Setup and building

These instructions cover how to get a working copy of the source code and a compiled version of the CPython interpreter (CPython is the version of Python available from https://www.python.org/). It also gives an overview of the directory structure of the CPython source code.

Alternatively, if you have Docker installed you might want to use our official images. These contain the latest releases of several Python versions, along with Git head, and are provided for development and testing purposes only.

Ver também

The Quick reference gives brief summary of the process from installing Git to submitting a pull request.

Install Git

CPython is developed using Git for version control. The Git command line program is named git; this is also used to refer to Git itself. Git is easily available for all common operating systems.

• Install

  As the CPython repo is hosted on GitHub, please refer to either the GitHub setup instructions or the Git project instructions for step-by-step installation directions. You may also want to consider a graphical client such as TortoiseGit or GitHub Desktop.

• Configure

  Configure your name and email and create an SSH key as this will allow you to interact with GitHub without typing a username and password each time you execute a command, such as git pull, git push, or git fetch. On Windows, you should also enable autocrlf.

Get the source code

The CPython repo is hosted on GitHub. To get a copy of the source code you should fork the Python repository on GitHub, create a local clone of your personal fork, and configure the remotes.

You will only need to execute these steps once per machine:

1. Go to https://github.com/python/cpython.

2. Press Fork on the top right.

3. When asked where to fork the repository, choose to fork it to your username.

4. Your fork will be created at https://github.com/<username>/cpython.

5. Clone your GitHub fork (replace <username> with your username):

   $ git clone git@github.com:<username>/cpython.git
   

   (You can use both SSH-based or HTTPS-based URLs.)

6. Add an upstream remote, then configure git to pull main from upstream and always push to origin:

   $ cd cpython
   $ git remote add upstream https://github.com/python/cpython
   $ git config --local branch.main.remote upstream
   $ git remote set-url --push upstream git@github.com:<your-username>/cpython.git
   

7. Verify that your setup is correct:

   $ git remote -v
   origin  git@github.com:<your-username>/cpython.git (fetch)
   origin  git@github.com:<your-username>/cpython.git (push)
   upstream        https://github.com/python/cpython (fetch)
   upstream        git@github.com:<your-username>/cpython.git (push)
   $ git config branch.main.remote
   upstream
   

For more information about these commands see Git Bootcamp and Cheat Sheet.

If you did everything correctly, you should now have a copy of the code in the cpython directory and two remotes that refer to your own GitHub fork (origin) and the official CPython repository (upstream).

If you want a working copy of an already-released version of Python, i.e., a version in maintenance mode, you can checkout a release branch. For instance, to checkout a working copy of Python 3.8, do git switch 3.8.

You will need to re-compile CPython when you do such an update.

Do note that CPython will notice that it is being run from a working copy. This means that if you edit CPython’s source code in your working copy, changes to Python code will be picked up by the interpreter for immediate use and testing. (If you change C code, you will need to recompile the affected files as described below.)

Patches for the documentation can be made from the same repository; see Getting started.

Compile and build

CPython provides several compilation flags which help with debugging various things. While all of the known flags can be found in the Misc/SpecialBuilds.txt file, the most critical one is the Py_DEBUG flag which creates what is known as a “pydebug” build. This flag turns on various extra sanity checks which help catch common issues. The use of the flag is so common that turning on the flag is a basic compile option.

You should always develop under a pydebug build of CPython (the only instance of when you shouldn’t is if you are taking performance measurements). Even when working only on pure Python code the pydebug build provides several useful checks that one should not skip.

Ver também

The effects of various configure and build flags are documented in the Python configure docs.

Unix

The core CPython interpreter only needs a C compiler to be built, however, some of the extension modules will need development headers for additional libraries (such as the zlib library for compression). Depending on what you intend to work on, you might need to install these additional requirements so that the compiled interpreter supports the desired features.

If you want to install these optional dependencies, consult the Install dependencies section below.

If you don’t need to install them, the basic steps for building Python for development is to configure it and then compile it.

Configuration is typically:

$ ./configure --with-pydebug

More flags are available to configure, but this is the minimum you should do to get a pydebug build of CPython.

