fix changelog (#1452)

* fix changelog

* formatting

* add ddp_cpu

* docs

* add another

CHANGELOG.md

@@ -9,15 +9,20 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Added
 
 - Added flag `replace_sampler_ddp` to manually disaple sampler replacement in ddp  ([#1513](https://github.com/PyTorchLightning/pytorch-lightning/pull/1513))
+
+- Added speed parity tests (max 1 sec difference per epoch)([#1482](https://github.com/PyTorchLightning/pytorch-lightning/pull/1482))
+
 - Added `auto_select_gpus` flag to trainer that enables automatic selection of available GPUs on exclusive mode systems.
 
 - Added learining rate finder ([#1347](https://github.com/PyTorchLightning/pytorch-lightning/pull/1347))
 
 - Added support for ddp mode in clusters without SLURM ([#1345](https://github.com/PyTorchLightning/pytorch-lightning/issues/1345))
 
-- Added learning rate finder ([#1347](https://github.com/PyTorchLightning/pytorch-lightning/pull/1347))
+- Added `test_dataloaders` parameter to `Trainer.test()` ([#1393](https://github.com/PyTorchLightning/pytorch-lightning/issues/1393))
 
-- Added `terminate_on_nan` flag to trainer that performs a NaN check with each training iteration when set to `True`. ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
+- Added `terminate_on_nan` flag to trainer that performs a NaN check with each training iteration when set to `True` ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
+
+- Added `ddp_cpu` backend for testing ddp without GPUs ([#1158](https://github.com/PyTorchLightning/pytorch-lightning/pull/1158))
 
 
 ### Changed
@@ -31,42 +36,38 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Deprecated
 
--
 
 
 ### Removed
 
--
+- Removed `test_dataloaders` parameter from `Trainer.fit()` ([#1393](https://github.com/PyTorchLightning/pytorch-lightning/issues/1393))
 
--
 
 ### Fixed
 
+- Fixed memory leak from opt return ([#1528](https://github.com/PyTorchLightning/pytorch-lightning/pull/1528))
 
 - Fixed saving checkpoint before deleting old ones ([#1453](https://github.com/PyTorchLightning/pytorch-lightning/pull/1453))
 
 - Fixed loggers - flushing last logged metrics even before continue, e.g. `trainer.test()` results ([#1459](https://github.com/PyTorchLightning/pytorch-lightning/pull/1459))
 
+- Fixed optimizer configuration when `configure_optimizers` returns dict without `lr_scheduler` ([#1443](https://github.com/PyTorchLightning/pytorch-lightning/pull/1443))
+
 - Fixed LightningModule - Mixing hparams and arguments in `LightningModule.__init__()` crashes load_from_checkpoint() ([#1505](https://github.com/PyTorchLightning/pytorch-lightning/pull/1505))
 
 - Added a missing call to the `on_before_zero_grad` model hook ([#1493](https://github.com/PyTorchLightning/pytorch-lightning/pull/1493)). 
 
 - Fixed a bug that caused the `callbacks` Trainer argument to reference a global variable ([#1534](https://github.com/PyTorchLightning/pytorch-lightning/pull/1534)).
 
+
 ## [0.7.3] - 2020-04-09
 
 ### Added
 
 - Added `rank_zero_warn` for warning only in rank 0 ([#1428](https://github.com/PyTorchLightning/pytorch-lightning/pull/1428))
-- Added `test_dataloaders` parameter to `Trainer.test()` ([#1393](https://github.com/PyTorchLightning/pytorch-lightning/issues/1393))
-
-### Changed
-
-- Removed `test_dataloaders` parameter from `Trainer.fit()` ([#1393](https://github.com/PyTorchLightning/pytorch-lightning/issues/1393))
 
