deepspeedai / deepspeedai/DeepSpeed
Support configuration for general devices and backends.
Open
Nobody has claimed this yet.
enhancement
- Dominant language
- Python
- Stars
- 43.1k
- Forks
- 5k
- Avg merge
- 4d 15h
- Merged PRs (30d)
- 112
Description
DeepSpeed is currently assumes NVIDIA GPUs using the NCCL backend. It would be nice to support more general configurations.
Non-exhaustive list of things to consider:
- Configuration mechanisms (e.g., JSON config file,
deepspeed.initialize()) - Data movement
- Resource querying and specification: we currently query the number of local GPUs and would need to add additional capabilities for CPUs, etc.
- Documentation: often assumes GPUs and would need to be revised to be more general (e.g., https://github.com/microsoft/DeepSpeed#resource-configuration-multi-node)
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
Begin with the deepspeed.initialize() configuration mechanism and the README's resource configuration and multi-node documentation linked in the issue. Map the existing assumptions about NVIDIA GPUs and NCCL across configuration, data movement, and resource querying. Done would require a defined approach for general devices and backends, implementation across those areas, and revised documentation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- distributed-systems, documentation, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100