[Delegate Testing] Determine tolerance / numerical accuracy validation strategy
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- Dominant language
- Python
- Stars
- 5k
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- Avg merge
- 2d 10h
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Description
We need to figure out how to best test numerical accuracy. Delegates can do many things, like run models in reduced precision (fp16, commonly) or quantize. We want to avoid hitting false positives, but we do want to ensure that we catch correctness issues.
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
No files, tests, or entry points are named. Start by examining the delegate testing workflow and define validation criteria for reduced-precision and quantized models that avoid false positives while still detecting correctness issues.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100