deephyper 0.1.2

Scalable asynchronous neural architecture and hyperparameter search for deep neural networks.

What is DeepHyper?

DeepHyper is an automated machine learning (AutoML) package for deep neural networks. It comprises two components: 1) Neural architecture search is an approach for automatically searching for high-performing the deep neural network search_space. 2) Hyperparameter search is an approach for automatically searching for high-performing hyperparameters for a given deep neural network. DeepHyper provides an infrastructure that targets experimental research in neural architecture and hyperparameter search methods, scalability, and portability across HPC systems. It comprises three modules: benchmarks, a collection of extensible and diverse benchmark problems; search, a set of search algorithms for neural architecture search and hyperparameter search; and evaluators, a common interface for evaluating hyperparameter configurations on HPC platforms.

Documentation

Deephyper documentation is on ReadTheDocs

Install instructions

From pip:

pip install deephyper

From github:

git clone https://github.com/deephyper/deephyper.git
cd deephyper/
pip install -e .

if you want to install deephyper with test and documentation packages:

# From Pypi
pip install 'deephyper[tests,docs]'

# From github
git clone https://github.com/deephyper/deephyper.git
cd deephyper/
pip install -e '.[tests,docs]'

Directory search_space

benchmark/
    a set of problems for hyperparameter or neural architecture search which the user can use to compare our different search algorithms or as examples to build their own problems.
evaluator/
    a set of objects which help to run search on different systems and for different cases such as quick and light experiments or long and heavy runs.
search/
    a set of algorithms for hyperparameter and neural architecture search. You will also find a modular way to define new search algorithms and specific sub modules for hyperparameter or neural architecture search.
hps/
        hyperparameter search applications
nas/
        neural architecture search applications

How do I learn more?

• Documentation: https://deephyper.readthedocs.io

• GitHub repository: https://github.com/deephyper/deephyper

Quickstart

Hyperparameter Search (HPS)

deephyper hps ambs --evaluator ray --problem deephyper.benchmark.hps.polynome2.Problem --run deephyper.benchmark.hps.polynome2.run --n-jobs 1

Neural Architecture Search (NAS)

deephyper nas ambs --evaluator ray --problem deephyper.benchmark.nas.polynome2Reg.Problem --n-jobs 1

Who is responsible?

Currently, the core DeepHyper team is at Argonne National Laboratory:

• Prasanna Balaprakash [e-mail], Lead and founder
• Romain Egele [e-mail]
• Misha Salim [e-mail]
• Romit Maulik [e-mail]
• Venkat Vishwanath [e-mail]
• Stefan Wild [e-mail]

Modules, patches (code, documentation, etc.) contributed by:

• Elise Jennings [e-mail]
• Dipendra Kumar Jha [e-mail]

Citing DeepHyper

If you are referencing DeepHyper in a publication, please cite the following papers:

• P. Balaprakash, M. Salim, T. Uram, V. Vishwanath, and S. M. Wild. DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. In 25th IEEE International Conference on High Performance Computing, Data, and Analytics. IEEE, 2018.
• P. Balaprakash, R. Egele, M. Salim, S. Wild, V. Vishwanath, F. Xia, T. Brettin, and R. Stevens. Scalable reinforcement-learning-based neural architecture search for cancer deep learning research. In SC ’19: IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis, 2019.

How can I participate?

Questions, comments, feature requests, bug reports, etc. can be directed to:

• Our mailing list: [e-mail] or https://groups.io/g/deephyper

• Issues on GitHub

Patches are much appreciated on the software itself as well as documentation. Optionally, please include in your first patch a credit for yourself in the list above.

The DeepHyper Team uses git-flow to organize the development: Git-Flow cheatsheet. For tests we are using: Pytest.

Acknowledgements

• Scalable Data-Efficient Learning for Scientific Domains, U.S. Department of Energy 2018 Early Career Award funded by the Advanced Scientific Computing Research program within the DOE Office of Science (2018--Present)
• Argonne Leadership Computing Facility: This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.
• SLIK-D: Scalable Machine Learning Infrastructures for Knowledge Discovery, Argonne Computing, Environment and Life Sciences (CELS) Laboratory Directed Research and Development (LDRD) Program (2016--2018)

Copyright and license

Copyright © 2019, UChicago Argonne, LLC

DeepHyper is distributed under the terms of BSD License. See LICENSE

Argonne Patent & Intellectual Property File Number: SF-19-007