TensorFlow.js is a powerful tool for machine learning in JavaScript, but what do you do when your TensorFlow.js application outgrows the browser? In this post, we'll explore how to scale TensorFlow.js applications with Node.js.
TensorFlow.js is an open-source library for machine learning in JavaScript. It's used by developers to create sophisticated machine learning models that run in the browser or in Node.js.
Node.js is a great choice for scaling TensorFlow.js applications. It's fast, efficient, and has a large ecosystem of libraries and tools.
There are two main ways to scale TensorFlow.js applications with Node.js:
tensorflow.js
Node.js module.tfjs-node
.We'll explore each of these methods in more detail below.
tensorflow.js
Node.js moduleThe tensorflow.js
Node.js module is the official way to run TensorFlow.js in Node.js. It's easy to install and use.
To install the tensorflow.js
Node.js module, run the following command:
npm install @tensorflow/tfjs
To use the tensorflow.js
Node.js module, require it in your code:
const tf = require('@tensorflow/tfjs');
Now you can use all the TensorFlow.js APIs in your Node.js application.
If you want to use a TensorFlow.js-compatible library like tfjs-node
, you'll need to install it first.
To install tfjs-node
, run the following command:
npm install tfjs-node
To use tfjs-node
, require it in your code:
const tf = require('tfjs-node');
Now you can use all the TensorFlow.js APIs in your Node.js application.