Building from sources
Building from sources#
If you are building the latest development version of Pyodide from the
branch, please make sure to follow the build instructions from the dev
version of the documentation at
Building Pyodide 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 Windows Subsystem for Linux to create a Linux build environment.
We provide a Debian-based Docker image
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.
These Docker images are also available from the Github packages at
From a git checkout of Pyodide, run
You can control the resources allocated to the build by setting the env
PYODIDE_JOBS (the default for each is
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.)
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
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.
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 need Python 3.10.2 to run the build scripts. To make sure that the correct Python is used during the build it is recommended to use a virtual environment,
Additional build prerequisites are:
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 https://github.com/pyenv/pyenv/issues/1219#issuecomment-428305417)
coreutils for md5sum and other essential Unix utilities (
brew install coreutils).
brew install cmake)
brew install pkg-config)
brew install openssl)
autoconf, automaker & libtool (
brew install autoconf automaker libtool)
brew cask install gfortran)
f2c: Install wget (
brew install wget), and then run the buildf2c script from the root directory (
It is also recommended installing the GNU patch (
brew install gpatch), and GNU sed (
brew install gnu-sed) and re-defining them temporarily as
(optional) SWIG to compile NLopt (
brew install swig)
(optional) sqlite3 to compile libproj (
brew install sqlite3)
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:
To build a subset of available packages in Pyodide, set the environment variable
PYODIDE_PACKAGES to a comma separated list of packages. For instance,
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.
PYODIDE_PACKAGES is not set, a minimal set of packages necessary to run
the core test suite is installed, including “micropip”, “pyparsing”, “pytz”,
“packaging”, “Jinja2”, “regex”. This is equivalent to setting
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 exhaustive and is mainly intended to make build faster while testing a diverse set of scientific packages.
“*” builds all packages
You can exclude a package by prefixing it with “!”.
micropip and distutils are always automatically included.
The cryptography package is a Rust extension. If you want to build it, you will
need Rust >= 1.41, you need the
environment variable set appropriately, and you need the
wasm32-unknown-emscripten toolchain installed. If you run
make rust, Pyodide
will install this stuff automatically. If you want to build every package except
for cryptography, you can set
The following environment variables additionally impact the build:
-joption passed to the
emmake makecommand when applicable for parallel compilation. Default: 3.
PYODIDE_BASE_URL: Base URL where Pyodide packages are deployed. It must end with a trailing
./to load Pyodide packages from the same base URL path as where
pyodide.jsis located. Example:
EXTRA_CFLAGS: Add extra compilation flags.
EXTRA_LDFLAGS: Add extra linker flags.
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
automatically sets this flag.
In certain cases, setting
EXTRA_LDFLAGS="-s ASSERTIONS=1 or
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.