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https://github.com/pygfx/wgpu-py
WebGPU for Python
https://github.com/pygfx/wgpu-py
dx12 gpu gui metal python vulkan webgpu wgpu
Last synced: 9 days ago
JSON representation
WebGPU for Python
- Host: GitHub
- URL: https://github.com/pygfx/wgpu-py
- Owner: pygfx
- License: bsd-2-clause
- Created: 2019-12-16T22:19:14.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-10-28T10:39:57.000Z (15 days ago)
- Last Synced: 2024-10-28T13:42:09.942Z (15 days ago)
- Topics: dx12, gpu, gui, metal, python, vulkan, webgpu, wgpu
- Language: Python
- Homepage: https://wgpu-py.readthedocs.io
- Size: 2.38 MB
- Stars: 458
- Watchers: 17
- Forks: 36
- Open Issues: 34
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
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[](https://wgpu-py.readthedocs.io)
[](https://badge.fury.io/py/wgpu)
# 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](https://github.com/pygfx/pygfx) to create a modern Pythonic render
engine.
To get an idea of what this API looks like have a look at
[triangle.py](https://github.com/pygfx/wgpu-py/blob/main/examples/triangle.py)
and the other [examples](https://github.com/pygfx/wgpu-py/blob/main/examples/).
## Status
* Until WebGPU settles as a standard, its specification may change, and with
that our API will probably too. Check the [changelog](CHANGELOG.md) when you
upgrade!
* Coverage of the WebGPU spec is complete enough to build e.g.
[pygfx](https://github.com/pygfx/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](https://gpuweb.github.io/gpuweb/), 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](https://github.com/gfx-rs/wgpu-native), exposing its functionality with a Pythonic
API closely resembling the [WebGPU spec](https://gpuweb.github.io/gpuweb/).
## 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](https://wgpu-py.readthedocs.io/en/stable/start.html)
for details.
## Usage
Also see the [online documentation](https://wgpu-py.readthedocs.io) and the [examples](https://github.com/pygfx/wgpu-py/tree/main/examples).
The full API is accessible via the main namespace:
```py
import wgpu
```
To render to the screen you can use a variety of GUI toolkits:
```py
# 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](https://github.com/pygfx/pygfx) - A python render engine running on wgpu.
* [shadertoy](https://github.com/pygfx/shadertoy) - Shadertoy implementation using wgpu-py.
* [tinygrad](https://github.com/tinygrad/tinygrad) - deep learning framework
* [fastplotlib](https://github.com/fastplotlib/fastplotlib) - A fast plotting library
* [xdsl](https://github.com/xdslproject/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](https://github.com/pre-commit/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](codegen/README.md)
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.