resolving documentation warnings

.github/workflows/greetings.yml

@@ -10,5 +10,5 @@ jobs:
     - uses: actions/first-interaction@v1
       with:
         repo-token: ${{ secrets.GITHUB_TOKEN }}
-        issue-message: 'Hey, thanks for your contribution! Great first issue!'
-        pr-message: 'Hey, thanks for the input! Please give us a bit of time to review it!'
+        issue-message: 'Hi! thanks for your contribution!, great first issue!'
+        pr-message: 'Hey thanks for the input! Please give us a bit of time to review it!'

.pep8speaks.yml

@@ -5,7 +5,7 @@ scanner:
     linter: pycodestyle  # Other option is flake8
 
 pycodestyle:  # Same as scanner.linter value. Other option is flake8
-    max-line-length: 120  # Default is 79 in PEP 8
+    max-line-length: 100  # Default is 79 in PEP 8
     ignore:  # Errors and warnings to ignore
         - W504  # line break after binary operator
         - E402  # module level import not at top of file

docs/source/callbacks.rst

@@ -4,11 +4,12 @@
 Callbacks
 =========
 .. automodule:: pytorch_lightning.callbacks
+   :noindex:
    :exclude-members:
         _del_model,
         _save_model,
         on_epoch_end,
         on_train_end,
         on_epoch_start,
         check_monitor_top_k,
-        on_train_start,
+        on_train_start,

docs/source/checkpointing.rst

@@ -71,7 +71,7 @@ If you want to pick up training from where you left off, you have a few options.
    trainer = Trainer(logger=logger)
    trainer.fit(model)
 
-2. A second option is to pass in a path to a checkpoint (see: :ref:`pytorch_lightning.trainer`).
+2. A second option is to pass in a path to a checkpoint (see: :ref:`pytorch_lightning.trainer.trainer.Trainer`).
 
 .. code-block:: python
 

docs/source/conf.py

@@ -355,10 +355,10 @@ def find_source():
 autodoc_default_options = {
     'members': None,
     'special-members': '__call__',
-    'undoc-members': True,
     # 'exclude-members': '__weakref__',
     'show-inheritance': True,
     'private-members': True,
+    'noindex': True,
 }
 
 # Sphinx will add “permalinks” for each heading and description environment as paragraph signs that

docs/source/index.rst

@@ -69,6 +69,7 @@ PyTorch-Lightning Documentation
    CODE_OF_CONDUCT.md
    CONTRIBUTING.md
    BECOMING_A_CORE_CONTRIBUTOR.md
+   PULL_REQUEST_TEMPLATE.md
    governance.md
 
 Indices and tables

docs/source/lightning-module.rst

@@ -5,6 +5,7 @@ LightningModule
 ===============
 
 .. automodule:: pytorch_lightning.core
+   :noindex:
    :exclude-members:
         _abc_impl,
         summarize,

docs/source/loggers.rst

@@ -4,6 +4,7 @@
 Loggers
 ===========
 .. automodule:: pytorch_lightning.loggers
+   :noindex:
    :exclude-members:
         _abc_impl,
         _save_model,

docs/source/optimizers.rst

@@ -53,9 +53,9 @@ Lightning will call each optimizer sequentially:
 
 
 Step optimizers at arbitrary intervals
--------------------------------------
+----------------------------------------
 To do more interesting things with your optimizers such as learning rate warm-up or odd scheduling,
-override the :meth:`optimizer_step' function.
+override the :meth:`optimizer_step` function.
 
