284 lines
8.8 KiB
ReStructuredText
284 lines
8.8 KiB
ReStructuredText
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.. _advanced-installation:
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===================================
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Advanced installation instructions
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===================================
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There are different ways to get scikit-learn installed:
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* :ref:`Install an official release <install_official_release>`. This
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is the best approach for most users. It will provide a stable version
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and pre-build packages are available for most platforms.
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* Install the version of scikit-learn provided by your
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:ref:`operating system or Python distribution <install_by_distribution>`.
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This is a quick option for those who have operating systems
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that distribute scikit-learn. It might not provide the latest release
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version.
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* :ref:`Building the package from source
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<install_bleeding_edge>`. This is best for users who want the
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latest-and-greatest features and aren't afraid of running
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brand-new code. This document describes how to build from source.
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.. note::
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If you wish to contribute to the project, you need to
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:ref:`install the latest development version<install_bleeding_edge>`.
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.. _install_nightly_builds:
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Installing nightly builds
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=========================
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The continuous integration servers of the scikit-learn project build, test
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and upload wheel packages for the most recent Python version on a nightly
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basis to help users test bleeding edge features or bug fixes::
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pip install --pre -f https://sklearn-nightly.scdn8.secure.raxcdn.com scikit-learn
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.. _install_bleeding_edge:
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Building from source
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=====================
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In the vast majority of cases, building scikit-learn for development purposes
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can be done with::
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pip install cython pytest flake8
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Then, in the main repository::
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pip install --editable .
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Please read below for details and more advanced instructions.
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Dependencies
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------------
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Scikit-learn requires:
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- Python (>= 3.5),
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- NumPy (>= 1.11),
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- SciPy (>= 0.17),
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- Joblib (>= 0.11).
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.. note::
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For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+
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are required. For PyPy, only installation instructions with pip apply.
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Building Scikit-learn also requires
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- Cython >=0.28.5
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- OpenMP
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.. note::
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It is possible to build scikit-learn without OpenMP support by setting the
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``SKLEARN_NO_OPENMP`` environment variable (before cythonization). This is
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not recommended since it will force some estimators to run in sequential
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mode and their ``n_jobs`` parameter will be ignored.
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Running tests requires
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.. |PytestMinVersion| replace:: 3.3.0
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- pytest >=\ |PytestMinVersion|
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Some tests also require `pandas <https://pandas.pydata.org>`_.
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.. _git_repo:
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Retrieving the latest code
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--------------------------
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We use `Git <https://git-scm.com/>`_ for version control and
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`GitHub <https://github.com/>`_ for hosting our main repository.
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You can check out the latest sources with the command::
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git clone git://github.com/scikit-learn/scikit-learn.git
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If you want to build a stable version, you can ``git checkout <VERSION>``
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to get the code for that particular version, or download an zip archive of
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the version from github.
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Once you have all the build requirements installed (see below for details),
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you can build and install the package in the following way.
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If you run the development version, it is cumbersome to reinstall the
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package each time you update the sources. Therefore it's recommended that you
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install in editable mode, which allows you to edit the code in-place. This
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builds the extension in place and creates a link to the development directory
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(see `the pip docs <https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs>`_)::
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pip install --editable .
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.. note::
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This is fundamentally similar to using the command ``python setup.py develop``
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(see `the setuptool docs <https://setuptools.readthedocs.io/en/latest/setuptools.html#development-mode>`_).
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It is however preferred to use pip.
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.. note::
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You will have to re-run::
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pip install --editable .
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every time the source code of a compiled extension is changed (for
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instance when switching branches or pulling changes from upstream).
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Compiled extensions are Cython files (ending in `.pyx` or `.pxd`).
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On Unix-like systems, you can equivalently type ``make in`` from the
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top-level folder. Have a look at the ``Makefile`` for additional utilities.
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Mac OSX
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-------
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The default C compiler, Apple-clang, on Mac OSX does not directly support
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OpenMP. The first solution to build scikit-learn is to install another C
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compiler such as gcc or llvm-clang. Another solution is to enable OpenMP
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support on the default Apple-clang. In the following we present how to
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configure this second option.
