.. _advanced-installation: =================================== Advanced installation instructions =================================== There are different ways to get scikit-learn installed: * Install the version of scikit-learn provided by your :ref:`operating system or Python distribution `. This is the quickest option for those who have operating systems that distribute scikit-learn. * :ref:`Install an official release `. This is the best approach for users who want a stable version number and aren't concerned about running a slightly older version of scikit-learn. * :ref:`Install the latest development version `. This is best for users who want the latest-and-greatest features and aren't afraid of running brand-new code. .. note:: If you wish to contribute to the project, you need to :ref:`install the latest development version`. .. _install_official_release: Installing an official release ============================== Scikit-learn requires: - Python (>= 2.6 or >= 3.3), - NumPy (>= 1.6.1), - SciPy (>= 0.9). Mac OSX ------- Scikit-learn and its dependencies are all available as wheel packages for OSX:: pip install -U numpy scipy scikit-learn Linux ----- At this time scikit-learn does not provide official binary packages for Linux so you have to build from source if you want the lastest version. If you don't need the newest version, consider using your package manager to install scikit-learn. it is usually the easiest way, but might not provide the newest version. installing build dependencies ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ installing from source requires you to have installed the scikit-learn runtime dependencies, python development headers and a working c/c++ compiler. under debian-based operating systems, which include ubuntu, if you have python 2 you can install all these requirements by issuing:: sudo apt-get install build-essential python-dev python-setuptools \ python-numpy python-scipy \ libatlas-dev libatlas3gf-base if you have python 3:: sudo apt-get install build-essential python3-dev python3-setuptools \ python3-numpy python3-scipy \ libatlas-dev libatlas3gf-base on recent debian and ubuntu (e.g. ubuntu 13.04 or later) make sure that atlas is used to provide the implementation of the blas and lapack linear algebra routines:: sudo update-alternatives --set libblas.so.3 \ /usr/lib/atlas-base/atlas/libblas.so.3 sudo update-alternatives --set liblapack.so.3 \ /usr/lib/atlas-base/atlas/liblapack.so.3 .. note:: in order to build the documentation and run the example code contains in this documentation you will need matplotlib:: sudo apt-get install python-matplotlib .. note:: the above installs the atlas implementation of blas (the basic linear algebra subprograms library). ubuntu 11.10 and later, and recent (testing) versions of debian, offer an alternative implementation called openblas. using openblas can give speedups in some scikit-learn modules, but can freeze joblib/multiprocessing prior to openblas version 0.2.8-4, so using it is not recommended unless you know what you're doing. if you do want to use openblas, then replacing atlas only requires a couple of commands. atlas has to be removed, otherwise numpy may not work:: sudo apt-get remove libatlas3gf-base libatlas-dev sudo apt-get install libopenblas-dev sudo update-alternatives --set libblas.so.3 \ /usr/lib/openblas-base/libopenblas.so.0 sudo update-alternatives --set liblapack.so.3 \ /usr/lib/lapack/liblapack.so.3 on red hat and clones (e.g. centos), install the dependencies using:: sudo yum -y install gcc gcc-c++ numpy python-devel scipy building scikit-learn with pip ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ this is usually the fastest way to install or upgrade to the latest stable release:: pip install --user --install-option="--prefix=" -u scikit-learn the ``--user`` flag asks pip to install scikit-learn in the ``$home/.local`` folder therefore not requiring root permission. this flag should make pip ignore any old version of scikit-learn previously installed on the system while benefiting from system packages for numpy and scipy. those dependencies can be long and complex to build correctly from source. the ``--install-option="--prefix="`` flag is only required if python has a ``distutils.cfg`` configuration with a predefined ``prefix=`` entry. from source package ~~~~~~~~~~~~~~~~~~~ download the source package from `pypi `_, , unpack the sources and cd into the source directory. this packages uses distutils, which is the default way of installing python modules. the install command is:: python setup.py install or alternatively (also from within the scikit-learn source folder):: pip install . .. warning:: packages installed with the ``python setup.py install`` command cannot be uninstalled nor upgraded by ``pip`` later. to properly uninstall scikit-learn in that case it is necessary to delete the ``sklearn`` folder from your python ``site-packages`` directory. windows ------- first, you need to install `numpy `_ and `scipy `_ from their own official installers. wheel packages (.whl files) for scikit-learn from `pypi `_ can be installed with the `pip `_ utility. open a console and type the following to install or upgrade scikit-learn to the latest stable release:: pip install -u scikit-learn if there are no binary packages matching your python, version you might to try to install scikit-learn and its dependencies from `christoph gohlke unofficial windows installers `_ or from a :ref:`python distribution ` instead. .. _install_by_distribution: third party distributions of scikit-learn ========================================= some third-party distributions are now providing versions of scikit-learn integrated with their package-management systems. these can make installation and upgrading much easier for users since the integration includes the ability to automatically install dependencies (numpy, scipy) that scikit-learn requires. the following is an