mlcommons / mlcommons/algorithmic-efficiency
Clean up train diff tests
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Good First Issue
- Dominant language
- Python
- Stars
- 425
- Forks
- 78
- PR merge metrics
- No merged PRs in 30d
Description
The train diff tests are difficult to run at the moment.
- Add documentation on how to run them
- Eliminate IO errors related to writing temporary results to files
Train diff test: https://github.com/mlcommons/algorithmic-efficiency/blob/main/tests/test_traindiffs.py
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 with tests/test_traindiffs.py and determine how the train diff tests are currently run and where temporary results are written. Document the run instructions and eliminate the reported IO errors related to temporary result files. Done means the documented tests can be run without those IO errors.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, testing-qa
- Issue type
- Refactor
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100