Gretel Tuner
Automate hyperparameter sweeps to create the best synthetic data for your task
Before you start training a machine learning model, you have to make important choices like the number of layers in a neural network, the embedding dimension, and the details of how training should proceed. These so-called “hyperparameters” are not learned during training and can have a dramatic impact on the quality of your model. In the case of generative models, optimized hyperparameters can be the difference between low- and high-quality synthetic data.
It is therefore essential to systematically and objectively tune your model’s hyperparameters for your particular use case. Gretel Tuner is a config-driven tool for efficiently sweeping the hyperparameters of Gretel Synthetics models.
Gretel Tuner is built directly into our Python SDK. Our implementation was designed with the following features in mind:
Simplicity: Running Gretel Tuner only requires a YAML configuration and a single command using the Gretel SDK's high-level interface.
Efficiency: Gretel Tuner efficiently samples the search space by leveraging Optuna’s Bayesian optimization framework to home in on an optimal model configuration.
Customizability: In addition to all of Gretel’s synthetic data quality scores, Gretel Tuner supports custom user-implemented optimization metrics, making it possible to find optimal model parameters for highly specific tasks. For example, we have found optimization metrics based on downstream machine learning tasks to be very effective at tuning hyperparameters to yield models that generate synthetic data of high fidelity and utility for machine learning.
Installation
To install Gretel Tuner, add the tuner option to the SDK installation command:
This will install the Gretel client along with the new tuner module and its associated dependencies.
Getting Started
Try our Google Colab notebooks to get started.
Gretel Tuner: Introductory Tutorial — Learn the basics of how to configure Gretel Tuner to sweep the hyperparameters of a Gretel Synthetics model.
Gretel Tuner: Advanced Tutorial — Learn advanced features of Gretel Tuner, including how to implement custom optimization metrics and how to enforce arbitrary constraints on the sampled model configurations.
Gretel Tuner is also available in Workflows as the autotune option.
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