BwUniCluster2.0/Software/Python Dask: Difference between revisions
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== Using Dask == |
== Using Dask == |
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In a new interactive shell, |
In a new interactive shell, execute the following commands in Python: |
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<pre> |
<pre> |
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>>> from dask_jobqueue import SLURMCluster |
>>> from dask_jobqueue import SLURMCluster |
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>>> cluster = SLURMCluster(cores= |
>>> cluster = SLURMCluster(cores=X, memory='X GB', queue='X') |
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>>> |
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</pre> |
</pre> |
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You have to specify how many cores and memory you want for one dask worker. |
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<pre> |
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>>> cluster.scale (X) |
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</pre> |
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[[Category:bwUniCluster_2.0|Access]][[Category:Access|bwUniCluster 2.0]] |
[[Category:bwUniCluster_2.0|Access]][[Category:Access|bwUniCluster 2.0]] |
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Revision as of 10:49, 19 April 2020
This guide explains how to use Python Dask and dask-jobqueue on bwUniCluster2.0.
Installation
Use on of our pre-configured Python modules and load them with 'module load ...'. You have to install the packages 'dask' and 'das-jobqueue' if your are yousing an own conda environment.
Using Dask
In a new interactive shell, execute the following commands in Python:
>>> from dask_jobqueue import SLURMCluster >>> cluster = SLURMCluster(cores=X, memory='X GB', queue='X')
You have to specify how many cores and memory you want for one dask worker.
>>> cluster.scale (X)
Replace