Usage
This package adds a GPU accelerated WebGPU
backend to TensorFlow.js. It currently supports
the following models:
- BlazeFace
- BodyPix
- Face landmarks detection
- HandPose
- MobileNet
- PoseDetection
- Universal sentence encoder
- AutoML Image classification
- AutoML Object detection
- Speech commands
Google Chrome started to support WebGPU by default in M113 on May 2, 2023.
Importing the backend
Via NPM
import * as tf from '@tensorflow/tfjs';
import '@tensorflow/tfjs-backend-webgpu';
tf.setBackend('webgpu').then(() => main());
Via a script tag
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgpu/dist/tf-backend-webgpu.js"></script>
<script>
tf.setBackend('webgpu').then(() => main());
</script>
FAQ
When should I use the WebGPU backend?
The mission of WebGPU backend is to achieve the best performance among all
approaches. However, this target can not be met overnight, but we are committed
to supporting it with rapid and continuous performance improvement. Many
exciting features, like FP16, DP4A, will be brought in soon.
How many ops have you implemented?
See register_all_kernels.ts
for an up-to-date list of supported ops. We love contributions. See the
contributing
document for more info.
Do you support training?
Maybe. There are still a decent number of ops that we are missing in WebGPU that
are needed for gradient computation. At this point we are focused on making
inference as fast as possible.
Do you work in node?
Yes. If you run into issues, please let us know.
How do I give feedback?
We'd love your feedback as we develop this backend! Please file an issue
here.
Development
Building
yarn build
Testing
Currently the Canary channel of Chrome is used for testing of the WebGPU
backend:
yarn test