update Docs [links & formatting]

.run_local_tests.sh

@@ -2,11 +2,11 @@
 
 # use this to run tests
 rm -rf _ckpt_*
-rm -rf tests/save_dir*
-rm -rf tests/mlruns_*
-rm -rf tests/cometruns*
-rm -rf tests/wandb*
-rm -rf tests/tests/*
-rm -rf lightning_logs
+rm -rf ./tests/save_dir*
+rm -rf ./tests/mlruns_*
+rm -rf ./tests/cometruns*
+rm -rf ./tests/wandb*
+rm -rf ./tests/tests/*
+rm -rf ./lightning_logs
 coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules
 coverage report -m

docs/requirements.txt

@@ -4,7 +4,7 @@ m2r  # fails with multi-line text
 nbsphinx
 pandoc
 docutils
-git+https://github.com/PytorchLightning/lightning_sphinx_theme.git
 sphinxcontrib-fulltoc
 sphinxcontrib-mockautodoc
-pip_shims
+git+https://github.com/PytorchLightning/lightning_sphinx_theme.git
+# pip_shims

docs/source/conf.py

@@ -348,7 +348,11 @@ def find_source():
 
 autodoc_member_order = 'groupwise'
 autoclass_content = 'both'
-autodoc_default_flags = [
-    'members', 'undoc-members', 'show-inheritance', 'private-members',
-    # 'special-members', 'inherited-members'
-]
+autodoc_default_options = {
+    'members': True,
+    'special-members': '__call__',
+    'undoc-members': True,
+    # 'exclude-members': '__weakref__',
+    'show-inheritance': True,
+    'private-members': True,
+}

docs/source/index.rst

@@ -4,7 +4,7 @@
    contain the root `toctree` directive.
 
 PyTorch-Lightning Documentation
-=============================
+===============================
 
 .. toctree::
    :maxdepth: 1

docs/source/lightning-module.rst

@@ -2,7 +2,8 @@
     :class: hidden-section
 
 LightningModule
-===========
+===============
+
 .. automodule:: pytorch_lightning.core
    :exclude-members:
         _abc_impl,

docs/source/tutorials.rst

@@ -1,20 +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>`_
+----------------------------------
+`How to refactor your PyTorch code to get these 42 benefits of PyTorch-Lighting <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>`_
+-------------------
+`Supercharge your AI research with PyTorch-Lightning <https://towardsdatascience.com/supercharge-your-ai-research-with-pytorch-lightning-337948a99eec>`_
 
 9 key Lightning  tricks
-========================
-`Tutorial <https://towardsdatascience.com/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565>`_
+-----------------------
+`Tutorial on 9 key speed features in PyTorch-Lightning <9 key speed features in Pytorch-Lightning>`_
 
 Multi-node training on SLURM
-=============================
-`Tutorial <https://towardsdatascience.com/trivial-multi-node-training-with-pytorch-lightning-ff75dfb809bd>`_
+----------------------------
+`Trivial multi node training with PyTorch-Lightning <https://towardsdatascience.com/trivial-multi-node-training-with-pytorch-lightning-ff75dfb809bd>`_
 

pl_examples/__init__.py

@@ -43,6 +43,7 @@
 
 The main function is your entry into the program. This is where you init your model, checkpoint directory,
  and launch the training. The main function should have 3 arguments:
+
 - hparams: a configuration of hyperparameters.
 - slurm_manager: Slurm cluster manager object (can be None)
 - dict: for you to return any values you want (useful in meta-learning, otherwise set to)

pl_examples/basic_examples/cpu_template.py

@@ -7,8 +7,8 @@
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -28,7 +28,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer()
+    trainer = pl.Trainer()
 
     # ------------------------
     # 3 START TRAINING

pl_examples/basic_examples/gpu_template.py

@@ -7,8 +7,8 @@
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -28,7 +28,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer(
+    trainer = pl.Trainer(
         gpus=hparams.gpus,
         distributed_backend=hparams.distributed_backend,
         use_amp=hparams.use_16bit

pl_examples/basic_examples/lightning_module_template.py

@@ -16,10 +16,9 @@
 from torchvision.datasets import MNIST
 
 import pytorch_lightning as pl
-from pytorch_lightning.core.lightning import LightningModule
 
 
-class LightningTemplateModel(LightningModule):
+class LightningTemplateModel(pl.LightningModule):
     """
     Sample model to show how to define a template
     """

pl_examples/multi_node_examples/multi_node_ddp2_demo.py

@@ -7,8 +7,8 @@
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -29,7 +29,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer(
+    trainer = pl.Trainer(
         gpus=2,
         num_nodes=2,
         distributed_backend='ddp2'

pl_examples/multi_node_examples/multi_node_ddp_demo.py

@@ -7,8 +7,8 @@
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -29,7 +29,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer(
+    trainer = pl.Trainer(
         gpus=2,
         num_nodes=2,
         distributed_backend='ddp'

pytorch_lightning/__init__.py

@@ -1,6 +1,6 @@
 """Root package info."""
 
-__version__ = '0.6.0.dev'
+__version__ = '0.6.1.dev'
 __author__ = 'William Falcon et al.'
 __author_email__ = 'waf2107@columbia.edu'
 __license__ = 'Apache-2.0'
@@ -10,7 +10,6 @@
 __docs__ = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers." \
            " Scale your models. Write less boilerplate."
 
