Building from sources


If you are building the latest development version of Pyodide from the main branch, please make sure to follow the build instructions from the dev version of the documentation at

Building on any operating system is easiest using the Pyodide Docker image. This approach works with any native operating system as long as Docker is installed. You can also build on your native Linux OS if the correct build prerequisites are installed. Building on MacOS is possible, but there are known issues as of version 0.18 that you will need to work around. It is not possible to build on Windows, but you can use WSL2 to create a Linux build environment.

Build instructions

Using Docker

We provide a Debian-based Docker image (pyodide/pyodide-env) on Docker Hub with the dependencies already installed to make it easier to build Pyodide. On top of that we provide a pre-built image (pyodide/pyodide) which can be used for fast custom and partial builds. Note that building from the non pre-built Docker image is very slow on Mac, building on the host machine is preferred if at all possible.

  1. Install Docker

  2. From a git checkout of Pyodide, run ./run_docker or ./run_docker --pre-built

  3. Run make to build.


You can control the resources allocated to the build by setting the env vars EMSDK_NUM_CORE, EMCC_CORES and PYODIDE_JOBS (the default for each is 4).

If running make deterministically stops at some point, increasing the maximum RAM usage available to the docker container might help. (The RAM available to the container is different from the physical RAM capacity of the machine.) Ideally, at least 3 GB of RAM should be available to the docker container to build Pyodide smoothly. These settings can be changed via Docker preferences (see here).

You can edit the files in the shared pyodide source folder on your host machine (outside of Docker), and then repeatedly run make inside the Docker environment to test your changes.

Using make

Make sure the prerequisites for emsdk are installed. Pyodide will build a custom, patched version of emsdk, so there is no need to build it yourself prior.

You would need Python 3.9.5 to run the build scripts. To make sure that the correct Python is used during build it is recommended to use a Python virtual environment,

Additional build prerequisites are:

  • A working native compiler toolchain, enough to build CPython.

  • CMake

  • FreeType 2 development libraries to compile Matplotlib.

  • gfortran (GNU Fortran 95 compiler)

  • f2c

  • ccache (optional) highly recommended for much faster rebuilds.

  • (optional) SWIG to compile NLopt

To build on MacOS, you need:

  • Homebrew for installing dependencies

  • System libraries in the root directory ( sudo installer -pkg /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg -target / should do it, see

  • coreutils for md5sum and other essential Unix utilities (brew install coreutils).

  • cmake (brew install cmake)

  • pkg-config (brew install pkg-config)

  • openssl (brew install openssl)

  • gfortran (brew cask install gfortran)

  • f2c: Install wget (brew install wget), and then run the buildf2c script from the root directory (sudo ./tools/buildf2c)

  • It is also recommended installing the GNU patch (brew install gpatch), and GNU sed (brew install gnu-sed) and re-defining them temporarily as patch and sed.

  • (optional) SWIG to compile NLopt (brew install swig)


If you encounter issues with the requirements, it is useful to check the exact list in the Dockerfile which is tested in the CI.

You can install the Python dependencies from the requirement file at the root of Pyodide folder: pip install -r requirements.txt

After installing the build prerequisites, run from the command line:


Partial builds

To build a subset of available packages in Pyodide, set the environment variable PYODIDE_PACKAGES to a comma separated list of packages. For instance,

PYODIDE_PACKAGES="toolz,attrs" make

Dependencies of the listed packages will be built automatically as well. The package names must match the folder names in packages/ exactly; in particular they are case-sensitive.

If PYODIDE_PACKAGES is not set, a minimal set of packages necessairy to run the core test suite is installed, including “micropip”, “pyparsing”, “pytz”, “packaging”, “Jinja2”, “regex”. This is equivalent to setting PYODIDE_PACKAGES='core' meta-package. Other supported meta-packages are,

  • “min-scipy-stack”: includes the “core” meta-package as well as some core packages from the scientific python stack and their dependencies: “numpy”, “scipy”, “pandas”, “matplotlib”, “scikit-learn”, “joblib”, “pytest”. This option is non exaustive and is mainly intended to make build faster while testing a diverse set of scientific packages.

  • “*” builds all packages

micropip and distutils are always automatically included.

Environment variables

The following environment variables additionally impact the build:

  • PYODIDE_JOBS: the -j option passed to the emmake make command when applicable for parallel compilation. Default: 3.

  • PYODIDE_BASE_URL: Base URL where Pyodide packages are deployed. It must end with a trailing /. Default: ./ to load Pyodide packages from the same base URL path as where pyodide.js is located. Example:

  • EXTRA_CFLAGS : Add extra compilation flags.

  • EXTRA_LDFLAGS : Add extra linker flags.

Setting EXTRA_CFLAGS="-D DEBUG_F" provides detailed diagnostic information whenever error branches are taken inside the Pyodide core code. These error messages are frequently helpful even when the problem is a fatal configuration problem and Pyodide cannot even be initialized. These error branches occur also in correctly working code, but they are relatively uncommon so in practice the amount of noise generated isn’t too large. The shorthand make debug automatically sets this flag.

In certain cases, setting EXTRA_LDFLAGS="-s ASSERTIONS=1 or ASSERTIONS=2 can also be helpful, but this slows down the linking and the runtime speed of Pyodide a lot and generates a large amount of noise in the console.