In this post, we combine the powers of NVIDIA RAPIDS and Amazon SageMaker to accelerate hyperparameter optimization (HPO). HPO runs many training jobs on your dataset using different settings to find the best-performing model configuration. HPO helps data scientists reach top performance, and is applied when models go into production, or to periodically refresh deployed models as new data arrives. However, HPO can feel out of reach on non-accelerated platforms as dataset sizes continue to grow. With RAPIDS and SageMaker working together, workloads like HPO are GPU scaled up (multi-GPU) within a node and cloud scaled out over parallel instances.