Test adoption of the new Python Extension API

Testing adoption of the new Python Extension API for discovering Python Enviornmnents

Refs: #7583

• MacOS @IanMatthewHuff
• anyOS @rebornix
• Windows @amunger
• WSL @roblourens

Complexity: 5

Authors: @DonJayamanne

Create Issue

---

Pre-Test

• Keep track of the kernels displayed in the kernel picker

Requirements

• Install pre-release Jupyter extension
• Install pre-release Python extension
• Install VS Code Insiders

Scenarios to test

• Verify the same kernels (kernel specs and Python environments) are still displayed in the kernle picker

• New Virtual environments are detected and displayed in the Kernel picker Note: You need to have already executed a cell in some notebook Then create a virtual environment within VS Code I.e. the goal is to test detection of new virtual envs after the extension has been used for a while

  • Verify you are prompted to install IPyKernel into this Python enviornment when running a Notebook cell against this kernel
  • Verify you are prompted to install IPyKernel into this Python enviornment when running an Interactive Window cell such a new Python Environment (either re-create a new env or uninstall IPyKernel)
  • Verify you are prompted to install Pandas data frame into this Python enviornment when using the Data Frame viewer To test this, create a few variables, at least one with a list e.g. a = [1,2,3] and use the variable viewer to open the variable a

• New Conda environments are detected and displayed in the Kernle picker Note: You need to have already executed a cell in some notebook Then create a conda environment within VS Code I.e. the goal is to test detection of new virtual envs after the extension has been used for a while

  • Verify you are prompted to install IPyKernel into this Python enviornment when running a Notebook cell against this kernel
  • Verify you are prompted to install IPyKernel into this Python enviornment when running an Interactive Window cell such a new Python Environment (either re-create a new env or uninstall IPyKernel)
  • Verify you are prompted to install Pandas data frame into this Python enviornment when using the Data Frame viewer To test this, create a few variables, at least one with a list e.g. a = [1,2,3] and use the variable viewer to open the variable a

• Re-load VS Code and verify you can pick a Kernel Spec before Python environments I’m hoping some machines are slow enough and we can see Kernel Specifications before Python environments in the kernel picker. In the past we regressed in this space and Jupyter extension waited for ALL python environnents to be loaded before displaying Kernel Specs on disc. We’re verifyhing the fact that some kernels will appear before others

• Verify local kernel specs appera before Remote Kernel specs Connect to a local Jupyter server after having started a jupyter server using jupyter notebook --no-browser --NotebookApp.allow_origin=* In the past we regressed in this space and Jupyter extension waited for ALL python environnents to be loaded before displaying Kernel Specs on disc. We’re verifyhing the fact that some kernels will appear before others

• Verify debugging works and uses the right Python environment

• Test other parts of the extension that you believe could be impacted by adopting the new Python Extension API

[IanMatthewHuff]

How do I do this? I’ve only ever used the conda terminal to work directly with conda.

Python did add a new Python Create Environment Command, I’m testing both that and creating one manually.

[DonJayamanne]

Does this just mean I should see environments loaded gradually, rather than none until they are all available?

If you are lucky to have a slow machine, then yo’ll see environments load gradually. Its unlikely you’ll have none, as the Python Extension API returns the active interpreter pretty quickly, hence you’ll most likely see at least one python env in the list.

Re-load VS Code and verify you can pick a Kernel Spec before Python environments

Sorry, I should have been more specific. I shoudl have stated that you can pick kernel specs before ALL python envrionmetns are loaded. I.e. over time you should see more & more python envs getting added to the list. However kernel specs should be loaded very quickly in comparison.