 ### Fixed
 
-- Fixed optimizer configuration when `configure_optimizers` returns dict without `lr_scheduler` ([#1443](https://github.com/PyTorchLightning/pytorch-lightning/pull/1443))
 - Fixed default `DistributedSampler` for DDP training ([#1425](https://github.com/PyTorchLightning/pytorch-lightning/pull/1425))
 - Fixed workers warning not on windows ([#1430](https://github.com/PyTorchLightning/pytorch-lightning/pull/1430))
 - Fixed returning tuple from `run_training_batch` ([#1431](https://github.com/PyTorchLightning/pytorch-lightning/pull/1431))
@@ -77,7 +78,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
-- Added speed parity tests (max 1 sec difference per epoch)([#1482](https://github.com/PyTorchLightning/pytorch-lightning/pull/1482))
 - Added same step loggers' metrics aggregation ([#1278](https://github.com/PyTorchLightning/pytorch-lightning/pull/1278))
 - Added parity test between a vanilla MNIST model and lightning model ([#1284](https://github.com/PyTorchLightning/pytorch-lightning/pull/1284))
 - Added parity test between a vanilla RNN model and lightning model ([#1351](https://github.com/PyTorchLightning/pytorch-lightning/pull/1351))

docs/source/conf.py

@@ -227,7 +227,8 @@ man_pages = [
 #  dir menu entry, description, category)
 texinfo_documents = [
     (master_doc, project, project + ' Documentation', author, project,
-     'One line description of project.', 'Miscellaneous'),
+     'The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.',
+     'Miscellaneous'),
 ]
 
 # -- Options for Epub output -------------------------------------------------

pytorch_lightning/__init__.py

@@ -9,6 +9,27 @@ __homepage__ = 'https://github.com/PyTorchLightning/pytorch-lightning'
 # this has to be simple string, see: https://github.com/pypa/twine/issues/522
 __docs__ = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers." \
            " Scale your models. Write less boilerplate."
+__long_docs__ = """
+Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
+ It's more of a style-guide than a framework.
+
+In Lightning, you organize your code into 3 distinct categories:
+
+1. Research code (goes in the LightningModule).
+2. Engineering code (you delete, and is handled by the Trainer).
+3. Non-essential research code (logging, etc. this goes in Callbacks).
+
+Although your research/production project might start simple, once you add things like GPU AND TPU training,
+ 16-bit precision, etc, you end up spending more time engineering than researching.
+ Lightning automates AND rigorously tests those parts for you.
+
+Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
+
+Documentation
+-------------
+- https://pytorch-lightning.readthedocs.io/en/latest
+- https://pytorch-lightning.readthedocs.io/en/stable
+"""
 
 import logging as python_logging
 

setup.cfg

@@ -38,7 +38,6 @@ ignore =
 [check-manifest]
 ignore =
     *.yml
-    tox.ini
     .github
     .github/*
     .circleci

tests/collect_env_details.py

@@ -69,13 +69,13 @@ def info_packages():
 def nice_print(details, level=0):
     lines = []
     for k in sorted(details):
-        key = f'{k}:'
+        key = f'* {k}:' if level == 0 else f'- {k}:'
         if isinstance(details[k], dict):
             lines += [level * LEVEL_OFFSET + key]
             lines += nice_print(details[k], level + 1)
         elif isinstance(details[k], (set, list, tuple)):
             lines += [level * LEVEL_OFFSET + key]
-            lines += [(level + 1) * LEVEL_OFFSET + v for v in details[k]]
+            lines += [(level + 1) * LEVEL_OFFSET + '- ' + v for v in details[k]]
         else:
             template = '{:%is} {}' % KEY_PADDING
             key_val = template.format(key, details[k])
@@ -85,9 +85,9 @@ def nice_print(details, level=0):
 
 def main():
     details = {
-        "system": info_system(),
-        'cuda': info_cuda(),
-        'packages': info_packages(),
+        "System": info_system(),
+        'CUDA': info_cuda(),
+        'Packages': info_packages(),
     }
     lines = nice_print(details)
     text = os.linesep.join(lines)