Advanced installation instructions

There are different ways to get scikit-learn installed:

  • Install an official release. This is the best approach for most users. It will provide a stable version and pre-build packages are available for most platforms.
  • Install the version of scikit-learn provided by your operating system or Python distribution. This is a quick option for those who have operating systems that distribute scikit-learn. It might not provide the latest release version.
  • Building the package from source. This is best for users who want the latest-and-greatest features and aren’t afraid of running brand-new code. This document describes how to build from source.


If you wish to contribute to the project, you need to install the latest development version.

Building from source

Scikit-learn requires:

  • Python (>= 2.7 or >= 3.4),
  • NumPy (>= 1.8.2),
  • SciPy (>= 0.13.3).


For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required. For PyPy, only installation instructions with pip apply.

Building Scikit-learn also requires

  • Cython >=0.23

Running tests requires

  • pytest >=3.3.0

Some tests also require pandas.

Retrieving the latest code

We use Git for version control and GitHub for hosting our main repository.

You can check out the latest sources with the command:

git clone git://

If you want to build a stable version, you can git checkout <VERSION> to get the code for that particular version, or download an zip archive of the version from github.

If you have all the build requirements installed (see below for details), you can build and install the package in the following way.

If you run the development version, it is cumbersome to reinstall the package each time you update the sources. Therefore it’s recommended that you install in editable, which allows you to edit the code in-place. This builds the extension in place and creates a link to the development directory (see the pip docs):

pip install --editable .


This is fundamentally similar to using the command python develop (see the setuptool docs). It is however preferred to use pip.


If you decide to do an editable install you have to rerun:

pip install --editable .

every time the source code of a compiled extension is changed (for instance when switching branches or pulling changes from upstream).

On Unix-like systems, you can simply type make in the top-level folder to build in-place and launch all the tests. Have a look at the Makefile for additional utilities.

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 libatlas3-base

If you have Python 3:

sudo apt-get install build-essential python3-dev python3-setuptools \
                     python3-numpy python3-scipy \
                     libatlas-dev libatlas3-base

On recent Debian and Ubuntu (e.g. Ubuntu 14.04 or later) make sure that ATLAS is used to provide the implementation of the BLAS and LAPACK linear algebra routines:

sudo update-alternatives --set \
sudo update-alternatives --set \


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


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 \
sudo update-alternatives --set \

On Red Hat and clones (e.g. CentOS), install the dependencies using:

sudo yum -y install gcc gcc-c++ numpy python-devel scipy


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.

Python >= 3.5

For Python versions as of 3.5, you need Build Tools for Visual Studio 2017.

For 64-bit Python, configure the build environment with:

"C:\Program Files (x86)\Microsoft Visual Studio\2017\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64

And build scikit-learn from this environment:

python install

Replace x64 by x86 to build for 32-bit Python.

32-bit Python (<= 3.4)

For 32-bit Python versions up to 3.4 use Microsoft Visual C++ Express 2010.

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 install

64-bit Python (<= 3.4)

For 64-bit Python versions up to 3.4, 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.

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:

"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 install

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 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 build --compiler=my_compiler install

where my_compiler should be one of mingw32 or msvc.


Testing scikit-learn once installed

Testing requires having pytest >=3.3.0. Some tests also require having pandas <> installed. After installation, the package can be tested by executing from outside the source directory:

$ pytest sklearn

This should give you a lot of output (and some warnings) but eventually should finish with a message similar to:

=========== 8304 passed, 26 skipped, 4659 warnings in 557.76 seconds ===========

Otherwise, please consider posting an issue into the GitHub issue tracker or to the Mailing List including the traceback of the individual failures and errors. Please include your operating 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 build_ext --inplace

Test can now be run using pytest:

pytest sklearn

This is automated by the commands:

make in


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 .