Auto-PyTorch

Copyright (C) 2019 AutoML Group Freiburg

This a very early pre-alpha version of our upcoming Auto-PyTorch. So far, Auto-PyTorch supports featurized data (classification, regression) and image data (classification).

The newest features in Auto-PyTorch for tabular data are described in the paper "Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL".

Installation

Clone repository

$ cd install/path
$ git clone https://github.com/automl/Auto-PyTorch.git
$ cd Auto-PyTorch

If you want to contribute to this repository switch to our current develop branch

$ git checkout develop

Install pytorch: https://pytorch.org/

Install Auto-PyTorch:

$ cat requirements.txt | xargs -n 1 -L 1 pip install
$ python setup.py install

Examples

Code for the paper is available under examples/ensemble.

For a detailed tutorial, please refer to the jupyter notebook in https://github.com/automl/Auto-PyTorch/tree/master/examples/basics.

In a nutshell:

from autoPyTorch import AutoNetClassification

# data and metric imports
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics
X, y = sklearn.datasets.load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = \
        sklearn.model_selection.train_test_split(X, y, random_state=1)

# running Auto-PyTorch
autoPyTorch = AutoNetClassification("tiny_cs",  # config preset
                                    log_level='info',
                                    max_runtime=300,
                                    min_budget=30,
                                    max_budget=90)

autoPyTorch.fit(X_train, y_train, validation_split=0.3)
y_pred = autoPyTorch.predict(X_test)

print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_pred))

More examples with datasets:

$ cd examples/

Configuration

How to configure Auto-PyTorch for your needs:

# Print all possible configuration options.
AutoNetClassification().print_help()

# You can use the constructor to configure Auto-PyTorch.
autoPyTorch = AutoNetClassification(log_level='info', max_runtime=300, min_budget=30, max_budget=90)

# You can overwrite this configuration in each fit call.
autoPyTorch.fit(X_train, y_train, log_level='debug', max_runtime=900, min_budget=50, max_budget=150)

# You can use presets to configure the config space.
# Available presets: full_cs, medium_cs (default), tiny_cs.
# These are defined in autoPyTorch/core/presets.
# tiny_cs is recommended if you want fast results with few resources.
# full_cs is recommended if you have many resources and a very high search budget.
autoPyTorch = AutoNetClassification("full_cs")

# Enable or disable components using the Auto-PyTorch config:
autoPyTorch = AutoNetClassification(networks=["resnet", "shapedresnet", "mlpnet", "shapedmlpnet"])

# You can take a look at the search space.
# Each hyperparameter belongs to a node in Auto-PyTorch's ML Pipeline.
# The names of the hyperparameters are prefixed with the name of the node: NodeName:hyperparameter_name.
# If a hyperparameter belongs to a component: NodeName:component_name:hyperparameter_name.
# Call with the same arguments as fit.
autoPyTorch.get_hyperparameter_search_space(X_train, y_train, validation_split=0.3)

# You can configure the search space of every hyperparameter of every component:
from autoPyTorch import HyperparameterSearchSpaceUpdates
search_space_updates = HyperparameterSearchSpaceUpdates()

search_space_updates.append(node_name="NetworkSelector",
                            hyperparameter="shapedresnet:activation",
                            value_range=["relu", "sigmoid"])
search_space_updates.append(node_name="NetworkSelector",
                            hyperparameter="shapedresnet:blocks_per_group",
                            value_range=[2,5],
                            log=False)
autoPyTorch = AutoNetClassification(hyperparameter_search_space_updates=search_space_updates)

Enable ensemble building (for featurized data):

from autoPyTorch import AutoNetEnsemble
autoPyTorchEnsemble = AutoNetEnsemble(AutoNetClassification, "tiny_cs", max_runtime=300, min_budget=30, max_budget=90)

Disable pynisher if you experience issues when using cuda:

autoPyTorch = AutoNetClassification("tiny_cs", log_level='info', max_runtime=300, min_budget=30, max_budget=90, cuda=True, use_pynisher=False)

License

This program is free software: you can redistribute it and/or modify it under the terms of the Apache license 2.0 (please see the LICENSE file).

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

You should have received a copy of the Apache license 2.0 along with this program (see LICENSE file).

Reference

@incollection{mendoza-automlbook18a,
  author    = {Hector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter},
  title     = {Towards Automatically-Tuned Deep Neural Networks},
  year      = {2018},
  month     = dec,
  editor    = {Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin},
  booktitle = {AutoML: Methods, Sytems, Challenges},
  publisher = {Springer},
  chapter   = {7},
  pages     = {141--156},
  note      = {To appear.},
}

Note: Previously, the name of the project was AutoNet. Since this was too generic, we changed the name to AutoPyTorch. AutoNet 2.0 in the reference mention above is indeed AutoPyTorch.

Contact

Auto-PyTorch is developed by the AutoML Group of the University of Freiburg.