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- Passing a distributed.Future to the kwargs of apply_ufunc should resolve the future · 5 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1280786780 | https://github.com/pydata/xarray/issues/6803#issuecomment-1280786780 | https://api.github.com/repos/pydata/xarray/issues/6803 | IC_kwDOAMm_X85MV0Fc | alessioarena 33886395 | 2022-10-17T12:33:18Z | 2022-10-17T12:33:18Z | NONE | I will try that. I still find it weird that I need to wrap a numpy object into a task/xarray object to be able to send it to workers when there is dask.scatter made for exactly that purpose. Thanks for opening that issue. I do feel there is the need to revisit scatter functionality and role particularly around dynamic clusters. Having a better look at your initial comment, that may still work if you call |
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Passing a distributed.Future to the kwargs of apply_ufunc should resolve the future 1307523148 | |
| 1280759221 | https://github.com/pydata/xarray/issues/6803#issuecomment-1280759221 | https://api.github.com/repos/pydata/xarray/issues/6803 | IC_kwDOAMm_X85MVtW1 | alessioarena 33886395 | 2022-10-17T12:11:05Z | 2022-10-17T12:11:05Z | NONE | I'm not sure I understand the code above. In my case I have an array of approximately 300k elements that each and every function call needs to have access. I can pass it as a kwargs in its numpy form, but once I scale up the calculation across a large dataset (many large chunks) such array gets replicated for every task pushing the scheduler out of memory. That is why I tried to send the dataset to the cluster beforehand using scatter, but I cannot resolve the Future at the workers |
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Passing a distributed.Future to the kwargs of apply_ufunc should resolve the future 1307523148 | |
| 1280743293 | https://github.com/pydata/xarray/issues/6803#issuecomment-1280743293 | https://api.github.com/repos/pydata/xarray/issues/6803 | IC_kwDOAMm_X85MVpd9 | alessioarena 33886395 | 2022-10-17T11:59:19Z | 2022-10-17T11:59:19Z | NONE | I can add that this problem is augmented in a dask_gateway system where the task just fails. With My interpretation is that the Future is resolved at the worker (or in case of apply_ufunc a thread of this worker) and embeds a reference to the Client object. This last however uses a gateway connection that is not understood by the worker as generally is the scheduler dealing with those |
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Passing a distributed.Future to the kwargs of apply_ufunc should resolve the future 1307523148 | |
| 1264523142 | https://github.com/pydata/xarray/issues/6803#issuecomment-1264523142 | https://api.github.com/repos/pydata/xarray/issues/6803 | IC_kwDOAMm_X85LXxeG | alessioarena 33886395 | 2022-10-02T01:29:35Z | 2022-10-02T01:29:35Z | NONE | I think I may have narrowed down the problem to a limitation in dask using dask_gateway. If passing a Future to a worker, the worker will try to unpickle that Future, and as part of that unpickle the Client object passed when creating such Future. Unfortunately, in a dask_gateway context the client is behind a |
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Passing a distributed.Future to the kwargs of apply_ufunc should resolve the future 1307523148 | |
| 1260319916 | https://github.com/pydata/xarray/issues/6803#issuecomment-1260319916 | https://api.github.com/repos/pydata/xarray/issues/6803 | IC_kwDOAMm_X85LHvSs | alessioarena 33886395 | 2022-09-28T02:53:25Z | 2022-09-28T02:53:25Z | NONE | This is still an issue.
I noticed that the documentation of Is this the case for |
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Passing a distributed.Future to the kwargs of apply_ufunc should resolve the future 1307523148 |
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