Auptimizer 1.0

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[](https://LGE-ARC-AdvancedAI.github.io/auptimizer) [](https://opensource.org/licenses/GPL-3.0) [](https://travis-ci.org/LGE-ARC-AdvancedAI/auptimizer) [](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.

` git clone <REPO URL> cd <REPO FOLDER> pip install -r requirements.txt python setup.py install `

## 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.


More examples are under [Examples](https://github.com/LGE-ARC-AdvancedAI/auptimizer/tree/master/Examples).

## License

[GPL 3.0 License](./LICENSE)