Fix setup-doc for pypi (#472)

* add Twine to CI

* freeze Twine

* freeze Twine

* minor refactoring

* try another

* fix req.

* update README

* fix __doc__

* fix multiple req. test-tube

.travis.yml

@@ -16,16 +16,13 @@ language: python
 
 matrix:
   include:
-    - os: linux
-      dist: xenial  # Ubuntu 16.04
+#    - dist: xenial  # Ubuntu 16.04
+#      python: 3.5
+#      env: TOXENV=py35
+    - dist: bionic  # Ubuntu 18.04
       python: 3.6
       env: TOXENV=py36
-    - os: linux
-      dist: bionic  # Ubuntu 18.04
-      python: 3.6
-      env: TOXENV=py36
-    - os: linux
-      dist: bionic  # Ubuntu 18.04
+    - dist: bionic  # Ubuntu 18.04
       python: 3.7
       env: TOXENV=py37
     - os: osx
@@ -58,6 +55,7 @@ script:
   # integration
   - tox --sitepackages
   - pip install --editable .
+  #- python setup.py install --dry-run --user
 
 after_success:
   - coverage report

README.md

@@ -63,85 +63,85 @@ Lightning sets up all the boilerplate state-of-the-art training for you so you c
 ---
 
 ## How do I do use it?   
-Think about Lightning as refactoring your research code instead of using a new framework. The research code goes into a [LightningModule]((https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)) which you fit using a Trainer.    
+Think about Lightning as refactoring your research code instead of using a new framework. The research code goes into a [LightningModule](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/) which you fit using a Trainer.    
 
 The LightningModule defines a *system* such as seq-2-seq, GAN, etc... It can ALSO define a simple classifier such as the example below.     
 
 To use lightning do 2 things:  
-1. [Define a LightningModule](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)     
-
-**WARNING:** This syntax is for version 0.5.0+ where abbreviations were removed.    
-```python
-import os
-import torch
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torchvision.datasets import MNIST
-import torchvision.transforms as transforms
-
-import pytorch_lightning as pl
-
-class CoolSystem(pl.LightningModule):
-
-    def __init__(self):
-        super(CoolSystem, self).__init__()
-        # not the best model...
-        self.l1 = torch.nn.Linear(28 * 28, 10)
-
-    def forward(self, x):
-        return torch.relu(self.l1(x.view(x.size(0), -1)))
-
-    def training_step(self, batch, batch_nb):
-        # REQUIRED
-        x, y = batch
-        y_hat = self.forward(x)
-        loss = F.cross_entropy(y_hat, y)
-        tensorboard_logs = {'train_loss': loss}
-        return {'loss': loss, 'log': tensorboard_logs}
-
-    def validation_step(self, batch, batch_nb):
-        # OPTIONAL
-        x, y = batch
-        y_hat = self.forward(x)
-        return {'val_loss': F.cross_entropy(y_hat, y)}
-
-    def validation_end(self, outputs):
-        # OPTIONAL
-        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
-        tensorboard_logs = {'val_loss': avg_loss}
-        return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
-
-    def configure_optimizers(self):
-        # REQUIRED
-        # can return multiple optimizers and learning_rate schedulers
-        # (LBFGS it is automatically supported, no need for closure function)
-        return torch.optim.Adam(self.parameters(), lr=0.02)
-
-    @pl.data_loader
-    def train_dataloader(self):
-        # REQUIRED
-        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
-
-    @pl.data_loader
-    def val_dataloader(self):
-        # OPTIONAL
-        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
-
-    @pl.data_loader
-    def test_dataloader(self):
-        # OPTIONAL
-        return DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor()), batch_size=32)
-```
+1. [Define a LightningModule](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)
+**WARNING:** This syntax is for version 0.5.0+ where abbreviations were removed.
+    ```python
+    import os
+    
+    import torch
+    from torch.nn import functional as F
+    from torch.utils.data import DataLoader
+    from torchvision.datasets import MNIST
+    from torchvision import transforms
+    
+    import pytorch_lightning as pl
+    
+    class CoolSystem(pl.LightningModule):
+    
+        def __init__(self):
+            super(CoolSystem, self).__init__()
+            # not the best model...
+            self.l1 = torch.nn.Linear(28 * 28, 10)
+    
+        def forward(self, x):
+            return torch.relu(self.l1(x.view(x.size(0), -1)))
+    
+        def training_step(self, batch, batch_nb):
+            # REQUIRED
+            x, y = batch
+            y_hat = self.forward(x)
+            loss = F.cross_entropy(y_hat, y)
+            tensorboard_logs = {'train_loss': loss}
+            return {'loss': loss, 'log': tensorboard_logs}
+    
+        def validation_step(self, batch, batch_nb):
+            # OPTIONAL
+            x, y = batch
+            y_hat = self.forward(x)
+            return {'val_loss': F.cross_entropy(y_hat, y)}
+    
+        def validation_end(self, outputs):
+            # OPTIONAL
+            avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
+            tensorboard_logs = {'val_loss': avg_loss}
+            return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
+    
+        def configure_optimizers(self):
+            # REQUIRED
+            # can return multiple optimizers and learning_rate schedulers
+            # (LBFGS it is automatically supported, no need for closure function)
+            return torch.optim.Adam(self.parameters(), lr=0.02)
+    
+        @pl.data_loader
+        def train_dataloader(self):
+            # REQUIRED
+            return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
+    
+        @pl.data_loader
+        def val_dataloader(self):
+            # OPTIONAL
+            return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
+    
+        @pl.data_loader
+        def test_dataloader(self):
+            # OPTIONAL
+            return DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor()), batch_size=32)
+    ```
 2. Fit with a [trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)    
-```python
-from pytorch_lightning import Trainer
-
-model = CoolSystem()
-
-# most basic trainer, uses good defaults
-trainer = Trainer()    
-trainer.fit(model)   
-```     
+    ```python
+    from pytorch_lightning import Trainer
+    
+    model = CoolSystem()
+    
+    # most basic trainer, uses good defaults
+    trainer = Trainer()    
+    trainer.fit(model)   
+    ```
 
