deepspeedai / deepspeedai/DeepSpeed
[REQUEST]Inference Optimized Pipeline Parallelism
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- Dominant language
- Python
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Description
As mentioned in the paper https://arxiv.org/abs/2207.00032, DeepSpeed inference supports pipeline parallelism, including hybrid scheduling, offloading activations, and communication, which have led to significant performance improvements.
However, does DeepSpeed currently support these features? If not, is there a timeline for when they will be supported?
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 cited paper and DeepSpeed's existing inference pipeline-parallelism entry points. Compare support for hybrid scheduling, activation offloading, and communication, then document which features are available and what remains; the issue is complete when their support status and implementation scope are recorded.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100