@jetsonjs/tfjs-node-jetson

TensorFlow backend for TensorFlow.js via Node.js

This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime, accelerated by the TensorFlow C binary under the hood. It provides the same API as TensorFlow.js.

This package will work on Linux, Windows, and Mac platforms where TensorFlow is supported.

Installing

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, tfjs-node-gpu@1.2.4 or later requires the following NVIDIA® software installed on your system:

Name	Version
NVIDIA® GPU drivers	>410.x
CUDA® Toolkit	10.0
cuDNN SDK	>=7.4.1

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.

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:

$ 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:

// 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.

Development

# 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 libtensorflow:

./configure
bazel 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 tfjs-node repo:

cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz ~/myproject/node_modules/@tensorflow/tfjs-node/deps
cd path-to-my-project/node_modules/@tensorflow/tfjs-node/deps
tar -xf libtensorflow.tar.gz