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CI Documentation Status PyPI version

wgpu-py

A Python implementation of WebGPU - the next generation GPU API. 🚀

Introduction

The purpose of wgpu-py is to provide Python with a powerful and reliable GPU API.

It serves as a basis to build a broad range of applications and libraries related to visualization and GPU compute. We use it in pygfx to create a modern Pythonic render engine.

To get an idea of what this API looks like have a look at triangle.py and the other examples.

Status

  • Until WebGPU settles as a standard, its specification may change, and with that our API will probably too. Check the changelog when you upgrade!
  • Coverage of the WebGPU spec is complete enough to build e.g. pygfx.
  • Test coverage of the API is close to 100%.
  • Support for Windows, Linux (x86 and aarch64), and MacOS (Intel and M1).

What is WebGPU / wgpu?

WGPU is the future for GPU graphics; the successor to OpenGL.

WebGPU is a JavaScript API with a well-defined spec, the successor to WebGL. The somewhat broader term "wgpu" is used to refer to "desktop" implementations of WebGPU in various languages.

OpenGL is old and showing its cracks. New API's like Vulkan, Metal and DX12 provide a modern way to control the GPU, but these are too low-level for general use. WebGPU follows the same concepts, but with a simpler (higher level) API. With wgpu-py we bring WebGPU to Python.

Technically speaking, wgpu-py is a wrapper for wgpu-native, exposing its functionality with a Pythonic API closely resembling the WebGPU spec.

Installation

pip install wgpu glfw

Linux users should make sure that pip >= 20.3. That should do the trick on most systems. See getting started for details.

Usage

Also see the online documentation and the examples.

The full API is accessible via the main namespace:

import wgpu

To render to the screen you can use a variety of GUI toolkits:

# The auto backend selects either the glfw, qt or jupyter backend
from wgpu.gui.auto import WgpuCanvas, run, call_later

# Visualizations can be embedded as a widget in a Qt application.
# Import PySide6, PyQt6, PySide2 or PyQt5 before running the line below.
# The code will detect and use the library that is imported.
from wgpu.gui.qt import WgpuCanvas

# Visualizations can be embedded as a widget in a wx application.
from wgpu.gui.wx import WgpuCanvas

Some functions in the original wgpu-native API are async. In the Python API, the default functions are all sync (blocking), making things easy for general use. Async versions of these functions are available, so wgpu can also work well with Asyncio or Trio.

License

This code is distributed under the 2-clause BSD license.

Projects using wgpu-py

  • pygfx - A python render engine running on wgpu.
  • shadertoy - Shadertoy implementation using wgpu-py.
  • tinygrad - deep learning framework
  • fastplotlib - A fast plotting library
  • xdsl - A Python Compiler Design Toolkit (optional wgpu interpreter)

Developers

  • Clone the repo.
  • Install devtools using pip install -e .[dev].
  • Using pip install -e . will also download the upstream wgpu-native binaries.
    • You can use python tools/download_wgpu_native.py when needed.
    • Or point the WGPU_LIB_PATH environment variable to a custom build of wgpu-native.
  • Use ruff format to apply autoformatting.
  • Use ruff check to check for linting errors.
  • Optionally, if you install pre-commit hooks with pre-commit install, lint fixes and formatting will be automatically applied on git commit.

Updating to a later version of WebGPU or wgpu-native

To update to upstream changes, we use a combination of automatic code generation and manual updating. See the codegen utility for more information.

Testing

The test suite is divided into multiple parts:

  • pytest -v tests runs the unit tests.
  • pytest -v examples tests the examples.
  • pytest -v wgpu/__pyinstaller tests if wgpu is properly supported by pyinstaller.
  • pytest -v codegen tests the code that autogenerates the API.
  • pytest -v tests_mem tests against memoryleaks.

There are two types of tests for examples included:

Type 1: Checking if examples can run

When running the test suite, pytest will run every example in a subprocess, to see if it can run and exit cleanly. You can opt out of this mechanism by including the comment # run_example = false in the module.

Type 2: Checking if examples output an image

You can also (independently) opt-in to output testing for examples, by including the comment # test_example = true in the module. Output testing means the test suite will attempt to import the canvas instance global from your example, and call it to see if an image is produced.

To support this type of testing, ensure the following requirements are met:

  • The WgpuCanvas class is imported from the wgpu.gui.auto module.
  • The canvas instance is exposed as a global in the module.
  • A rendering callback has been registered with canvas.request_draw(fn).

Reference screenshots are stored in the examples/screenshots folder, the test suite will compare the rendered image with the reference.

Note: this step will be skipped when not running on CI. Since images will have subtle differences depending on the system on which they are rendered, that would make the tests unreliable.

For every test that fails on screenshot verification, diffs will be generated for the rgb and alpha channels and made available in the examples/screenshots/diffs folder. On CI, the examples/screenshots folder will be published as a build artifact so you can download and inspect the differences.

If you want to update the reference screenshot for a given example, you can grab those from the build artifacts as well and commit them to your branch.