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0.0.1-alpha.10 • Public • Published

TFLite support for Tensorflow.js


This package enables users to run arbitary TFLite models on the web. Users can load a TFLite model from a URL, use TFJS tensors to set the model's input data, run inference, and get the output back in TFJS tensors. Under the hood, the TFLite C++ runtime is packaged in a set of WASM modules, and the one with the best performance will be automatically loaded based on user's current environment (e.g. whether WebAssembly SIMD and/or multi-threading is supported or not).

Check out this demo where we use this package to run a CartoonGAN TFLite model on the web.


Import the packages

To use this package, you will need a TFJS backend installed. We recommend the CPU backend. You will also need to import @tensorflow/tfjs-core for manipulating tensors.


// Adds the CPU backend.
import '@tensorflow/tfjs-backend-cpu';
// Import @tensorflow/tfjs-core
import * as tf from '@tensorflow/tfjs-core';
// Import @tensorflow/tfjs-tflite.
import * as tflite from '@tensorflow/tfjs-tflite';

Via a script tag

<!-- Import @tensorflow/tfjs-core -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
<!-- Adds the CPU backend -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-cpu"></script>
  Import @tensorflow/tfjs-tflite

  Note that we need to explicitly load dist/tf-tflite.min.js so that it can
  locate WASM module files from their default location (dist/).
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-tflite/dist/tf-tflite.min.js"></script>

Set WASM modules location (optional)

By default, it will try to load the WASM modules from the same location where the package or your own script is served. Use setWasmPath to set your own location. See src/tflite_web_api_client.d.ts for more details.


Load a TFLite model

const tfliteModel = await tflite.loadTFLiteModel('url/to/your/model.tflite');

Run inference

// Prepare input tensors.
const img = tf.browser.fromPixels(document.querySelector('img'));
const input = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);

// Run inference and get output tensors.
let outputTensor = tfliteModel.predict(input) as tf.Tensor;


This package uses XNNPACK to accelerate inference for floating-point and quantized models. See XNNPACK documentation for the full list of supported floating-point and quantized operators.

To achieve the best performance, use a browser that supports "WebAssembly SIMD" and "WebAssembly threads". In Chrome 92+, these features are enabled by default. In older versions of Chrome, they can be enabled in chrome://flags/.

Starting from Chrome 92, cross-origin isolation needs to be set up in your site in order to take advantage of the multi-threading support. Without this, it will fallback to the WASM binary with SIMD-only support (or the vanila version if SIMD is not enabled). Without multi-threading support, certain models might not achieve the best performance. See here for the high-level steps to set up the cross-origin isolation.

By default, the runtime uses the number of physical cores as the thread count. You can tune this number by setting the numThreads option when loading the TFLite model:

const tfliteModel = await tflite.loadTFLiteModel(
    {numThreads: navigator.hardwareConcurrency / 2});


Profiling can be enabled by setting the enableProfiling option to true when loading the TFLite model:

const tfliteModel = await tflite.loadTFLiteModel(
    {enableProfiling: true});

Once it is enabled, the runtime will record per-op latency data when the predict method is called. The profiling results can be retrieved in two ways:

  • tfliteModel.getProfilingResults(): this method will return an array of {nodeType, nodeName, execTimeInMs}.
  • tfliteModel.getProfilingSummary(): this method will return a human-readable profiling result summary that looks like this.



$ yarn
$ yarn build


$ yarn test


$ yarn build-npm
# (TODO): publish




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