Updated June 2026

High-Performance Python with Zig

Class Duration

7 hours of live training delivered over 1 day.

Student Prerequisites

All students should have significant experience with Python
Working knowledge of Zig: pointers, slices, error unions, and allocators
The class has no review of the Python or Zig programming languages
If you need to learn Zig, please consider the Zig Essentials course first

Target Audience

This course is for Python developers who need to accelerate CPU-bound Python code and want Zig as the native layer. It suits teams who value Zig's small toolchain, C-ABI fluency, and effortless cross-compilation, and engineers comparing Zig against the approaches covered in our High-Performance Python with C and C++, High-Performance Python with Cython, and High-Performance Python with Rust courses before committing to one.

Description

This one-day course is for Python developers who want to rewrite CPU-bound hot paths in Zig and ship the result as a normal Python package. Because Zig speaks the C ABI natively and imports Python.h directly with @cImport, a Zig source file can be a CPython extension module with no binding generator at all — the course starts there, teaching PyObject* handling, argument parsing, and reference counting discipline from Zig. It then moves up a level to Ziggy Pydust, the actively maintained toolkit for building Python extensions in Zig, which uses comptime to wrap and unwrap arguments between native Zig types and Python objects and ships a pytest plugin for running Zig tests from your Python test suite. The course is candid about ecosystem maturity: Pydust targets CPython 3.11+ and historically trails new Zig compiler releases by a few months, so teams learn both the high-level and raw C API routes and when each is the right call.

The distribution story is where Zig shines: the same zig binary that compiles your extension is a zero-dependency C cross-compiler, so building manylinux, musllinux, macOS, and Windows wheels with cibuildwheel is dramatically simpler than with a traditional toolchain zoo. Students benchmark their Zig extensions honestly against pure Python, Cython, Rust, and C++ equivalents — cross-referencing the sibling courses High-Performance Python with Cython, High-Performance Python with Rust, and High-Performance Python with C and C++ — examine buffer protocol and NumPy interop for zero-copy data exchange, consider what officially supported free-threaded Python (PEP 703 / PEP 779) means for a Zig extension author, and finish by packaging a wheel for PyPI.

Learning Outcomes

Profile Python applications to identify the hot paths actually worth rewriting in Zig
Write a CPython extension module in pure Zig via the C ABI and @cImport, handling PyObject*, argument parsing, and reference counting correctly
Build extensions with Ziggy Pydust and evaluate its comptime argument wrapping, pytest integration, and version-support trade-offs
Exchange data with NumPy and other libraries zero-copy through the buffer protocol
Cross-compile wheels for Linux (manylinux and musllinux), macOS, and Windows with zig cc and cibuildwheel
Benchmark Zig extensions honestly against Cython, Rust, and C/C++ alternatives and present defensible numbers
Reason about the GIL, releasing it for CPU-bound Zig work, and the implications of free-threaded Python (PEP 703 / PEP 779) for Zig extensions
Package and publish a Zig-powered Python wheel to PyPI

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training.

Software Requirements

Students will need a free, personal GitHub account to access the courseware. Students will need permission to install Python 3.12+, Zig, and Visual Studio Code on their computers, along with Python packages including ziggy-pydust, cibuildwheel, and pytest. No separate C compiler is required — the Zig toolchain fills that role. If students are unable to configure a local environment, a cloud-based environment can be provided.

Training Topics

When to Drop to Native Code

Why Python is slow for CPU-bound workloads
CPU-bound vs IO-bound: choosing the right tool
Profiling with cProfile and py-spy to find the real hot path
Where Zig fits among Cython, Rust, and C/C++ for Python acceleration
The cost model: build complexity, maintenance, and team skills

Zig Extensions via the C ABI

Zig speaks C natively: @cImport("Python.h") and extern functions
Module initialization and method tables from Zig
PyObject* in Zig: argument parsing and building return values
Reference counting: ownership rules, Py_INCREF/Py_DECREF discipline, and leak hunting
Mapping Zig error unions to Python exceptions
Allocators and lifetime at the Python boundary
Building the shared object with build.zig

Ziggy Pydust

What Pydust provides: extension packaging, comptime argument wrapping between Zig and Python types, and a pytest plugin for Zig tests
Project layout with the Pydust template
Defining modules, functions, and classes
Honest status check: CPython 3.11+ support, the lag behind new Zig releases, and when to prefer the raw C API
Alternatives in brief: setuptools-zig and thin C shims

Buffer Protocol and NumPy Interop

The buffer protocol: zero-copy views of Python memory
Receiving NumPy arrays in Zig as slices
Strides, dtypes, and contiguity checks
Writing results back without copies
A vectorized hot loop in Zig over NumPy data

Cross-Compiling Wheels with zig cc and cibuildwheel

zig cc as a zero-dependency drop-in cross-compiler
Targeting manylinux (pinned glibc) and musllinux from any host
cibuildwheel configuration for a Zig-built extension
macOS and Windows wheels from a single CI pipeline
The ziglang PyPI package: installing the toolchain with pip

Benchmarking Honestly

pytest-benchmark methodology: warmup, variance, and fair baselines
Comparing the same kernel in Zig, Cython, Rust, and C++
What our High-Performance Python with Cython, High-Performance Python with Rust, and High-Performance Python with C and C++ courses each conclude — and why the boundary-crossing cost often dominates
Avoiding benchmark theater: amortizing call overhead, realistic data sizes

The GIL and Free-Threaded Python

What the GIL means for a Zig extension
Releasing the GIL around CPU-bound Zig code
Free-threaded Python (PEP 703), officially supported in 3.14 (PEP 779)
Thread-safety obligations for Zig extension authors under free threading
Zig's thread-safe allocator design and shared state at the boundary

Packaging to PyPI

Wheel metadata, abi3 considerations, and version support windows
Source distributions when the user has (or pip-installs) Zig
Publishing with twine and trusted publishing from GitHub Actions