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[RFC] Feature: ParametricUMAP PyTorch implementation

See original GitHub issue

In the spirit of the PR #578 which allows dependencies not to interfere too much with the test suite, I was wondering whether you would consider adding an extra dependency 😅

More seriously, I started working to an alternative PyTorch-based implementation for ParametricUMAP (will soon sync the branch on my fork)

If you would generally appreciate the idea and the contribution, I will elaborate better here my UEP (UMAP Enhancement Proposal) to have the two implementations co-existing, with the less impact possible in the API for backward compatibility.

Issue Analytics

State:
Created 3 years ago
Reactions:4
Comments:15 (3 by maintainers)

Top GitHub Comments

4reactions

timsainbcommented, May 19, 2021

hey @leriomaggio . Is this still something you’re thinking about? I’ve been playing around with pytorch and I’m sold on it’s advantages over tensorflow. It seems like it might be a good idea to even switch the primary backend over to pytorch. From a user standpoint pytorch is just a bit easier to deal with. And it seems like pytorch lightning allows us the same level of abstraction as keras.

3reactions

timsainbcommented, Feb 11, 2021

I think that would be a great addition.

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