Auto-PyTorch is an automated machine learning toolkit based on PyTorch:

>>> import autoPyTorch
>>> cls = autoPyTorch.api.tabular_classification.TabularClassificationTask()
>>>, y_train)
>>> predictions = cls.predict(X_test)

Auto-PyTorch frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Learn more about Auto-PyTorch by reading our paper Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL .




Auto-PyTorch is licensed the same way as scikit-learn, namely the 3-clause BSD license.

Citing Auto-PyTorch

If you use Auto-PyTorch in a scientific publication, we would appreciate a reference to the following paper:

Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL,

Bibtex entry:

   title={Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl},
   author={Zimmer, Lucas and Lindauer, Marius and Hutter, Frank},
   journal={arXiv preprint arXiv:2006.13799},


We appreciate all contribution to Auto-PyTorch, from bug reports and documentation to new features. If you want to contribute to the code, you can pick an issue from the issue tracker which is marked with Needs contributer.


To avoid spending time on duplicate work or features that are unlikely to get merged, it is highly advised that you contact the developers by opening a github issue before starting to work.

When developing new features, please create a new branch from the refactor_development branch. When to submitting a pull request, make sure that all tests are still passing.