Guidance on Implementing CPU Binding for UNet3D (PyTorch) with MPI
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- Dominant language
- Python
- Stars
- 203
- Forks
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- Avg merge
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- Merged PRs (30d)
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Description
I recently joined the MLCommons group and have been looking into UNet3D's performance. It was mentioned during our discussions that UNet3D does not fully utilize bandwidth, potentially due to being the only model implemented in PyTorch.
To address this, I understand we may need to implement CPU binding via MPI. I wanted to reach out to ask:
- Has anyone in the group already explored or implemented CPU binding for UNet3D?
- Are there recommended practices or existing examples that I could refer to?
- Should this be done within the training script or as part of the MPI launch configuration?
Any guidance, documentation, or pointers would be greatly appreciated!
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 locating the UNet3D training script and the MPI launch configuration, then compare how CPU binding is handled in any existing examples. The issue is complete when the project has a decided implementation location and documented guidance or an example for running UNet3D with CPU binding.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100