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As Sebastian and Matthew, we would like to be able to run GPU-intensive workloads on Kyma Runtime so that we can build and deploy machine learning models or data analytics tasks efficiently. That story is part of https://github.tools.sap/kyma/backlog/issues/4633
Allowing GPU workloads would make Kyma Runtime a more versatile and competitive product, attracting users who require heavy computational tasks, including but not limited to data scientists, ML engineers, and computational physicists.
Acceptance Criteria
Users should be able to specify GPU resources in their Kyma Runtime configurations.
The system should schedule the workload on nodes with available GPU resources.
Performance metrics specific to GPU should be available for monitoring.
Must comply with security and resource isolation standards.
Additional infrastructure setup like drivers provisioned via a DaemonSet should be provided in a convenient way
Non-functional Requirements
Scalability: Should support clusters with multiple GPUs.
Security: Isolation of GPU resources to prevent unauthorized access.
Performance: Minimal latency when scheduling and running GPU workloads.
Description
As Sebastian and Matthew, we would like to be able to run GPU-intensive workloads on Kyma Runtime so that we can build and deploy machine learning models or data analytics tasks efficiently. That story is part of https://github.tools.sap/kyma/backlog/issues/4633
Allowing GPU workloads would make Kyma Runtime a more versatile and competitive product, attracting users who require heavy computational tasks, including but not limited to data scientists, ML engineers, and computational physicists.
Acceptance Criteria
Non-functional Requirements
Useful Links
Tasks
Preparation
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