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onnxruntime-common

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onnxruntime-common

ONNXRuntime JavaScript API library


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increased by11.54%
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What is onnxruntime-common?

The onnxruntime-common npm package provides common utilities and types for the ONNX Runtime JavaScript API. It is typically used in conjunction with other ONNX Runtime packages to facilitate the loading, running, and managing of ONNX models in JavaScript environments.

What are onnxruntime-common's main functionalities?

Model Management

This feature allows you to load an ONNX model into an inference session. The code sample demonstrates how to create an inference session from a model file.

const { InferenceSession } = require('onnxruntime-common');

async function loadModel(modelPath) {
  const session = await InferenceSession.create(modelPath);
  console.log('Model loaded successfully');
  return session;
}

loadModel('path/to/model.onnx');

Inference

This feature allows you to run inference on an ONNX model. The code sample demonstrates how to create a tensor from input data, feed it into the model, and retrieve the inference results.

const { InferenceSession, Tensor } = require('onnxruntime-common');

async function runInference(session, inputData) {
  const inputTensor = new Tensor('float32', inputData, [1, inputData.length]);
  const feeds = { input: inputTensor };
  const results = await session.run(feeds);
  console.log('Inference results:', results);
  return results;
}

(async () => {
  const session = await InferenceSession.create('path/to/model.onnx');
  const inputData = new Float32Array([1.0, 2.0, 3.0, 4.0]);
  await runInference(session, inputData);
})();

Tensor Management

This feature allows you to create and manage tensors, which are the primary data structure used in ONNX models. The code sample demonstrates how to create a tensor from a data array and dimensions.

const { Tensor } = require('onnxruntime-common');

function createTensor(data, dims) {
  const tensor = new Tensor('float32', data, dims);
  console.log('Tensor created:', tensor);
  return tensor;
}

const data = new Float32Array([1.0, 2.0, 3.0, 4.0]);
const dims = [2, 2];
createTensor(data, dims);

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Package last updated on 20 Nov 2023

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