@litertjs/core

@litertjs/core

Core web runtime for LiteRT (previously TFLite), Google's open-source high performance runtime for on-device AI.

This package provides the core functionality to load and run arbitrary LiteRT (.tflite) models directly in the browser, with support for WebGPU and CPU acceleration.

This is the primary package for LiteRT.js. For integration with TensorFlow.js, see @litertjs/tfjs-interop. To test and benchmark your models, check out the @litertjs/model-tester.

Features

• Load and run a LiteRT (.tflite) model.
• Run with WebGPU acceleration on supported browsers.
• Run with (XNNPack)-accelerated CPU kernels on any browser.
• Slot into existing TensorFlow.js pipelines as a replacement for TFJS Graph Models. See @litertjs/tfjs-interop package for details.

Usage

For a complete guide, see our docs at ai.google.dev/edge/litert/web.

The following code snippet loads LiteRT.js, loads a MobileNetV2 model, and runs it on a sample input tensor.

import {loadLiteRt, loadAndCompile, Tensor} from '@litertjs/core';

// Initialize LiteRT.js's Wasm files.
// These files are located in `node_modules/@litertjs/core/wasm/`
// and need to be served by your web server.
await loadLiteRt('/path/to/wasm/directory/');

const model = await loadAndCompile(
  '/path/to/your/model/torchvision_mobilenet_v2.tflite',
  {accelerator: 'webgpu'},
);

// Create an input tensor.
const inputTypedArray = new Float32Array(1 * 3 * 224 * 224);
const inputTensor = new Tensor(inputTypedArray, [1, 3, 224, 224]);

// Move the tensor to the GPU for a WebGPU-accelerated model.
const gpuTensor = await inputTensor.moveTo('webgpu');

// Run the model.
const results = model.run(gpuTensor);

// All tensors that are not moved to another backend with `moveTo` must
// eventually be freed with `.delete()`.
gpuTensor.delete();

// Move the result back to the CPU to read the data.
const result = results[0].moveTo('wasm');
console.log(result.toTypedArray());

// Clean up the result tensor.
result.delete();