improve pytorch tutorial for Data Parallelism
Open
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 9.3k
- Forks
- 4.4k
- Avg merge
- 1d 21h
- Merged PRs (30d)
- 4
Description
in this tutorial for data parallel (link)
it can be useful if you can add how to handle loss function for the case that we are using multiple gpus.
usually naive way will cause unbalance gpu memory usage
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 with the linked Data Parallel tutorial at pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html and review how it currently handles the loss function across multiple GPUs. Add guidance for avoiding unbalanced GPU memory usage, then verify that the tutorial clearly explains the multi-GPU loss-handling case and its expected outcome.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100