Building from source#

Note

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

Building NumPy 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 NumPy for local usage, develop NumPy 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 NumPy build works, and links to up-to-date guides for generic Python build & packaging documentation that is relevant.

System-level dependencies#

NumPy 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 mamba env create -f environment.yml command.

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

  • C and C++ compilers (typically GCC).

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

  • BLAS and LAPACK libraries. OpenBLAS is the NumPy default; other variants include Apple Accelerate, MKL, ATLAS and Netlib (or “Reference”) BLAS and LAPACK.

  • pkg-config for dependency detection.

  • A Fortran compiler is needed only for running the f2py tests. The instructions below include a Fortran compiler, however you can safely leave it out.

To install NumPy 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 numpy

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

To install NumPy build requirements, you can do:

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

Alternatively, you can do:

sudo dnf builddep numpy

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

To install NumPy build requirements, you can do:

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

Alternatively, you can do:

sudo yum-builddep numpy

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

To install NumPy build requirements, you can do:

sudo pacman -S gcc-fortran openblas pkgconf

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.

On macOS >=13.3, the easiest build option is to use Accelerate, which is already installed and will be automatically used by default.

On older macOS versions you need a different BLAS library, most likely OpenBLAS, plus pkg-config to detect OpenBLAS. These are easiest to install with Homebrew:

brew install openblas pkg-config gfortran

On Windows, the use of a Fortran compiler is more tricky than on other platforms, because MSVC does not support Fortran, and gfortran and MSVC can’t be used together. If you don’t need to run the f2py tests, simply using MSVC is easiest. Otherwise, you will need one of these sets of compilers:

  1. MSVC + Intel Fortran (ifort)

  2. Intel compilers (icc, ifort)

  3. Mingw-w64 compilers (gcc, g++, gfortran)

Compared to macOS and Linux, building NumPy 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).

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.

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.

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

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 NumPy from source#

If you want to only install NumPy 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 Mambaforge; any conda flavor will work though.

Building from source to use NumPy#

If you are using a conda environment, pip is still the tool you use to invoke a from-source build of NumPy. 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 NumPy 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 compilers openblas meson-python pkg-config

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

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

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

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

Building from source for NumPy development#

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

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

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 NumPy with the spin developer interface.

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

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

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

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

python -m venv venv
source venv/bin/activate

python -m venv venv
.\venv\Scripts\activate

Then install the Python-level dependencies from PyPI with:

python -m pip install -r requirements/all_requirements.txt

To build NumPy in an activated development environment, run:

spin build

This will install NumPy 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 interface is our recommended way of working on NumPy, it has one limitation: because of the custom install location, NumPy installed using spin 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. It is important to understand that you may use either an editable install or ``spin`` in a given repository clone, but not both. If you use editable installs, you have to use pytest and other development tools directly instead of using spin.

To use an editable install, ensure you start from a clean repository (run git clean -xdf if you’ve built with spin before) and have all dependencies set up correctly as described higher up on this page. Then do:

# Note: the --no-build-isolation is important!
pip install -e . --no-build-isolation

# To run the tests for, e.g., the `numpy.linalg` module:
pytest numpy/linalg

When making changes to NumPy code, including to compiled code, there is no need to manually rebuild or reinstall. NumPy is automatically rebuilt each time NumPy is imported by the Python interpreter; see the meson-python documentation on editable installs for more details on how that works under the hood.

When you run git clean -xdf, which removes the built extension modules, remember to also uninstall NumPy with pip uninstall numpy.

Warning

Note that editable installs are fundamentally incomplete installs. Their only guarantee is that import numpy works - so they are suitable for working on NumPy itself, and for working on pure Python packages that depend on NumPy. Headers, entrypoints, and other such things may not be available from an editable install.

Customizing builds#

Background information#

On this page