Torch and TorchVision, for your Node servers. Get up and running with PyTorch models within your NodeJS infrastructure in seconds.

Getting Started

via yarn

Assuming nothing's broken: yarn add pytorchjs

The same old PyTorch models, in NodeJS

Run your PyTorch models in Javascript, just like you would in Python.

import { torch, torchvision } from 'pytorchjs';

const { load } = torch;
const { DataLoader } = torch.utils.data;
const { ImageFolder } = torchvision.datasets;

const { Compose, Resize, InvertAxes, Normalize } = torchvision.transforms;

const squeezeNet = load("./test/resources/squeezenet_ts.pt");
const transforms = new Compose([
  new Resize({height: 224, width: 224}),
  new Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
  new InvertAxes()
]);

const loader = new DataLoader(new ImageFolder("./test/resources/dataset"), 1, transforms);
const results = await squeezeNet(loader);

More Examples

Additional examples of both setup and usage involving features like Torchvision Transforms and CUDA (in development) may be found here.

Key Features

• Run your PyTorch models in a Javascript environment, without worrying about setting up Torchscript or downloading custom binaries
• Deploy your model using configurations identical to what you used during training
• Built-in CUDA support
  • CUDA support is a work in progress
• Support for TorchVision, including transforms, dataset classes, and pre-trained models
  • Support for TorchVision models is a work in progress

Development

• yarn install should allow you to install project dependencies
• yarn test to run the test suite for this project

Misc

• This project uses arition's fork of torch-js to run TorchScript - check the project out if you're curious about how we do it!
• Distributed under the MIT license. See LICENSE for more information.
• This project was originally developed as a part of COMSW4995 - Open Source Development at Columbia University.