Here you will learn some basic things you need to know to get started with mypyc.
You need a Python C extension development environment. The way to set this up depends on your operating system.
Install Xcode command line tools:
$ xcode-select --install
You need a C compiler and CPython headers and libraries. The specifics of how to install these varies by distribution. Here are instructions for Ubuntu 18.04, for example:
$ sudo apt install python3-dev
From Build Tools for Visual Studio 2022, install MSVC C++ build tools for your architecture and a Windows SDK. (latest versions recommended)
Mypyc is shipped as part of the mypy distribution. Install mypy like this (you need Python 3.5 or later):
$ python3 -m pip install -U mypy
On some systems you need to use this instead:
$ python -m pip install -U mypy
Let’s start with a classic micro-benchmark, recursive fibonacci. Save
this file as
import time def fib(n: int) -> int: if n <= 1: return n else: return fib(n - 2) + fib(n - 1) t0 = time.time() fib(32) print(time.time() - t0)
Note that we gave the
fib function a type annotation. Without it,
performance won’t be as impressive after compilation.
Mypy documentation is a good introduction if you are new to type annotations or mypy. Mypyc uses mypy to perform type checking and type inference, so some familiarity with mypy is very useful.
Compiling and running#
We can run
fib.py as a regular, interpreted program using CPython:
$ python3 fib.py 0.4125328063964844
It took about 0.41s to run on my computer.
mypyc to compile the program to a binary C extension:
$ mypyc fib.py
This will generate a C extension for
fib in the current working
directory. For example, on a Linux system the generated file may be
Since C extensions can’t be run as programs, use
python3 -c to run
the compiled module as a program:
$ python3 -c "import fib" 0.04097270965576172
After compilation, the program is about 10x faster. Nice!
fib.py would now be
You can also pass most
mypy command line options
Deleting compiled binary#
You can manually delete the C extension to get back to an interpreted version (this example works on Linux):
$ rm fib.*.so
You can also use
setup.py to compile modules using mypyc. Here is an
from setuptools import setup from mypyc.build import mypycify setup( name='mylib', packages=['mylib'], ext_modules=mypycify([ 'mylib/__init__.py', 'mylib/mod.py', ]), )
mypycify(...) to specify which files to compile using
setup.py can include additional Python files outside
mypycify(...) that won’t be compiled.
Now you can build a wheel (.whl) file for the package:
python3 setup.py bdist_wheel
The wheel is created under
You can also compile the C extensions in-place, in the current directory (similar
mypyc to compile modules):
python3 setup.py build_ext --inplace
You can include most mypy command line options in the
list of arguments passed to
mypycify(). For example, here we use
--disallow-untyped-defs flag to require that all functions
have type annotations:
... setup( name='frobnicate', packages=['frobnicate'], ext_modules=mypycify([ '--disallow-untyped-defs', # Pass a mypy flag 'frobnicate.py', ]), )
A simple way to use mypyc is to always compile your code after any code changes, but this can get tedious, especially if you have a lot of code. Instead, you can do most development in interpreted mode. This development workflow has worked smoothly for developing mypy and mypyc (often we forget that we aren’t working on a vanilla Python project):
During development, use interpreted mode. This gives you a fast edit-run cycle.
Use type annotations liberally and use mypy to type check your code during development. Mypy and tests can find most errors that would break your compiled code, if you have good type annotation coverage. (Running mypy is pretty quick.)
After you’ve implemented a feature or a fix, compile your project and run tests again, now in compiled mode. Usually nothing will break here, assuming your type annotation coverage is good. This can happen locally or in a Continuous Integration (CI) job. If you have CI, compiling locally may be rarely needed.
Release or deploy a compiled version. Optionally, include a fallback interpreted version for platforms that mypyc doesn’t support.
This mypyc workflow only involves minor tweaks to a typical Python workflow. Most of development, testing and debugging happens in interpreted mode. Incremental mypy runs, especially when using the mypy daemon, are very quick (often a few hundred milliseconds).
You can sometimes get good results by just annotating your code and compiling it. If this isn’t providing meaningful performance gains, if you have trouble getting your code to work under mypyc, or if you want to optimize your code for maximum performance, you should read the rest of the documentation in some detail.
Here are some specific recommendations, or you can just read the documentation in order: