Use pytorch as backend for xarrays
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- Dominant language
- Python
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Description
I would be interested in using pytorch as a backend for xarrays - because:
a) pytorch is very similar to numpy - so the conceptual overhead is small
b) [most helpful] enable having a GPU as the underlying hardware for compute - which would provide non-trivial speed up
c) it would allow seamless integration with deep-learning algorithms and techniques
Any thoughts on what the interest for such a feature might be ? I would be open to implementing parts of it - so any suggestions on where I could start ?
Thanks
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
No files, tests, or entry points are named. Start by reviewing xarray's existing backend architecture and NumPy integration, then determine how PyTorch tensors and GPU execution should be supported. Done requires an agreed feature scope and an implementation and testing plan for a PyTorch backend.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python, pytorch
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100