pytorch / pytorch/executorch

Establish a process for monitoring and resolving CoreML GH issues, including an SLA

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triaged
Dominant language
Python
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5k
Forks
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Avg merge
2d 10h
Merged PRs (30d)
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Description

🚀 The feature, motivation and pitch

Needed by GA release

Alternatives

No response

Additional context

No response

RFC (Optional)

No response

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

No files, tests, or entry points are named. Start by reviewing the repository’s existing CoreML ownership and GitHub issue workflows, then define where the monitoring process and SLA should live. Done means the process identifies responsible owners, response targets, and how unresolved issues are tracked.

Written by the indexing model from the issue text.

Assessment

Tech stack
github
Domain
devtools, machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
15/100

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