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LightOn technologies for Large scale Machine Learning with Optical Processing Unit

Project description

LightOnML library

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LightOnML is a high level machine learning-oriented API that allows to perform random projections on LightOn’s optical processing units (OPUs). LightOn’s OPUs are available through LightOn’s Cloud service.

Features

  • Run large-scale non-linear and linear random projections using LightOn’s Aurora OPUs
  • Simulate these projections on any machine without access to an OPU
  • Encode input data in a binary form using various encoders, for OPU input

Installation

lightonml doesn't require access to an OPU for some functionalities, but for performing computations on an OPU you'll need one. Otherwise, a simulated OPU can be used.

To install, use pip:

pip install lightonml

Optional dependencies are :

  • torch, required for the encoder classes, and the PyTorch OPUMap.
  • scikit-learn, required for using the corresponding OPUMap to work.

Documentation, examples and help

Main documentation can be found at the API docs website.

Check the examples directory in the repo, if you don't have access to an OPU you can run the code locally with a simulated OPU

For getting help on the LightOn Cloud service check the Community website

For help on the library itself, you can use issues on this repository.

Access to Optical Processing Units

To request access to LightOn Cloud and try our photonic co-processor, please visit: https://cloud.lighton.ai/

For researchers, we also have a LightOn Cloud for Research program, please visit https://cloud.lighton.ai/lighton-research/ for more information.

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