SW Architecture Enhancements
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roadmap
SW Architecture
- Dominant language
- Python
- Stars
- 14.7k
- Forks
- 2.8k
- Avg merge
- 2d 23h
- Merged PRs (30d)
- 489
Description
- torch.compile based graph compiler backend
- Merging trition-inference-server/tensorrtllm_backend into nvidia/tensorrt-llm
- Architecture unification of sampling logics in PyTorch backend, details can be referred here.
- Ray integration
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
No files, tests, or entry points are named. Start by reading the four proposed enhancements and the linked issue #3539, then inspect the existing PyTorch backend and integration boundaries for the compiler, TensorRT-LLM merger, sampling logic, and Ray work. Done would require a defined scope and implementation plan for these architectural changes, but this issue does not specify acceptance criteria.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- ai, distributed-systems, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100