Fix callback metrics for non-Tensor types

.circleci/config.yml

@@ -10,8 +10,8 @@ references:
     run:
       name: Install Dependences
       command: |
-        # PyPI
         pip install "$TORCH_VERSION" --user
+        # this is temporal fix til test-tube is not merged and released
         pip install -r requirements.txt --user
         sudo pip install pytest pytest-cov pytest-flake8
         pip install -r ./tests/requirements.txt --user
@@ -22,9 +22,27 @@ references:
      command: |
        python --version ; pip --version ; pip list
        py.test pytorch_lightning tests pl_examples -v --doctest-modules --junitxml=test-reports/pytest_junit.xml --flake8
+     no_output_timeout: 15m
+
+  make_docs: &make_docs
+   run:
+     name: Make Documentation
+     command: |
+       # sudo apt-get install pandoc
+       pip install -r requirements.txt --user
+       sudo pip install -r docs/requirements.txt
+       # sphinx-apidoc -o ./docs/source ./pytorch_lightning **/test_* --force --follow-links
+       cd docs; make clean ; make html
 
 jobs:
 
+  Build-Docs:
+    docker:
+      - image: circleci/python:3.7
+    steps:
+      - checkout
+      - *make_docs
+
   PyTorch:
     docker:
       - image: circleci/python:3.7
@@ -66,6 +84,7 @@ workflows:
   version: 2
   build:
     jobs:
+      - Build-Docs
       - PyTorch-v1.1
       - PyTorch-v1.2
       - PyTorch-v1.3

.github/ISSUE_TEMPLATE/bug_report.md

@@ -11,26 +11,32 @@ assignees: ''
 1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/williamFalcon/pytorch-lightning/issues/79).    
 2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/williamFalcon/pytorch-lightning#faq)    
 
-**Describe the bug**
+### Describe the bug
 A clear and concise description of what the bug is.
 
-**To Reproduce**
+#### To Reproduce
 Steps to reproduce the behavior:
 1. Go to '...'
-2. Click on '....'
+2. Run '....'
 3. Scroll down to '....'
 4. See error
 
-**Expected behavior**
+#### Code sample
+Ideally attach a minimal code sample to reproduce the decried issue. 
+[Minimal means having the shortest code but still preserving the bug]
+
+#### Expected behavior
 A clear and concise description of what you expected to happen.
 
-**Screenshots**
+#### Screenshots
 If applicable, add screenshots to help explain your problem.
 
-**Desktop (please complete the following information):**
- - OS: [e.g. iOS]
- - Browser [e.g. chrome, safari]
- - Version [e.g. 22]
+### Environment information
+
+Desktop (please complete the following information):
+ - OS: [e.g. iOS, Linux, Win]
+ - Packaging [e.g. pip, conda]
+ - Version [e.g. 0.5.2.1]
 
-**Additional context**
+### Additional context
 Add any other context about the problem here.

.github/ISSUE_TEMPLATE/typos-and-doc-fixes.md

@@ -7,11 +7,12 @@ assignees: ''
 
 ---
 
+## Typos and docs fixes
+
 For typos and doc fixes, please go ahead and:
 
 1. Create an issue.
 2. Fix the typo.   
 3. Submit a PR.
 
-
 Thanks!

.readthedocs.yml

@@ -5,9 +5,13 @@
 # Required
 version: 2
 
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/source/conf.py
+
 # Build documentation with MkDocs
-mkdocs:
-  configuration: mkdocs.yml
+#mkdocs:
+#  configuration: mkdocs.yml
 
 # Optionally build your docs in additional formats such as PDF and ePub
 formats: all
@@ -16,4 +20,5 @@ formats: all
 python:
   version: 3.7
   install:
-    - requirements: docs/requirements.txt
+    #- requirements: requirements.txt
+    - requirements: docs/requirements.txt

.travis.yml

@@ -70,8 +70,14 @@ before_script:
 script:
   # integration
   - tox --sitepackages
-  - pip install --editable .
+
   #- python setup.py install --dry-run --user
+  - virtualenv vEnv ;
+    source vEnv/bin/activate
+  - pip install --editable . ;
+    cd .. & python -c "import pytorch_lightning ; print(pytorch_lightning.__version__)"
+  - deactivate ;
+    rm -rf vEnv
 
 after_success:
   - coverage report

README.md

@@ -1,6 +1,6 @@
 <div align="center">
 
-![Logo](./docs/source/_static/lightning_logo_small.png)
+![Logo](docs/source/_static/images/lightning_logo_small.png)
 