 For example, here step optimizer A every 2 batches and optimizer B every 4 batches
 
@@ -96,4 +96,4 @@ Here we add a learning-rate warm up
 
         # update params
         optimizer.step()
-        optimizer.zero_grad()
+        optimizer.zero_grad()

docs/source/profiler.rst

@@ -3,8 +3,9 @@
 
 
 Performance and Bottleneck Profiler
-===========
+===================================
 .. automodule:: pytorch_lightning.profiler
+   :noindex:
    :exclude-members:
         _abc_impl,
         summarize,

docs/source/trainer.rst

@@ -6,6 +6,7 @@ Trainer
 
 .. automodule:: pytorch_lightning.trainer
    :members: fit, test
+   :noindex:
    :exclude-members: 
         run_pretrain_routine, 
         _abc_impl, 

pl_examples/__init__.py

@@ -3,8 +3,8 @@
 -------------------------
 
 In 99% of cases you want to just copy `one of the examples
- <https://github.com/PyTorchLightning/pytorch-lightning/tree/master/pl_examples>`_
- to start a new lightningModule and change the core of what your model is actually trying to do.
+<https://github.com/PyTorchLightning/pytorch-lightning/tree/master/pl_examples>`_
+to start a new lightningModule and change the core of what your model is actually trying to do.
 
 .. code-block:: bash
 

pl_examples/basic_examples/lightning_module_template.py

@@ -15,10 +15,11 @@
 from torch.utils.data.distributed import DistributedSampler
 from torchvision.datasets import MNIST
 
-import pytorch_lightning as pl
+from pytorch_lightning.core import LightningModule
+from pytorch_lightning.core import data_loader
 
 
-class LightningTemplateModel(pl.LightningModule):
+class LightningTemplateModel(LightningModule):
     """
     Sample model to show how to define a template
     """

pl_examples/domain_templates/gan.py

@@ -19,7 +19,9 @@
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
 
-import pytorch_lightning as pl
+from pytorch_lightning.core import LightningModule
+from pytorch_lightning.core import data_loader
+from pytorch_lightning.trainer import Trainer
 
 
 class Generator(nn.Module):
@@ -69,7 +71,7 @@ def forward(self, img):
         return validity
 
 
-class GAN(pl.LightningModule):
+class GAN(LightningModule):
 
     def __init__(self, hparams):
         super(GAN, self).__init__()
@@ -165,7 +167,7 @@ def configure_optimizers(self):
         opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
         return [opt_g, opt_d], []
 
-    @pl.data_loader
+    @data_loader
     def train_dataloader(self):
         transform = transforms.Compose([transforms.ToTensor(),
                                         transforms.Normalize([0.5], [0.5])])
@@ -193,7 +195,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = pl.Trainer()
+    trainer = Trainer()
 
     # ------------------------
     # 3 START TRAINING

pl_examples/full_examples/imagenet/imagenet_example.py

@@ -19,6 +19,8 @@
 import torchvision.transforms as transforms
 
 import pytorch_lightning as pl
+from pytorch_lightning.core import LightningModule
+from pytorch_lightning.core import data_loader
 
 # pull out resnet names from torchvision models
 MODEL_NAMES = sorted(
@@ -27,9 +29,11 @@
 )
 
 
-class ImageNetLightningModel(pl.LightningModule):
-
+class ImageNetLightningModel(LightningModule):
     def __init__(self, hparams):
+        """
+        TODO: add docstring here
+        """
         super(ImageNetLightningModel, self).__init__()
         self.hparams = hparams
         self.model = models.__dict__[self.hparams.arch](pretrained=self.hparams.pretrained)
@@ -128,7 +132,7 @@ def configure_optimizers(self):
         scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.1)
         return [optimizer], [scheduler]
 
-    @pl.data_loader
+    @data_loader
     def train_dataloader(self):
         normalize = transforms.Normalize(
             mean=[0.485, 0.456, 0.406],
@@ -159,7 +163,7 @@ def train_dataloader(self):
         )
         return train_loader
 
