Add PR \ Issue templates (#742)

* Add PR \ Issue templates

* Add pipe decorator get step's task example

Co-authored-by: Erez Schnaider <erez@clear.ml>

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,29 @@
+---
+name: Bug report
+about: Create a bug report to help us improve
+labels: bug
+assignees: ''
+
+---
+
+Thank you for helping us making ClearML better!
+
+
+## Describe the bug
+A clear and concise description of what the bug is.
+
+
+## To reproduce
+Exact steps to reproduce the bug. Provide example code if possible.
+
+## Expected behaviour
+What is the expected behaviour? What should've happened but didn't?
+
+## Environment
+* Server type (self hosted \ app.clear.ml)
+* ClearML SDK Version
+* ClearML Server Version (Only for self hosted). Can be found on the bottom right corner of the settings screen.
+* Python Version
+* OS (Windows \ Linux \ Macos)
+## Related Discussion
+If this continues a slack thread, please provide a link to the original slack thread. 

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,16 @@
+---
+name: Feature Request
+about: Suggest an idea for ClearML
+title: ''
+assignees: ''
+
+---
+
+## Proposal Summary
+Explain your proposed feature.
+
+## Motivation
+Explain the use case that needs this feature
+
+## Related Discussion
+If this continues a slack thread, please provide a link to the original slack thread. 

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,11 @@
+## Related Issue \ discussion
+If this patch originated from a github issue or slack conversation, please paste a link to the original discussion<br/>
+
+## Patch Description
+Description of what does the patch do. If the patch solves a bug, explain what the bug is and how to reproduce it 
+(If not already mentioned in the original conversation)<br/>
+
+## Testing Instructions
+Instructions of how to test the patch or reproduce the issue the patch is aiming to solve
+
+## Other Information

examples/pipeline/pipeline_from_decorator.py

@@ -52,9 +52,14 @@ def step_three(data):
     # make sure we have pandas for this step, we need it to use the data_frame
     import pandas as pd  # noqa
     from sklearn.linear_model import LogisticRegression
+    from clearml import Task
     X_train, X_test, y_train, y_test = data
     model = LogisticRegression(solver='liblinear', multi_class='auto')
     model.fit(X_train, y_train)
+    score = model.score(X_test,y_test)
+    # Get current step's Task
+    task = Task.current_task()
+    task.get_logger().report_single_value(name='accuracy',value=score)
     return model