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 architecture. 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 structure

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)

python -m deephyper.search.hps.ambs --problem deephyper.benchmark.hps.polynome2.Problem --run deephyper.benchmark.hps.polynome2.run

Neural Architecture Search (NAS)

python -m deephyper.search.nas.ppo_a3c_sync --problem deephyper.benchmark.nas.mnist1D.problem.Problem --run deephyper.search.nas.model.run.alpha.run

Who is responsible?

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

Prasanna Balaprakash pbalapra@anl.gov, Lead and founder
Romain Egele regele@anl.gov
Misha Salim msalim@anl.gov
Venkat Vishwanath venkat@anl.gov
Stefan Wild wild@anl.gov

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

Elise Jennings ejennings@anl.gov
Dipendra Kumar Jha dipendrajha2018@u.northwestern.edu

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.

How can I participate?

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

Our mailing list: deephyper@groups.io 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 (2018--Present)
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

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

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

zhanghonglishanzai/deephyper