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| version: 6 | |
| environments: | |
| default: | |
| channels: | |
| - url: https://conda.anaconda.org/conda-forge/ | |
| packages: | |
| linux-64: | |
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| channels: | |
| - url: https://conda.anaconda.org/conda-forge/ | |
| packages: | |
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| - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h85ac4a6_6.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h500dc9f_9.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.6.17-hbd8a1cb_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.6-py314hd8ed1ab_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/icu-78.3-h25d91c4_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.11.0-8_he492b99_openblas.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.11.0-8_h9b27e0a_openblas.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-22.1.8-h19cb2f5_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.8.1-hcc62823_1.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/libgcc-15.2.0-h08519bb_19.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.11.0-8_h859234e_openblas.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.53.2-h8f8c405_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.2-hbb4bfdb_2.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.14.6-ha91e2d8_0_cp314t.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.14.6-h92d6c8b_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/readline-8.3-h68b038d_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h3eecb57_6.conda | |
| osx-arm64: | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_9.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.6.17-hbd8a1cb_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.6-py314hd8ed1ab_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.3-hef89b57_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.11.0-8_h51639a9_openblas.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.11.0-8_hb0561ab_openblas.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-22.1.8-h55c6f16_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.8.1-hf6b4638_1.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_19.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.6-h1d4f5a5_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.6-he51be1b_0_cp314t.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.3-h46df422_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.18.0-py314h122b93a_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda | |
| win-64: | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h0ad9c76_9.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.6.17-h4c7d964_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.6-py314hd8ed1ab_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.11.0-8_h8455456_mkl.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-8_h2a3cdd5_mkl.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.8.1-hac47afa_1.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.5.2-h3d046cb_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.13.0-default_h049141e_1000.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-hc1393d2_2.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-8_hf9ab0e9_mkl.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.3-hfd05255_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libmpdec-4.0.0-hfd05255_1.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libopenblas-0.3.33-openmp_hdb726d1_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.53.2-hf5d6505_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libwinpthread-12.0.0.r4.gg4f2fc60ca-h57928b3_10.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-16-2.15.3-h692994f_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.3-hbc0d294_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.2-hfd05255_2.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/llvm-openmp-22.1.8-h4fa8253_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2026.0.0-hac47afa_908.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.5.0-py314hffb9209_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/onemkl-license-2026.0.0-h57928b3_908.