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# Building from source

Note

If you are only trying to install SciPy, we recommend using binaries - see Installation for details on that.

Building SciPy from source requires setting up system-level dependencies (compilers, BLAS/LAPACK libraries, etc.) first, and then invoking a build. The build may be done in order to install SciPy for local usage, develop SciPy itself, or build redistributable binary packages. And it may be desired to customize aspects of how the build is done. This guide will cover all these aspects. In addition, it provides background information on how the SciPy build works, and links to up-to-date guides for generic Python build & packaging documentation that is relevant.

## System-level dependencies

SciPy uses compiled code for speed, which means you need compilers and some other system-level (i.e, non-Python / non-PyPI) dependencies to build it on your system.

Note

If you are using Conda, you can skip the steps in this section - with the exception of installing compilers for Windows or the Apple Developer Tools for macOS. All other dependencies will be installed automatically by the `conda env create -f environment.yml` command.

Linux

If you want to use the system Python and `pip`, you will need:

- C, C++, and Fortran compilers (typically `gcc`, `g++`, and `gfortran`).

- Python header files (typically a package named `python3-dev` or `python3-devel`)

- BLAS and LAPACK libraries. OpenBLAS is the SciPy default; other variants include ATLAS and MKL.

- `pkg-config` for dependency detection.

Debian/Ubuntu Linux

To install SciPy build requirements, you can do:

    sudo apt install -y gcc g++ gfortran libopenblas-dev liblapack-dev pkg-config python3-pip python3-dev

Alternatively, you can do:

    sudo apt build-dep scipy

This command installs whatever is needed to build SciPy, with the advantage that new dependencies or updates to required versions are handled by the package managers.

Fedora / RHEL & CentOS 8+

To install SciPy build requirements, you can do:

    sudo dnf install gcc-gfortran python3-devel openblas-devel lapack-devel pkgconfig

Alternatively, you can do:

    sudo dnf builddep scipy

This command installs whatever is needed to build SciPy, with the advantage that new dependencies or updates to required versions are handled by the package managers.

CentOS & RHEL <=7

To install SciPy build requirements, you can do:

    sudo yum install gcc-gfortran python3-devel openblas-devel lapack-devel pkgconfig

Alternatively, you can do:

    sudo yum-builddep scipy

This command installs whatever is needed to build SciPy, with the advantage that new dependencies or updates to required versions are handled by the package managers.

Arch

To install SciPy build requirements, you can do:

    sudo pacman -S gcc-fortran openblas pkgconf

macOS

Install Apple Developer Tools. An easy way to do this is to open a terminal window, enter the command:

    xcode-select --install

and follow the prompts. Apple Developer Tools includes Git, the Clang C/C++ compilers, and other development utilities that may be required.

Do not use the macOS system Python. Instead, install Python with the python.org installer or with a package manager like Homebrew, MacPorts or Fink.

The other system dependencies you need are a Fortran compiler, BLAS and LAPACK libraries, and pkg-config. They’re easiest to install with Homebrew:

    brew install gfortran openblas pkg-config

To allow the build tools to find OpenBLAS, you must run:

    brew info openblas | grep PKG_CONFIG_PATH

This will give you a command starting with `export PKG_CONFIG_PATH=`, which you must run.

Note

As of SciPy 1.14.0, we have added support for the Accelerate library for BLAS and LAPACK. It requires macOS 13.3 or greater. To build with Accelerate instead of OpenBLAS, see Selecting BLAS and LAPACK libraries.

Windows

A compatible set of C, C++ and Fortran compilers is needed to build SciPy. This is trickier on Windows than on other platforms, because MSVC does not support Fortran, and gfortran and MSVC can’t be used together. You will need one of these sets of compilers:

1. Mingw-w64 compilers (`gcc`, `g++`, `gfortran`) - recommended, because it’s easiest to install and is what we use for SciPy’s own CI and binaries

2. MSVC + Intel Fortran (`ifort`)

3. Intel compilers (`icc`, `ifort`)

Compared to macOS and Linux, building SciPy on Windows is a little more difficult, due to the need to set up these compilers. It is not possible to just call a one-liner on the command prompt as you would on other platforms.

First, install Microsoft Visual Studio - the 2019 Community Edition or any newer version will work (see the Visual Studio download site). This is needed even if you use the MinGW-w64 or Intel compilers, in order to ensure you have the Windows Universal C Runtime (the other components of Visual Studio are not needed when using Mingw-w64, and can be deselected if desired, to save disk space).

MinGW-w64

There are several sources of binaries for MinGW-w64. We recommend the RTools versions, which can be installed with Chocolatey (see Chocolatey install instructions here):

    choco install rtools -y --no-progress --force --version=4.0.0.20220206

In case of issues, we recommend using the exact same version as used in the SciPy GitHub Actions CI jobs for Windows.

MSVC

The MSVC installer does not put the compilers on the system path, and the install location may change. To query the install location, MSVC comes with a `vswhere.exe` command-line utility. And to make the C/C++ compilers available inside the shell you are using, you need to run a `.bat` file for the correct bitness and architecture (e.g., for 64-bit Intel CPUs, use `vcvars64.bat`).

For detailed guidance, see Use the Microsoft C++ toolset from the command line.

Intel

Similar to MSVC, the Intel compilers are designed to be used with an activation script (`Intel\oneAPI\setvars.bat`) that you run in the shell you are using. This makes the compilers available on the path. For detailed guidance, see Get Started with the Intel® oneAPI HPC Toolkit for Windows.

Note

Compilers should be on the system path (i.e., the `PATH` environment variable should contain the directory in which the compiler executables can be found) in order to be found, with the exception of MSVC which will be found automatically if and only if there are no other compilers on the `PATH`. You can use any shell (e.g., Powershell, `cmd` or Git Bash) to invoke a build. To check that this is the case, try invoking a Fortran compiler in the shell you use (e.g., `gfortran --version` or `ifort --version`).

