/python_on_codespaces

Primary LanguagePython

Description

This is a quick demo of the use of codespaces in GitHub for workshop purposes.

all participants need a GH account ( can be free version ) this will give them access to the codespace feature on public repos

  • full and preconfigured VSCode , in browser or local
  • CPython or conda possible based on the underlying base image
  • Fast to starty ( Github hase a pool of preconfigured images, and very fast storage / network)
  • You do need to take care to prevent secret sharing ( .gitignore, template .env files, etc)
  • saving state will happen to codespaces, no branch or fork needed per se
  • but if you want permanent changes can be done by creating a fork
  • It is possible to create a deeplink to create a code-space
Account Plan Storage per Month Core Hours per Month
GitHub Free for personal accounts 15 GB-month 120

so with a 2-core machine you can run 60 hours of codespaces per month for free with a 4 core machine you can run 30 hour of codespaces per month for free

https://docs.github.com/en/billing/managing-billing-for-your-products/managing-billing-for-github-codespaces/about-billing-for-github-codespaces

Also see

This repository contains a collection of codespaces which can be used to demonstrate the feature at various levels, from a default codespace (i.e., with no configuration specified) to a complex one with full support for developing machine learning applications using Python.

Each of the following branches can be used to showcase a different type of codespace:

  • default: No codespace configuration. This can be used to illustrate what a barebones codespace looks like and how it can be used to bootstrap more complex codespaces or to simply create and modify files using a more powerful tool than the default GitHub editor.
  • simple: Basic, manually-crafted codespace which can be used to develop a simple Python application.
  • complex: Complex codespace containing a large collection of libraries, modules and tools which together form an environment where a machine learning application can be developed with little to no initial setup effort needed.

Start with the default branch, then proceed to simple, then finally complex. Each branch will contain a README.md with further instructions.