mlcommons / mlcommons/modelbench
Why was nvidia-llama-3-1-nemotron-nano-8b-v1 run so slow?
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Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 134
- Forks
- 36
- Avg merge
- 1d 11h
- Merged PRs (30d)
- 17
Description
This run took more than 6 hours, but it only needed to run two items:
sut_cache: DiskCache(run/sut_cache)
sut_cache: started with 11997
sut_cache: finished with 11997
annotator_cache: DiskCache(run/annotator_cache)
annotator_cache: started with 47340
annotator_cache: finished with 47318
And according to the cache info, it did slightly less than nothing:
sut_cache: DiskCache(run/sut_cache)
sut_cache: started with 11997
sut_cache: finished with 11997
annotator_cache: DiskCache(run/annotator_cache)
annotator_cache: started with 47340
annotator_cache: finished with 47318
More signs that something about the cache isn't right.
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 by reproducing the nvidia-llama-3-1-nemotron-nano-8b-v1 run and inspect the run/sut_cache and run/annotator_cache entries and counts. Trace why the run exceeds six hours and why annotator_cache decreases from 47340 to 47318; done means the cache behavior and runtime cause are explained and the issue has a verified fix.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100