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.3-hf411b9b_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-3.0.3-py314he4c2ca6_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.6-h7c1dbca_0_cp314t.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.14.6-h92d6c8b_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.2-pyhd8ed1ab_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314t.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/scikit-learn-1.9.0-np2py314hb10d3ac_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.18.0-py314h9c5632b_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/win-64/tbb-2023.0.0-hd3d4ead_2.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.26100.0-h57928b3_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.5-h1b7c187_39.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.51.36231-h1b9f54f_39.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/vcomp14-14.51.36231-h1b9f54f_39.conda | |
| - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda | |
| openblas-openmp-gil: | |
| channels: | |
| - url: https://conda.anaconda.org/conda-forge/ | |
| packages: | |
| linux-64: | |
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| - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h4777abc_9.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.6.17-hbd8a1cb_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-8_haddc8a3_openblas.conda | |
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| packages: | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.6.17-h4c7d964_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-8_h2a3cdd5_mkl.conda | |
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| openblas-pthreads-gil: | |
| channels: | |
| - url: https://conda.anaconda.org/conda-forge/ | |
| packages: | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.53.2-h8f8c405_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.13.14-h3d5d122_100_cp313.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/readline-8.3-h68b038d_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/scikit-learn-1.9.0-np2py313h4d167ef_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-64/scipy-1.18.0-py313h9cbb6b6_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_9.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.6.17-hbd8a1cb_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.3-hef89b57_0.conda | |
| - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda | |
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| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.11.0-8_hb0561ab_openblas.conda | |
| - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-22.1.8-h55c6f16_0.conda | |
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| channels: | |
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| indexes: | |
| - https://pypi.org/simple | |
| packages: | |
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| [workspace] | |
| name = "regression-pipeline-tuning" | |
| version = "0.1.0" | |
| channels = ["conda-forge"] | |
| platforms = ["linux-64", "linux-aarch64", "osx-64", "osx-arm64", "win-64"] | |
| # --------------------------------------------------------------------------- | |
| # Python interpreter variants (pick one per environment). | |
| # --------------------------------------------------------------------------- | |
| [feature.gil.dependencies] | |
| python = ">=3.11,<3.14" | |
| [feature.freethreading.dependencies] | |
| python-freethreading = ">=3.14,<3.15" | |
| # --------------------------------------------------------------------------- | |
| # PyPI-only environment: scientific stack from PyPI. | |
| # --------------------------------------------------------------------------- | |
| [feature.pypi-deps.pypi-dependencies] | |
| joblib = "*" | |
| numpy = "*" | |
| pandas = "*" | |
| scikit-learn = ">=1.8" | |
| [feature.pypi-gil.tasks] | |
| pypi-gil-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| pypi-gil-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.pypi-ft.tasks] | |
| pypi-freethreading-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| pypi-freethreading-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| # --------------------------------------------------------------------------- | |
