This repo is under active development and is not production-ready. We are actively developing as an open source project.

TensorFlow.js for Node currently supports the following platforms:

• Mac OS X CPU (10.12.6 Siera or higher)
• Linux CPU (Ubuntu 14.04 or higher)
• Linux GPU (Ubuntu 14.04 or higher and Cuda 9.0 w/ CUDNN v7) (see installation instructions)
• Windows CPU (Win 7 or higher)
• Windows GPU (Win 7 or higher and Cuda 9.0 w/ CUDNN v7) (see installation instructions)

Other Linux variants might also work but this project matches core TensorFlow installation requirements.

npm install @tensorflow/tfjs-node (or) yarn add @tensorflow/tfjs-node

npm install @tensorflow/tfjs-node-gpu (or) yarn add @tensorflow/tfjs-node-gpu

If you do not have Xcode setup on your machine, please run the following commands:

$ xcode-select --install

After that operation completes, re-run yarn add or npm install for the @tensorflow/tfjs-node package.

Before executing any TensorFlow.js code, import the node package:

import * as tf from '@tensorflow/tfjs'; // Load the binding import '@tensorflow/tfjs-node'; // Or if running with GPU: import '@tensorflow/tfjs-node-gpu';

# Download and install JS dependencies, including libtensorflow 1.8. yarn # Run TFJS tests against Node.js backend: yarn test

# Switch to GPU for local development: yarn enable-gpu

See the tfjs-examples repository for training the MNIST dataset using the Node.js bindings.

This requires installing bazel first.

bazel build --config=monolithic //tensorflow/tools/lib_package:libtensorflow