Copyright (C) 2018 AutoML Group
This a very early pre-alpha version of our upcoming Auto-PyTorch. So far, Auto-PyTorch only supports featurized data.
Clone repository
$ cd install/path
$ git clone https://github.com/automl/Auto-PyTorch.git
$ cd Auto-PyTorchIf you want to contribute to this repository switch to our current develop branch
$ git checkout developInstall pytorch: https://pytorch.org/
Install Auto-PyTorch:
$ cat requirements.txt | xargs -n 1 -L 1 pip install
$ python setup.py installIn 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/
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:
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)This program is free software: you can redistribute it and/or modify it under the terms of the 3-clause BSD license (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 3-clause BSD license along with this program (see LICENSE file). If not, see https://opensource.org/licenses/BSD-3-Clause.
@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.},
}
Auto-PyTorch is developed by the AutoML Group of the University of Freiburg.