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.

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:

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
mamba env create -f environment.yml

# Or, install only the required build dependencies
mamba install python numpy cython pythran pybind11 compilers openblas 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
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
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
git submodule update --init

Then you want to do the following:

Create a dedicated development environment (virtual environment or conda environment),
Install all needed dependencies (build, and also test, doc and optional dependencies),
Build SciPy with our dev.py developer interface.

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

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):

python -m venv venv
source venv/bin/activate

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

# Build dependencies
python -m pip install numpy cython pythran pybind11 meson ninja pydevtool rich-click

# Test and optional runtime dependencies
python -m pip install pytest pytest-xdist pytest-timeout pooch threadpoolctl asv gmpy2 mpmath

# Doc build dependencies
python -m pip sphinx "pydata-sphinx-theme==0.9.0" sphinx-design matplotlib numpydoc jupytext myst-nb

# Dev dependencies (static typing and linting)
python -m pip mypy typing_extensions types-psutil pycodestyle ruff cython-lint

Conda env

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

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

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

Note

On Windows 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 under the Windows tab of the System-level dependencies section higher up.

To build SciPy in an activated development environment, run:

python dev.py build

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

Building from source#

System-level dependencies#

Building SciPy from source#

Building from source for SciPy development#

Customizing builds#

Background information#