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
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.
For more troubleshooting on Windows, check out WINDOWS_TROUBLESHOOTING.md.
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.
Before executing any TensorFlow.js code, import the node package:
// Load the binding
import * as tf from '@tensorflow/tfjs-node';
// Or if running with GPU:
import * as tf from '@tensorflow/tfjs-node-gpu';
Note: you do not need to add the @tensorflow/tfjs
package to your dependencies or import it directly.
# 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