this post was submitted on 25 Nov 2023
1 points (100.0% liked)
Machine Learning
1 readers
1 users here now
Community Rules:
- Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
- Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
- Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
- Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I'm not very knowledgeable in this realm, so somebody clue me in. I always thought of JAX as targeted towards more "bespoke" stuff, is there any advantage to using it in a high-level way instead of Torch or TF? Anything in the performance or ecosystem etc?
Biggest 'advantage' i can see is that, since Google is deprecating tf soon, JAX is the only googly deep learning lib left. It fills a niche, insofar a that is a definable niche. I'm sticking with pytorch for now.
No clue about things like speed/efficiency, which may be a factor.
Google isn’t deprecating TF.