Warning

When using a conda environment it is possible that the environment creation will not work due to an outdated Fortran compiler. If that happens, remove the `compilers` entry from `environment.yml` and try again. The Fortran compiler should be installed as described in this section.

## Building SciPy from source

If you want to only install SciPy from source once and not do any development work, then the recommended way to build and install is to use `pip`. Otherwise, conda is recommended.

Note

If you don’t have a conda installation yet, we recommend using Miniforge; any conda flavor will work though.

### Building from source to use SciPy

Conda env

If you are using a conda environment, `pip` is still the tool you use to invoke a from-source build of SciPy. It is important to always use the `--no-build-isolation` flag to the `pip install` command, to avoid building against a `numpy` wheel from PyPI. In order for that to work you must first install the remaining build dependencies into the conda environment:

    # Either install all SciPy dev dependencies into a fresh conda environment
    conda env create -f environment.yml

    # Or, install only the required build dependencies
    conda install python numpy cython pythran pybind11 compilers openblas meson-python pkg-config

    # To build the latest stable release:
    pip install scipy --no-build-isolation --no-binary scipy

    # To build a development version, you need a local clone of the SciPy git repository:
    git clone https://github.com/scipy/scipy.git
    cd scipy
    git submodule update --init
    pip install . --no-build-isolation

Virtual env or system Python

    # To build the latest stable release:
    pip install scipy --no-binary scipy

    # To build a development version, you need a local clone of the SciPy git repository:
    git clone https://github.com/scipy/scipy.git
    cd scipy
    git submodule update --init
    pip install .

### Building from source for SciPy development

If you want to build from source in order to work on SciPy itself, first clone the SciPy repository:

    git clone https://github.com/scipy/scipy.git
    cd scipy
    git submodule update --init

Tip

Many of the steps described below can now be accomplished automatically with commands which execute tasks in SciPy’s Pixi workspace, like `pixi run build`. To use this workspace, install Pixi and execute `pixi task list` in a local clone of SciPy’s source to see the various tasks available.

This removes the need for developers to keep track of development environments and installed dependencies, as running a task automatically installs and uses a suitable environment. A future update to this guide will provide full details on using the Pixi workspace for SciPy development.

Then you want to do the following:

1. Create a dedicated development environment (virtual environment or conda environment),

2. Install all needed dependencies (build, and also test, doc and optional dependencies),

3. Build SciPy with our `spin` developer interface.

Step (3) is always the same, steps (1) and (2) are different between conda and virtual environments:

Conda env

To create a `scipy-dev` development environment with every required and optional dependency installed, run:

    conda env create -f environment.yml
    conda activate scipy-dev

Virtual env or system Python

Note

There are many tools to manage virtual environments, like `venv`, `virtualenv`/`virtualenvwrapper`, `pyenv`/`pyenv-virtualenv`, Poetry, PDM, Hatch, and more. Here we use the basic `venv` tool that is part of the Python stdlib. You can use any other tool; all we need is an activated Python environment.

Create and activate a virtual environment in a new directory named `venv` ( note that the exact activation command may be different based on your OS and shell - see “How venvs work” in the `venv` docs).

Linux

    python -m venv venv
    source venv/bin/activate

macOS

    python -m venv venv
    source venv/bin/activate

Windows

    python -m venv venv
    venv\Scripts\Activate.ps1

Then install the Python-level dependencies (see `pyproject.toml`) from PyPI with:

    # All dependencies
    python -m pip install -r requirements/all.txt

    # Alternatively, you can install just the dependencies for certain
    # development tasks:

    # Build and dev dependencies (for `spin {build, lint, mypy}`)
    python -m pip install -r requirements/build.txt -r requirements/dev.txt

    # Doc dependencies (for `spin {doc, refguide-check}`)
    python -m pip install -r requirements/doc.txt

    # Test dependencies (for `spin {test, bench, refguide-check}`)
    python -m pip install -r requirements/test.txt

To build SciPy in an activated development environment, run:

    spin build

This will install SciPy inside the repository (by default in a `build-install` directory). You can then run tests (`spin test`), drop into IPython (`spin ipython`), or take other development steps like build the html documentation or running benchmarks. The `spin` interface is self-documenting, so please see `spin --help` and `spin <subcommand> --help` for detailed guidance.

IDE support & editable installs

While the `spin build` interface is our recommended way of working on SciPy, it has one limitation: because of the custom install location, SciPy will not be recognized automatically within an IDE (e.g., for running a script via a “run” button, or setting breakpoints visually). This will work better with an in-place build (or “editable install”).

Editable installs are supported via `spin install`. When making changes to SciPy code, including to compiled code, there is no need to manually rebuild or reinstall. However, should you need to run `git clean -xdf`, which removes the built extension modules, remember to also uninstall SciPy with `pip uninstall scipy`.

See the meson-python documentation on editable installs for more details on how things work under the hood.

Note that editable installations are unsuitable for some forms of development, such as working on sections of C/Cython API where tests are disabled for editable installations. They also tend to hit weird corner cases more frequently than regular installations, and have some known limitations like a lack of support for static typing.

## Installing static type stubs

If you would like to install static type stubs to aid your development of SciPy, you can include the `scipy-stubs` package in your development environment. It is available on PyPI and conda-forge - see the scipy-stubs installation guide.

## Customizing builds

- Compiler selection and customizing a build
- BLAS and LAPACK
- Cross compilation
- Building redistributable binaries

## Background information

- Understanding Meson
- Introspecting build steps
- Meson and `distutils` ways of doing things