Metadata-Version: 2.1
Name: deephyper
Version: 0.0.5
Summary: Scalable asynchronous neural architecture and hyperparameter search for deep neural networks.
Home-page: https://github.com/deephyper/deephyper
Author: Prasanna Balaprakash
Author-email: pbalapra@anl.gov
License: BSD
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scikit-optimize
Requires-Dist: scikit-learn
Requires-Dist: tqdm
Requires-Dist: tensorflow (>=1.11.0)
Requires-Dist: keras
Requires-Dist: deap
Requires-Dist: gym
Requires-Dist: networkx
Requires-Dist: joblib
Requires-Dist: mpi4py (>=3.0.0)
Provides-Extra: docs
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Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'


<p align="center">
<img src="docs/_static/logo/medium.png">
</p>

[![Documentation Status](https://readthedocs.org/projects/deephyper/badge/?version=latest)](https://deephyper.readthedocs.io/en/latest/?badge=latest)

# 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](https://deephyper.readthedocs.io)

# 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](https://danielkummer.github.io/git-flow-cheatsheet/). For tests we are using: [Pytest](https://docs.pytest.org/en/latest/).

# 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