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You first need to install the OpenMP library::
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brew install libomp
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Then you need to set the following environment variables::
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export CC=/usr/bin/clang
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export CXX=/usr/bin/clang++
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export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp"
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export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include"
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export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include"
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export LDFLAGS="$LDFLAGS -L/usr/local/opt/libomp/lib -lomp"
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export DYLD_LIBRARY_PATH=/usr/local/opt/libomp/lib
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Finally you can build the package using the standard command.
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FreeBSD
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-------
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The clang compiler included in FreeBSD 12.0 and 11.2 base systems does not
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include OpenMP support. You need to install the `openmp` library from packages
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(or ports)::
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sudo pkg install openmp
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This will install header files in ``/usr/local/include`` and libs in
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``/usr/local/lib``. Since these directories are not searched by default, you
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can set the environment variables to these locations::
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export CFLAGS="$CFLAGS -I/usr/local/include"
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export CXXFLAGS="$CXXFLAGS -I/usr/local/include"
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export LDFLAGS="$LDFLAGS -L/usr/local/lib -lomp"
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export DYLD_LIBRARY_PATH=/usr/local/lib
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Finally you can build the package using the standard command.
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For the upcomming FreeBSD 12.1 and 11.3 versions, OpenMP will be included in
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the base system and these steps will not be necessary.
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Installing build dependencies
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=============================
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Linux
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-----
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Installing from source without conda requires you to have installed the
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scikit-learn runtime dependencies, Python development headers and a working
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C/C++ compiler. Under Debian-based operating systems, which include Ubuntu::
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sudo apt-get install build-essential python3-dev python3-setuptools \
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python3-pip
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and then::
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pip3 install numpy scipy cython
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.. note::
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In order to build the documentation and run the example code contains in
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this documentation you will need matplotlib::
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pip3 install matplotlib
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When precompiled wheels are not avalaible for your architecture, you can
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install the system versions::
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sudo apt-get install cython3 python3-numpy python3-scipy python3-matplotlib
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On Red Hat and clones (e.g. CentOS), install the dependencies using::
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sudo yum -y install gcc gcc-c++ python-devel numpy scipy
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.. note::
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To use a high performance BLAS library (e.g. OpenBlas) see
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`scipy installation instructions
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<https://docs.scipy.org/doc/scipy/reference/building/linux.html>`_.
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Windows
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-------
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To build scikit-learn on Windows you need a working C/C++ compiler in
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addition to numpy, scipy and setuptools.
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The building command depends on the architecture of the Python interpreter,
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32-bit or 64-bit. You can check the architecture by running the following in
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``cmd`` or ``powershell`` console::
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python -c "import struct; print(struct.calcsize('P') * 8)"
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The above commands assume that you have the Python installation folder in your
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PATH environment variable.
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You will need `Build Tools for Visual Studio 2017
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<https://visualstudio.microsoft.com/de/downloads/>`_.
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For 64-bit Python, configure the build environment with::
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SET DISTUTILS_USE_SDK=1
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"C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64
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And build scikit-learn from this environment::
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python setup.py install
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Replace ``x64`` by ``x86`` to build for 32-bit Python.
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Building binary packages and installers
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---------------------------------------
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The ``.whl`` package and ``.exe`` installers can be built with::
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pip install wheel
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python setup.py bdist_wheel bdist_wininst -b doc/logos/scikit-learn-logo.bmp
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The resulting packages are generated in the ``dist/`` folder.
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Using an alternative compiler
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-----------------------------
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It is possible to use `MinGW <http://www.mingw.org>`_ (a port of GCC to Windows
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OS) as an alternative to MSVC for 32-bit Python. Not that extensions built with
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mingw32 can be redistributed as reusable packages as they depend on GCC runtime
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libraries typically not installed on end-users environment.
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To force the use of a particular compiler, pass the ``--compiler`` flag to the
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build step::
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python setup.py build --compiler=my_compiler install
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where ``my_compiler`` should be one of ``mingw32`` or ``msvc``.
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