[DOC] Discussion board `blog` about scalability of multi-GPU out-of-core K-means (single node)
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- Dominant language
- Cuda
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Description
@viclafargue to plan content but theme should be around demonstrating training single node multi-gpu K-means at extreme scale when the training data doesn't fit into the memory of the GPUs.
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 with the issue brief and confirm the blog or discussion-board location and content plan with @viclafargue. Define the demonstration around single-node multi-GPU K-means at extreme scale when training data exceeds GPU memory; done means the planned content is agreed and delivered in the project’s designated location.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100