Nota

You might need to run make clean before or after re-running configure in a particular build directory.

Once configure is done, you can then compile CPython with:

$ make -s -j2

This will build CPython with only warnings and errors being printed to stderr and utilize up to 2 CPU cores. If you are using a multi-core machine with more than 2 cores (or a single-core machine), you can adjust the number passed into the -j flag to match the number of cores you have (or if your version of Make supports it, you can use -j without a number and Make will not limit the number of steps that can run simultaneously.).

At the end of the build you should see a success message, followed by a list of extension modules that haven’t been built because their dependencies were missing:

The necessary bits to build these optional modules were not found:
_gdbm
To find the necessary bits, look in configure.ac and config.log.

Checked 106 modules (31 built-in, 74 shared, 0 n/a on macosx-13.4-arm64, 0 disabled, 1 missing, 0 failed on import)

If the build failed and you are using a C89 or C99-compliant compiler, please open a bug report on the issue tracker.

If you decide to Install dependencies, you will need to re-run both configure and make.

Once CPython is done building you will then have a working build that can be run in-place; ./python on most machines (and what is used in all examples), ./python.exe wherever a case-insensitive filesystem is used (e.g. on macOS by default), in order to avoid conflicts with the Python directory. There is normally no need to install your built copy of Python! The interpreter will realize where it is being run from and thus use the files found in the working copy. If you are worried you might accidentally install your working copy build, you can add --prefix=/tmp/python to the configuration step. When running from your working directory, it is best to avoid using the --enable-shared flag to configure; unless you are very careful, you may accidentally run with code from an older, installed shared Python library rather than from the interpreter you just built.

Clang

If you are using clang to build CPython, some flags you might want to set to quiet some standard warnings which are specifically superfluous to CPython are -Wno-unused-value -Wno-empty-body -Qunused-arguments. You can set your CFLAGS environment variable to these flags when running configure.

If you are using clang with ccache, turn off the noisy parentheses-equality warnings with the -Wno-parentheses-equality flag. These warnings are caused by clang not having enough information to detect that extraneous parentheses in expanded macros are valid, because the preprocessing is done separately by ccache.

If you are using LLVM 2.8, also use the -no-integrated-as flag in order to build the ctypes module (without the flag the rest of CPython will still build properly).

Optimization

If you are trying to improve CPython’s performance, you will probably want to use an optimized build of CPython. It can take a lot longer to build CPython with optimizations enabled, and it’s usually not necessary to do so. However, it’s essential if you want accurate benchmark results for a proposed performance optimization.

For an optimized build of Python, use configure --enable-optimizations --with-lto. This sets the default make targets up to enable Profile Guided Optimization (PGO) and may be used to auto-enable Link Time Optimization (LTO) on some platforms. See --enable-optimizations and --with-lto to learn more about these options.

$ ./configure --enable-optimizations --with-lto

Windows

Nota

If you are using the Windows Subsystem for Linux (WSL), clone the repository from a native Windows shell program like PowerShell or the cmd.exe command prompt, and use a build of Git targeted for Windows, e.g. the Git for Windows download from the official Git website. Otherwise, Visual Studio will not be able to find all the project’s files and will fail the build.

For a concise step by step summary of building Python on Windows, you can read Victor Stinner’s guide.

All supported versions of Python can be built using Microsoft Visual Studio 2017 or later. You can download and use any of the free or paid versions of Visual Studio.

When installing it, select the Python development workload and the optional Python native development tools component to obtain all of the necessary build tools. You can find Git for Windows on the Individual components tab if you don’t already have it installed.

Nota

If you want to build MSI installers, be aware that the build toolchain for them has a dependency on the Microsoft .NET Framework Version 3.5 (which may not be included on recent versions of Windows, such as Windows 10). If you are building on a recent Windows version, use the Control Panel (Programs ‣ Programs and Features ‣ Turn Windows Features on or off) and ensure that the entry .NET Framework 3.5 (includes .NET 2.0 and 3.0) is enabled.

