Metadata-Version: 2.1
Name: Auptimizer
Version: 1.1
Summary: UNKNOWN
Home-page: https://github.com/LGE-ARC-AdvancedAI/auptimizer
Author: LG Electronics Inc.
Author-email: auptimizer@lge.com
License: SPDX-License-Identifier: GPL-3.0-or-later
Project-URL: Bug Tracker, https://github.com/LGE-ARC-AdvancedAI/auptimizer/issues
Project-URL: Documentation, https://lge-arc-advancedai.github.io/auptimizer/
Project-URL: Source Code, https://github.com/LGE-ARC-AdvancedAI/auptimizer
Description: # ![Auptimizer Logo](AuptimizerBlackLong.png)
        
        [![Documentation](https://img.shields.io/badge/doc-reference-blue.svg)](https://LGE-ARC-AdvancedAI.github.io/auptimizer)
        [![GPL 3.0 License](https://img.shields.io/badge/License-GPL%203.0-blue.svg)](https://opensource.org/licenses/GPL-3.0)
        [![pipeline status](https://travis-ci.org/LGE-ARC-AdvancedAI/auptimizer.svg?branch=master)](https://travis-ci.org/LGE-ARC-AdvancedAI/auptimizer)
        [![coverage report](https://codecov.io/gh/LGE-ARC-AdvancedAI/auptimizer/branch/master/graph/badge.svg)](https://codecov.io/gh/LGE-ARC-AdvancedAI/auptimizer)
        
        **Auptimizer** is an optimization tool for Machine Learning (ML) that automates many of the tedious parts of the model building process.
        Currently, **Auptimizer** helps with:
        
        + **Automating tedious experimentation** - Start using Auptimizer by changing just a few lines of your code. It will
          run and record sophisticated hyperparameter optimization (HPO) experiments for you, resulting in effortless
          consistency and reproducibility.
        
        + **Making the best use of your compute-resources** - Whether you are using a couple of GPUs or AWS, Auptimizer will
          help you orchestrate compute resources for faster hyperparameter tuning.
        
        + **Getting the best models in minimum time** - Generate optimal models and achieve better performance by employing
          state-of-the-art HPO techniques. Auptimizer provides a single seamless access point to top-notch HPO algorithms,
          including Bayesian optimization, multi-armed bandit. You can even integrate your own proprietary solution.
        
        Best of all, **Auptimizer** offers a consistent interface that allows users to switch between different HPO algorithms
        and computing resources with minimal changes to their existing code.
        
        In the future, **Auptimizer** will support end-to-end model building for edge devices, including model compression and
        neural architecture search. The table below shows a full list of currently supported techniques.
        
        | Supported HPO Algorithms      | Supported Infrastructure |
        | ----------- | ----------- |
        | Random<br>Grid<br>[Hyperband](https://github.com/zygmuntz/hyperband)<br>[Hyperopt](https://github.com/hyperopt/hyperopt)<br>[Spearmint](https://github.com/JasperSnoek/spearmint)<br>[EAS (experimental)](https://github.com/han-cai/EAS)<br>Passive      | Multiple CPUs<br>Multiple GPUs<br>Multiple Machines (SSH)<br>AWS EC2 instances |
        
        
        ## Install
        
        **Auptimizer** currently is well tested on Linux systems, it may require some tweaks for Windows users.
        
        ```
        pip install auptimizer
        ```
        
        **Note** Dependencies are not included. Using `pip install`
        [requirements.txt](https://github.com/LGE-ARC-AdvancedAI/auptimizer/blob/master/requirements.txt) will install
        necessary libraries for all functionalities.
        
        ## Documentation
        
        See more in [documentation](https://lge-arc-advancedai.github.io/auptimizer/) 
        
        ## Example
        
        ```
        cd Examples/demo
        # Setup environment (Interactively create the environment file based on user input)
        python -m aup.setup
        # Setup experiment
        python -m aup.init
        # Create training script - auto.py
        python -m aup.convert origin.py experiment.json demo_func
        # Run aup for this experiment
        python -m aup experiment.json
        ```
        
        Each job's hyperparameter configuration is saved separately under `jobs/*.json` and is also recorded in the SQLite file `.aup/sqlite3.db`.
        
        ![gif demo](docs/images/demo.gif)
        
        More examples are under [Examples](https://github.com/LGE-ARC-AdvancedAI/auptimizer/tree/master/Examples).
        
        ## License
        
        [GPL 3.0 License](./LICENSE)
        
        
        ## Cite
        
        If you have used this software for research, please cite the following paper (accepted at IEEE Big Data 2019):
        
        ```
        @misc{liu2019auptimizer,
            title={Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning},
            author={Jiayi Liu and Samarth Tripathi and Unmesh Kurup and Mohak Shah},
            year={2019},
            eprint={1911.02522},
            archivePrefix={arXiv},
            primaryClass={cs.LG}
        }
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