| # conda-forge environments with selectable BLAS/LAPACK implementations. | |
| # --------------------------------------------------------------------------- | |
| [feature.openblas-pthreads-deps.dependencies] | |
| libopenblas = { version = "*", build = "*pthreads*" } | |
| numpy = "*" | |
| scipy = "*" | |
| scikit-learn = "*" | |
| joblib = "*" | |
| pandas = "*" | |
| [feature.openblas-pthreads-gil.tasks] | |
| openblas-pthreads-gil-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| openblas-pthreads-gil-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.openblas-pthreads-ft.tasks] | |
| openblas-pthreads-freethreading-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| openblas-pthreads-freethreading-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.openblas-openmp-deps.dependencies] | |
| libopenblas = { version = "*", build = "*openmp*" } | |
| numpy = "*" | |
| scipy = "*" | |
| scikit-learn = "*" | |
| joblib = "*" | |
| pandas = "*" | |
| [feature.openblas-openmp-gil.tasks] | |
| openblas-openmp-gil-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| openblas-openmp-gil-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.openblas-openmp-ft.tasks] | |
| openblas-openmp-freethreading-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| openblas-openmp-freethreading-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.newaccelerate-deps] | |
| platforms = ["osx-64", "osx-arm64"] | |
| [feature.newaccelerate-deps.system-requirements] | |
| macos = "13.3" | |
| [feature.newaccelerate-deps.dependencies] | |
| libblas = { version = "*", build = "*newaccelerate*" } | |
| liblapack = { version = "*", build = "*newaccelerate*" } | |
| numpy = "*" | |
| scipy = "*" | |
| scikit-learn = "*" | |
| joblib = "*" | |
| pandas = "*" | |
| [feature.newaccelerate-gil.tasks] | |
| newaccelerate-gil-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| newaccelerate-gil-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.newaccelerate-ft.tasks] | |
| newaccelerate-freethreading-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| newaccelerate-freethreading-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.mkl-deps] | |
| platforms = ["linux-64", "osx-64", "win-64"] | |
| [feature.mkl-deps.dependencies] | |
| libblas = { version = "*", build = "*mkl*" } | |
| liblapack = { version = "*", build = "*mkl*" } | |
| mkl = "*" | |
| numpy = "*" | |
| scipy = "*" | |
| scikit-learn = "*" | |
| joblib = "*" | |
| pandas = "*" | |
| [feature.mkl-gil.target.linux-64.tasks] | |
| mkl-gil-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| mkl-gil-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.mkl-gil.target.osx-64.tasks] | |
| mkl-gil-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| mkl-gil-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.mkl-gil.target.win-64.tasks] | |
| mkl-gil-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| mkl-gil-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.mkl-ft.target.linux-64.tasks] | |
| mkl-freethreading-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| mkl-freethreading-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.mkl-ft.target.osx-64.tasks] | |
| mkl-freethreading-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| mkl-freethreading-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [feature.mkl-ft.target.win-64.tasks] | |
| mkl-freethreading-threading = "python regression_pipeline_tuning.py --joblib-backend threading" | |
| mkl-freethreading-loky = "python regression_pipeline_tuning.py --joblib-backend loky" | |
| [environments] | |
| pypi-gil = { features = ["pypi-deps", "gil", "pypi-gil"], no-default-feature = true, solve-group = "pypi-gil" } | |
| pypi-freethreading = { features = ["pypi-deps", "freethreading", "pypi-ft"], no-default-feature = true } | |
| openblas-pthreads-gil = { features = ["openblas-pthreads-deps", "gil", "openblas-pthreads-gil"], no-default-feature = true, solve-group = "openblas-pthreads-gil" } | |
| openblas-pthreads-freethreading = { features = ["openblas-pthreads-deps", "freethreading", "openblas-pthreads-ft"], no-default-feature = true } | |
| openblas-openmp-gil = { features = ["openblas-openmp-deps", "gil", "openblas-openmp-gil"], no-default-feature = true, solve-group = "openblas-openmp-gil" } | |
