Use `numba` to accelerate numerical CPU code
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enhancement
- Dominant language
- Python
- Stars
- 2.5k
- Forks
- 320
- Avg merge
- 1d 14h
- Merged PRs (30d)
- 133
Description
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- I have searched the Inference issues and found no similar feature requests.
Description
Hi all, I was wondering if there's a possibility to use numba to accelerate CPU code in this repository? With minor refactors, we can see massive performance boosts by adding numba's @jit decorator. Hope to hear your thoughts. Best,
Use case
No response
Additional
No response
Are you willing to submit a PR?
- Yes I'd like to help by submitting a PR!
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
The issue names no files, tests, or entry points; begin by locating the numerical CPU code and identifying which paths could be evaluated with numba. A contribution would need an agreed scope, minor refactors, and evidence of the expected CPU performance improvement.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100