首先,你需要确认threadpoolctl模块是否已经正确安装在你的Python环境中。可以通过在命令行中运行以下命令来检查: bash pip show threadpoolctl 如果threadpoolctl已安装,这个命令会显示该模块的详细信息。如果命令没有返回任何信息,说明threadpoolctl可能尚未安装。 如果未安装,使用pip安装'threadpoolctl'模块: 如果确认...
python -m threadpoolctl -i numpy scipy.linalg [ { "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so", "prefix": "libmkl_rt", "user_api": "blas", "internal_api": "mkl", "version": "2019.0.4", "num_threads": 2, "threading_layer": "intel" }, { "filepath"...
That being said, I am in favor of moving dependencies and other information intopyproject.toml.pyproject.tomlseems like what most of the Python ecosystem is moving toward. 0reactions thomasjpfancommented, Aug 9, 2022 No. setup.py had/has tags install_require, test_re...
The changelog is available here:https://github.com/joblib/threadpoolctl/blob/master/CHANGES.md. This version supports Python versions 3.8 to 3.12. You can install with pip: pip install -U threadpoolctl or conda: conda install -c conda-forge threadpoolctl...
Python helpers to limit the number of threads used in native libraries that handle their own internal threadpool (BLAS and OpenMP implementations) - threadpoolctl/multiple_openmp.md at master · joblib/threadpoolctl
https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.2-h996f2a0_0.conda#bc4748d0d26253c8499a3abeca289469 https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.3-h996f2a0_0.conda#77af2bd351a8311d1e780bcfa7819bb8 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2...
python -m threadpoolctl -i numpy scipy.linalg [ { "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so", "prefix": "libmkl_rt", "user_api": "blas", "internal_api": "mkl", "version": "2019.0.4", "num_threads": 2, "threading_layer": "intel" }, { "filepath"...