# 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.

Note

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

## Installing nightly builds¶

The continuous integration servers of the scikit-learn project build, test and upload wheel packages for the most recent Python version on a nightly basis to help users test bleeding edge features or bug fixes:

pip install --pre -f https://sklearn-nightly.scdn8.secure.raxcdn.com scikit-learn


## Building from source¶

In the vast majority of cases, building scikit-learn for development purposes can be done with:

pip install cython pytest flake8


Then, in the main repository:

pip install --editable .


### Dependencies¶

Scikit-learn requires:

• Python (>= 3.5),

• NumPy (>= 1.11),

• SciPy (>= 0.17),

• Joblib (>= 0.11).

Note

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 >=0.28.5

• OpenMP

Note

It is possible to build scikit-learn without OpenMP support by setting the SKLEARN_NO_OPENMP environment variable (before cythonization). This is not recommended since it will force some estimators to run in sequential mode and their n_jobs parameter will be ignored.

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://github.com/scikit-learn/scikit-learn.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.

Once 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 mode, 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 .


Note

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

Note

You will have to re-run:

pip install --editable .


every time the source code of a compiled extension is changed (for instance when switching branches or pulling changes from upstream). Compiled extensions are Cython files (ending in .pyx or .pxd).

On Unix-like systems, you can equivalently type make in from the top-level folder. Have a look at the Makefile for additional utilities.

### Mac OSX¶

The default C compiler, Apple-clang, on Mac OSX does not directly support OpenMP. The first solution to build scikit-learn is to install another C compiler such as gcc or llvm-clang. Another solution is to enable OpenMP support on the default Apple-clang. In the following we present how to configure this second option.

You first need to install the OpenMP library:

brew install libomp


Then you need to set the following environment variables:

export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
export CPPFLAGS="$CPPFLAGS -Xpreprocessor -fopenmp" export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include"
export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include" export LDFLAGS="$LDFLAGS -L/usr/local/opt/libomp/lib -lomp"
export DYLD_LIBRARY_PATH=/usr/local/opt/libomp/lib


Finally you can build the package using the standard command.

### FreeBSD¶

The clang compiler included in FreeBSD 12.0 and 11.2 base systems does not include OpenMP support. You need to install the openmp library from packages (or ports):

sudo pkg install openmp


This will install header files in /usr/local/include and libs in /usr/local/lib. Since these directories are not searched by default, you can set the environment variables to these locations:

export CFLAGS="$CFLAGS -I/usr/local/include" export CXXFLAGS="$CXXFLAGS -I/usr/local/include"
export LDFLAGS="\$LDFLAGS -L/usr/local/lib -lomp"
export DYLD_LIBRARY_PATH=/usr/local/lib


Finally you can build the package using the standard command.

For the upcomming FreeBSD 12.1 and 11.3 versions, OpenMP will be included in the base system and these steps will not be necessary.

## Installing build dependencies¶

### Linux¶

Installing from source without conda 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:

sudo apt-get install build-essential python3-dev python3-setuptools \
python3-pip


and then:

pip3 install numpy scipy cython


Note

In order to build the documentation and run the example code contains in this documentation you will need matplotlib:

pip3 install matplotlib


When precompiled wheels are not avalaible for your architecture, you can install the system versions:

sudo apt-get install cython3 python3-numpy python3-scipy python3-matplotlib


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

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


Note

To use a high performance BLAS library (e.g. OpenBlas) see scipy installation instructions.

### Windows¶

To build scikit-learn on Windows you need a working C/C++ compiler in addition to numpy, scipy and setuptools.

The building command depends on the architecture of the Python interpreter, 32-bit or 64-bit. You can check the architecture by running the following in cmd or powershell console:

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.

You will need Build Tools for Visual Studio 2017.

Warning

You DO NOT need to install Visual Studio 2019. You only need the “Build Tools for Visual Studio 2019”, under “All downloads” -> “Tools for Visual Studio 2019”.

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

SET DISTUTILS_USE_SDK=1
"C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvarsall.bat" x64


Please be aware that the path above might be different from user to user. The aim is to point to the “vcvarsall.bat” file.

And build scikit-learn from this environment:

python setup.py install


Replace x64 by x86 to build for 32-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.