Hummingbird: A library for compiling trained traditional machine learning models into tensor computations – MarkTechPost

Hummingbird: A library for compiling trained traditional machine learning models into tensor computations – MarkTechPost

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Source: http://learningsys.org/neurips19/assets/papers/27_CameraReadySubmission_Hummingbird%20(5).pdf
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This is really a cool work out of Microsoft research called hummingbird. You can convert traditional machine learning models to tensor computations to take advantage of hardware acceleration like GPUs and TPUs. It allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models. 

It has many features and benefits as follows:

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  • User can benefit from current and future optimizations implemented in neural network frameworks;
  • User can benefit from native hardware acceleration;
  • User can benefit from having a unique platform to support both traditional and neural network models;
  • User does not have to re-engineer their models.

Hummingbird is compatible with a number of tree-based classifiers and regressors. These models include scikit-learn Decision Trees and Random Forest.

Github: https://github.com/microsoft/hummingbird

Paper: http://learningsys.org/neurips19/assets/papers/27_CameraReadySubmission_Hummingbird%20(5).pdf

Here they convert a random forest model to PyTorch (https://github.com/microsoft/hummingbird)

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Asif Razzaq is an AI Tech Blogger and Digital Health Business Strategist with robust medical device and biotech industry experience and an enviable portfolio in development of Health Apps, AI, and Data Science. An astute entrepreneur, Asif has distinguished himself as a startup management professional by successfully growing startups from launch phase into profitable businesses. This has earned him awards including, the SGPGI NCBL Young Biotechnology Entrepreneurs Award.

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