When resuming an unwrapped checkpoint, LR is not updated
Nobody has claimed this yet.
Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 38/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start by tracing the checkpoint-resume entry point and how the optimizer learning rate is restored when an unwrapped checkpoint is loaded. Compare that behavior with the current model configuration and verify that resuming with a different LR uses the configured value; add or run a regression test covering two runs with different learning rates.
Written by the indexing model from the issue text.
Description
Even if the model config is changed, the LR is not updated but it continues from where it was when the checkpoint was saved. This is a bit tricky, since now I can't run different runs with different LRs.
- Dominant language
- Python
- Stars
- 3.9k
- Forks
- 490
- PR merge metrics
- No merged PRs in 30d
Getting set up
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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.
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