mlcommons / mlcommons/algorithmic-efficiency
Add tests for scoring code
Open
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 425
- Forks
- 78
- PR merge metrics
- No merged PRs in 30d
Description
Add unit and integration tests to test the following requirements:
In both strict=False and strict=True, to receive a finite score for a workload a submission must:
- Reach the validation target on the fixed workload within the maximum runtime.
- Reach the validation target fixed workload within 4x of the fastest submission.
In strict=True, to receive a finite score for a workload a submission must:
- Reach the validation target for at least 3/5 studies (the median).
- Take the best over 5 trials per study.
- Reach the validation target on the held-out workload (corresponding to the fixed workload) within the maximum runtime.
- Reach the validation target on the held-out workload (corresponding to the fixed workload) within 4x of the fastest submission.
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 or existing tests; start by locating the scoring implementation and its current unit and integration test coverage. Add tests for both strict modes covering runtime, fastest-submission, study-median, trial-selection, and held-out-workload requirements, then run the relevant test suite to verify finite-score behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- testing-qa
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100