-    @pl.data_loader
+    @data_loader
     def val_dataloader(self):
         normalize = transforms.Normalize(
             mean=[0.485, 0.456, 0.406],

pytorch_lightning/core/lightning.py

@@ -365,7 +365,6 @@ def validation_step(self, batch, batch_idx, dataset_idx):
 
     def test_step(self, *args, **kwargs):
         """return whatever outputs will need to be aggregated in test_end
-
         :param batch: The output of your dataloader. A tensor, tuple or list
         :param int batch_idx: Integer displaying which batch this is
         :param int dataloader_idx: Integer displaying which dataloader this is (only if multiple test datasets used)
@@ -381,11 +380,13 @@ def test_step(self, batch, batch_idx, dataloader_idxdx)
 
 
         **OPTIONAL**
-        If you don't need to test you don't need to implement this method. In this step you'd normally
-         generate examples or calculate anything of interest such as accuracy.
+        If you don't need to test you don't need to implement this method.
+        In this step you'd normally generate examples or
+        calculate anything of interest such as accuracy.
 
-        When the validation_step is called, the model has been put in eval mode and PyTorch gradients
-         have been disabled. At the end of validation, model goes back to training mode and gradients are enabled.
+        When the validation_step is called, the model has been put in eval mode
+        and PyTorch gradients have been disabled.
+        At the end of validation, model goes back to training mode and gradients are enabled.
 
         The dict you return here will be available in the `test_end` method.
 
@@ -578,7 +579,7 @@ def configure_ddp(self, model, device_ids):
         3. On a testing batch, the call goes to model.test_step
 
         Args:
-            model (LightningModule): the LightningModule currently being optimized
+            model (:class:`.LightningModule`): the LightningModule currently being optimized
             device_ids (list): the list of GPU ids
 
         Return:
@@ -692,7 +693,7 @@ def configure_apex(self, amp, model, optimizers, amp_level):
 
         Args:
             amp (object): pointer to amp library object
-            model (LightningModule): pointer to current lightningModule
+            model (:class:`.LightningModule`): pointer to current lightningModule
             optimizers (list): list of optimizers passed in configure_optimizers()
             amp_level (str): AMP mode chosen ('O1', 'O2', etc...)
 
@@ -1087,7 +1088,6 @@ def val_dataloader(self):
     @classmethod
     def load_from_metrics(cls, weights_path, tags_csv, map_location=None):
         r"""
-
         You should use `load_from_checkpoint` instead!
         However, if your .ckpt weights don't have the hyperparameters saved, use this method  to pass
         in a .csv with the hparams you'd like to use. These will  be converted  into a argparse.Namespace
@@ -1097,10 +1097,11 @@ def load_from_metrics(cls, weights_path, tags_csv, map_location=None):
 
             weights_path (str): Path to a PyTorch checkpoint
             tags_csv (str): Path to a .csv with two columns (key, value) as in this
-                Example::
-                    key,value
-                    drop_prob,0.2
-                    batch_size,32
+
+            Example::
+                key,value
+                drop_prob,0.2
+                batch_size,32
 
             map_location (dict | str | torch.device | function):
                 If your checkpoint saved a GPU model and you now load on CPUs
@@ -1163,7 +1164,7 @@ def load_from_checkpoint(cls, checkpoint_path, map_location=None):
 
             model = MyModel(hparams)
 
-            class MyModel(pl.LightningModule):
+            class MyModel(LightningModule):
                 def __init__(self, hparams):
                     self.learning_rate = hparams.learning_rate
 
@@ -1172,7 +1173,7 @@ def __init__(self, hparams):
             # when using a dict
             model = MyModel({'learning_rate': 0.1})
 
-            class MyModel(pl.LightningModule):
+            class MyModel(LightningModule):
                 def __init__(self, hparams):
                     self.learning_rate = hparams['learning_rate']
 

pytorch_lightning/core/memory.py

@@ -277,15 +277,17 @@ def get_human_readable_count(number):
     """
     Abbreviates an integer number with K, M, B, T for thousands, millions,
     billions and trillions, respectively.
+
     Examples:
         123     -> 123
         1234    -> 1 K       (one thousand)
         2e6     -> 2 M       (two million)
         3e9     -> 3 B       (three billion)
         4e12    -> 4 T       (four trillion)
         5e15    -> 5,000 T
+
     :param number: a positive integer number
-    :returns a string formatted according to the pattern described above.
+    :return: a string formatted according to the pattern described above.
     """
     assert number >= 0
     labels = [' ', 'K', 'M', 'B', 'T']

pytorch_lightning/loggers/mlflow.py

@@ -1,5 +1,5 @@
 """
-Log using `mlflow <https://mlflow.org>'_
+Log using `mlflow <https://mlflow.org>`_
 