 # PyTorch Lightning
 
@@ -11,11 +11,11 @@
 [![PyPI Status](https://pepy.tech/badge/pytorch-lightning)](https://pepy.tech/project/pytorch-lightning)
 [![Build Status](https://travis-ci.org/williamFalcon/pytorch-lightning.svg?branch=master)](https://travis-ci.org/williamFalcon/pytorch-lightning)
 [![Build status](https://ci.appveyor.com/api/projects/status/NEW-PROJECT-ID?svg=true)](https://ci.appveyor.com/project/williamFalcon/pytorch-lightning)
-[![Coverage](https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/coverage.svg)](https://github.com/williamFalcon/pytorch-lightning/tree/master/tests#running-coverage)
+[![Coverage](docs/source/_static/images/coverage.svg)](https://github.com/williamFalcon/pytorch-lightning/tree/master/tests#running-coverage)
 [![CodeFactor](https://www.codefactor.io/repository/github/borda/pytorch-lightning/badge)](https://www.codefactor.io/repository/github/borda/pytorch-lightning)    
 
 [![ReadTheDocs](https://readthedocs.org/projects/pytorch-lightning/badge/?version=latest)](https://pytorch-lightning.readthedocs.io/en/latest)
-[![Gitter](https://badges.gitter.im/PyTorch-Lightning/community.svg)](https://gitter.im/PyTorch-Lightning/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pytorch-lightning/shared_invite/enQtODU5ODIyNTUzODQwLTFkMDg5Mzc1MDBmNjEzMDgxOTVmYTdhYjA1MDdmODUyOTg2OGQ1ZWZkYTQzODhhNzdhZDA3YmNhMDhlMDY4YzQ)
 [![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/williamFalcon/pytorch-lightning/blob/master/LICENSE)
 [![Next Release](https://img.shields.io/badge/Next%20Release-Dec%206-<COLOR>.svg)](https://shields.io/)
 
@@ -96,15 +96,15 @@ To use lightning do 2 things:
         def forward(self, x):
             return torch.relu(self.l1(x.view(x.size(0), -1)))
 
-        def training_step(self, batch, batch_nb):
+        def training_step(self, batch, batch_idx):
             # 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):
+        def validation_step(self, batch, batch_idx):
             # OPTIONAL
             x, y = batch
             y_hat = self.forward(x)
@@ -154,16 +154,16 @@ use something other than tensorboard).
 Here are more advanced examples
 ```python   
 # train on cpu using only 10% of the data (for demo purposes)
-trainer = Trainer(max_nb_epochs=1, train_percent_check=0.1)
+trainer = Trainer(max_epochs=1, train_percent_check=0.1)
 
 # train on 4 gpus (lightning chooses GPUs for you)
-# trainer = Trainer(max_nb_epochs=1, gpus=4, distributed_backend='ddp')  
+# trainer = Trainer(max_epochs=1, gpus=4, distributed_backend='ddp')  
 
 # train on 4 gpus (you choose GPUs)
-# trainer = Trainer(max_nb_epochs=1, gpus=[0, 1, 3, 7], distributed_backend='ddp')   
+# trainer = Trainer(max_epochs=1, gpus=[0, 1, 3, 7], distributed_backend='ddp')   
 
 # train on 32 gpus across 4 nodes (make sure to submit appropriate SLURM job)
-# trainer = Trainer(max_nb_epochs=1, gpus=8, nb_gpu_nodes=4, distributed_backend='ddp')
+# trainer = Trainer(max_epochs=1, gpus=8, num_gpu_nodes=4, distributed_backend='ddp')
 
 # train (1 epoch only here for demo)
 trainer.fit(model)
@@ -183,14 +183,14 @@ trainer.test()
 Everything in gray!    
 You define the blue parts using the LightningModule interface:  
 
-![Overview](./docs/source/_static/overview_flat.jpg)
+![Overview](docs/source/_static/images/overview_flat.jpg)
 
 ```python
 # what to do in the training loop
-def training_step(self, batch, batch_nb):
+def training_step(self, batch, batch_idx):
 
 # what to do in the validation loop
-def validation_step(self, batch, batch_nb):
+def validation_step(self, batch, batch_idx):
 
 # how to aggregate validation_step outputs
 def validation_end(self, outputs):
@@ -205,7 +205,7 @@ def test_dataloader():
 
 ```python
 # define what happens for training here
-def training_step(self, batch, batch_nb):
+def training_step(self, batch, batch_idx):
     x, y = batch
 
     # define your own forward and loss calculation
@@ -232,7 +232,7 @@ def training_step(self, batch, batch_nb):
 
 ```python
 # define what happens for validation here
-def validation_step(self, batch, batch_nb):    
+def validation_step(self, batch, batch_idx):    
     x, y = batch
 
     # or as basic as a CNN classification
@@ -266,11 +266,11 @@ def validation_end(self, outputs):
 ## Tensorboard    
 Lightning is fully integrated with tensorboard, MLFlow and supports any logging module.   
 
-![tensorboard-support](./docs/source/_static/tf_loss.png)
+![tensorboard-support](docs/source/_static/images/tf_loss.png)
 
 Lightning also adds a text column with all the hyperparameters for this experiment.      
 
-![tensorboard-support](./docs/source/_static/tf_tags.png)
+![tensorboard-support](docs/source/_static/images/tf_tags.png)
 
 ## Lightning automates all of the following ([each is also configurable](https://williamfalcon.github.io/pytorch-lightning/Trainer/)):