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# ![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.
## 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.
![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)
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