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Distributed Hyperparameter Optimization on SageMaker

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Syne Tune: Large-Scale and Reproducible Hyperparameter Optimization

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Syne Tune

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Syne Tune provides state-of-the-art algorithms for hyperparameter optimization (HPO) with the following key features:

  • Lightweight and platform-agnostic: Syne Tune is designed to work with different execution backends, so you are not locked into a particular distributed system architecture. Syne Tune runs with minimal dependencies.
  • Wide coverage of different HPO methods: Syne Tune supports more than 20 different optimization methods across multi-fidelity HPO, constrained HPO, multi-objective HPO, transfer learning, cost-aware HPO, and population-based training.
  • Simple, modular design: Rather than wrapping other HPO frameworks, Syne Tune provides simple APIs and scheduler templates, which can easily be extended to your specific needs. Studying the code will allow you to understand what the different algorithms are doing, and how they differ from each other.
  • Industry-strength Bayesian optimization: Syne Tune has comprehensive support for Gaussian Process-based Bayesian optimization. The same code powers modalities such as multi-fidelity HPO, constrained HPO, and cost-aware HPO, and has been tried and tested in production for several years.
  • Support for distributed workloads: Syne Tune lets you move fast, thanks to the parallel compute resources AWS SageMaker offers. Syne Tune allows ML/AI practitioners to easily set up and run studies with many experiments running in parallel. Run on different compute environments (locally, AWS, simulation) by changing just one line of code.
  • Out-of-the-box tabulated benchmarks: Tabulated benchmarks let you simulate results in seconds while preserving the real dynamics of asynchronous or synchronous HPO with any number of workers.

Syne Tune is developed in collaboration with the team behind the Automatic Model Tuning service.


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

pip install 'syne-tune[basic]'

or to install the latest version from source:

git clone
cd syne-tune
python3 -m venv st_venv
. st_venv/bin/activate
pip install --upgrade pip
pip install -e '.[basic]'

This installs everything in a virtual environment st_venv. Remember to activate this environment before working with Syne Tune. We also recommend building the virtual environment from scratch now and then, in particular when you pull a new release, as dependencies may have changed.

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 below:

import logging
import time

from syne_tune import Reporter
from argparse import ArgumentParser

if __name__ == '__main__':
    root = logging.getLogger()
    parser = ArgumentParser()
    parser.add_argument('--epochs', 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.epochs):
        dummy_score = 1.0 / (0.1 + args.width * step / 100) + args.height * 0.1
        # Feed the score back to Syne Tune.
        report(epoch=step + 1, mean_loss=dummy_score)

Once you have a training script reporting a metric, you can launch a tuning as follows:

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 = {
    'width': randint(1, 20),
    'height': randint(1, 20),
    'epochs': 100,

tuner = Tuner(
        search_options={'debug_log': False},
    n_workers=4,  # how many trials are evaluated in parallel

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

Experimentation with Syne Tune

If you plan to use advanced features of Syne Tune, such as different execution backends or running experiments remotely, writing launcher scripts like examples/ can become tedious. Syne Tune provides an advanced experimentation framework, which you can learn about in this tutorial or also in this one.

Supported HPO methods

The following hyperparameter optimization (HPO) methods are available in Syne Tune:

Method Reference Searcher Asynchronous? Multi-fidelity? Transfer?
Grid Search deterministic yes no no
Random Search Bergstra, et al. (2011) random yes no no
Bayesian Optimization Snoek, et al. (2012) model-based yes no no
BORE Tiao, et al. (2021) model-based yes no no
CQR Salinas, et al. (2023) model-based yes no no
MedianStoppingRule Golovin, et al. (2017) any yes yes no
SyncHyperband Li, et al. (2018) random no yes no
SyncBOHB Falkner, et al. (2018) model-based no yes no
SyncMOBSTER Klein, et al. (2020) model-based no yes no
ASHA Li, et al. (2019) random yes yes no
BOHB Falkner, et al. (2018) model-based yes yes no
MOBSTER Klein, et al. (2020) model-based yes yes no
DEHB Awad, et al. (2021) evolutionary no yes no
HyperTune Li, et al. (2022) model-based yes yes no
DyHPO* Wistuba, et al. (2022) model-based yes yes no
ASHABORE Tiao, et al. (2021) model-based yes yes no
ASHACQR Salinas, et al. (2023) model-based yes yes no
PASHA Bohdal, et al. (2022) random or model-based yes yes no
REA Real, et al. (2019) evolutionary yes no no
KDE Falkner, et al. (2018) model-based yes no no
PBT Jaderberg, et al. (2017) evolutionary no yes no
ZeroShotTransfer Wistuba, et al. (2015) deterministic yes no yes
ASHA-CTS Salinas, et al. (2021) random yes yes yes
RUSH Zappella, et al. (2021) random yes yes yes
BoundingBox Perrone, et al. (2019) any yes yes yes

*: We implement the model-based scheduling logic of DyHPO, but use the same Gaussian process surrogate models as MOBSTER and HyperTune. The original source code for the paper is here.

The searchers fall into four broad categories, deterministic, random, evolutionary and model-based. The random searchers sample candidate hyperparameter configurations uniformly at random, while the model-based searchers sample them non-uniformly at random, according to a model (e.g., Gaussian process, density ration estimator, etc.) and an acquisition function. The evolutionary searchers make use of an evolutionary algorithm.

Syne Tune also supports BoTorch searchers.

Supported multi-objective optimization methods

Method Reference Searcher Asynchronous? Multi-fidelity? Transfer?
Constrained Bayesian Optimization Gardner, et al. (2014) model-based yes no no
MOASHA Schmucker, et al. (2021) random yes yes no
NSGA-2 Deb, et al. (2002) evolutionary no no no
Multi Objective Multi Surrogate (MSMOS) Guerrero-Viu, et al. (2021) model-based no no no
MSMOS wihh random scalarization Paria, et al. (2018) model-based no no no

HPO methods listed can be used in a multi-objective setting by scalarization or non-dominated sorting. See for details.


You will find many examples in the examples/ folder illustrating different functionalities provided by Syne Tune. For example:

Examples for Experimentation and Benchmarking

You will find many examples for experimentation and benchmarking in benchmarking/examples/ and in benchmarking/nursery/.

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.

Blog Posts



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.

  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={International Conference on Automated Machine Learning, AutoML 2022},


This project is licensed under the Apache-2.0 License.

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