx, 'val'):
+            return self._shared_eval(batch, batch_idx, 'val')
 
         def test_step(self, batch, batch_idx):
-            return self._shared_eval(batch, batch_idx, 'test'):
+            return self._shared_eval(batch, batch_idx, 'test')
 
         def _shared_eval(self, batch, batch_idx, prefix):
             x, y = batch
@@ -43,6 +67,7 @@ that change in the `Autoencoder` model are the init, forward, training, validati
             loss = F.nll_loss(logits, y)
             return {f'{prefix}_loss': loss}
 
+
 and we can train this using the same trainer
 
 .. code-block:: python
@@ -58,5 +83,3 @@ In this case, we want to use the `AutoEncoder` to extract image representations
 
     some_images = torch.Tensor(32, 1, 28, 28)
     representations = autoencoder(some_images)
-
-..

docs/source/conf.py

@@ -307,7 +307,7 @@ def setup(app):
 # https://stackoverflow.com/questions/15889621/sphinx-how-to-exclude-imports-in-automodule
 
 MOCK_REQUIRE_PACKAGES = []
-with open(os.path.join(PATH_ROOT, 'requirements.txt'), 'r') as fp:
+with open(os.path.join(PATH_ROOT, 'requirements-extra.txt'), 'r') as fp:
     for ln in fp.readlines():
         found = [ln.index(ch) for ch in list(',=<>#') if ch in ln]
         pkg = ln[:min(found)] if found else ln
@@ -316,19 +316,10 @@ def setup(app):
 
 # TODO: better parse from package since the import name and package name may differ
 MOCK_MANUAL_PACKAGES = [
-    'torch',
     'torchvision',
     'PIL',
-    'test_tube',
-    'mlflow',
-    'comet_ml',
-    'wandb',
-    'neptune',
-    'trains',
 ]
 autodoc_mock_imports = MOCK_REQUIRE_PACKAGES + MOCK_MANUAL_PACKAGES
-# for mod_name in MOCK_REQUIRE_PACKAGES:
-#     sys.modules[mod_name] = mock.Mock()
 
 
 # Options for the linkcode extension
@@ -403,3 +394,13 @@ def find_source():
 #  Useful for avoiding ambiguity when the same section heading appears in different documents.
 # http://www.sphinx-doc.org/en/master/usage/extensions/autosectionlabel.html
 autosectionlabel_prefix_document = True
+
+# only run doctests marked with a ".. doctest::" directive
+doctest_test_doctest_blocks = ''
+doctest_global_setup = """
+
+import os
+import torch
+
+"""
+coverage_skip_undoc_in_source = True

docs/source/debugging.rst

@@ -1,3 +1,7 @@
+.. testsetup:: *
+
+    from pytorch_lightning.trainer.trainer import Trainer
+
 Debugging
 =========
 The following are flags that make debugging much easier.
@@ -11,9 +15,9 @@ a full epoch to crash.
 (See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.fast_dev_run`
 argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
-.. code-block:: python
+.. testcode::
 
-    trainer = pl.Trainer(fast_dev_run=True)
+    trainer = Trainer(fast_dev_run=True)
 
 Inspect gradient norms
 ----------------------
@@ -22,10 +26,10 @@ Logs (to a logger), the norm of each weight matrix.
 (See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.track_grad_norm`
 argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
-.. code-block:: python
+.. testcode::
 
     # the 2-norm
-    trainer = pl.Trainer(track_grad_norm=2)
+    trainer = Trainer(track_grad_norm=2)
 
 Log GPU usage
 -------------
@@ -34,9 +38,9 @@ Logs (to a logger) the GPU usage for each GPU on the master machine.
 (See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.log_gpu_memory`
 argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
-.. code-block:: python
+.. testcode::
 
-    trainer = pl.Trainer(log_gpu_memory=True)
+    trainer = Trainer(log_gpu_memory=True)
 
 Make model overfit on subset of data
 ------------------------------------
@@ -47,9 +51,9 @@ and try to get your model to overfit. If it can't, it's a sign it won't work wit
 (See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.overfit_pct`
 argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
-.. code-block:: python
+.. testcode::
 
-    trainer = pl.Trainer(overfit_pct=0.01)
+    trainer = Trainer(overfit_pct=0.01)
 
 Print the parameter count by layer
 ----------------------------------
@@ -59,9 +63,9 @@ To disable this behavior, turn off this flag:
 (See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.weights_summary`
 argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
-.. code-block:: python
+.. testcode::
 
-    trainer = pl.Trainer(weights_summary=None)
+    trainer = Trainer(weights_summary=None)
 
 
 Set the number of validation sanity steps
@@ -72,7 +76,7 @@ This avoids crashing in the validation loop sometime deep into a lengthy trainin
 (See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.num_sanity_val_steps`
 argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
-.. code-block:: python
+.. testcode::
 
     # DEFAULT
     trainer = Trainer(num_sanity_val_steps=5)

docs/source/early_stopping.rst

@@ -1,3 +1,9 @@
+.. testsetup:: *
+
+    from pytorch_lightning.trainer.trainer import Trainer
+    from pytorch_lightning.callbacks.early_stopping import EarlyStopping
+
+
 Early stopping
 ==============
 
@@ -17,23 +23,25 @@ Enable Early Stopping using Callbacks on epoch end
 --------------------------------------------------
 There are two ways to enable early stopping using callbacks on epoch end.
 
-.. doctest::
+-   Set early_stop_callback to True. Will look for 'val_loss' in validation_epoch_end() return dict.
+    If it is not found an error is raised.
+
+    .. testcode::
+
+        trainer = Trainer(early_stop_callback=True)
+
+-   Or configure your own callback
 
-    >>> from pytorch_lightning import Trainer
-    >>> from pytorch_lightning.callbacks import EarlyStopping
+    .. testcode::
 
-    # A) Set early_stop_callback to True. Will look for 'val_loss'
-    # in validation_epoch_end() return dict. If it is not found an error is raised.
-    >>> trainer = Trainer(early_stop_callback=True)
-    # B) Or configure your own callback
-    >>> early_stop_callback = EarlyStopping(
-    ...    monitor='val_loss',
-    ...    min_delta=0.00,
-    ...    patience=3,
-    ...    verbose=False,
-    ...    mode='min'
-    ... )
-    >>> trainer = Trainer(early_stop_callback=early_stop_callback)
+        early_stop_callback = EarlyStopping(
+            monitor='val_loss',
+            min_delta=0.00,
+            patience=3,
+            verbose=False,
+            mode='min'
+        )
+        trainer = Trainer(early_stop_callback=early_stop_callback)
 
 In any case, the callback will fall back to the training metrics (returned in
 :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`,
@@ -43,7 +51,8 @@ looking for a key to monitor if validation is disabled or
 is not defined.
 
 .. seealso::
-    :class:`~pytorch_lightning.trainer.trainer.Trainer`
+    - :class:`~pytorch_lightning.trainer.trainer.Trainer`
+    - :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping`
 
 Disable Early Stopping with callbacks on epoch end
 --------------------------------------------------
@@ -53,4 +62,5 @@ Note that ``None`` will not disable early stopping but will lead to the
 default behaviour.
 
 .. seealso::
-    :class:`~pytorch_lightning.trainer.trainer.Trainer`
+    - :class:`~pytorch_lightning.trainer.trainer.Trainer`
+    - :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping`