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HomeArtificial Intelligence and Machine LearningAmazon SageMaker Automatic Model Tuning now automatically chooses tuning configurations to improve...

Amazon SageMaker Automatic Model Tuning now automatically chooses tuning configurations to improve usability and cost efficiency

Amazon SageMaker Automatic Model Tuning has introduced Autotune, a new feature to automatically choose hyperparameters on your behalf. This provides an accelerated and more efficient way to find hyperparameter ranges, and can provide significant optimized budget and time management for your automatic model tuning jobs.

In this post, we discuss this new capability and some of the benefits it brings.

Hyperparameter overview

When training any machine learning (ML) model, you are generally dealing with three types of data: input data (also called the training data), model parameters, and hyperparameters. You use the input data to train your model, which in effect learns your model parameters. During the training process, your ML algorithms are trying to find the optimal model parameters based on data while meeting the goals of your objective function. For example, when a neural network is trained, the weight of the network nodes is learned from the training, and indicates how much impact it has on the final prediction. These weights are the model parameters.

Hyperparameters, on the other hand, are parameters of a learning algorithm and not the model itself. The number of hidden layers and the number of nodes are some of the examples of hyperparameters you can set for a neural network. The difference between model parameters and hyperparameters is that model parameters are learned during the training process, whereas hyperparameters are set prior to the training and remain constant during the training process.

Pain points

SageMaker automatic model tuning, also called hyperparameter tuning, runs many training jobs on your dataset using a range of hyperparameters that you specify. It can accelerate your productivity by trying many variations of a model. It looks for the best model automatically by focusing on the most promising combinations of hyperparameter values within the ranges that you specify. However, to get good results, you must choose the right ranges to explore.

But how do you know what the right range is to begin with? With hyperparameter tuning jobs, we are assuming that the optimal set of hyperparameters lies within the range that we specified. What happens if the chosen range is not right, and the optimal hyperparameter actually falls outside of the range?

Choosing the right hyperparameters requires experience with the ML technique you are using and understanding how its hyperparameters behave. It’s important to understand the hyperparameter implications because every hyperparameter that you choose to tune has the potential to increase the number of trials required for a successful tuning job. You need to strike an optimal trade-off between resources allocated to the tuning job and achieving the goals you’ve set.

The SageMaker Automatic Model Tuning team is constantly innovating on behalf of our customers to optimize their ML workloads. AWS recently announced support of new completion criteria for hyperparameter optimization: the max runtime criteria, which is a budget control completion criteria that can be used to bound cost and runtime. Desired target metrics, improvement monitoring, and convergence detection monitors the performance of the model and assists with early stopping if the models don’t improve after a defined number of training jobs. Autotune is a new feature of automatic model tuning that helps save you time and reduce wasted resources on finding optimal hyperparameter ranges.

Benefits of Autotune and how automatic model tuning alleviates those pain points

Autotune is a new configuration in the CreateHyperParameterTuningJob API and in the HyperparameterTuner SageMaker Python SDK that alleviates the need to specify the hyperparameter ranges, tuning strategy, objective metrics, or the number of jobs that were required as part of the job definition. Autotune automatically chooses the optimal configurations for your tuning job, helps prevent wasted resources, and accelerates productivity.

The following example showcases how many of the parameters are not necessary when using Autotune.

The following code creates a hyperparameter tuner using the SageMaker Python SDK without Autotune:

estimator = PyTorch(
“epochs”: 1, “backend”: “gloo”

tuner = HyperparameterTuner(
hyperparameter_ranges = {
“lr”: ContinuousParameter(0.001, 0.1),
“batch-size”: CategoricalParameter([32, 64, 128, 256, 512])

The following example showcases how many of the parameters are not necessary when using Autotune:

estimator = PyTorch(
“epochs”: 1, “backend”: “gloo”, “lr”: 0.01, “batch-size”: 32
tuner = HyperparameterTuner(

If you are using API, the equivalent code would be as follows:

Autotune={‘Mode’: ‘Enabled’},

The code example illustrates some of the key benefits of Autotune:

A key choice for a tuning job is which hyperparameters to tune and their ranges. Autotune makes this choice for you based on a list of hyperparameters that you provide. Using the previous example, the hyperparameters that Autotune can choose to be tunable are lr and batch-size.
Autotune will automatically select the hyperparameter ranges on your behalf. Autotune uses best practices as well as internal benchmarks for selecting the appropriate ranges.
Autotune automatically selects the strategy on how to choose the combinations of hyperparameter values to use for the training job.
Early stopping is enabled by default when using Autotune. When using early stopping, SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs to avoid additional resource utilization.
Maximum expected resources to be consumed by the tuning job (parallel jobs, max runtime, and so on) will be calculated and set in the tuning job record as soon as the tuning job is created. Such reserved resources will not increase during the course of the tuning job; this will maintain an upper bound of cost and duration of the tuning job that is easily predictable by the user. A max runtime of 48 hours will be used by default.

You can override any settings chosen automatically by Autotune. As an example, if you specify your own hyperparameter ranges, those will be used alongside the inferred ranges. Any user-specified hyperparameter range will take precedence over the same named inferred ranges:

estimator = PyTorch(

“epochs”: 100, “backend”: “gloo”, “lr”: 0.01, “beta1”: 0.8

tuner = HyperparameterTuner(

hyperparameter_ranges = {
“lr”: ContinuousParameter(0.001, 0.01) # takes precedence over inferred “lr”

Autotune generates a set of settings as part of the tuning job. Any customer-specified settings that have the same name will override the Autotune-selected settings. Any customer-provided settings (that aren’t the same as the named Autotune settings) are added in addition to the Autotune-selected settings.

Inspecting parameters chosen by Autotune

Autotune reduces the time you would normally have spent in deciding on the initial set of hyperparameters to tune. But how do you get insights into what hyperparameter values Autotune ended up choosing? You can get information about decisions made for you in the description of the running tuning job (in the response of the DescribeHyperParameterTuningJob operation). After you submit a request to create a tuning job, the request is processed, and all missing fields are set by Autotune. All set fields are reported in the DescribeHyperParameterTuningJob operation.

Alternatively, you can inspect HyperparameterTuner class fields to see the settings chosen by Autotune.

The following is an XGBoost example of how you may use the DescribeHyperParameterTuningJob to inspect the hyperparameters chosen by Autotune.

First, we create a tuning job with automatic model tuning:

hyperparameters = {
“objective”: “reg:squarederror”,
“num_round”: “50”,
“verbosity”: “2”,
“max_depth”: “5”, # overlap with ranges is ok when Autotune is enabled
estimator = XGBoost(hyperparameters=hyperparameters, …)

hp_tuner = HyperparameterTuner(estimator, autotune=True)

After the tuning job is created successfully, we can discover what settings Autotune chose. For example, we can describe the tuning job by the name given by it from hp_tuner:

import boto3
sm = boto3.client(‘sagemaker’)

response = sm.describe_hyper_parameter_tuning_job(


Then we can inspect the generated response to review the settings chosen by Autotune on our behalf.

If the current tuning job settings are not satisfactory, you can stop the tuning job:



SageMaker Automatic Model Tuning allows you to reduce the time to tune a model by automatically searching for the best hyperparameter configuration within the ranges that you specify. However, choosing the right hyperparameter ranges can be a time-consuming process and can have direct implications on your training cost and duration.

In this post, we discussed how you can now use Autotune, a new feature introduced as part of automatic model tuning, to automatically pick an initial set of hyperparameter ranges on your behalf. This can reduce the time it takes for you to get started with your model tuning process. Additionally, you can evaluate the ranges picked by Autotune and adjust them according to your needs.

We also showed how Autotune can automatically pick the optimal parameter settings on your behalf, such as the number of training jobs, the strategy to choose the hyperparameter combinations, and enabling early stopping by default. This can result in significantly optimized budget and time bounds that are easily predictable.

To learn more, refer to Perform Automatic Model Tuning with SageMaker.

About the Authors

Jas Singh is a Senior Solutions Architect helping public sector customers achieve their business outcomes through architecting and implementing innovative and resilient solutions at scale. Jas has over 20 years of experience in designing and implementing mission-critical applications and holds a master’s degree in computer science from Baylor University.

Gopi Mudiyala is a Senior Technical Account Manager at AWS. He helps customers in the Financial Services industry with their operations in AWS. As a machine learning enthusiast, Gopi works to help customers succeed in their ML journey. In his spare time, he likes to play badminton, spend time with family, and travel.

Raviteja Yelamanchili is an Enterprise Solutions Architect with Amazon Web Services based in New York. He works with large financial services enterprise customers to design and deploy highly secure, scalable, reliable, and cost-effective applications on the cloud. He brings over 11 years of risk management, technology consulting, data analytics, and machine learning experience. When he is not helping customers, he enjoys traveling and playing PS5.

Iaroslav Shcherbatyi is a Machine Learning Engineer at AWS. He works mainly on improvements to the Amazon SageMaker platform and helping customers best use its features. In his spare time, he likes to go to gym, do outdoor sports such as ice skating or hiking, and to catch up on new AI research.

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