Today, we’re very excited to offer three new enhancements to our Amazon SageMaker Studio service.
As of now, users of SageMaker Studio can create, terminate, manage, discover, and connect to Amazon EMR clusters running within a single AWS account and in shared accounts across an organization—all directly from SageMaker Studio. Furthermore, SageMaker Studio Notebook users can able to utilize SparkUI to monitor and debug Spark jobs running on an Amazon EMR cluster—directly from the SageMaker Studio Notebooks!
The story so far…
Before today, SageMaker Studio users had some ability to find and connect with EMR clusters, provided that they were running in the same account as SageMaker Studio. While useful in many circumstances, if a cluster did not exist that would suit the requirements of the model or analysis being run, then data scientists would have to leave their development environment and manually configure a cluster that suited their needs. As well as being disruptive to workflow of data scientists, there are no guarantees that the data scientists would have either the permissions or depth of knowledge required to provision a cluster that would enable them to continue with their work. Additionally, being restricted to creating and managing clusters in a single account could become prohibitive in organizations working across many AWS accounts.
Data scientists can:
- Discover, manage, create, terminate, and connect to Amazon EMR clusters from within SageMaker Studio
- Utilize “templates” – a new way to configure and provision clusters for your workload needs with support from seasoned DevOps practitioners
- Connect to, debug, and monitor Spark jobs running on an Amazon EMR cluster from within a SageMaker Studio Notebook
Creating, Connecting to, and Managing EMR Clusters
With the ability to connect to and manage EMR clusters from within SageMaker Studio, data scientists no longer have to leave their familiar environment to create, configure and provision the EMR clusters where they run their workloads.
A template is a collection of off-the-shelf cluster configurations optimized for numerous workloads. Templates can be created and managed by DevOps administrators and made available through the AWS Service Catalog to data scientists within SageMaker Studio. This lets them quickly spin up a cluster to meet their needs, all while safe in the knowledge that a trusted DevOps admin has correctly configured a cluster per the project’s requirements. Furthermore, this lets data scientists get on with the work they do best, and it gives DevOps administrators within these teams greater ability to manage the types of provisioned infrastructure.
Directly Connect to and monitor Spark Jobs
Finally, to make the job of data scientists even simpler, we’ve built the ability to connect to, debug, and monitor Spark jobs running on an Amazon EMR cluster from within a SageMaker Studio Notebook. Before now, to access the monitoring UI of a Spark Job, one needed to configure secure tunnels and web proxies to gain direct access to currently executing jobs, adding friction to the workflow of a data scientist trying to observe and debug their workloads. Now, with these new features, users will have one-click access directly from the interface that they already know. This enables them to build and put their workloads to work, rather than spending time on configuring infrastructure and workloads.
These new features let data scientists can use a simple, consistent UI to provision and manage infrastructure as needed without ever having to leave SageMaker Studio or dive into the minutiae of the provisioning of such hardware – Moreover, they won’t have to spend time configuring proxies and SSH tunnels to debug and monitor ongoing Spark jobs.
Find out more
These features are generally available in all AWS Regions where SageMaker Studio is available, and there are no additional charges to use this capability. For complete information on pricing and regional availability, please refer to the SageMaker Studio pricing page .
To learn more, see our documentation.