TensorFlow backend for TensorFlow.js via Node.js
This package will work on Linux, Windows, and Mac platforms where TensorFlow is supported.
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 10.0 w/ CUDNN v7) (see installation instructions)
- Windows CPU (Win 7 or higher)
- Windows GPU (Win 7 or higher and Cuda 10.0 w/ CUDNN v7) (see installation instructions)
For GPU support, firstname.lastname@example.org or later requires the following NVIDIA® software installed on your system:
|NVIDIA® GPU drivers||>410.x|
Other Linux variants might also work but this project matches core TensorFlow installation requirements.
Installing CPU TensorFlow.js for Node:
npm install @tensorflow/tfjs-nodeyarn add @tensorflow/tfjs-node
Installing Linux/Windows GPU TensorFlow.js for Node:
npm install @tensorflow/tfjs-node-gpuyarn add @tensorflow/tfjs-node-gpu
Windows / Mac OS X Requires Python 2.7
Windows & OSX build support for
node-gyp requires Python 2.7. Be sure to have this version before installing
@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.
Mac OS X Requires Xcode
If you do not have Xcode setup on your machine, please run the following commands:
After that operation completes, re-run
yarn add or
npm install for the
You only need to include
@tensorflow/tfjs-node-gpu in the package.json file, since those packages ship with
Rebuild the package on Raspberry Pi
To use this package on Raspberry Pi, you need to rebuild the node native addon with the following command after you installed the package:
$ npm rebuild @tensorflow/tfjs-node --build-from-source
Using the binding
Before executing any TensorFlow.js code, import the node package:
// Load the binding;// Or if running with 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
MNIST demo for Node.js
See the tfjs-examples repository for training the MNIST dataset using the Node.js bindings.
Optional: Build optimal TensorFlow from source
To get the most optimal TensorFlow build that can take advantage of your specific hardware (AVX512, MKL-DNN), you can build the
libtensorflow library from source:
- Install bazel
- Checkout the main tensorflow repo and follow the instructions in here with one difference: instead of building the pip package, build
./configurebazel build --config=opt --config=monolithic //tensorflow/tools/lib_package:libtensorflow
The build might take a while and will produce a
bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz file, which should be unpacked and replace the files in
deps folder of
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz ~/myproject/node_modules/@tensorflow/tfjs-node/depscd path-to-my-project/node_modules/@tensorflow/tfjs-node/depstar -xf libtensorflow.tar.gz