| openblas-openmp-freethreading = { features = ["openblas-openmp-deps", "freethreading", "openblas-openmp-ft"], no-default-feature = true } | |
| newaccelerate-gil = { features = ["newaccelerate-deps", "gil", "newaccelerate-gil"], no-default-feature = true, solve-group = "newaccelerate-gil" } | |
| newaccelerate-freethreading = { features = ["newaccelerate-deps", "freethreading", "newaccelerate-ft"], no-default-feature = true } | |
| mkl-gil = { features = ["mkl-deps", "gil", "mkl-gil"], no-default-feature = true, solve-group = "mkl-gil" } | |
| mkl-freethreading = { features = ["mkl-deps", "freethreading", "mkl-ft"], no-default-feature = true } | |
| # --------------------------------------------------------------------------- | |
| # Run all benchmark tasks that are compatible with the host platform. | |
| # --------------------------------------------------------------------------- | |
| [tasks] | |
| run-all = { depends-on = [ | |
| "pypi-gil-threading", | |
| "pypi-gil-loky", | |
| "pypi-freethreading-threading", | |
| "pypi-freethreading-loky", | |
| "openblas-pthreads-gil-threading", | |
| "openblas-pthreads-gil-loky", | |
| "openblas-pthreads-freethreading-threading", | |
| "openblas-pthreads-freethreading-loky", | |
| "openblas-openmp-gil-threading", | |
| "openblas-openmp-gil-loky", | |
| "openblas-openmp-freethreading-threading", | |
| "openblas-openmp-freethreading-loky", | |
| ] } | |
| [target.osx-64.tasks] | |
| run-all = { depends-on = [ | |
| "pypi-gil-threading", | |
| "pypi-gil-loky", | |
| "pypi-freethreading-threading", | |
| "pypi-freethreading-loky", | |
| "openblas-pthreads-gil-threading", | |
| "openblas-pthreads-gil-loky", | |
| "openblas-pthreads-freethreading-threading", | |
| "openblas-pthreads-freethreading-loky", | |
| "openblas-openmp-gil-threading", | |
| "openblas-openmp-gil-loky", | |
| "openblas-openmp-freethreading-threading", | |
| "openblas-openmp-freethreading-loky", | |
| "newaccelerate-gil-threading", | |
| "newaccelerate-gil-loky", | |
| "newaccelerate-freethreading-threading", | |
| "newaccelerate-freethreading-loky", | |
| "mkl-gil-threading", | |
| "mkl-gil-loky", | |
| "mkl-freethreading-threading", | |
| "mkl-freethreading-loky", | |
| ] } | |
| [target.osx-arm64.tasks] | |
| run-all = { depends-on = [ | |
| # XXX: RuntimeWarning: divide by zero encountered in matmul on macOS. Skip for now. | |
| # "pypi-gil-threading", | |
| # "pypi-gil-loky", | |
| # "pypi-freethreading-threading", | |
| # "pypi-freethreading-loky", | |
| "openblas-pthreads-gil-threading", | |
| "openblas-pthreads-gil-loky", | |
| "openblas-pthreads-freethreading-threading", | |
| "openblas-pthreads-freethreading-loky", | |
| "openblas-openmp-gil-threading", | |
| "openblas-openmp-gil-loky", | |
| "openblas-openmp-freethreading-threading", | |
| "openblas-openmp-freethreading-loky", | |
| "newaccelerate-gil-threading", | |
| "newaccelerate-gil-loky", | |
| "newaccelerate-freethreading-threading", | |
| "newaccelerate-freethreading-loky", | |
| ] } | |
| [target.linux-64.tasks] | |
| run-all = { depends-on = [ | |
| "pypi-gil-threading", | |
| "pypi-gil-loky", | |
| "pypi-freethreading-threading", | |
| "pypi-freethreading-loky", | |
| "openblas-pthreads-gil-threading", | |
| "openblas-pthreads-gil-loky", | |
| "openblas-pthreads-freethreading-threading", | |
| "openblas-pthreads-freethreading-loky", | |
| "openblas-openmp-gil-threading", | |
| "openblas-openmp-gil-loky", | |
| "openblas-openmp-freethreading-threading", | |
| "openblas-openmp-freethreading-loky", | |
| "mkl-gil-threading", | |
| "mkl-gil-loky", | |
| "mkl-freethreading-threading", | |
| "mkl-freethreading-loky", | |
| ] } | |
| [target.win-64.tasks] | |
| run-all = { depends-on = [ | |
| "pypi-gil-threading", | |
| "pypi-gil-loky", | |
| "pypi-freethreading-threading", | |
| "pypi-freethreading-loky", | |
| "openblas-pthreads-gil-threading", | |
| "openblas-pthreads-gil-loky", | |
| "openblas-pthreads-freethreading-threading", | |
| "openblas-pthreads-freethreading-loky", | |
| "openblas-openmp-gil-threading", | |
| "openblas-openmp-gil-loky", | |
| "openblas-openmp-freethreading-threading", | |
| "openblas-openmp-freethreading-loky", | |
