meta-pytorch / meta-pytorch/data
[Nodes] Ensure get_worker_info works correctly in ParallelMapper
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.3k
- Forks
- 179
- Avg merge
- 6d 1h
- Merged PRs (30d)
- 2
Description
🚀 The feature
Support for torch.utils.data.IterableDataset
Motivation, pitch
Currently IterableDataset (and possibly some map datasets) rely on torch.utils.data.get_worker_info. To ensure drop-in compatibility, we should make sure this works correctly for ParallelMapper by setting the environment up correctly. We could do this directly in ParallelMapper, or in a new IterableDatasetWrapper class.
Alternatives
No response
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
Start by tracing ParallelMapper and the behavior of torch.utils.data.get_worker_info for IterableDataset and map-style datasets. Determine how worker context should be established for drop-in compatibility, then validate that datasets using get_worker_info work correctly through ParallelMapper.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100