YellowFin

YellowFin: Open-Source AutoML Library for Machine Learning

Automate hyperparameter tuning and model selection with YellowFin, an open-source autoML library designed for users with no machine learning expertise to achieve high accuracy on various tasks.

What is YellowFin?

YellowFin is an open-source autoML library for Python that automates the tuning of hyperparameters and model architecture search to help users achieve high accuracy with machine learning models. Developed by researchers at MIT, IIT, and Adobe Research, YellowFin aims to make state-of-the-art machine learning techniques accessible to non-experts.

Some key capabilities and benefits of YellowFin include:

• Automated hyperparameter optimization using a novel tuning algorithm that customizes tuning for different hyperparameters like learning rate and momentum.
• Automatic model selection between frameworks like PyTorch and TensorFlow.
• Multi-objective tuning to simultaneously optimize for accuracy, training time, and model size.
• Support for various data modalities including image, text, audio, and time-series data.
• Integration with popular ML frameworks like Scikit-Learn, Keras, and PyTorch for easy use in existing workflows.
• Significant reduction in time and effort for non-expert users to build highly accurate models compared to manual tuning or other autoML tools.

With its innovative algorithms and flexibility across frameworks and data types, YellowFin makes state-of-the-art machine learning more accessible for non-experts across a variety of applications like computer vision, NLP, recommendation systems, predictive analytics, and more.

YellowFin Features

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