| "mkl-gil-threading", | |
| "mkl-gil-loky", | |
| "mkl-freethreading-threading", | |
| "mkl-freethreading-loky", | |
| ] } | |
| [dependencies] | |
| ipython = ">=9.14.1,<10" |
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| # %% | |
| import argparse | |
| import json | |
| import os | |
| import platform | |
| import subprocess | |
| import sys | |
| import sysconfig | |
| import uuid | |
| from datetime import datetime, timezone | |
| from functools import partial | |
| from importlib import import_module | |
| from importlib.metadata import PackageNotFoundError, distribution | |
| from pathlib import Path | |
| from urllib.parse import urlparse | |
| import time | |
| from pprint import pprint | |
| import warnings | |
| import joblib | |
| import numpy as np | |
| import pandas as pd | |
| import threadpoolctl | |
| import sklearn | |
| from sklearn.compose import ( | |
| ColumnTransformer, | |
| TransformedTargetRegressor, | |
| make_column_selector, | |
| ) | |
| from sklearn.datasets import fetch_openml | |
| from sklearn.kernel_approximation import Nystroem | |
| from sklearn.linear_model import RidgeCV | |
| from sklearn.model_selection import RandomizedSearchCV, ShuffleSplit, KFold | |
| from sklearn.pipeline import FeatureUnion, make_pipeline | |
| from sklearn.preprocessing import ( | |
| FunctionTransformer, | |
| MinMaxScaler, | |
| OneHotEncoder, | |
| SplineTransformer, | |
| TargetEncoder, | |
| PowerTransformer, | |
| ) | |
| from sklearn.utils.parallel import delayed, Parallel | |
| RESULTS_DIR = Path(__file__).resolve().parent / "results" / "regression_pipeline_tuning" | |
| BENCHMARK_SCRIPT = Path(__file__).name | |
| def write_json_result(path, record): | |
| with path.open("w", encoding="utf-8") as file: | |
| json.dump(record, file, indent=2, sort_keys=True) | |
| file.write("\n") | |
| def _run_command(args): | |
| try: | |
| return subprocess.check_output( | |
| args, text=True, stderr=subprocess.DEVNULL | |
| ).strip() | |
| except (OSError, subprocess.CalledProcessError): | |
| return None | |
| def _os_family(): | |
| system = platform.system() | |
| if system == "Darwin": | |
| return "macOS" | |
| if system == "Windows": | |
| return "windows" | |
| if system == "Linux": | |
| return "linux" | |
| return system.lower() | |
| def _gil_enabled(): | |
| if hasattr(sys, "_is_gil_enabled"): | |
| return sys._is_gil_enabled() | |
| return sysconfig.get_config_var("Py_GIL_DISABLED") != 1 | |
| def _linux_cpuinfo(): | |
| info = {} | |
| try: | |
| with open("/proc/cpuinfo", encoding="utf-8") as file: | |
| for line in file: | |
| if ":" not in line: | |
| continue | |
| key, value = (part.strip() for part in line.split(":", 1)) | |
| if key in {"vendor_id", "model name", "cpu cores"}: | |
| info[key] = value | |
| except OSError: | |
| pass | |
| return info | |
| def _cpu_details(): | |
| family = _os_family() | |
| details = { | |
| "architecture": platform.machine(), | |
| "manufacturer": None, | |
| "model": None, | |
| "physical_cores": joblib.cpu_count(only_physical_cores=True), | |
| "logical_cores": joblib.cpu_count(only_physical_cores=False), | |
| } | |
| if family == "macOS": | |
| brand = _run_command(["sysctl", "-n", "machdep.cpu.brand_string"]) | |
| details["model"] = brand | |
| if brand: | |
| if "Apple" in brand: | |
| details["manufacturer"] = "Apple" | |
| elif "Intel" in brand: | |
| details["manufacturer"] = "Intel" | |
| elif family == "linux": | |
| cpuinfo = _linux_cpuinfo() | |
| details["manufacturer"] = cpuinfo.get("vendor_id") | |
| details["model"] = cpuinfo.get("model name") | |
| elif family == "windows": | |
| details["manufacturer"] = platform.processor() or None | |
| details["model"] = _run_command(["wmic", "cpu", "get", "Name", "/value"]) | |
| if details["model"]: | |
| for line in details["model"].splitlines(): | |
| if line.startswith("Name="): | |
| details["model"] = line.split("=", 1)[1].strip() | |
| break | |
| return details | |
| def _normalize_channel_url(channel_url): | |
| if not channel_url: | |
| return None | |
| if channel_url.startswith("pypi"): | |
| return "pypi.org" | |
| parsed = urlparse(channel_url) | |
| if parsed.netloc in {"pypi.org", "files.pythonhosted.org"}: | |
