Coffee Time Papers: Google Vizier

A Service for Black-Box Optimization

This blog post is part of the series Coffee Time Papers.

Paper

https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46180.pdf

Overview

Google Vizier is a black-box optimization service developed by Google. Black-box optimization means finding the best input values for a function to optimize its output, without knowing the function’s internal workings. Vizier is used at Google to optimize various systems, including machine learning models and user interfaces.

The paper discusses the design goals and constraints of Vizier, emphasizing ease of use, support for state-of-the-art algorithms, scalability, and flexibility for experimentation. The system architecture is described, including its parallel processing capabilities and algorithm playground for testing new optimization methods.

The paper also details the algorithms used in Vizier, such as Gaussian Process Bandits for smaller studies and proprietary algorithms for larger ones. It also covers automated early stopping techniques to save resources and transfer learning to leverage knowledge from previous studies.

The effectiveness of Vizier is demonstrated through empirical results on benchmark functions and real-world use cases like hyperparameter tuning for machine learning models and A/B testing for web properties. The paper concludes by highlighting Vizier’s value as a platform for research and development in black-box optimization.

Q & A

Q: What is Google Vizier?

A: Google Vizier is a black-box optimization service developed by Google. It is designed to optimize complex systems where the inner workings of the objective function are unknown or difficult to model.

Q: What are the main applications of Google Vizier?

A: Google Vizier is primarily used for:

• Hyperparameter tuning of machine learning models: It helps find the best combination of settings for machine learning algorithms to improve their performance.
• A/B testing of web properties: It optimizes user interface parameters and traffic-serving parameters to enhance user experiences.
• Solving complex black-box optimization problems: It can be applied to various domains, such as optimizing physical design or logistical problems.

Q: What are the key design goals of Google Vizier?

A: The key design goals of Google Vizier include:

• Ease of use: Minimal user configuration and setup.
• State-of-the-art algorithms: Hosting advanced black-box optimization algorithms.
• Scalability: Handling millions of trials per study and thousands of parallel evaluations.
• Flexibility: Easy experimentation with new algorithms and seamless switching between algorithms.

Q: How does Vizier work?

Google Vizier is a black-box optimization service. In a black-box optimization problem, the goal is to find the input values that result in the best possible output of a function, without needing to know the function’s internal workings. Vizier guides this process by suggesting which input values to try next, based on the results of previous trials.

Here’s a simplified explanation of how Vizier works:

1. Study Configuration: The user defines the problem to be optimized, including the range of possible input values (parameters) and the metric to be optimized (the objective).
2. Trial Suggestions: Vizier’s algorithms suggest a set of input values (a trial) to evaluate. The specific algorithm used can vary depending on the nature of the problem and the number of trials already conducted.
3. Trial Evaluation: The user’s system evaluates the objective function using the suggested input values. This could involve training a machine learning model, running an A/B test, or any other process where the goal is to optimize the output.
4. Feedback: The results of the trial evaluation are reported back to Vizier, including the objective value achieved.
5. Model Update: Vizier updates its internal model of the objective function based on the new results. This model is used to guide future trial suggestions.
6. Iteration: Steps 2–5 are repeated iteratively. With each iteration, Vizier’s suggestions are expected to get closer to the optimal solution.

Vizier also includes features like automated early stopping (terminating unpromising trials early) and transfer learning (using knowledge from previous studies to speed up optimization). These features make it a powerful tool for solving a wide range of optimization problems.

Q: How does Google Vizier handle automated early stopping?

A: Google Vizier supports automated early stopping to save resources by terminating unpromising trials before they complete. It uses algorithms like the performance curve stopping rule and the median stopping rule to make these decisions based on intermediate trial results.

Q: What is transfer learning in the context of Google Vizier?

A: Transfer learning in Google Vizier leverages data from prior studies to guide and accelerate the current study. It uses a stack of Gaussian Process regressors, where each regressor is associated with a study and trained on the residuals relative to the regressor below it. This approach helps share information across studies and improve optimization efficiency.

Q: How is Google Vizier evaluated?

A: Google Vizier’s performance is evaluated using benchmark functions with known optimal points. The success of an optimizer is measured by its final optimality gap, which is the difference between the best solution found and the actual optimal value. The results are normalized and averaged over multiple runs to assess the optimizer’s overall performance.