홈페이지Data + AI Summit 2022 로고
Watch on demand

Fugue Tune: Distributed Hybrid Hyperparameter Tuning

On Demand

Type

  • Session

Format

  • Hybrid

Track

  • 데이터 사이언스, 머신 러닝 및 MLOps

Difficulty

  • Beginner

Room

  • Moscone South | Upper Mezzanine | 156

Duration

  • 35 min
Download session slides

개요

Hyperparameter tuning is used in model development to search for optimal model parameters. Spark hyperparameter tuning has generally been done on memory-bound problems, where one dataset is split across different machines, and multiple models are trained in a sequential way. In this talk, we’ll explore how to use Apache Spark as an engine for parallelizing compute-bound tuning problems, where hundreds or thousands of smaller models are trained in parallel.

There are multiple approaches to hyperparameter tuning. Grid search is exploring a finite combination of values, while Bayesian Optimization is building over the last attempts to create a better hyperparameter combination. Approaches like grid search are trivially parallelizable, while Bayesian Optimization has a sequential dependency. But actually, we can combine these two ideas to parallelize a Grid of Bayesian Optimization trials over Spark. This will be done through Fugue-tune, a general interface that abstracts existing machine learning frameworks such as Optuna and Hyperopt, by providing a scalable interface on top of them.

In this talk, we'll explore how to tune a general ML objective on a hybrid search space at where model search, grid search, random search and Bayesian optimization are combined intuitively using Fugue-Tune's simple interface. Using Greykite as an example, we will demo tuning a forecasting model distributedly and monitoring the best result at realtime.

Session Speakers

Headshot of Jun Liu

Jun Liu

선임 데이터 사이언티스트

Lyft

Data+AI Summit 하이라이트 보기

Watch on demand