Auto-PyTorch

Finding the right architecture and hyperparameter settings for training a deep neural network is crucial to achieve top performance. Auto-PyTorch automates these two aspects by using multi-fidelity optimization and Bayesian optimization (BOHB) to search for the best settings.

The current version of Auto-PyTorch is a first beta and only supports featurized and image data.

References

  • Lucas Zimmer, Marius Lindauer and Frank Hutter: Auto-PyTorch Tabular: Multi-Fidelity Meta Learning for Efficient and Robust AutoDL https://arxiv.org/abs/2006.13799
  • Mendoza, Hector and Klein, Aaron and Feurer, Matthias and Springenberg, Jost Tobias and Urban, Matthias and Burkart, Michael and Dippel, Max and Lindauer, Marius and Hutter, Frank: Towards Automatically-Tuned Deep Neural Networks In: AutoML: Methods, Sytems, Challenge. 2019