DistML.js is the web browser-based deep learning library with distributed training tools.

• Multi-dimensional tensor
  • Acceleration with GPU
    • WebGL (WebGL2 only), WebGPU (experimental)
  • Useful tensor operations for pre / post processing
• Neural network building with define-by-run
  • All operators needed by ResNet are implemented
  • PyTorch-like API
• Template for distributed training server
  • Low-latency communication with WebSocket
  • Low-overhead tensor serialization
  • Implementation of data-parallel SGD

node 20.x is needed.

npm install

Python 3.8+ is needed for dataset preprocessing in samples and distributed training feature. Dataset download and export of trained model to ONNX requires PyTorch.

The installation of distributed training server library is described in distributed.

This commands are needed only when WebGPU shader is modified.

python tools/generate_webgputensor_glsl_unary_op.py
node tools/compile_webgpu_shader.js

CommonJS format to be loaded by application built with webpack. The output is generated in dist directory.

npm run build

Then run below to generate archive for distribution.

npm pack

distmljs-<version>.tgz is generated.

Single file format for directly loading from HTML using <script> tag. It is generated to webpack/distmljs.js.

npm run webpack

DistML.js needs to unit test elements such as WebGL that do not work in node.js and have implementation differences between Web browsers. For this reason, testing is performed on a Web browser using mocha.

npm run webpack:test

npm run serve

Open http://localhost:8080/test/ with web browser. Test automatically starts and the result will be displayed.

This section describes scalar_regression as an sample. For other samples, see sample directory.

npm run build

cd sample/scalar_regression
npm install
npm run build

Run HTTP server

npm run serve

Open http://localhost:8080/sample/scalar_regression/output/ with web browser.

MIT