-
 try:
     # This variable is injected in the __builtins__ by the build
     # process. It used to enable importing subpackages of skimage when
@@ -28,12 +27,12 @@
     import logging as log
     log.basicConfig(level=log.INFO)
 
-    from .trainer.trainer import Trainer
-    from .core.lightning import LightningModule
-    from .core.decorators import data_loader
+    from .core import data_loader, LightningModule
+    from .trainer import Trainer
 
     __all__ = [
         'Trainer',
         'LightningModule',
         'data_loader',
     ]
+    # __call__ = __all__

pytorch_lightning/core/__init__.py

@@ -96,6 +96,9 @@ def test_dataloader(self):
 for a live demo.
 
 """
+
+from .decorators import data_loader
 from .lightning import LightningModule
 
-__all__ = ['LightningModule']
+__all__ = ['LightningModule', 'data_loader']
+# __call__ = __all__

pytorch_lightning/core/decorators.py

@@ -3,12 +3,11 @@
 
 
 def data_loader(fn):
-    """
-    Decorator to make any fx with this use the lazy property
+    """Decorator to make any fx with this use the lazy property.
+
     :param fn:
     :return:
     """
-
     wraps(fn)
     attr_name = '_lazy_' + fn.__name__
 

pytorch_lightning/core/lightning.py

@@ -18,6 +18,7 @@
 
 
 class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
+
     def __init__(self, *args, **kwargs):
         super(LightningModule, self).__init__(*args, **kwargs)
 
@@ -115,6 +116,7 @@ def training_step(self, *args, **kwargs):
         :param int batch_idx: Integer displaying which batch this is
         :return: dict with loss key and optional log, progress keys
          if implementing training_step, return whatever you need in that step:
+
             - loss -> tensor scalar [REQUIRED]
             - progress_bar -> Dict for progress bar display. Must have only tensors
             - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
@@ -1061,30 +1063,30 @@ def load_from_checkpoint(cls, checkpoint_path, map_location=None):
         it  stores  the hyperparameters in the checkpoint if you initialized your  LightningModule
         with an argument  called `hparams` which is a Namespace or dictionary of hyperparameters
 
-            Example
-            -------
-            .. code-block:: python
+        Example
+        -------
+        .. code-block:: python
 
-                # --------------
-                # Case 1
-                # when using Namespace (output of using Argparse to parse command line arguments)
-                from argparse import Namespace
-                hparams = Namespace(**{'learning_rate': 0.1})
+            # --------------
+            # Case 1
+            # when using Namespace (output of using Argparse to parse command line arguments)
+            from argparse import Namespace
+            hparams = Namespace(**{'learning_rate': 0.1})
 
-                model = MyModel(hparams)
+            model = MyModel(hparams)
 
-                class MyModel(pl.LightningModule):
-                    def __init__(self, hparams):
-                        self.learning_rate = hparams.learning_rate
+            class MyModel(pl.LightningModule):
+                def __init__(self, hparams):
+                    self.learning_rate = hparams.learning_rate
 
-                # --------------
-                # Case 2
-                # when using a dict
-                model = MyModel({'learning_rate': 0.1})
+            # --------------
+            # Case 2
+            # when using a dict
+            model = MyModel({'learning_rate': 0.1})
 
-                class MyModel(pl.LightningModule):
-                    def __init__(self, hparams):
-                        self.learning_rate = hparams['learning_rate']
+            class MyModel(pl.LightningModule):
+                def __init__(self, hparams):
+                    self.learning_rate = hparams['learning_rate']
 
         Args:
             checkpoint_path (str): Path to checkpoint.

pytorch_lightning/testing/model_base.py

@@ -16,7 +16,7 @@
     # TODO: this should be discussed and moved out of this package
     raise ImportError('Missing test-tube package.')
 
-from pytorch_lightning import data_loader
+from pytorch_lightning.core.decorators import data_loader
 from pytorch_lightning.core.lightning import LightningModule
 
 

pytorch_lightning/testing/model_mixins.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from pytorch_lightning import data_loader
+from pytorch_lightning.core.decorators import data_loader
 
 
 class LightningValidationStepMixin:

pytorch_lightning/trainer/__init__.py

@@ -26,4 +26,5 @@
 """
 
 from .trainer import Trainer
+
 __all__ = ['Trainer']

pytorch_lightning/trainer/trainer.py

@@ -49,6 +49,7 @@ class Trainer(TrainerIOMixin,
               TrainerTrainLoopMixin,
               TrainerCallbackConfigMixin,
               ):
+
     def __init__(
             self,
             logger=True,

setup.py

@@ -58,6 +58,12 @@ def load_requirements(path_dir=PATH_ROOT, comment_char='#'):
     setup_requires=[],
     install_requires=load_requirements(PATH_ROOT),
 
+    project_urls={
+        "Bug Tracker": "https://github.com/PyTorchLightning/pytorch-lightning/issues",
+        "Documentation": "https://pytorch-lightning.rtfd.io/en/latest/",
+        "Source Code": "https://github.com/PyTorchLightning/pytorch-lightning",
+    },
+
     classifiers=[
         'Environment :: Console',
         'Natural Language :: English',