meta-pytorch / meta-pytorch/data
fault tolerant training with dataloader
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.3k
- Forks
- 179
- Avg merge
- 6d 1h
- Merged PRs (30d)
- 2
Description
🚀 The feature, motivation and pitch
When training with a dataloader, we might stop training in the middle of the run, and start it again later. Then, we usually want to start when we finished last time. Currently, I need to go over all of the data I went over in the previous run, which can be very slow. It would be nice to change it.
Alternatives
If we could save the state of the dataloader, and then load it, or if we could have a skipping mode that returns dummy batches until we get to the step we stopped in the previous run, it will probably lead to much faster results, when continuing a run that was stopped.
Additional context
No response
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
No files, tests, or entry points are named in the issue. Start by locating the dataloader state and training-resume paths, then determine how progress is represented. Done means an interrupted run can resume without revisiting already-consumed data, with coverage for saving/loading state or the proposed skipping behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100