Upgrade to latest stable pytorch and torchvision
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- Dominant language
- Python
- Stars
- 2.1k
- Forks
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Description
- [ ] Bump packages for both cpu as well as gpu docker images.
- [ ] Re-calculate dependencies and lock dependency tree via pip-compile.
- [ ] Test end to end
Don't expect anything breaking for this version update; but we need to test end to end.
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 locating the CPU and GPU Docker image definitions and the dependency inputs used by pip-compile. Bump the PyTorch packages, recalculate and lock both dependency trees, then run the end-to-end tests; done means both images build and end-to-end testing passes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- docker, python, pytorch
- Domain
- devops, machine-learning
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100