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@tensorflow/tfjs-node
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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.
TensorFlow.js for Node currently supports the following platforms:
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.
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 & OSX 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.
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:
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
FAQs
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](https://js.tensorflow.org/api/latest/).
We found that @tensorflow/tfjs-node demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 10 open source maintainers collaborating on the project.
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