aesara-devs / aesara

Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
https://aesara.readthedocs.io
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Warnings emitted on import in Windows #1514

Open maresb opened 9 months ago

maresb commented 9 months ago

Description of your problem or feature request

On conda-forge for the Windows builds

import: 'aesara.configdefaults'
import: 'aesara'
import: 'aesara.configdefaults'

Please provide the full tracebacks for any relevant errors and/or warning messages.

WARN: Could not locate executable g77
WARN: Could not locate executable f77
WARN: Could not locate executable ifort
WARN: Could not locate executable ifl
WARN: Could not locate executable f90
WARN: Could not locate executable efl
D:\bld\aesara-suite_1695284749238\_test_env\Lib\site-packages\numpy\distutils\system_info.py:2159: UserWarning: 
    Optimized (vendor) Blas libraries are not found.
    Falls back to netlib Blas library which has worse performance.
    A better performance should be easily gained by switching
    Blas library.
  if self._calc_info(blas):
D:\bld\aesara-suite_1695284749238\_test_env\Lib\site-packages\numpy\distutils\system_info.py:2159: UserWarning: 
    Blas (http://www.netlib.org/blas/) libraries not found.
    Directories to search for the libraries can be specified in the
    numpy/distutils/site.cfg file (section [blas]) or by setting
    the BLAS environment variable.
  if self._calc_info(blas):
D:\bld\aesara-suite_1695284749238\_test_env\Lib\site-packages\numpy\distutils\system_info.py:2159: UserWarning: 
    Blas (http://www.netlib.org/blas/) sources not found.
    Directories to search for the sources can be specified in the
    numpy/distutils/site.cfg file (section [blas_src]) or by setting
    the BLAS_SRC environment variable.
  if self._calc_info(blas):

Please provide any additional information below.

Versions and main components

Aesara config: Place the results of `python -c "import aesara; print(aesara.config)"` here.
maresb commented 9 months ago

I will try pinning numpy<1.26...