Use cluster.scheduler_info rather than cluster.scheduler #73
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Supercedes dask#72 This depends on dask/distributed#2902 , which adds a `Cluster.scheduler_info` attribute to clusters which holds necessary scheduler information. We prefer this over querying a Scheduler object directly in case that scheduler is not local, as in increasingly becoming the case.
This was referenced Jul 28, 2019
Otherwise we didn't seem to be getting the baseline config, which was causing errors.
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OK, I think that this is good to go. It should be safe both for old and new releases. |
ian-r-rose
reviewed
Aug 1, 2019
dask_labextension/manager.py
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| ), | ||
| cores=sum(ws.ncores for ws in cluster.scheduler.workers.values()), | ||
| cores=sum(d["nthreads"] for d in info["workers"].values()), |
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What is the intended compatibility profile for this? I am getting failures here due to nthreads not being available in my workers with dask v1.2 and distributed v1.28.
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2.0+, but it's fairly easy to go handle this to go back further. I'll push up a small fix.
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👍 I'd be in favor of maintaining 1.0 compatibility, if it's a small check.
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@ian-r-rose is it safe for me to push out a micro release on the Python side? |
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Yeah, absolutely! |
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Had a tiny snafu, but eventually got there. Version 1.0.3 is on PyPI
…On Wed, Jul 31, 2019 at 10:06 PM Ian Rose ***@***.***> wrote:
Yeah, absolutely!
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This was referenced Aug 8, 2019
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Supercedes #72
This depends on dask/distributed#2902 , which
adds a
Cluster.scheduler_infoattribute to clusters which holdsnecessary scheduler information. We prefer this over querying a
Scheduler object directly in case that scheduler is not local, as in
increasingly becoming the case.