| return "pypi.org" | |
| path_parts = [part for part in parsed.path.split("/") if part] | |
| if parsed.netloc in {"conda.anaconda.org", "anaconda.org"} and path_parts: | |
| return path_parts[0] | |
| if parsed.netloc: | |
| return parsed.netloc | |
| return channel_url | |
| def _conda_package_channel(package_name): | |
| conda_prefix = os.environ.get("CONDA_PREFIX") | |
| if not conda_prefix: | |
| return None | |
| meta_dir = Path(conda_prefix) / "conda-meta" | |
| if not meta_dir.is_dir(): | |
| return None | |
| for meta_file in sorted(meta_dir.glob(f"{package_name}-*.json")): | |
| try: | |
| metadata = json.loads(meta_file.read_text(encoding="utf-8")) | |
| except (OSError, json.JSONDecodeError): | |
| continue | |
| if metadata.get("name") != package_name: | |
| continue | |
| channel_url = metadata.get("channel") or metadata.get("url") | |
| return _normalize_channel_url(channel_url) | |
| return None | |
| def _pixi_environment(): | |
| return os.environ.get("PIXI_ENVIRONMENT_NAME") | |
| def _package_info(package_name): | |
| try: | |
| dist = distribution(package_name) | |
| except PackageNotFoundError: | |
| return {"version": None, "channel": None} | |
| info = {"version": dist.version, "channel": _conda_package_channel(package_name)} | |
| if info["channel"] is not None: | |
| return info | |
| try: | |
| installer = dist.read_text("INSTALLER").strip() | |
| except (FileNotFoundError, OSError, TypeError): | |
| installer = None | |
| if installer in {"pip", "uv", "uv-pixi"}: | |
| info["channel"] = "pypi.org" | |
| return info | |
| try: | |
| direct_url = json.loads(dist.read_text("direct_url.json")) | |
| info["channel"] = ( | |
| _normalize_channel_url(direct_url.get("url", "")) or "pypi.org" | |
| ) | |
| except (FileNotFoundError, OSError, TypeError, json.JSONDecodeError): | |
| pass | |
| return info | |
| def _threadpool_libraries(): | |
| libraries = [] | |
| for entry in threadpoolctl.threadpool_info(): | |
| libraries.append( | |
| { | |
| "user_api": entry.get("user_api"), | |
| "internal_api": entry.get("internal_api"), | |
| "version": entry.get("version"), | |
| "threading_layer": entry.get("threading_layer"), | |
| "num_threads": entry.get("num_threads"), | |
| "architecture": entry.get("architecture"), | |
| } | |
| ) | |
| if not any(library["user_api"] == "blas" for library in libraries): | |
| libraries.append( | |
| { | |
| "user_api": "blas", | |
| "internal_api": "newaccelerate", | |
| "version": None, | |
| "threading_layer": None, | |
| "num_threads": None, | |
| "architecture": None, | |
| } | |
| ) | |
| return libraries | |
| def create_run_record( | |
| *, | |
| run_id, | |
| joblib_backend, | |
| array_api_namespace, | |
| device, | |
| max_n_workers, | |
| n_iter, | |
| cv, | |
| openml_data_id, | |
| results_file, | |
| ): | |
| packages = { | |
| package: _package_info(package) | |
| for package in ("numpy", "scipy", "scikit-learn", "pandas", "joblib") | |
| } | |
| cpu = _cpu_details() | |
| return { | |
| "run_id": str(run_id), | |
| "recorded_at": datetime.now(timezone.utc).isoformat(), | |
| "results_file": results_file, | |
| "run": { | |
| "joblib_backend": joblib_backend, | |
| "array_api_namespace": array_api_namespace, | |
| "device": device, | |
| "max_n_workers": max_n_workers, | |
| }, | |
| "benchmark": { | |
| "script": BENCHMARK_SCRIPT, | |
| "openml_data_id": openml_data_id, | |
| "n_iter": n_iter, | |
| "cv_n_splits": cv.n_splits, | |
| "cv_test_size": cv.test_size, | |
| "random_state": cv.random_state, | |
| }, | |
| "system": { | |
| "python": { | |
| "version": sys.version.replace("\n", " "), | |
| "gil_enabled": _gil_enabled(), | |
| }, | |
| "os": { | |
| "family": _os_family(), | |
| "version": platform.release(), | |
| "platform": platform.platform(), | |
| }, | |
| "cpu": cpu, | |
| "pixi": { | |
| "environment": _pixi_environment(), | |
| }, | |
| "packages": packages, | |
| "threadpool_libraries": _threadpool_libraries(), | |
| }, | |
| "timings": [], | |
| } | |
| # %% | |
| warnings.filterwarnings("ignore", category=UserWarning, message=".*A worker stopped.*") | |
| warnings.filterwarnings("error", category=RuntimeWarning) | |
| # %% | |
| X, y = fetch_openml(data_id=42165, as_frame=True, return_X_y=True) | |
