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Francois Chollet is leaving Google

(developers.googleblog.com)
377 points xnx | 2 comments | 13 Nov 24 22:28 UTC | HN request time: 0s | source
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fchollet ◴[14 Nov 24 07:27 UTC] No.42133844[source]▶
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)!

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1. cowsaymoo ◴[14 Nov 24 10:29 UTC] No.42134819[source]▶
I’m really going through it, trying to get legacy Theano and TensorFlow 1.x models from 2016 running on modern GPUs due to compatibility headaches due to OS, NVIDIA CUDA, CuDNN, drivers, docker, python, and package/image hubs all contributing their own roadblocks to actually coding. Ideally we would abandon this code, but we kind of need it running if we want to thoroughly understand our new model's performance on unseen old data, and/or understand Kappa scores between models. Will the move towards freeing Keras from TF again potentially reintroduce version chaos, or will it future proof it from that? Do you see a potential for something like this to once again befall tomorrow's legacy code relying on TF 1.x and 2.x?
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2. fchollet ◴[14 Nov 24 10:54 UTC] No.42134984[source]▶
Keras is now standalone and multi-backend again. Keras weights files from older versions are still loadable and Keras code from older versions are still runnable (on any backend as long as they only used Keras APIs)!

In general the ability to move across backends makes your code much longer-lived: you can take your Keras models with you (on a new backend) after something like TF or PyTorch stops development. Also, it reduces version compatibility issues, since tf.keras 2.n could only work with TF 2.n, but each Keras 3 version can work with a wide range of older and newer TF versions.