Run ONNX and TensorFlow inference in the browser. A thin wrapper on top of tract.
There is currently one other usable ONNX runner for the browser, ONNX.js. There are a couple of things tractjs does better:
tractjs.min.js which contains the inlined WASM and WebWorker. The WASM backend of ONNX.js can not as easily be used without a build system.There are however also some downsides to tractjs. See the FAQ.
<html>
<head>
<meta charset="utf-8" />
<script src="https://unpkg.com/tractjs/dist/tractjs.min.js"></script>
<script>
const model = new tractjs.Model("path/to/your/model");
model
.predict([new tractjs.Tensor(new Float32Array([1, 2, 3, 4]), [2, 2])])
.then((preds) => {
console.log(preds);
});
</script>
</head>
</html>
npm install tractjs
import * as tractjs from "tractjs";
const model = new tractjs.Model("path/to/your/model");
model
.predict([new tractjs.Tensor(new Float32Array([1, 2, 3, 4]), [2, 2])])
.then((preds) => {
console.log(preds);
});
My model with dynamic input dimensions doesn't work. Why?
Currently, tract requires fully determined input dimensions to optimize a model. There are two options:
optimize off:const model = new tractjs.Model("path/to/your/model", {
optimize: false,
});
This will however significantly impact performance.
const model = new tractjs.Model("path/to/your/model", {
inputFacts: {
0: ["float32", [1, 3, 224, 224]],
},
});
Be aware that the model will only work properly with inputs of this exact shape though.
There is ongoing work in tract to allow dynamically sized inputs.
What about size?
At the time of writing, tractjs is very large for web standards (8.5MB raw, 2.5MB gzipped). This is due to tract being quite large, and due to some overhead from inlining the WASM. But it's not as bad as it sounds. You can load tractjs lazily along your demo, where you will likely have to load significantly large weights too.
If you are working on a very size-sensitive application, get in touch and we can work on decreasing the size. There are some more optimizations to be done (e. g. an option not to inline WASM, and removing panics from the build). There is also ongoing work in tract to decrease size.
What about WebGL / WebNN support?
tractjs are bindings to the tract Rust library which was originally not intended to be run on the web. WebGL / WebNN support would be great, but would require lots of web-specific changes in tract so it is currently not under consideration.
Why do I still get some output back when passing an input with invalid shape / rank?
Currently tract does not do much error handling in regard to input dimensionality. So most of the time you will get some output back, it will just not be correct. There is ongoing work in tract to be more strict about input dimensionality.
All original work licensed under either of
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.
FAQs
A library for running ONNX and TensorFlow inference in the browser.
The npm package tractjs receives a total of 509 weekly downloads. As such, tractjs popularity was classified as not popular.
We found that tractjs demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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