Compute
On a project's Compute page in Amazon SageMaker Unified Studio, you can view compute information and add compute resources such as Amazon Redshift and Amazon EMR Serverless clusters to your project. Amazon SageMaker Unified Studio supports different kinds of compute resources:
Data warehouse: This includes Amazon Redshift Serverless workgroups and Amazon Redshift provisioned clusters. Workgroups are a collection of compute resources that you can use to run data warehousing queries and engineering notebooks without managing underlying infrastructure. Clusters are scalable compute environments that enable the processing and analysis of large datasets. For more information, see Amazon Redshift compute connections in Amazon SageMaker Unified Studio.
Data processing: This includes connections to Amazon EMR on EC2 clusters and EMR Serverless applications, and Glue ETL computes. For more information, see the following links:
HyperPod clusters: In Amazon SageMaker Unified Studio, you can launch machine learning workloads on Amazon SageMaker AI HyperPod clusters. For more information, see HyperPod clusters.
Spaces: Spaces are used to manage the storage and resource needs of applications running on JupyterLab. On the Spaces tab of the Compute page, you can view information about your JupyterLab environment in Amazon SageMaker Unified Studio, such as the EBS volume and the status of the IDE. For more information about spaces, see IDE spaces in Amazon SageMaker Unified Studio.
MLflow tracking servers: MLflow tracking servers make it possible to use MLflow in Amazon SageMaker Unified Studio to create, manage, analyze, and compare machine learning experiments. For more information, see Track experiments using MLflow.
Workflow environments: Use a workflow environment to share scheduled workflows with other project members. For more information, see Create a workflow environment.
Note
Adding a serverless or cluster compute connection adds the compute resource to the project space, so all project members can access it.
