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The best solution is to install Python modules into a container, and use the containers on supercomputers.  Pawsey provides several Python containers with common modules pre-installed:

Using a container will keep the Python startup IO to within the compute node's memory, which is very fast.  Running pip and anaconda to create containers for research is fine if the containers are kept under version control, since you keep a record of the versions of all Python modules used in the research.

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