Supporting multiple GPU models
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Description
Should supporting running the embedding models on multiple-GPUs be prioritized? Here are the pros/cons as I see it (not necessarily equally weighted in terms of importance):
## Pros
- Allows users to take advantage of multiple GPUs for faster running time
## Cons
- Adds an extra parameter to most API calls, though this can be optional
- Adds meat to the codebase (though we already have it)
- Can we test this on Travis?
All in all, I think that if we believe that using multiple GPUs will be a common use case, then we should include it. But if it's something that will be rarely used, if at all, we shouldn't prioritize it (at least for an MVP).
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- Read the whole issue, then the project's contributing guide.
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- Fork the repository and make your change on a branch.
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Research direction
This issue is a prioritization discussion rather than an implementation task, and it names no files, tests, or entry points. First establish whether multi-GPU embedding execution is in scope for the project and how it should be tested, then define the API and implementation boundaries before coding.
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Assessment
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100