.. LightOnML documentation master file, created by sphinx-quickstart. LightOnML documentation =========================================== LightOnML is a high level machine learning-oriented library that allows to control LightOn’s Optical Processing Units (OPUs). Our current product performs random projections using light. LightOn's OPUs are available through `LightOn's Cloud service `_, or physically with a `LightOn Appliance `_ . The library is developed for Python 3, with interfaces for `scikit-learn `_ and `pytorch `_ to offer to ML researchers and practitioners a familiar and user-friendly environment. It is available on `Github `_ and `PyPI `_, so it can be installed with ``pip install lightonml``. 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 Below shows a minimal usage example of lightonml's `OPU ` class: .. figure:: _static/img/showcase_api3.gif :alt: OPU library showcase :align: center .. toctree:: :glob: :hidden: :maxdepth: 1 :caption: Notes notes/opu.rst notes/get_started.ipynb notes/opu_in_depth.rst notes/release_notes.rst notes/migration_guide.rst .. toctree:: :hidden: :maxdepth: 1 :caption: API Reference lightonml.opu lightonml.projections lightonml.encoding lightonml.datasets lightonml.utils .. toctree:: :hidden: :glob: :maxdepth: 1 :caption: Examples examples/kernels_and_random_features.ipynb examples/pipeline_and_crossval_OPU.ipynb examples/saving_and_loading.ipynb examples/bitplan_encoding_tutorial.rst examples/transfer_learning.ipynb examples/randomized_svd.ipynb examples/newma_online.ipynb examples/autoencoder_tutorial.ipynb examples/dfa.rst examples/randomized_linear_algebra.ipynb examples/others.rst .. toctree:: :glob: :hidden: :maxdepth: 1 :caption: Advanced advanced/extending.rst Version |version|