| y = y.astype(np.float32).values | |
| cv = ShuffleSplit(n_splits=3, test_size=0.2, random_state=42) | |
| OPENML_DATA_ID = 42165 | |
| # %% | |
| # Parse the CLI arguments to retrieve the array API namespace, device and number of workers, but | |
| # only when not run as a Jupyter notebook. | |
| def in_notebook(): | |
| try: | |
| cfg = get_ipython().config # pyright: ignore[reportUndefinedVariable] | |
| if "IPKernelApp" in cfg: | |
| return True | |
| except NameError: | |
| return False | |
| xp = np | |
| device_name = "cpu" | |
| joblib_backend = "loky" | |
| max_n_workers = 1 | |
| array_api_namespace = "numpy" | |
| if not in_notebook(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--array-api-namespace", type=str, default="numpy") | |
| parser.add_argument("--device", type=str, default="cpu") | |
| parser.add_argument("--max-n-workers", type=int, default=1) | |
| parser.add_argument("--joblib-backend", type=str, default=None) | |
| args = parser.parse_args() | |
| max_n_workers = args.max_n_workers | |
| if args.joblib_backend is not None: | |
| joblib_backend = args.joblib_backend | |
| if args.array_api_namespace is not None: | |
| os.environ["SCIPY_ARRAY_API"] = "1" | |
| os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" | |
| warnings.filterwarnings( | |
| "ignore", | |
| category=UserWarning, | |
| message=".*is not currently supported on the MPS.*", | |
| ) | |
| sklearn.set_config(array_api_dispatch=True) | |
| xp = import_module(args.array_api_namespace) | |
| device_name = args.device | |
| array_api_namespace = args.array_api_namespace | |
| print(f"Array API namespace: {xp.__name__}") | |
| print(f"Device: {device_name}") | |
| joblib.parallel_config(backend=joblib_backend) | |
| print(f"Joblib backend: {joblib_backend}") | |
| # %% | |
| print("Runtime environment:") | |
| sklearn.show_versions() | |
| print() | |
| # %% | |
| target_encoder = make_pipeline( | |
| TargetEncoder( | |
| target_type="continuous", cv=KFold(n_splits=5, shuffle=True, random_state=42) | |
| ), | |
| MinMaxScaler(), | |
| ) | |
| preprocessor = ColumnTransformer( | |
| transformers=[ | |
| ( | |
| "categorical", | |
| FeatureUnion( | |
| [ | |
| ( | |
| "onehot", | |
| OneHotEncoder( | |
| sparse_output=False, | |
| handle_unknown="infrequent_if_exist", | |
| max_categories=10, | |
| min_frequency=5, | |
| ), | |
| ), | |
| ("target", target_encoder), | |
| ] | |
| ), | |
| make_column_selector(dtype_include=["category", "string"]), | |
| ), | |
| ( | |
| "numeric", | |
| SplineTransformer(n_knots=10, degree=2, handle_missing="zeros"), | |
| make_column_selector(dtype_include=["number"]), | |
| ), | |
| ] | |
| ) | |
| poly_reg = make_pipeline( | |
| preprocessor, | |
| FunctionTransformer( | |
| lambda X: X.astype(np.float32), | |
| feature_names_out="one-to-one", | |
| check_inverse=False, | |
| ), | |
| FunctionTransformer( | |
| partial(xp.asarray, device=device_name), | |
| feature_names_out="one-to-one", | |
| check_inverse=False, | |
| ), | |
| Nystroem(kernel="poly", degree=2, n_components=300, random_state=42), | |
| # TODO: add array API support for PowerTransformer and TransformedTargetRegressor. | |
| # TransformedTargetRegressor( | |
| # RidgeCV(alphas=np.logspace(-6, 6, 13)), | |
| # transformer=PowerTransformer(), | |
| # ), | |
| RidgeCV(alphas=np.logspace(-6, 6, 13)), | |
| ) | |
| param_grid = { | |
| "columntransformer__numeric__n_knots": [5, 10, 20, 50], | |
| "columntransformer__categorical__onehot__max_categories": [ | |
| 2, | |
| 5, | |
| 10, | |
| 20, | |
| 50, | |
| ], | |
| "columntransformer__categorical__onehot__min_frequency": [None, 2, 5, 10, 20], | |
| "columntransformer__categorical__target": ["drop", target_encoder], | |
| "nystroem__n_components": [10, 30, 100, 300], | |
| "nystroem__kernel": ["poly", "rbf"], | |
| "nystroem__degree": [2, 3], | |
| "nystroem__gamma": np.logspace(-6, 6, 25), | |
| } | |
| # TODO: run for various values of n_jobs and plot a scalability curve. | |
| cpu_count = joblib.cpu_count(only_physical_cores=True) | |
| n_jobs_list = 2 ** np.arange(int(np.log2(cpu_count) + 1)) | |
| n_iter = 30 | |
| RESULTS_DIR.mkdir(parents=True, exist_ok=True) | |
| run_id = uuid.uuid4() | |