Your first build should use the command line to ensure any external dependencies are downloaded:

PCbuild\build.bat -c Debug

The above command line build uses the -c Debug argument to build in the Debug configuration, which enables checks and assertions helpful for developing Python. By default, it builds in the Release configuration and for the 64-bit x64 platform rather than 32-bit Win32; use -c and -p to control build config and platform, respectively.

After this build succeeds, you can open the PCbuild\pcbuild.sln solution in the Visual Studio IDE to continue development, if you prefer. When building in Visual Studio, make sure to select build settings that match what you used with the script (the Debug configuration and the x64 platform) from the dropdown menus in the toolbar.

Nota

If you need to change the build configuration or platform, build once with the build.bat script set to those options first before building with them in VS to ensure all files are rebuilt properly, or you may encounter errors when loading modules that were not rebuilt.

Avoid selecting the PGInstrument and PGUpdate configurations, as these are intended for PGO builds and not for normal development.

You can run the build of Python you’ve compiled with:

PCbuild\amd64\python_d.exe

See the PCBuild readme for more details on what other software is necessary and how to build.

Install dependencies

This section explains how to install additional extensions (e.g. zlib) on Linux and macOS. On Windows, extensions are already included and built automatically.

Linux

For Unix-based systems, we try to use system libraries whenever available. This means optional components will only build if the relevant system headers are available. The best way to obtain the appropriate headers will vary by distribution, but the appropriate commands for some popular distributions are below.

On Fedora, Red Hat Enterprise Linux and other yum based systems:

$ sudo yum install yum-utils
$ sudo yum-builddep python3

On Fedora and other DNF based systems:

$ sudo dnf install dnf-plugins-core  # install this to use 'dnf builddep'
$ sudo dnf builddep python3

On Debian, Ubuntu, and other apt based systems, try to get the dependencies for the Python you’re working on by using the apt command.

First, make sure you have enabled the source packages in the sources list. You can do this by adding the location of the source packages, including URL, distribution name and component name, to /etc/apt/sources.list. Take Ubuntu 22.04 LTS (Jammy Jellyfish) for example:

deb-src http://archive.ubuntu.com/ubuntu/ jammy main

Alternatively, uncomment lines with deb-src using an editor, e.g.:

sudo nano /etc/apt/sources.list

For other distributions, like Debian, change the URL and names to correspond with the specific distribution.

Then you should update the packages index:

$ sudo apt-get update

Now you can install the build dependencies via apt:

$ sudo apt-get build-dep python3
$ sudo apt-get install pkg-config

If you want to build all optional modules, install the following packages and their dependencies:

$ sudo apt-get install build-essential gdb lcov pkg-config \
      libbz2-dev libffi-dev libgdbm-dev libgdbm-compat-dev liblzma-dev \
      libncurses5-dev libreadline6-dev libsqlite3-dev libssl-dev \
      lzma lzma-dev tk-dev uuid-dev zlib1g-dev

macOS

For macOS systems (versions 10.9+), the Developer Tools can be downloaded and installed automatically; you do not need to download the complete Xcode application.

If necessary, run the following:

$ xcode-select --install

This will also ensure that the system header files are installed into /usr/include.

Also note that macOS does not include several libraries used by the Python standard library, including libzma, so expect to see some extension module build failures unless you install local copies of them. As of OS X 10.11, Apple no longer provides header files for the deprecated system version of OpenSSL which means that you will not be able to build the _ssl extension. One solution is to install these libraries from a third-party package manager, like Homebrew or MacPorts, and then add the appropriate paths for the header and library files to your configure command.

For example, with Homebrew, install the dependencies:

$ brew install pkg-config openssl@3.0 xz gdbm tcl-tk

Then, for Python 3.11 and newer, run configure:

$ GDBM_CFLAGS="-I$(brew --prefix gdbm)/include" \
  GDBM_LIBS="-L$(brew --prefix gdbm)/lib -lgdbm" \
  ./configure --with-pydebug \
              --with-openssl="$(brew --prefix openssl@3.0)"

Or, for Python 3.8 through 3.10:

