meta-pytorch / meta-pytorch/data
Roadmap for mixed chain of multithread and multiprocessing pipelines?
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.3k
- Forks
- 179
- Avg merge
- 6d 1h
- Merged PRs (30d)
- 2
Description
🚀 The feature
pypeln has a nice feature to chain pipelines which may run on different kind of workers including process, thread or asyncio.
data = (
range(10)
| pl.process.map(slow_add1, workers=3, maxsize=4)
| pl.thread.filter(slow_gt3, workers=2)
| pl.sync.map(lambda x: print x)
| list
)
I remembered that in the first proposal of pytorch/data, it claims to support something alike. I'd like to ask if it's still planed and the concrete roadmap.
Motivation, pitch
Initial proposed
Alternatives
No response
Additional context
No response
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 reading the linked pypeln mixed-pipelines example and the initial PyTorch Data proposal referenced in the issue. Compare the proposed support for process, thread, and asyncio workers with the current project scope. Done would mean an agreed, documented roadmap or decision about whether mixed pipelines are planned.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- data-engineering
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100