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  • fjanoos · 6 ✖

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  • Use pytorch as backend for xarrays · 6 ✖

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766090834 https://github.com/pydata/xarray/issues/3232#issuecomment-766090834 https://api.github.com/repos/pydata/xarray/issues/3232 MDEyOklzc3VlQ29tbWVudDc2NjA5MDgzNA== fjanoos 923438 2021-01-23T14:50:04Z 2021-01-23T14:50:04Z NONE

@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.

 
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   Use pytorch as backend for xarrays 482543307
656372249 https://github.com/pydata/xarray/issues/3232#issuecomment-656372249 https://api.github.com/repos/pydata/xarray/issues/3232 MDEyOklzc3VlQ29tbWVudDY1NjM3MjI0OQ== fjanoos 923438 2020-07-09T22:01:25Z 2020-07-09T22:02:30Z NONE

@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 ?

 
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   Use pytorch as backend for xarrays 482543307
606322579 https://github.com/pydata/xarray/issues/3232#issuecomment-606322579 https://api.github.com/repos/pydata/xarray/issues/3232 MDEyOklzc3VlQ29tbWVudDYwNjMyMjU3OQ== fjanoos 923438 2020-03-31T00:24:06Z 2020-03-31T00:24:06Z NONE

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

 
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   Use pytorch as backend for xarrays 482543307
606216839 https://github.com/pydata/xarray/issues/3232#issuecomment-606216839 https://api.github.com/repos/pydata/xarray/issues/3232 MDEyOklzc3VlQ29tbWVudDYwNjIxNjgzOQ== fjanoos 923438 2020-03-30T20:05:24Z 2020-03-30T20:05:24Z NONE

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 

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   Use pytorch as backend for xarrays 482543307
524411995 https://github.com/pydata/xarray/issues/3232#issuecomment-524411995 https://api.github.com/repos/pydata/xarray/issues/3232 MDEyOklzc3VlQ29tbWVudDUyNDQxMTk5NQ== fjanoos 923438 2019-08-23T18:13:35Z 2019-08-23T18:13:35Z NONE

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? 

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   Use pytorch as backend for xarrays 482543307
524348393 https://github.com/pydata/xarray/issues/3232#issuecomment-524348393 https://api.github.com/repos/pydata/xarray/issues/3232 MDEyOklzc3VlQ29tbWVudDUyNDM0ODM5Mw== fjanoos 923438 2019-08-23T15:00:02Z 2019-08-23T15:00:02Z NONE

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 ?

 
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   Use pytorch as backend for xarrays 482543307

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