{
    "info": {
        "author": "Prasanna Balaprakash",
        "author_email": "pbalapra@anl.gov",
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            "Programming Language :: Python",
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        "description": "\n<p align=\"center\">\n<img src=\"docs/_static/logo/medium.png\">\n</p>\n\n![GitHub tag (latest by date)](https://img.shields.io/github/tag-date/deephyper/deephyper.svg?label=version)\n[![Build Status](https://travis-ci.com/deephyper/deephyper.svg?branch=develop)](https://travis-ci.com/deephyper/deephyper)\n[![Documentation Status](https://readthedocs.org/projects/deephyper/badge/?version=latest)](https://deephyper.readthedocs.io/en/latest/?badge=latest)\n![PyPI - License](https://img.shields.io/pypi/l/deephyper.svg)\n![PyPI - Downloads](https://img.shields.io/pypi/dm/deephyper.svg?label=Pypi%20downloads)\n\n# What is DeepHyper?\n\nDeepHyper is an automated machine learning ([AutoML](https://en.wikipedia.org/wiki/Automated_machine_learning)) 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\nsearch_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\nand hyperparameter search methods, scalability, and portability across HPC systems. It comprises three modules:\nbenchmarks, a collection of extensible and diverse benchmark problems;\nsearch, a set of search algorithms for neural architecture search and hyperparameter search;\nand evaluators, a common interface for evaluating hyperparameter configurations\non HPC platforms.\n\n# Documentation\n\nDeephyper documentation is on [ReadTheDocs](https://deephyper.readthedocs.io)\n\n# Install instructions\n\nFrom pip:\n```\npip install deephyper\n```\n\nFrom github:\n```\ngit clone https://github.com/deephyper/deephyper.git\ncd deephyper/\npip install -e .\n```\n\nif you want to install deephyper with test and documentation packages:\n```\n# From Pypi\npip install 'deephyper[tests,docs]'\n\n# From github\ngit clone https://github.com/deephyper/deephyper.git\ncd deephyper/\npip install -e '.[tests,docs]'\n```\n\n# Directory search_space\n\n```\nbenchmark/\n    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.\nevaluator/\n    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.\nsearch/\n    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.\nhps/\n        hyperparameter search applications\nnas/\n        neural architecture search applications\n```\n\n\n# How do I learn more?\n\n* Documentation: https://deephyper.readthedocs.io\n\n* GitHub repository: https://github.com/deephyper/deephyper\n\n# Quickstart\n\n## Hyperparameter Search (HPS)\n\n```\ndeephyper hps ambs --evaluator ray --problem deephyper.benchmark.hps.polynome2.Problem --run deephyper.benchmark.hps.polynome2.run --n-jobs 1\n```\n\n## Neural Architecture Search (NAS)\n\n```\ndeephyper nas ambs --evaluator ray --problem deephyper.benchmark.nas.polynome2Reg.Problem --n-jobs 1\n```\n\n# Who is responsible?\n\nCurrently, the core DeepHyper team is at Argonne National Laboratory:\n\n* Prasanna Balaprakash <pbalapra@anl.gov>, Lead and founder\n* Romain Egele <regele@anl.gov>\n* Misha Salim <msalim@anl.gov>\n* Venkat Vishwanath <venkat@anl.gov>\n* Stefan Wild <wild@anl.gov>\n\nModules, patches (code, documentation, etc.) contributed by:\n\n* Elise Jennings <ejennings@anl.gov>\n* Dipendra Kumar Jha <dipendrajha2018@u.northwestern.edu>\n\n\n# Citing DeepHyper\n\nIf you are referencing DeepHyper in a publication, please cite the following papers:\n\n * P. Balaprakash, M. Salim, T. Uram, V. Vishwanath, and S. M. Wild. **DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks**.\n    In 25th IEEE International Conference on High Performance Computing, Data, and Analytics. IEEE, 2018.\n <!-- * R. Egele, D. Jha, P. Balaprakash, M. Salim, V. Vishwanath, and S. M. Wild. **Scalable Reinforcement-Learning-Based Neural Architecture Search for Scientific and Engineering Applications**. In 34th International Conference on High Performance Computing, 2019. -->\n\n# How can I participate?\n\nQuestions, comments, feature requests, bug reports, etc. can be directed to:\n\n* Our mailing list: *deephyper@groups.io* or https://groups.io/g/deephyper\n\n* Issues on GitHub\n\nPatches are much appreciated on the software itself as well as documentation.\nOptionally, please include in your first patch a credit for yourself in the\nlist above.\n\nThe 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/).\n\n# Acknowledgements\n\n* 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)\n* Argonne Leadership Computing Facility (2018--Present)\n* 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)\n\n# Copyright and license\n\nCopyright \u00a9 2019, UChicago Argonne, LLC\n\nDeepHyper is distributed under the terms of BSD License. See [LICENSE](https://github.com/deephyper/deephyper/blob/master/LICENSE.md)\n\nArgonne Patent & Intellectual Property File Number: SF-19-007\n\n\n\n",
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