Scalable asynchronous neural architecture and hyperparameter search for deep neural networks.
Project description
What is DeepHyper?
DeepHyper is a Python package that 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 DL hyperparameter search problems; search, a set of search algorithms for DL hyperparameter search; and evaluators, a common interface for evaluating hyperparameter configurations on HPC platforms.
Documentation
Deephyper documentation is on : ReadTheDocs
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
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]'
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?
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
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
TBD
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