TensorFlow backend for TensorFlow.js via Node.js
This repo is under active development and is not production-ready. We are
actively developing as an open source project.
Installing
TensorFlow.js for Node currently supports the following platforms:
Other Linux variants might also work but this project matches core TensorFlow installation requirements.
Installing CPU TensorFlow.js for Node:
npm install @tensorflow/tfjs-node
(or)
yarn add @tensorflow/tfjs-node
Installing Linux/Windows GPU TensorFlow.js for Node:
npm install @tensorflow/tfjs-node-gpu
(or)
yarn add @tensorflow/tfjs-node-gpu
Windows Requires Python 2.7
Windows build support for node-gyp
requires Python 2.7. Be sure to have this version before installing @tensorflow/tfjs-node
or @tensorflow/tfjs-node-gpu
. Machines with Python 3.x will not install the bindings properly.
Mac OS X Requires Xcode
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.
You only need to include @tensorflow/tfjs-node
or @tensorflow/tfjs-node-gpu
in the package.json file, since those packages ship with @tensorflow/tfjs
already.
Using the binding
Before executing any TensorFlow.js code, import the node package:
import * as tf from '@tensorflow/tfjs';
import '@tensorflow/tfjs-node';
import '@tensorflow/tfjs-node-gpu';
Development
yarn
yarn test
yarn enable-gpu
MNIST demo for Node.js
See the tfjs-examples repository for training the MNIST dataset using the Node.js bindings.
Optional: Build libtensorflow From TensorFlow source
This requires installing bazel first.
bazel build --config=monolithic //tensorflow/tools/lib_package:libtensorflow