pymc-devs / pymc-devs/pytensor
Move more "experimental" backends to its own library
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- Dominant language
- Python
- Stars
- 644
- Forks
- 208
- Avg merge
- 2d 14h
- Merged PRs (30d)
- 16
Description
Description
We have some limited support for PyTorch and coming up MLX, which are at a stage where they don't yet integrate well enough with PyTensor in general.
I suggest we create a separate package pytensor-backends for these. That way development stays more focused and we don't need to test for every PR to main. User experience would still be similar, except when they do pytensor.function(..., mode="PYTORCH") we try to import the pytensor-backends library and if missing tell user to go about installing it (together with the backend library).
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at pytensor.function(..., mode="PYTORCH") and trace how that mode loads its backend; then map the existing PyTorch support and planned MLX support before deciding package boundaries. Done means experimental backends live in pytensor-backends, missing dependencies produce an installation message, and they no longer need testing on every PR to main.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- backend, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100