8000 BLD Migrate away from distutils and only use setuptools (#24563) · andportnoy/scikit-learn@6258ce6 · GitHub
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BLD Migrate away from distutils and only use setuptools (scikit-learn#24563)
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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‎MANIFEST.in

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include *.rst
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recursive-include doc *
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recursive-include examples *
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recursive-include sklearn *.c *.h *.pyx *.pxd *.pxi *.tp
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recursive-include sklearn *.c *.cpp *.h *.pyx *.pxd *.pxi *.tp
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recursive-include sklearn/datasets *.csv *.csv.gz *.rst *.jpg *.txt *.arff.gz *.json.gz
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include COPYING
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include README.rst

‎azure-pipelines.yml

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LOCK_FILE: './build_tools/azure/python_nogil_lock.txt'
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COVERAGE: 'false'
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# Check compilation with intel C++ compiler (ICC)
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- template: build_tools/azure/posix.yml
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parameters:
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name: Linux_Nightly_ICC
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vmImage: ubuntu-20.04
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dependsOn: [git_commit, linting]
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condition: |
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and(
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succeeded(),
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not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),
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or(eq(variables['Build.Reason'], 'Schedule'),
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contains(dependencies['git_commit']['outputs']['commit.message'], '[icc-build]')
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)
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)
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matrix:
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pylatest_conda_forge_mkl:
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DISTRIB: 'conda'
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LOCK_FILE: 'build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock'
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COVERAGE: 'false'
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BUILD_WITH_ICC: 'true'
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- template: build_tools/azure/posix-docker.yml
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parameters:
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name: Linux_Nightly_PyPy
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DISTRIB: 'conda'
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LOCK_FILE: './build_tools/azure/py38_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock'
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COVERAGE: 'false'
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BUILD_WITH_ICC: 'false'
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SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '0' # non-default seed
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- template: build_tools/azure/posix.yml

‎build_tools/azure/install.sh

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export PYTHON_NOGIL_PATH="${PYTHON_NOGIL_CLONE_PATH}/python"
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cd $OLDPWD
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elif [[ "$BUILD_WITH_ICC" == "true" ]]; then
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wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
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sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
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rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
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sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
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sudo apt-get update
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sudo apt-get install intel-oneapi-compiler-dpcpp-cpp-and-cpp-classic
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source /opt/intel/oneapi/setvars.sh
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fi
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}
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export LDFLAGS="$LDFLAGS -Wl,--sysroot=/"
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fi
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if [[ "$BUILD_WITH_ICC" == "true" ]]; then
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# The "build_clib" command is implicitly used to build "libsvm-skl".
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# To compile with a different compiler, we also need to specify the
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# compiler for this command
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python setup.py build_ext --compiler=intelem -i build_clib --compiler=intelem
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fi
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# TODO use a specific variable for this rather than using a particular build ...
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if [[ "$DISTRIB" == "conda-pip-latest" ]]; then
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# Check that pip can automatically build scikit-learn with the build

‎build_tools/azure/test_docs.sh

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source $VIRTUALENV/bin/activate
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fi
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if [[ "$BUILD_WITH_ICC" == "true" ]]; then
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source /opt/intel/oneapi/setvars.sh
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fi
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make test-doc

‎build_tools/azure/test_script.sh

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source $VIRTUALENV/bin/activate
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fi
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if [[ "$BUILD_WITH_ICC" == "true" ]]; then
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source /opt/intel/oneapi/setvars.sh
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fi
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if [[ "$BUILD_REASON" == "Schedule" ]]; then
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# Enable global random seed randomization to discover seed-sensitive tests
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# only on nightly builds.
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# removes its usage
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TEST_CMD="$TEST_CMD -Wignore:tostring:DeprecationWarning"
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# Python 3.10 deprecates distutils, which is imported by numpy internally
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TEST_CMD="$TEST_CMD -Wignore:The\ distutils:DeprecationWarning"
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# Ignore distutils deprecation warning, used by joblib internally
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TEST_CMD="$TEST_CMD -Wignore:distutils\ Version\ classes\ are\ deprecated:DeprecationWarning"
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‎build_tools/circle/list_versions.py

