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A Generic Resource-Aware Hyperparameter Tuning Execution Engine

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# Fluid: Resource-Aware Hyperparameter Tuning Engine

[![PyPI version](https://img.shields.io/pypi/v/fluidexec.svg)](https://pypi.org/project/fluidexec)
[![Python package](https://github.com/SymbioticLab/Fluid/actions/workflows/python-package.yml/badge.svg?event=release)](https://github.com/SymbioticLab/Fluid/actions/workflows/python-package.yml)

`Fluid` is an alternative [Ray](https://ray.io) executor that intelligently manages trial executions on behalf of hyperparameter tuning algorithms, in order to increase the resource utilization, and improve end-to-end makespan.

This is the implementation of our MLSys'21 [paper](https://symbioticlab.org/publications/#/venue:MLSys) "Fluid: Resource-Aware Hyperparameter Tuning Engine".

## Get Started
First follow the [instruction](https://docs.ray.io/en/master/tune/index.html) in Ray Tune to setup the Ray cluster and a tuning environment as usual.

Then make sure [Nvidia MPS](https://docs.nvidia.com/deploy/mps/index.html#topic_6_1) is correctly setup on all worker nodes.

`Fluid` itself is a normal python package that can be installed by `pip install fluidexec`. Note that the pypi package name is `fluidexec` because the name `fluid` is already taken.

To use `Fluid` in Ray Tune, pass an instance of it as the trial executor to `tune.run`. It should work with any other schedulers:

```python
from fluid.fliud_executor import FluidExecutor
tune.run(
    MyTrainable,
    trial_executor=FluidExecutor(),
    ...
)
```

## Reproduce Experiments
See the README in [`workloads`](workloads/) for more information.

## Notes

Please consider to cite our paper if you find this useful in your research project.

```bibtex
@inproceedings{fluid:mlsys21,
    author    = {Peifeng Yu and Jiachen Liu and Mosharaf Chowdhury},
    booktitle = {MLSys},
    title     = {Fluid: Resource-Aware Hyperparameter Tuning Engine},
    year      = {2021},
}
```

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