 .. code-block:: python
 

pytorch_lightning/loggers/neptune.py

@@ -103,8 +103,10 @@ def any_lightning_module_function_or_hook(...):
                Must be list of str or single str. Uploaded sources are displayed in the experiment’s Source code tab.
                If None is passed, Python file from which experiment was created will be uploaded.
                Pass empty list ([]) to upload no files. Unix style pathname pattern expansion is supported.
-               For example, you can pass '*.py' to upload all python source files from the current directory.
-               For recursion lookup use '**/*.py' (for Python 3.5 and later). For more information see glob library.
+               For example, you can pass '\*.py'
+                to upload all python source files from the current directory.
+               For recursion lookup use '\**/\*.py' (for Python 3.5 and later).
+               For more information see glob library.
             params (dict|None): Optional. Parameters of the experiment. After experiment creation params are read-only.
                Parameters are displayed in the experiment’s Parameters section and each key-value pair can be
                viewed in experiments view as a column.

pytorch_lightning/trainer/distrib_parts.py

@@ -269,14 +269,14 @@
 -------------------------
 
 Instead of manually building SLURM scripts, you can use the
- `SlurmCluster object <https://williamfalcon.github.io/test-tube/hpc/SlurmCluster>`_
- to do this for you. The SlurmCluster can also run a grid search if you pass
- in a `HyperOptArgumentParser
-  <https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser>`_.
+`SlurmCluster object <https://williamfalcon.github.io/test-tube/hpc/SlurmCluster>`_
+to do this for you. The SlurmCluster can also run a grid search if you pass
+in a `HyperOptArgumentParser
+<https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser>`_.
 
 Here is an example where you run a grid search of 9 combinations of hyperparams.
- The full examples are `here
- <https://github.com/PyTorchLightning/pytorch-lightning/tree/master/pl_examples/new_project_templates/multi_node_examples>`_.
+The full examples are
+`here <https://git.io/Jv87p>`_.
 
 .. code-block:: python
 

pytorch_lightning/trainer/trainer.py

@@ -153,8 +153,9 @@ def __init__(
 
                     trainer = Trainer(checkpoint_callback=checkpoint_callback)
 
-            early_stop_callback: Callback for early stopping. If
-                set to ``True``, then the default callback monitoring ``'val_loss'`` is created.
+            early_stop_callback (:class:`pytorch_lightning.callbacks.EarlyStopping`):
+                Callback for early stopping.
+                If set to ``True``, then the default callback monitoring ``'val_loss'`` is created.
                 Will raise an error if ``'val_loss'`` is not found.
                 If set to ``False``, then early stopping will be disabled.
                 If set to ``None``, then the default callback monitoring ``'val_loss'`` is created.
@@ -1168,7 +1169,7 @@ def test(self, model: Optional[LightningModule] = None):
         Separates from fit to make sure you never run on your test set until you want to.
 
         Args:
-            model: The model to test.
+            model (:class:`.LightningModule`): The model to test.
 
         Example::
 

pytorch_lightning/trainer/training_loop.py

@@ -114,7 +114,7 @@
 
 When using PackedSequence, do 2 things:
 1. return either a padded tensor in dataset or a list of variable length tensors
- in the dataloader collate_fn (example above shows the list implementation).
+in the dataloader collate_fn (example above shows the list implementation).
 2. Pack the sequence in forward or training and validation steps depending on use case.
 
 .. code-block:: python