Has Google completely abandoned TensorFlow?

[edwardyehuang]

Starting this year, I have raised some issues related to TensorFlow, but no one has addressed them at all. Recently, I have also submitted some pull requests to the TensorFlow repository, but no one has reviewed them. Does this mean that Google has abandoned TensorFlow? If so, I will shift my development focus to Jax.

[abhaskumarsinha]

Comments: I don't think Google has completely abandoned TF. But their main focus seems to be on JAX. Currently, in my opinion, JAX is the fastest, easiest, and simplest backend to run deep learning models on.
A Google's official engineer might comment better here.
But I often see this trend, in other repos, including - XLA (Accelerated Linear Algebra Compiler and so on).

[abhaskumarsinha]

@ghsanti They do a lot of things imo, but not a single thing properly. As @edwardyehuang pointed out, even PR reviews take like months to years for them to acknowledge. With such pace, they'll never bring up a thing of quality.

[edwardyehuang]

@fchollet @qlzh727 @jeffcarp @SamanehSaadat Any comments? As a researcher with limited time, I want to focus my limited time on a framework that provides support.

[abhaskumarsinha]

@edwardyehuang What exactly are you working on? I may suggest a better alternative if it is in my scope of knowledge?

[edwardyehuang]

@edwardyehuang What exactly are you working on? I may suggest a better alternative if it is in my scope of knowledge?

Thanks, I'm fine, and I'm also very familiar with PyTorch and Jax. I'm just considering whether it's time to migrate some of my core code out of TensorFlow.

[abhaskumarsinha]

@edwardyehuang I'm not an expert, but migrating to Keras would be worth, you might probably get support of PyT, JAX and TF simultaneously if possible.

[fchollet]

TensorFlow is still maintained, and will keep being maintained. Most production ML systems at Google use TensorFlow.

However JAX is definitely seeing more development capacity. IMO it's a good idea to switch to JAX, not so much because of fear that TF might not be supported in the future, but simply because JAX is a technologically better platform to develop on. In particular it is easier to scale (via sharding APIs, e.g. keras.distribution APIs) and it is generally more performant.

My personal style is to keep my code backend-agnostic and to switch from one backend to another based on current needs (might use torch to debug, JAX or TF to run). I still use many TF components though, in particular tf.data is indispensable.