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Build and use ML pipelines including AML Designer pipeline supportįor those Data Science professionals who have used AML Python SDK, existing AML Python SDK examples and notebooks, or your existing projects will work out-of-box with a simple change of compute target name in Python script.Azure Arc-enabled ML supports the following built-in AML training features seamlessly: Data Scientist can submit job either through AML 2.0 CLI or AML Python SDK, in either case Data Scientist will specify compute target name at job submission time. Data Scientist can choose a suitable compute target to submit training job, such as GPU compute target, or CPU compute target with proper resources requests for particular training job workload, such as the # of vCPU cores and memory.
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Once the attached Kubernetes compute target is available, Data Scientist can discover the list of compute targets in AML Studio UI compute section. We are pleased to announce that Azure Arc-enabled Machine Learning fully supports model training on-premises with outbound proxy server connection. Note when connecting Kubernetes cluster to Azure via Azure Arc, IT Operator can also specify configuration setting to enable outbound proxy server.
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For more information about creating compute targets using these custom properties, please refer to AML documentation.įor upcoming Azure Arc-enabled ML update release, we plan to support compute target creation through CLI command as well, and ML compute setup experience will be simplified to following 3 CLI commands: With these advanced configurations in compute target, IT Operator help Data Scientists target a subset of nodes such as GPU pool or CPU pool for training job, improves compute resource utilization and avoids fragmentation. In the process of Studio UI attach operation, IT Operator could provide an optional JSON configuration file specifying namespace, node selector, and resources requests/limits to be used for the compute target being created. Note by clicking “New->Kubernetes (preview)”, the Azure Arc-enabled Kubernetes clusters will automatically appear in a dropdown list for IT Operator to attach. With your cluster ready to take AML workload, now you can head over to AML Studio portal and create compute target for Data Scientists to use, see below AML Studio compute attach UI: Once AzureML extension installation completes in cluster, you will see following AzureML pods running inside the :