html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue https://github.com/pydata/xarray/issues/3232#issuecomment-766090834,https://api.github.com/repos/pydata/xarray/issues/3232,766090834,MDEyOklzc3VlQ29tbWVudDc2NjA5MDgzNA==,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.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,482543307 https://github.com/pydata/xarray/issues/3232#issuecomment-656372249,https://api.github.com/repos/pydata/xarray/issues/3232,656372249,MDEyOklzc3VlQ29tbWVudDY1NjM3MjI0OQ==,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 ?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,482543307 https://github.com/pydata/xarray/issues/3232#issuecomment-606322579,https://api.github.com/repos/pydata/xarray/issues/3232,606322579,MDEyOklzc3VlQ29tbWVudDYwNjMyMjU3OQ==,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. > > ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,482543307 https://github.com/pydata/xarray/issues/3232#issuecomment-606216839,https://api.github.com/repos/pydata/xarray/issues/3232,606216839,MDEyOklzc3VlQ29tbWVudDYwNjIxNjgzOQ==,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 ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,482543307 https://github.com/pydata/xarray/issues/3232#issuecomment-524411995,https://api.github.com/repos/pydata/xarray/issues/3232,524411995,MDEyOklzc3VlQ29tbWVudDUyNDQxMTk5NQ==,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? ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,482543307 https://github.com/pydata/xarray/issues/3232#issuecomment-524348393,https://api.github.com/repos/pydata/xarray/issues/3232,524348393,MDEyOklzc3VlQ29tbWVudDUyNDM0ODM5Mw==,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 ? ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,482543307