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Automated Parameter Tuning Service Integration

1 post · Apr 2019

What this trail covers

Two Sigma is integrating external services like SigOpt for automated parameter tuning to accelerate research and development. This involves using advanced optimization techniques for complex simulations and machine learning algorithms where traditional methods are insufficient. The integration aims to improve performance, enable multi-metric optimization, and ensure efficient utilization of compute resources by leveraging asynchronous parallelization. The post details the integration of SigOpt, highlighting its ability to deliver results faster and cheaper than previous solutions, including multi-metric optimization and asynchronous parallelization.

All Two Sigma trails
  1. Why Two Sigma is using SigOpt for Automated Parameter TuningThis post details the integration of SigOpt's optimization service into Two Sigma's research platform. It highlights the challenges of parameter tuning for 'black-box' functions in simulations and ML algorithms, contrasting grid search and Bayesian optimization with SigOpt's performance. Key benefits discussed include significantly faster tuning times (e.g., 24 days to 3 days for an ML algorithm), the ability to perform multi-metric optimization to explore Pareto frontiers, and efficient utilization of compute resources through asynchronous parallelization.