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.
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)
An example command line for 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)
An example command line for NAS:
deephyper nas ambs --evaluator ray --problem deephyper.benchmark.nas.polynome2Reg.Problem --n-jobs 1