$ CPPFLAGS="-I$(brew --prefix gdbm)/include -I$(brew --prefix xz)/include" \
  LDFLAGS="-L$(brew --prefix gdbm)/lib -L$(brew --prefix xz)/lib" \
  ./configure --with-pydebug \
              --with-openssl="$(brew --prefix openssl@3.0)" \
              --with-tcltk-libs="$(pkg-config --libs tcl tk)" \
              --with-tcltk-includes="$(pkg-config --cflags tcl tk)"

And finally, run make:

$ make -s -j2

Alternatively, with MacPorts:

$ sudo port install pkgconfig openssl xz gdbm tcl tk +quartz

Then, for Python 3.11 and newer, run configure:

$ GDBM_CFLAGS="-I$(dirname $(dirname $(which port)))/include" \
  GDBM_LIBS="-L$(dirname $(dirname $(which port)))/lib -lgdbm" \
  ./configure --with-pydebug

And finally, run make:

$ make -s -j2

There will sometimes be optional modules added for a new release which won’t yet be identified in the OS-level build dependencies. In those cases, just ask for assistance in the Core Development category on Discourse.

Explaining how to build optional dependencies on a Unix-based system without root access is beyond the scope of this guide.

For more details on various options and considerations for building, refer to the macOS README.

Nota

While you need a C compiler to build CPython, you don’t need any knowledge of the C language to contribute! Vast areas of CPython are written completely in Python: as of this writing, CPython contains slightly more Python code than C.

Regenerate configure

If a change is made to Python which relies on some POSIX system-specific functionality (such as using a new system call), it is necessary to update the configure script to test for availability of the functionality. Python’s configure script is generated from configure.ac using GNU Autoconf.

After editing configure.ac, run make regen-configure to generate configure, pyconfig.h.in, and aclocal.m4. When submitting a pull request with changes made to configure.ac, make sure you also commit the changes in the generated files.

The recommended and by far the easiest way to regenerate configure is:

$ make regen-configure

If you are regenerating configure in a clean repo, run one of the following containers instead:

$ podman run --rm --pull=always -v $(pwd):/src:Z quay.io/tiran/cpython_autoconf:271

$ docker run --rm --pull=always -v $(pwd):/src quay.io/tiran/cpython_autoconf:271

Notice that the images are tagged with 271. Python’s configure.ac script requires a specific version of GNU Autoconf. For Python 3.12 and newer, GNU Autoconf v2.71 is required. For Python 3.11 and earlier, GNU Autoconf v2.69 is required. For GNU Autoconf v2.69, change the :271 tag to :269.

If you cannot (or don’t want to) use the cpython_autoconf containers, install the autoconf-archive and pkg-config utilities, and make sure the pkg.m4 macro file located in the appropriate aclocal location:

$ ls $(aclocal --print-ac-dir) | grep pkg.m4

Nota

Running autoreconf is not the same as running autoconf. For example, running autoconf by itself will not regenerate pyconfig.h.in. autoreconf runs autoconf and a number of other tools repeatedly as appropriate.

Regenerate the ABI dump

Maintenance branches (not main) have a special file located in Doc/data/pythonX.Y.abi that allows us to know if a given Pull Request affects the public ABI. This file is used by the GitHub CI in a check called Check if the ABI has changed that will fail if a given Pull Request has changes to the ABI and the ABI file is not updated.

This check acts as a fail-safe and doesn’t necessarily mean that the Pull Request cannot be merged. When this check fails you should add the relevant release manager to the PR so that they are aware of the change and they can validate if the change can be made or not.

Importante

ABI changes are allowed before the first release candidate. After the first release candidate, all further releases must have the same ABI for ensuring compatibility with native extensions and other tools that interact with the Python interpreter. See the documentation about the release candidate phase.

When the PR check fails, the associated run will have the updated ABI file attached as an artifact. After release manager approval, you can download and add this file into your PR to pass the check.

You can regenerate the ABI file by yourself by invoking the regen abidump Make target. Note that for doing this you need to regenerate the ABI file in the same environment that the GitHub CI uses to check for it. This is because different platforms may include some platform-specific details that make the check fail even if the Python ABI is the same. The easier way to regenerate the ABI file using the same platform as the CI uses is by using Docker:

# In the CPython root:
$ docker run -v$(pwd):/src:Z -w /src --rm -it ubuntu:22.04 \
    bash /src/.github/workflows/regen-abidump.sh

Note that the ubuntu version used to execute the script matters and must match the version used by the CI to check the ABI. See the .github/workflows/build.yml file for more information.

