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PASHA: Efficient HPO and NAS with Progressive Resource Allocation

Abstract

Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly to run. When models are trained on large datasets, tuning them with HPO or NAS rapidly becomes prohibitively expensive for practitioners, even when efficient multi-fidelity methods are employed. We propose an approach to tackle the challenge of tuning machine learning models trained on large datasets with limited computational resources. Our approach, named PASHA, extends ASHA and is able to dynamically allocate maximum resources for the tuning procedure depending on the need. The experimental comparison shows that PASHA identifies well-performing hyperparameter configurations and architectures while consuming significantly fewer computational resources than ASHA.

How to use

This repository extends the open-source Syne Tune library and provides a Jupyter notebook to reproduce our experiments.

In order to run the notebook, it is necessary to install Syne Tune. We have installed the full version of Syne Tune using pip install -e .[extra].

In order to run experiments using NASBench201 it is necessary to build the dataset using the provided Syne Tune scripts. In particular you need to run python nasbench201_import.py that is in syne_tune/blackbox_repository/conversion_scripts/scripts. Note that this requires a lot of RAM memory, so it may be needed to have at least 32GB RAM.

The Jupyter notebook to replicate our experiments is located in notebooks directory, and it is called PASHA-ICLR23.ipynb.

PASHA is available also within Syne Tune library, together with a short tutorial.

How to cite

If you find PASHA useful for your research, please consider citing:

@inproceedings{bohdal2023pasha,
  title={PASHA: Efficient HPO and NAS with Progressive Resource Allocation},
  author={Bohdal, Ondrej and Balles, Lukas and Wistuba, Martin and Ermis, Beyza and Archambeau, Cedric and Zappella, Giovanni},
  booktitle={ICLR},
  year={2023}
}

The original descriptions for Syne Tune library follow next.

Syne Tune: Large-Scale and Reproducible Hyperparameter Optimization

Release Python Version License Downloads

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This package provides state-of-the-art distributed hyperparameter optimizers (HPO) with the following key features:

Installing

To install Syne Tune from pip, you can simply do:

pip install 'syne-tune[extra]==0.3.0'

or to get the latest version from git:

pip install --upgrade pip
git clone https://github.com/awslabs/syne-tune.git
cd syne-tune
pip install -e '.[extra]'

You can see the FAQ What are the different installations options supported? for more install options.

See our change log to see what changed in the latest version.

Getting started

To enable tuning, you have to report metrics from a training script so that they can be communicated later to Syne Tune, this can be accomplished by just calling report(epoch=epoch, loss=loss) as shown in the example bellow:

# train_height.py
import logging
import time

from syne_tune import Reporter
from argparse import ArgumentParser

if __name__ == '__main__':
    root = logging.getLogger()
    root.setLevel(logging.INFO)

    parser = ArgumentParser()
    parser.add_argument('--steps', type=int)
    parser.add_argument('--width', type=float)
    parser.add_argument('--height', type=float)

    args, _ = parser.parse_known_args()
    report = Reporter()

    for step in range(args.steps):
        dummy_score = (0.1 + args.width * step / 100) ** (-1) + args.height * 0.1
        # Feed the score back to Syne Tune.
        report(step=step, mean_loss=dummy_score, epoch=step + 1)
        time.sleep(0.1)

Once you have a script reporting metric, you can launch a tuning as-follow:

from syne_tune import Tuner, StoppingCriterion
from syne_tune.backend import LocalBackend
from syne_tune.config_space import randint
from syne_tune.optimizer.baselines import ASHA

# hyperparameter search space to consider
config_space = {
    'steps': 100,
    'width': randint(1, 20),
    'height': randint(1, 20),
}

tuner = Tuner(
    trial_backend=LocalBackend(entry_point='train_height.py'),
    scheduler=ASHA(
        config_space, metric='mean_loss', resource_attr='epoch', max_t=100,
        search_options={'debug_log': False},
    ),
    stop_criterion=StoppingCriterion(max_wallclock_time=15),
    n_workers=4,  # how many trials are evaluated in parallel
)
tuner.run()

The above example runs ASHA with 4 asynchronous workers on a local machine.

Examples

You will find the following examples in examples/ folder illustrating different functionalities provided by Syne Tune:

FAQ and Tutorials

You can check our FAQ, to learn more about Syne Tune functionalities.

Do you want to know more? Here are a number of tutorials.

Security

See CONTRIBUTING for more information.

Citing Syne Tune

If you use Syne Tune in a scientific publication, please cite the following paper:

"Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research" First Conference on Automated Machine Learning 2022

@inproceedings{
  salinas2022syne,
  title={Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research},
  author={David Salinas and Matthias Seeger and Aaron Klein and Valerio Perrone and Martin Wistuba and Cedric Archambeau},
  booktitle={First Conference on Automated Machine Learning (Main Track)},
  year={2022},
  url={https://openreview.net/forum?id=BVeGJ-THIg9}
}

License

This project is licensed under the Apache-2.0 License.