add autopep8 to Contributions guide (Lightning-AI#852)

* add autopep8 to Contrib.

* simplify cmd

* update GH templates

* add pytest-flake8

* update GH template

Author: Borda

.github/CONTRIBUTING.md

@@ -51,3 +51,7 @@ A lot of good work has already been done in project mechanics (requirements.txt,
 ## Coding Styleguide
 1. Test the code with flake8.
 2. Use f-strings.
+3. run locally PEP8 fixes:
+    ```
+    autopep8 -v -r --max-line-length 120 --in-place .
+    ```

.github/ISSUE_TEMPLATE/bug_report.md

@@ -2,14 +2,16 @@
 name: Bug report
 about: Create a report to help us improve
 title: ''
-labels: bug
+labels: bug, help wanted
 assignees: ''
 
 ---
 
+<!-- 
 ### Common bugs:
 1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).    
 2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq)    
+-->
 
 ## 🐛 Bug
 

.github/ISSUE_TEMPLATE/documentation.md

@@ -2,7 +2,7 @@
 name: Typos and doc fixes
 about: Typos and doc fixes
 title: ''
-labels: typo
+labels: typo, documentation
 assignees: ''
 
 ---

pytorch_lightning/loggers/comet.py

@@ -31,6 +31,7 @@ class CometLogger(LightningLoggerBase):
     r"""
     Log using `comet.ml <https://www.comet.ml>`_.
     """
+
     def __init__(self, api_key=None, save_dir=None, workspace=None,
                  rest_api_key=None, project_name=None, experiment_name=None, **kwargs):
         r"""

pytorch_lightning/loggers/neptune.py

@@ -27,6 +27,7 @@ class NeptuneLogger(LightningLoggerBase):
     Neptune logger can be used in the online mode or offline (silent) mode.
     To log experiment data in online mode, NeptuneLogger requries an API key:
     """
+
     def __init__(self, api_key=None, project_name=None, offline_mode=False,
                  experiment_name=None, upload_source_files=None,
                  params=None, properties=None, tags=None, **kwargs):

pytorch_lightning/trainer/trainer.py

@@ -862,7 +862,7 @@ def run_pretrain_routine(self, model):
 
         # check if we should run validation during training
         self.disable_validation = ((self.num_val_batches == 0 or
-                                   not self.is_overriden('validation_step')) and
+                                    not self.is_overriden('validation_step')) and
                                    not self.fast_dev_run)
 
         # run tiny validation (if validation defined)

tests/requirements.txt

@@ -4,6 +4,7 @@ coverage
 codecov
 pytest>=3.0.5
 pytest-cov
+pytest-flake8
 flake8
 check-manifest
 test-tube>=0.7.5