The problem

Our existing use of caching Conda content in GitHub Actions workflows clashes with Conda's own caching mechanism. Packages are stored in the Conda package-cache, and the packages are then included in individual environments via hard-links. GHA caching does not parse these hard-links, but we cache both the Conda package-cache AND the environment directories, causing two problems:

• Each package is cached TWICE (once per directory). With recent changes to GitHub, our older caches are being wiped almost daily - every time we blow our space limit - so workflows running less frequently (e.g. benchmarks) store caches that are NEVER used before disappearing.
• The restored package-cache is never used - each workflow run is associated with ~500MB of pointless IO. This is because there is no connection between the restored package-cache and the restored environments. The only time the package-cache would be used is when downloading new packages, but we invalidate the GHA caches when the lock-files change.

My proposed solution

• Only cache the Conda package-cache. Conda will populate the environments from this cache automatically.
• Share the package cache between workflows of the same Python version. I.e. for the latest Python version this would be tests, wheels and benchmarks.

Pros and cons

Pros

• Reduced cache size - sharing a single copy of a package between multiple workflows.
• Reduced downloads - sharing a single copy of a package between multiple workflows.
• Reduced IO - no redundant content is getting cached/restored.

Cons

• Slight runtime increase - Conda must recreate the environments each time.
  This is largely mitigated:
  • Cached package-cache avoids needing to re-download
  • Lock-files avoid needing to re-resolve dependencies
  • GHA's file system is fast