Hi HN, Francois here. Happy to answer any questions!
Here's a start --
"Did you get poached by Anthropic/etc": No, I am starting a new company with a friend. We will announce more about it in due time!
"Who uses Keras in production": Off the top of my head the current list includes Midjourney, YouTube, Waymo, Google across many products (even Ads started moving to Keras recently!), Netflix, Spotify, Snap, GrubHub, Square/Block, X/Twitter, and many non-tech companies like United, JPM, Orange, Walmart, etc. In total Keras has ~2M developers and powers ML at many companies big and small. This isn't all TF -- many of our users have started running Keras on JAX or PyTorch.
"Why did you decide to merge Keras into TensorFlow in 2019": I didn't! The decision was made in 2018 by the TF leads -- I was a L5 IC at the time and that was an L8 decision. The TF team was huge at the time, 50+ people, while Keras was just me and the open-source community. In retrospect I think Keras would have been better off as an independent multi-backend framework -- but that would have required me quitting Google back then. Making Keras multi-backend again in 2023 has been one of my favorite projects to work on, both from the engineering & architecture side of things but also because the product is truly great (also, I love JAX)!
I loved Keras at the beginning of my PhD, 2017. But it was just the wrong abstraction: too easy to start with, too difficult to create custom things (e.g., custom loss function).
I really tried to understand TensorFlow, I managed to make a for-loop in a week. Nested for-loop proved to be impossible.
PyTorch was just perfect out of the box. I don't think I would have finished my PhD in time if it wasn't for PyTorch.
I loved Keras. It was an important milestone, and it made me believe deep learning is feasible. It was just...not the final thing.
Strange. Had never read blog posts about individual engineers leaving Google on official Google Developers Blog before. Is this a first? Every day someone prominent leaves Google... Sounds like a big self-own if Google starts to post this kind of stuff. Looks like sole post by either of the (both new to Google) authors in the byline.
I guess they realized muilti-backend keras is futile? I never liked the tf.keras apis and the docs always promosed multi backend but then I guess they were never able to deliver that without breaking keras 3 changes. And even now.... "Keras 3 includes a brand new distribution API, the keras.distribution namespace, currently implemented for the JAX backend (coming soon to the TensorFlow and PyTorch backends)". I don't believe it. They are too different to reconcile under 1 api. And even if you could, I dont really see the benefit. Torch and Flax have similar goals to Keras and are imo better.
If I were to speculate, I would guess he quit Google. 2 days ago, his $1+ million Artificial General Intelligence competition ended. Chollet is now judging the submissions and will announce the winners in a few weeks. The timing there can't be a coincidence.
Genuine question: who is using Keras in production nowadays? I've done a few work projects in Keras/TensorFlow over the years and it created a lot of technical debt and lost time debugging it, with said issues disappearing once I switched to PyTorch.
The training loop with Keras for simple model is indeed easier and faster than PyTorch oriented helpers (e.g. Lightning AI, Hugging Face accelerate) but much, much less flexible.
Hi ! Thanks for ARC it's lots of fun. Did you think about expanding ARC beyond the current 32x32 relatively low dsl depth format ? Do you think there's anything to gain from it ?
Hello François, thank you for your great work to the Open Source community.
Aren't you worried that your work may only be profitable to some US-based interests that may backfire to your home country ? given the actual political situation...
France needs you, come back home.
This is not a judgment, just wondering about your opinion on it.
Francois Chollet is leaving Google
(developers.googleblog.com)352 points by xnx 13 November 2024 | 157 comments
Comments
Here's a start --
"Did you get poached by Anthropic/etc": No, I am starting a new company with a friend. We will announce more about it in due time!
"Who uses Keras in production": Off the top of my head the current list includes Midjourney, YouTube, Waymo, Google across many products (even Ads started moving to Keras recently!), Netflix, Spotify, Snap, GrubHub, Square/Block, X/Twitter, and many non-tech companies like United, JPM, Orange, Walmart, etc. In total Keras has ~2M developers and powers ML at many companies big and small. This isn't all TF -- many of our users have started running Keras on JAX or PyTorch.
"Why did you decide to merge Keras into TensorFlow in 2019": I didn't! The decision was made in 2018 by the TF leads -- I was a L5 IC at the time and that was an L8 decision. The TF team was huge at the time, 50+ people, while Keras was just me and the open-source community. In retrospect I think Keras would have been better off as an independent multi-backend framework -- but that would have required me quitting Google back then. Making Keras multi-backend again in 2023 has been one of my favorite projects to work on, both from the engineering & architecture side of things but also because the product is truly great (also, I love JAX)!
I really tried to understand TensorFlow, I managed to make a for-loop in a week. Nested for-loop proved to be impossible.
PyTorch was just perfect out of the box. I don't think I would have finished my PhD in time if it wasn't for PyTorch.
I loved Keras. It was an important milestone, and it made me believe deep learning is feasible. It was just...not the final thing.
The training loop with Keras for simple model is indeed easier and faster than PyTorch oriented helpers (e.g. Lightning AI, Hugging Face accelerate) but much, much less flexible.
https://x.com/fchollet/status/1638057646602489856
https://x.com/fchollet/status/1840486105118015901
https://x.com/fchollet/status/1845103528806662258
https://github.com/tensorflow/community/pull/24
Maybe he figured out a model that beats ARC-AGI by 85%?