 Trainer sets up a tensorboard logger, early stopping and checkpointing by default (you can modify all of them or
 use something other than tensorboard).   
@@ -166,7 +166,7 @@ trainer.fit(model)
 # view tensorboard logs 
 logging.info(f'View tensorboard logs by running\ntensorboard --logdir {os.getcwd()}')
 logging.info('and going to http://localhost:6006 on your browser')
-```    
+```
 
 When you're all done you can even run the test set separately.   
 ```python
@@ -348,7 +348,8 @@ Lightning also adds a text column with all the hyperparameters for this experime
 - [9 key speed features in Pytorch-Lightning](https://towardsdatascience.com/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565)    
 - [SLURM, multi-node training with Lightning](https://towardsdatascience.com/trivial-multi-node-training-with-pytorch-lightning-ff75dfb809bd)     
 
----    
+---
+
 ## Asking for help    
 Welcome to the Lightning community!   
 

pytorch_lightning/__init__.py

@@ -5,10 +5,9 @@ __author__ = ' William Falcon et al.'
 __author_email__ = 'waf2107@columbia.edu'
 __license__ = 'Apache-2.0'
 __homepage__ = 'https://github.com/williamFalcon/pytorch-lightning'
-__docs__ = """# PyTorch Lightning
-
-The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.
-"""
+# 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."
 
 
 try:

requirements.txt

@@ -1,6 +1,5 @@
 scikit-learn==0.20.2
 tqdm==4.35.0
-twine==1.13.0
 numpy==1.16.4
 torch>=1.2.0
 torchvision>=0.3.0

setup.cfg

@@ -41,12 +41,13 @@ exclude_lines =
     break
     pass
     os.makedirs
+# TODO: to be reviewed, this should not be skipped
 omit =
     pytorch_lightning/callbacks/pt_callbacks.py
     tests/test_models.py
     pytorch_lightning/testing_models/lm_test_module.py
     pytorch_lightning/utilities/arg_parse.py
-    examples/templates
+    pl_examples/templates
 
 [flake8]
 # TODO: this should be 88 or 100 according PEP8
@@ -58,3 +59,15 @@ verbose = 2
 # https://pep8.readthedocs.io/en/latest/intro.html#error-codes
 format = pylint
 ignore = E731,W504,F401,F841
+
+[check-manifest]
+ignore =
+    .travis.yml
+    tox.ini
+    .github
+    .github/*
+
+[metadata]
+license_file = LICENSE
+# long_description = file:README.md
+# long_description_content_type = text/markdown

setup.py

@@ -46,15 +46,18 @@ setup(
     url=pytorch_lightning.__homepage__,
     download_url='https://github.com/williamFalcon/pytorch-lightning',
     license=pytorch_lightning.__license__,
-    packages=find_packages(exclude=['examples']),
-    # long_description=open('README.md', encoding='utf-8').read(),
-    # long_description_content_type='text/markdown',
+    packages=find_packages(exclude=['tests']),
+
+    long_description=open('README.md', encoding='utf-8').read(),
+    long_description_content_type='text/markdown',
     include_package_data=True,
     zip_safe=False,
+
     keywords=['deep learning', 'pytorch', 'AI'],
     python_requires='>=3.6',
     setup_requires=[],
     install_requires=load_requirements(PATH_ROOT),
+
     classifiers=[
         'Environment :: Console',
         'Natural Language :: English',

tests/requirements.txt

@@ -5,6 +5,7 @@ pytest>=3.0.5
 pytest-cov
 flake8
 check-manifest
-test_tube
+# test_tube  # already installed in main req.
 mlflow
 comet_ml
+twine==1.13.0

tox.ini

@@ -35,10 +35,12 @@ deps =
     -r ./tests/requirements.txt
 commands =
     pip list
-    check-manifest --ignore tox.ini
+    check-manifest
     python setup.py check --metadata --strict
     coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules
     flake8 .
+    python setup.py sdist
+    twine check dist/*
 
 # DROP, it is duplication of setup.cfg
 # [flake8]