Merge branch 'master' into clean_docs

.gitignore

@@ -6,6 +6,7 @@ app/models/
 pip-wheel-metadata/
 lightning_logs/
 
+
 # Test-tube
 test_tube_logs/
 test_tube_data/
@@ -14,6 +15,7 @@ test_tube_exp/
 # Documentations
 docs/source/pl_examples*.rst
 docs/source/pytorch_lightning*.rst
+tests/tests/
 /docs/source/*.md
 
 # Byte-compiled / optimized / DLL files
@@ -22,6 +24,7 @@ __pycache__/
 *$py.class
 timit_data/
 
+
 # C extensions
 *.so
 

docs/source/callbacks.rst

@@ -0,0 +1,14 @@
+.. role:: hidden
+    :class: hidden-section
+
+Callbacks
+===========
+.. automodule:: pytorch_lightning.callbacks
+   :exclude-members:
+        _del_model,
+        _save_model,
+        on_epoch_end,
+        on_train_end,
+        on_epoch_begin,
+        check_monitor_top_k,
+        on_train_begin,

docs/source/common-cases.rst

@@ -0,0 +1,21 @@
+Multi-gpu (same node) training
+==============================
+
+Multi-node training
+====================
+
+16-bit precision
+=================
+
+gradient clipping
+=================
+
+modifying training via hooks
+=============================
+
+
+
+.. toctree::
+   :maxdepth: 3
+
+   pl_examples

docs/source/index.rst

@@ -44,22 +44,20 @@ PyTorch-Lightning Documentation
 
    common-cases
 
-
 .. toctree::
    :maxdepth: 1
    :name: community
    :caption: Community
 
+
    CODE_OF_CONDUCT.md
    CONTRIBUTING.md
    BECOMING_A_CORE_CONTRIBUTOR.md
    governance.md
 
-
 Indices and tables
 ------------------
 
 * :ref:`genindex`
 * :ref:`modindex`
 * :ref:`search`
-

docs/source/lightning-module.rst

@@ -0,0 +1,10 @@
+.. role:: hidden
+    :class: hidden-section
+
+LightningModule
+===========
+.. automodule:: pytorch_lightning.core
+   :exclude-members:
+        _abc_impl,
+        summarize,
+

docs/source/logging.rst

@@ -0,0 +1,12 @@
+.. role:: hidden
+    :class: hidden-section
+
+Logging
+===========
+.. automodule:: pytorch_lightning.logging
+   :exclude-members:
+        _abc_impl,
+        _save_model,
+        on_epoch_end,
+        on_train_end,
+        on_epoch_begin,

docs/source/modules.rst

@@ -0,0 +1,7 @@
+pl_examples
+===========
+
+.. toctree::
+   :maxdepth: 4
+
+   pl_examples

docs/source/trainer.rst

@@ -0,0 +1,21 @@
+.. role:: hidden
+    :class: hidden-section
+
+Trainer
+===========
+.. automodule:: pytorch_lightning.trainer
+   :members: fit, test
+   :exclude-members: 
+        run_pretrain_routine, 
+        _abc_impl, 
+        _Trainer__set_root_gpu, 
+        _Trainer__init_optimizers,
+        _Trainer__parse_gpu_ids,
+        _Trainer__configure_schedulers,
+        data_parallel,
+        num_gpus,
+        slurm_job_id,
+        tng_tqdm_dic,
+        training_tqdm_dict,
+        init_optimizers,
+        configure_schedulers

