AMP Checkpointing
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- Dominant language
- Python
- Stars
- 9k
- Forks
- 1.5k
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 3
Description
Assuming it differs from normal pytorch usage, would it be possible to provide an example of the steps required to save and load model checkpoints with amp? Are there any specific considerations to take into account (especially when using DDP)?
Contributor guide
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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.
Research direction
Start by locating the repository's existing AMP and DDP examples or documentation; the issue names no specific files or entry points. Add a checkpointing example covering save and load steps, explain AMP-specific considerations for DDP, and verify that the guidance matches the supported usage.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- distributed-systems, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100