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import re
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import sys
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from distutils.version import LooseVersion
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from sklearn.utils.fixes import parse_version
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from urllib.request import urlopen
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# The 'dev' branch should be explicitly handled
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return "zip"
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current_version = LooseVersion(version)
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min_zip_version = LooseVersion("0.24")
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current_version = parse_version(version)
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min_zip_version = parse_version("0.24")
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return "zip" if current_version >= min_zip_version else "pdf"
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# Output in order: dev, stable, decreasing other version
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seen = set()
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for name in NAMED_DIRS + sorted(
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(k for k in dirs if k[:1].isdigit()), key=LooseVersion, reverse=True
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(k for k in dirs if k[:1].isdigit()), key=parse_version, reverse=True
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):
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version_num, file_size = dirs[name]
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if version_num in seen:

‎build_tools/github/build_wheels.sh

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export CFLAGS="$CFLAGS -I$PREFIX/include"
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export CXXFLAGS="$CXXFLAGS -I$PREFIX/include"
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export LDFLAGS="$LDFLAGS -Wl,-rpath,$PREFIX/lib -L$PREFIX/lib -lomp"
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# Disable the use of setuptools's vendored copy distutils when invoking setuptools
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# See: https://setuptools.pypa.io/en/latest/deprecated/distutils-legacy.html
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# TODO: remove the definition of this environment variable when no
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# reference to distutils exist in the code-base for building scikit-learn.
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export SETUPTOOLS_USE_DISTUTILS=stdlib
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fi
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# The version of the built dependencies are specified

‎doc/computing/computational_performance.rst

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non-optimized BLAS.
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You can display the BLAS / LAPACK implementation used by your NumPy / SciPy /
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scikit-learn install with the following commands::
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scikit-learn install with the following command::
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from numpy import show_config
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show_config()
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python -c "import sklearn; sklearn.show_versions()"
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Optimized BLAS / LAPACK implementations include:
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- Atlas (need hardware specific tuning by rebuilding on the target machine)

‎doc/developers/advanced_installation.rst

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.. _conda environment: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
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.. _Miniforge3: https://github.com/conda-forge/miniforge#miniforge3
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Alternative compilers
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=====================
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The command:
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.. prompt:: bash $
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pip install --verbose --editable .
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will build scikit-learn using your default C/C++ compiler. If you want to build
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scikit-learn with another compiler handled by ``distutils`` or by
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``numpy.distutils``, use the following command:
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.. prompt:: bash $
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python setup.py build_ext --compiler=<compiler> -i build_clib --compiler=<compiler>
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To see the list of available compilers run:
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.. prompt:: bash $
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python setup.py build_ext --help-compiler
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If your compiler is not listed here, you can specify it via the ``CC`` and
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``LDSHARED`` environment variables (does not work on windows):
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.. prompt:: bash $
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CC=<compiler> LDSHARED="<compiler> -shared" python setup.py build_ext -i
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Building with Intel C Compiler (ICC) using oneAPI on Linux
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----------------------------------------------------------
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Intel provides access to all of its oneAPI toolkits and packages through a
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public APT repository. First you need to get and install the public key of this
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repository:
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.. prompt:: bash $
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wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
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sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
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rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
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Then, add the oneAPI repository to your APT repositories:
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.. prompt:: bash $
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sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
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sudo apt-get update
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Install ICC, packaged under the name
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``intel-oneapi-compiler-dpcpp-cpp-and-cpp-classic``:
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.. prompt:: bash $
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sudo apt-get install intel-oneapi-compiler-dpcpp-cpp-and-cpp-classic
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Before using ICC, you need to set up environment variables:
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.. prompt:: bash $
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source /opt/intel/oneapi/setvars.sh
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Finally, you can build scikit-learn. For example on Linux x86_64:
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.. prompt:: bash $
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python setup.py build_ext --compiler=intelem -i build_clib --compiler=intelem
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Parallel builds
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===============
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‎doc/developers/contributing.rst

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[lint skip] Azure pipeline skips linting
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[scipy-dev] Build & test with our dependencies (numpy, scipy, etc ...) development builds
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[nogil] Build & test with the nogil experimental branches of CPython, Cython, NumPy, SciPy...
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[icc-build] Build & test with the Intel C compiler (ICC)
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[pypy] Build & test with PyPy
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[float32] Run float32 tests by setting `SKLEARN_RUN_FLOAT32_TESTS=1`. See :ref:`environment_variable` for more details
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[doc skip] Docs are not built

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