issue_comments: 524403160

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https://github.com/pydata/xarray/issues/3232#issuecomment-524403160 https://api.github.com/repos/pydata/xarray/issues/3232 524403160 MDEyOklzc3VlQ29tbWVudDUyNDQwMzE2MA== 1217238 2019-08-23T17:45:54Z 2019-08-23T17:45:54Z MEMBER

Within a jit compiled function, JAX's execution speed should be quite competitive on GPUs. It uses the XLA compiler, which was recently enabled by default in TensorFlow.

For data loading and deep learning algorithms, take a look at the examples in the notebooks directory in the JAX repo. The APIs for deep learning in JAX are still undergoing rapid development, so APIs are not quite as stable/usable as pytorch or keras yet, but they are quite capable. See jax.experimental.stax and tensor2tensor.trax for examples.

 
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