BLAS / pymc installation problem with remote tests?

[drbenvincent]

When I was reviewing a PR, and looking at a failed test, I noticed that we are getting a warning about BLAS. https://github.com/pymc-labs/CausalPy/actions/runs/15776976858/job/44473554627?pr=488

causalpy/experiments/diff_in_diff.py::causalpy.experiments.diff_in_diff.DifferenceInDifferences
  /opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2968: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library

I'm not sure if this is a strange one-off or a recurring problem. This does seem to be a recurring issue, but seemingly only for the doctests, not the regular tests. This is going to cause the doctests to run quite slowly.

[ricardoV94]

Are you running heavy models with doctests? If not you can just ignore

[drbenvincent]

Not very heavy. Setting sampling kwargs to just a few chains and draws etc

[drbenvincent]

Though the doctests do take a bit of time in totality. Not done a deep dive on what is taking up that time

[ricardoV94]

I doubt the slowness will come from there unless you have some model that takes >10m to sample