Package Exports
- @tensorflow/tfjs-node
- @tensorflow/tfjs-node/dist/io/file_system
This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@tensorflow/tfjs-node) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.
Readme
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:
- Mac OS X CPU (10.12.6 Siera or higher)
- Linux CPU (Ubuntu 16.04 or higher)
- Linux GPU (Ubuntu 16.04 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.
Installing CPU TensorFlow.js for Node:
npm install @tensorflow/tfjs-node
(or)
yarn add @tensorflow/tfjs-node
Installing Linux GPU TensorFlow.js for Node:
npm install @tensorflow/tfjs-node-gpu
(or)
yarn add @tensorflow/tfjs-node-gpu
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.
Using the binding
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';
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 libtensorflow From TensorFlow source
This requires installing bazel first.
bazel build --config=monolithic //tensorflow/tools/lib_package:libtensorflow