issue_comments

@Duane321 While it would be fantastic to have gpu-enabled auto-diff-able xarrays / DataArrays, an interesting development worth looking into are the named tensor in https://pytorch.org/docs/stable/named_tensor.html. This appears to be an attempt to bridge the gap from the that they are making pytorch tensors increasingly dataarray like. I would not be surprised if within the next few iterations they add indexes to the tensors closing the gap even further.

@andersy005 I'm about to start working actively on cupy support in xarray. Would be great to get some of your input.

Cupy requests that instead of calling __array__ you instead call their .get method for explicit conversion to numpy. So we need to add a little compatibility code for this.

Do you have a sense of the overhead / effort of making jax vs cupy as the gpu backend for xarrays ? One advantage of jax would be built in auto-diff functionality that would enable xarray to be plugged directly into deep learning pipelines. Downside is that it is not as numpy compatible as cupy. How much of a non-starter would this be ?

If you have any pointers on how to go about this - I can give it a try.

This might be a good time to revive this thread and see if there is wider interest (and bandwidth) in having xarray use CuPy (https://cupy.chainer.org/ ) as a backend (along with numpy). It appears to be a plug-and-play replacement for numpy - so it might not have all the issues that were brought up regarding pytorch/jax ?

Any thoughts ? cc @mrocklin

While it is pretty straightforward to implement a lot of standard xarray operations with a pytorch / Jax backend (since they just fallback on native functions) - it will be interesting to think about how to implement rolling operations / expanding / exponential window in a way that is both efficient and maintains differentiability.

Expanding and exponential window operations would be easy to do leveraging RNN semantics - but doing rolling using convolutions is going to be very inefficient.

Do you have any thoughts on this?

I haven't used JAX - but was just browsing through its documentation and it looks super cool. Any ideas on how it compares with Pytorch in terms of:

a) Cxecution speed, esp. on GPU b) Memory management on GPUs. Pytorch has the 'Dataloader/Dataset' paradigm which uses background multithreading to shuttle batches of data back and forth - along with a lot of tips and tricks on efficient memory usage. c) support for deep-learning optimization algorithms ?