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Python: Difference between revisions
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m
bit more expicit on multi-node
(tone down the advocacy a bit and note that this works for single-node jobs.) |
m (bit more expicit on multi-node) |
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Parallel filesystems such as the ones used on our clusters are very good at reading or writing large chunks of data, but can be bad for intensive use of small files. Launching a software and loading libraries, such as starting python and loading a virtual environment, can be slow for this reason. | Parallel filesystems such as the ones used on our clusters are very good at reading or writing large chunks of data, but can be bad for intensive use of small files. Launching a software and loading libraries, such as starting python and loading a virtual environment, can be slow for this reason. | ||
As a workaround for this kind of slowdown, and especially for single-node Python jobs, you can create your virtual environment inside of your job, using the compute node's local disk. It may seem counter-intuitive to recreate your environment for every job, but it can be faster than running from the parallel filesystem, and will give you some protection against some filesystem performance issues. This | As a workaround for this kind of slowdown, and especially for single-node Python jobs, you can create your virtual environment inside of your job, using the compute node's local disk. It may seem counter-intuitive to recreate your environment for every job, but it can be faster than running from the parallel filesystem, and will give you some protection against some filesystem performance issues. This approach, of creating a node-local virtualenv, has to be done for each node in the job, since the virtualenv is only accessible on one node. Following job submission script demonstrates how to do this for a single-node job: | ||
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