| results_path = RESULTS_DIR / f"{datetime.now().strftime('%Y%m%dT%H%M%S')}_{run_id}.json" | |
| run_record = create_run_record( | |
| run_id=run_id, | |
| joblib_backend=joblib_backend, | |
| array_api_namespace=array_api_namespace, | |
| device=device_name, | |
| max_n_workers=max_n_workers, | |
| n_iter=n_iter, | |
| cv=cv, | |
| openml_data_id=OPENML_DATA_ID, | |
| results_file=results_path.name, | |
| ) | |
| write_json_result(results_path, run_record) | |
| print(f"Recording results to {results_path} (run_id={run_id})") | |
| print(f"Benchmarking hyper-parameter tuning with {n_iter=} and {cv.n_splits=}...") | |
| for n_jobs in n_jobs_list: | |
| # Warm-up joblib workers to ignore the process startup overhead: nominally, | |
| # Python programs should not change the number of workers so often. | |
| Parallel(n_jobs=n_jobs)([delayed(lambda: None)() for _ in range(10)]) | |
| # Measure the time taken to tuned hyperparameters. | |
| tic = time.time() | |
| hp_search = RandomizedSearchCV( | |
| poly_reg, | |
| param_grid, | |
| n_iter=n_iter, | |
| cv=cv, | |
| n_jobs=n_jobs, | |
| scoring="r2", | |
| error_score="raise", | |
| random_state=42, | |
| ) | |
| hp_search.fit(X, y) | |
| toc = time.time() | |
| duration = toc - tic | |
| speedup = ( | |
| run_record["timings"][0]["duration_s"] / duration | |
| if run_record["timings"] | |
| else 1.0 | |
| ) | |
| run_record["timings"].append( | |
| { | |
| "recorded_at": datetime.now(timezone.utc).isoformat(), | |
| "n_jobs": int(n_jobs), | |
| "duration_s": duration, | |
| "speedup": speedup, | |
| "best_r2": float(hp_search.best_score_), | |
| } | |
| ) | |
| write_json_result(results_path, run_record) | |
| timing = run_record["timings"][-1] | |
| print( | |
| f"n_jobs: {n_jobs}, duration: {duration:.3f} s, speedup: {timing['speedup']:.2f}x, Best R2: {hp_search.best_score_:.4f}" | |
| ) | |
| run_record["completed_at"] = datetime.now(timezone.utc).isoformat() | |
| write_json_result(results_path, run_record) | |
| # %% |
Author
Note that pandas is an optional dependency of scikit-learn. Ideally numpy support for string dtypes will improve and allow something similar with a variable length string dtype.
Author
@itamarst I still plan to iterate on this gist to add pixi config to make it easy to collect results on different envs and consolidate them together for reporting.
I filed the issue for the string dtypes, NumPy does actually have such a thing in newer versions.
One improvement to make to this benchmark: it's using joblib.Parallel/delayed, but you should use the ones from sklearn.utils.parallel so that it hits the oversaturation protection I added in the draft branch. In practice that doesn't seem to have any effect, so I am investigating. Possibly this is hitting some other bottleneck, unrelated to oversaturation, which is also useful to fix of course.
Author
I use joblib.Parallel / delayed just to call noops and move the loky worker pool resizing operations outside the timed section of the code.
But I agree, I will use the one from scikit-learn. Done.
Ah, right, sorry (woke up far too early).
Author
@itamarst I am in the process of moving the contents of this gist to a dedicated repo to be able to collect evaluation results in many environments and consolidate them in a subfolder for analysis:
https://github.com/ogrisel/example_pipelines
I found a bug in OpenBLAS on macOS M4 in the process. I think I have found a workaround, I will let you know once ready.
Thanks!
You saw Nathan's OpenMathLib/OpenBLAS#5836?
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Looking at a profile, a significant amount of time is spent in counting nans in pure Python on NumPy arrays with dtype
object, which is why it's in Python. So first, that means free-threaded Python benefits rather more from threads here.Second, this suggests a systemic performance issue: sklearn should really be trying to use Arrow arrays for strings (supported by Pandas 2) because then you're not in Python, and since missing data (as opposed to the hacky NaN usage Pandas does in its NumPy-based dtypes) are stored in a separate low-level array and counting that number is fast. But really almost all operations will be faster... I'll try to file an issue if there isn't one already.