incomplete list of python and os distributions that provide their own version of scikit-learn. macports for mac osx -------------------- the macports package is named ``py-scikits-learn``, where ``xy`` denotes the python version. it can be installed by typing the following command:: sudo port install py26-scikit-learn or:: sudo port install py27-scikit-learn arch linux ---------- arch linux's package is provided through the `official repositories `_ as ``python-scikit-learn`` for python 3 and ``python2-scikit-learn`` for python 2. it can be installed by typing the following command: .. code-block:: none # pacman -s python-scikit-learn or: .. code-block:: none # pacman -s python2-scikit-learn depending on the version of python you use. netbsd ------ scikit-learn is available via `pkgsrc-wip `_: http://pkgsrc.se/wip/py-scikit_learn fedora ------ the fedora package is called ``python-scikit-learn`` for the python 2 version and ``python3-scikit-learn`` for the python 3 version. both versions can be installed using ``yum``:: $ sudo yum install python-scikit-learn or:: $ sudo yum install python3-scikit-learn building on windows ------------------- to build scikit-learn on windows you need a working c/c++ compiler in addition to numpy, scipy and setuptools. picking the right compiler depends on the version of python (2 or 3) and the architecture of the python interpreter, 32-bit or 64-bit. you can check the python version by running the following in ``cmd`` or ``powershell`` console:: python --version and the architecture with:: python -c "import struct; print(struct.calcsize('p') * 8)" the above commands assume that you have the python installation folder in your path environment variable. 32-bit python ------------- for 32-bit python it is possible use the standalone installers for `microsoft visual c++ express 2008 `_ for python 2 or `microsoft visual c++ express 2010 `_ or python 3. once installed you should be able to build scikit-learn without any particular configuration by running the following command in the scikit-learn folder:: python setup.py install 64-bit python ------------- for the 64-bit architecture, you either need the full visual studio or the free windows sdks that can be downloaded from the links below. the windows sdks include the msvc compilers both for 32 and 64-bit architectures. they come as a ``grmsdkx_en_dvd.iso`` file that can be mounted as a new drive with a ``setup.exe`` installer in it. - for python 2 you need sdk **v7.0**: `ms windows sdk for windows 7 and .net framework 3.5 sp1 `_ - for python 3 you need sdk **v7.1**: `ms windows sdk for windows 7 and .net framework 4 `_ both sdks can be installed in parallel on the same host. to use the windows sdks, you need to setup the environment of a ``cmd`` console launched with the following flags (at least for sdk v7.0):: cmd /e:on /v:on /k then configure the build environment with:: set distutils_use_sdk=1 set mssdk=1 "c:\program files\microsoft sdks\windows\v7.0\setup\windowssdkver.exe" -q -version:v7.0 "c:\program files\microsoft sdks\windows\v7.0\bin\setenv.cmd" /x64 /release finally you can build scikit-learn in the same ``cmd`` console:: python setup.py install replace ``v7.0`` by the ``v7.1`` in the above commands to do the same for python 3 instead of python 2. replace ``/x64`` by ``/x86`` to build for 32-bit python instead of 64-bit python. building binary packages and installers --------------------------------------- the ``.whl`` package and ``.exe`` installers can be built with:: pip install wheel python setup.py bdist_wheel bdist_wininst -b doc/logos/scikit-learn-logo.bmp the resulting packages are generated in the ``dist/`` folder. using an alternative compiler ----------------------------- it is possible to use `mingw `_ (a port of gcc to windows os) as an alternative to msvc for 32-bit python. not that extensions built with mingw32 can be redistributed as reusable packages as they depend on gcc runtime libraries typically not installed on end-users environment. to force the use of a particular compiler, pass the ``--compiler`` flag to the build step:: python setup.py build --compiler=my_compiler install where ``my_compiler`` should be one of ``mingw32`` or ``msvc``. .. _install_bleeding_edge: bleeding edge ============= see section :ref:`git_repo` on how to get the development version. then follow the previous instructions to build from source depending on your platform. .. _testing: testing ======= testing scikit-learn once installed ----------------------------------- testing requires having the `nose `_ library. after installation, the package can be tested by executing *from outside* the source directory:: $ nosetests -v sklearn under windows, it is recommended to use the following command (adjust the path to the ``python.exe`` program) as using the ``nosetests.exe`` program can badly interact with tests that use ``multiprocessing``:: c:\python34\python.exe -c "import nose; nose.main()" -v sklearn this should give you a lot of output (and some warnings) but eventually should finish with a message similar to:: ran 3246 tests in 260.618s ok (skip=20) otherwise, please consider posting an issue into the `bug tracker `_ or to the :ref:`mailing_lists` including the traceback of the individual failures and errors. please include your operation system, your version of numpy, scipy and scikit-learn, and how you installed scikit-learn. testing scikit-learn from within the source folder -------------------------------------------------- scikit-learn can also be tested without having the package installed. for this you must compile the sources inplace from the source directory:: python setup.py build_ext --inplace test can now be run using nosetests:: nosetests -v sklearn/ this is automated by the commands:: make in and:: make test you can also install a symlink named ``site-packages/scikit-learn.egg-link`` to the development folder of scikit-learn with:: pip install --editable .