vscode-jupyter VS pyenv

https://github.com/microsoft/vscode/issues/4490

It looks like there are a number of vscode extensions for recording macros:

- https://www.google.com/search?q=vscode+macro+recorder

- https://marketplace.visualstudio.com/search?term=Macro&targe...

- the macro-commander README explains its JSON-based macro language. YAML might be easier to maintain than JSON. https://github.com/jeff-hykin/macro-commander#what-are-some-...

For teams with multiple editors, you can specify workflow automation scripts with shell scripts or ci container/cmd YAML, and/or pre-commit.yml instead of with an IDE-specific tool.

Isn't there native real-time collaboration functionality in vscode/vscodium that would be useful for a native macro recording feature? (Edit) Live Share can't be installed in vscodium. https://github.com/VSCodium/vscodium/issues/128

Support for jupyter-collaboration Y.js CRDT could be added to vscode-jupyter and/or a more generic extension: "Support for real-time collaboration in the extension?" https://github.com/microsoft/vscode-jupyter/discussions/1293...

jupyterlab/jupyter-collaboration:

Check out this GitHub issue. Similar to your needs, the reply said

It looks like this. I resubmitted a new feature request.

I don't like the fact that Visual Studio Code thinks a notebook has changed, while Git knows it isn't. According to issue #9514, this is something that should be fixed in the core of Visual Studio Code. So, I'm not really sure why issue #83232 (or #24883) is closed.

I wish for a simple option in VS Code: On close of a Jupyter Notebook clear its output. Or something that separate the display of the output from the saved file (Still `ipnyb` file). See [1].

[1]: https://github.com/microsoft/vscode-jupyter/issues/9514

jupyter won't work on vs-code

If that doesn't fix it, can you log an issue here? https://github.com/microsoft/vscode-jupyter/issues

For more information visit: pyenv repository

Note that virtual environments assume you are using the same global version of Python. Often, this is not the case and additional tools like pyenv can be used alongside virtual environments when you need to switch between versions of Python itself on your local machine.

Python version manager pyenv

First of all, install the Python virtual environment from these links: 1 and 2. I developed my GPT-based API in Python version 3.8.18. Pick any Python versions >= 3.7.

Note: Most modern systems will probably have a system environment that meets this requirement, but if yours does not or if you prefer not to install anything in your system environment (even if it's just PDM) check out asdf or pyenv to help install and manage additional Python environments.

When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage you to learn more about venv, pyenv or conda for a better understanding on how to create and manage virtual environments.

For Python, with your use case I would avoid system packages, no matter the distro. It sounds like it would be worth setting up pyenv and working exclusively with virtual environments.

For OSX there is homebrew or pyenv (pyenv is another solution on Linux). As pyenv compiles from source it will require setting up XCode (the Apple IDE) tools to support this which can be pretty bulky. Windows users have chocolatey but the issue there is it works off the binaries. That means it won't have the latest security release available since those are source only. Conda is also another solution which can be picked up by Visual Studio Code as available versions of Python making development easier. In the end it might be best to consider using WSL on Windows for installing a Linux version and using that instead.