科技回声

I find JAX really exciting. The idea of numpy with autograd is exactly what Pythonistas want. The elephant in the room, though, is “why not Pytorch?”<p>Everyone knows JAX is what Google realised Tensorflow should have been when they realised how much of a joy Pytorch was to use. I actually think JAX does offer some advantages, not least true numpy interoperability. However, not mentioning *torch a single time in the blog post seems a little disingenuous for a Google-owned deep learning enterprise.

The description says it's autograd + XLA. So I assumed it always compiles to GPUs via XLA.<p>But, had a look in the code and jax has cublas and RoCm blas, and it looks like there is a flow where it uses the gpu directly, unless I'm missing something.<p>Definitely worth having a closer look. Autograd via function reflection should be faster than backprop. And if it's running on AMD GPUs then it's quite intriguing.

From people with more experience than me: does JAX have a good story about how to manage state cleanly, where it needs to occur?

Is JAX in competition with TF? Is it something you use instead of TF, or in combination with it?

From people with more experience than me: does JAX have a good story about how to manage state cleanly, where it needs to occur?

Is JAX in competition with TF? Is it something you use instead of TF, or in combination with it?

Using JAX to Accelerate Research

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Using JAX to Accelerate Research

4 条评论