Troubleshoot the build

This section lists some of the common problems that may arise during the compilation of Python, with proposed solutions.

Avoid recreating auto-generated files

Under some circumstances you may encounter Python errors in scripts like Parser/asdl_c.py or Python/makeopcodetargets.py while running make. Python auto-generates some of its own code, and a full build from scratch needs to run the auto-generation scripts. However, this makes the Python build require an already installed Python interpreter; this can also cause version mismatches when trying to build an old (2.x) Python with a new (3.x) Python installed, or vice versa.

To overcome this problem, auto-generated files are also checked into the Git repository. So if you don’t touch the auto-generation scripts, there’s no real need to auto-generate anything.

Editors and tools

Python is used widely enough that practically all code editors have some form of support for writing Python code. Various coding tools also include Python support.

For editors and tools which the core developers have felt some special comment is needed for coding in Python, see Additional resources.

Directory structure

There are several top-level directories in the CPython source tree. Knowing what each one is meant to hold will help you find where a certain piece of functionality is implemented. Do realize, though, there are always exceptions to every rule.

Doc

The official documentation. This is what https://docs.python.org/ uses. See also Building the documentation.

Grammar

Contains the EBNF grammar file for Python.

Include

Contains all interpreter-wide header files.

Lib

The part of the standard library implemented in pure Python.

Mac

Mac-specific code (e.g., using IDLE as a macOS application).

Misc

Things that do not belong elsewhere. Typically this is varying kinds of developer-specific documentation.

Modules

The part of the standard library (plus some other code) that is implemented in C.

Objects

Code for all built-in types.

PC

Windows-specific code.

PCbuild

Build files for the version of MSVC currently used for the Windows installers provided on python.org.

Parser

Code related to the parser. The definition of the AST nodes is also kept here.

Programs

Source code for C executables, including the main function for the CPython interpreter.

Python

The code that makes up the core CPython runtime. This includes the compiler, eval loop and various built-in modules.

Tools

Various tools that are (or have been) used to maintain Python.

Contribute using GitHub Codespaces

What is GitHub Codespaces?

If you’d like to start contributing to CPython without needing to set up a local developer environment, you can use GitHub Codespaces. Codespaces is a cloud-based development environment offered by GitHub that allows developers to write, build, test, and debug code directly within their web browser or in Visual Studio Code (VS Code).

To help you get started, CPython contains a devcontainer folder with a JSON configuration file that provides consistent and versioned codespace configurations for all users of the project. It also contains a Dockerfile that allows you to set up the same environment but locally in a Docker container if you’d prefer to use that directly.

Create a CPython codespace

Here are the basic steps needed to contribute a patch using Codespaces. You first need to navigate to the CPython repo hosted on GitHub.

Then you will need to:

1. Press the , key to launch the codespace setup screen for the current branch (alternatively, click the green Code button and choose the codespaces tab and then press the green Create codespace on main button).

2. A screen should appear that lets you know your codespace is being set up. (Note: Since the CPython devcontainer is provided, codespaces will use the configuration it specifies.)

3. A web version of VS Code will open inside your web browser, already linked up with your code and a terminal to the remote codespace where CPython and its documentation have already been built.

4. Use the terminal with the usual Git commands to create a new branch, commit and push your changes once you’re ready!

If you close your repository and come back later you can always resume your codespace by navigating to the CPython repo, selecting the codespaces tab and selecting your most recent codespaces session. You should then be able to pick up from where you left off!

Use Codespaces locally

On the bottom left side of the codespace screen you will see a green or grey square that says Codespaces. You can click this for additional options. If you prefer working in a locally installed copy of VS Code you can select the option Open in VS Code. You will still be working on the remote codespace instance, thus using the remote instance’s compute power. The compute power may be a much higher spec than your local machine which can be helpful.