docs/source/tutorials.rst

@@ -0,0 +1,20 @@
+Refactoring PyTorch into Lightning
+==================================
+`Tutorial <https://towardsdatascience.com/how-to-refactor-your-pytorch-code-to-get-these-42-benefits-of-pytorch-lighting-6fdd0dc97538>`_
+
+Start a research project
+=========================
+`Research seed <https://github.com/PytorchLightning/pytorch-lightning-conference-seed>`_
+
+Basic Lightning use
+====================
+`Tutorial <https://towardsdatascience.com/supercharge-your-ai-research-with-pytorch-lightning-337948a99eec>`_
+
+9 key Lightning  tricks
+========================
+`Tutorial <9 key speed features in Pytorch-Lightning>`_
+
+Multi-node training on SLURM
+=============================
+`Tutorial <https://towardsdatascience.com/trivial-multi-node-training-with-pytorch-lightning-ff75dfb809bd>`_
+

pytorch_lightning/callbacks/pt_callbacks.py

@@ -1,15 +1,13 @@
 """
 Callbacks
-=========
+====================================
-
 Callbacks supported by Lightning
 """
 
-import logging
 import os
 import shutil
+import logging
 import warnings
-
 import numpy as np
 
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
@@ -165,7 +163,9 @@ class EarlyStopping(Callback):
 
 
 class ModelCheckpoint(Callback):
-    r"""Save the model after every epoch.
+    r"""
+
+    Save the model after every epoch.
 
     Args:
         filepath (str): path to save the model file.

pytorch_lightning/core/lightning.py

@@ -5,15 +5,16 @@ import warnings
 from abc import ABC, abstractmethod
 from argparse import Namespace
 
+
 import pandas as pd
 import torch
 import torch.distributed as dist
-
+#
 from pytorch_lightning.core.decorators import data_loader
 from pytorch_lightning.core.grads import GradInformation
 from pytorch_lightning.core.hooks import ModelHooks
-from pytorch_lightning.core.memory import ModelSummary
 from pytorch_lightning.core.saving import ModelIO
+from pytorch_lightning.core.memory import ModelSummary
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
 
 
@@ -679,7 +680,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
     @abstractmethod
     def configure_optimizers(self):
         r"""
-
         This is where you choose what optimizers and learning-rate schedulers to use in your optimization.
         Normally you'd need one. But in the case of GANs or something more esoteric you might have multiple.
 
@@ -1015,7 +1015,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
             map_location (dict): A dictionary mapping saved weight GPU devices to new
                 GPU devices (example: {'cuda:1':'cuda:0'})
-
         Return:
             LightningModule with loaded weights
 
@@ -1136,7 +1135,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
     def freeze(self):
         r"""
-
         Freeze all params for inference
 
         Example
@@ -1168,7 +1166,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
     def on_load_checkpoint(self, checkpoint):
         r"""
-
         Called by lightning to restore your model.
         If you saved something with **on_save_checkpoint** this is your chance to restore this.
 

pytorch_lightning/logging/__init__.py

@@ -71,7 +71,6 @@ Call the logger anywhere from your LightningModule by doing:
 Supported Loggers
 -----------------
 """
-
 from os import environ
 
 from .base import LightningLoggerBase, rank_zero_only

pytorch_lightning/trainer/__init__.py

@@ -26,5 +26,4 @@ This is the basic use of the trainer:
 """
 
 from .trainer import Trainer
-
 __all__ = ['Trainer']

pytorch_lightning/trainer/trainer.py

@@ -1,9 +1,8 @@
-
-
-import logging
 import os
 import sys
 import warnings
+import logging
+
 
 import torch
 import torch.distributed as dist
@@ -20,6 +19,7 @@ from pytorch_lightning.trainer.distrib_parts import (
     parse_gpu_ids,
     determine_root_gpu_device
 )
+
 from pytorch_lightning.trainer.evaluation_loop import TrainerEvaluationLoopMixin
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
@@ -850,7 +850,6 @@ class Trainer(TrainerIOMixin,
             # run test from a loaded model
             model = LightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')
             trainer = Trainer()
-
             trainer.test(model)
         """
         self.testing = True

pytorch_lightning/trainer/training_io.py

@@ -98,7 +98,6 @@ from abc import ABC
 from subprocess import call
 from argparse import Namespace
 
-import pandas as pd
 import torch
 import torch.distributed as dist