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Recipes to build, install and execute NVIDIA Rapids framework on Summit supercomputer
When starting dask-cuda-worker
, would recommend passing --rmm-pool-size
with an amount of memory equal to the memory on a single device. For example, if one has 4 GPUs with 8GB a piece, this should be --rmm-pool-size 7GB
.
cc @pentschev (in case you have more thoughts here)
Given that Summit only has five batch nodes, shouldn't the scheduler be configured to run on a random port so that more than one user can run a scheduler (on different ports) on the same batch node? This is something we encountered when setting up per-user jupyterlab servers on Summit.
If you have the time, I would love to see Dask performance reports for some of these runs:
https://docs.dask.org/en/latest/diagnostics-distributed.html#capture-diagnostics
from dask.distributed import performance_report
with performance_report(filename="dask-report.html"):
x.compute()
This would allow me or other Dask maintainers to understand current bottlenecks.
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