Metadata-Version: 2.1
Name: wgpu
Version: 0.8.3
Summary: Next generation GPU API for Python
Home-page: https://github.com/pygfx/wgpu-py
Author: Almar Klein
Author-email: almar.klein@gmail.com
License: BSD 2-Clause
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
Provides-Extra: jupyter
Provides-Extra: glfw
License-File: LICENSE

[![CI](https://github.com/pygfx/wgpu-py/workflows/CI/badge.svg)](https://github.com/pygfx/wgpu-py/actions)
[![Documentation Status](https://readthedocs.org/projects/wgpu-py/badge/?version=latest)](https://wgpu-py.readthedocs.io)
[![PyPI version](https://badge.fury.io/py/wgpu.svg)](https://badge.fury.io/py/wgpu)

# wgpu-py

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

<img width=300 src='https://raw.githubusercontent.com/pygfx/wgpu-py/main/examples/screenshots/cube.png' />
<img width=300 src='https://raw.githubusercontent.com/pygfx/wgpu-py/main/examples/screenshots/triangle_auto.png' />


## Introduction

In short, this is a Python lib wrapping
[wgpu-native](https://github.com/gfx-rs/wgpu) and exposing it with a Pythonic
API similar to the [WebGPU spec](https://gpuweb.github.io/gpuweb/).

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

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

> **Note** 
>
> The wgpu-API has not settled yet, use with care!

* Coverage of the WebGPU spec is complete enough to build e.g.
  [pygfx](https://github.com/pygfx/pygfx).
* Test coverage of the API is 100%.
* Support for Windows, Linux, and MacOS (Intel and M1).
* 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!


## Installation

```
pip install wgpu
```

The wheels include the prebuilt binaries of [wgpu-native](https://github.com/gfx-rs/wgpu-native).

Note that on Linux you need to use at least **pip >= 20.3**, and a recent 
distribution, otherwise the binaries will not be available. See "platform
requirements" for details.

If you need/want to build wgpu-native yourself, you need to set the environment variable
`WGPU_LIB_PATH` to let wgpu-py know where the DLL is located.

You may also want to install a GUI backend: 
```
pip install glfw  # a lightweight backend for the desktop
pip install jupyter_rfb  # only if you plan on using wgpu in Jupyter
```

## Platform requirements

Under the hood, `wgpu` runs on Vulkan, Metal, or DX12. The wgpu-backend is
selected automatically, but can be overridden by setting the `WGPU_BACKEND_TYPE`
environment variable to "Vulkan", "Metal", "D3D12", "D3D11", or "OpenGL".

On Windows 10+, things should just work. On older Windows versions you may need
to install the Vulkan drivers. You may want to force "Vulkan" while "D3D12" is
less mature.

On MacOS you need at least 10.13 (High Sierra) to have Vulkan support.

On Linux, it's advisable to install the proprietary drivers of your GPU (if you
have a dedicated GPU). You may need to `apt install mesa-vulkan-drivers`.
Wayland support is currently broken (we could use a hand to fix this).

**Note that on Linux, binary wheels are only available for manylinux_2_24**. That
means you can only install the binaries with **pip >= 20.3**, and need to use a recent distribution, listed [here](https://github.com/pypa/manylinux#manylinux). If you wish to work with an older distribution, you will have to build the [wgpu-native](https://github.com/gfx-rs/wgpu-native) library yourself, and point wgpu-py to the
resulting binary using the `WGPU_LIB_PATH` environment variable.

## Usage

Also see the [online documentation](https://wgpu-py.readthedocs.io).

The full API is accessable via the main namespace:
```py
import wgpu
```

But to use it, you need to select a backend first. You do this by importing it.
There is currently only one backend:
```py
import wgpu.backends.rs
```

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.


## Developers

* Clone the repo.
* Install devtools using `pip install -r dev-requirements.txt` (you can replace
  `pip` with `pipenv` to install to a virtualenv).
* Install wgpu-py in editable mode by running `pip install -e .`, this will also
  install runtime dependencies as needed.
* Run `python download-wgpu-native.py` to download the upstream wgpu-native
  binaries.
  * Or alternatively point the `WGPU_LIB_PATH` environment variable to a custom
    build.
* Use `black .` to apply autoformatting.
* Use `flake8 .` to check for flake errors.
* Use `pytest .` to run the tests.
* Use `pip wheel --no-deps .` to build a wheel.


### Changing the upstream wgpu-native version

* Use the optional arguments to `python download-wgpu-native.py --help` to
  download a different version of the upstream wgpu-native binaries.
* The file `wgpu/resources/wgpu_native-version` will be updated by the script to
  track which version we depend upon.

## Testing

The test suite is divided into multiple parts:

* `pytest -v tests` runs the core unit tests.
* `pytest -v examples` tests the examples.
* `pytest -v wgpu/__pyinstaller` tests if wgpu is properly supported by
  pyinstaller.
* `pytest -v codegen` lints the generated binding code.

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.
