Metadata-Version: 2.1
Name: deephyper
Version: 0.1.11
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: ANL
Description: 
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        <img src="docs/_static/logo/medium.png">
        </p>
        
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        # What is DeepHyper?
        
        DeepHyper 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
        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.
        
        # Documentation
        
        Deephyper documentation is on [ReadTheDocs](https://deephyper.readthedocs.io)
        
        # 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)
        
        ```
        deephyper hps ambs --evaluator ray --problem deephyper.benchmark.hps.polynome2.Problem --run deephyper.benchmark.hps.polynome2.run --n-jobs 1
        ```
        
        ## Neural Architecture Search (NAS)
        
        ```
        deephyper nas ambs --evaluator ray --problem deephyper.benchmark.nas.polynome2Reg.Problem --n-jobs 1
        ```
        
        # 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>
        * Romit Maulik <rmaulik@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.
         
         * P. Balaprakash, R. Egele, M. Salim, S. Wild, V. Vishwanath, F. Xia, T. Brettin, and R. Stevens. **Scalable reinforcement-learning-based neural architecture search for cancer deep learning research**.  In SC ’19:  IEEE/ACM International Conference on High Performance Computing, Network-ing, Storage and Analysis, 2019.
        
         <!-- * 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. -->
        
        # 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: This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.
        * 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
        
        Copyright © 2019, UChicago Argonne, LLC
        
        DeepHyper is distributed under the terms of BSD License. See [LICENSE](https://github.com/deephyper/deephyper/blob/master/LICENSE.md)
        
        Argonne Patent & Intellectual Property File Number: SF-19-007
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6, <3.8
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: dev
Provides-Extra: docs
Provides-Extra: analytics
