Amazon SageMaker Debugger UI in Amazon SageMaker Studio Experiments - Amazon SageMaker
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Amazon SageMaker Debugger UI in Amazon SageMaker Studio Experiments

Use the Amazon SageMaker Debugger Insights dashboard in Amazon SageMaker Studio Experiments to analyze your model performance and system bottlenecks while running training jobs on Amazon Elastic Compute Cloud (Amazon EC2) instances. Gain insights into your training jobs and improve your model training performance and accuracy with the Debugger dashboards. By default, Debugger monitors system metrics (CPU, GPU, GPU memory, network, and data I/O) every 500 milliseconds and basic output tensors (loss and accuracy) every 500 iterations for training jobs. You can also further customize Debugger configuration parameter values and adjust the saving intervals through the Studio UI or using the Amazon SageMaker Python SDK.


If you're using an existing Studio app, delete the app and restart to use the latest Studio features. For instructions on how to restart and update your Studio environment, see Update Amazon SageMaker Studio.