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https://issues.apache.org/jira/browse/FLINK-17044?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Piotr Nowojski updated FLINK-17044:
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Release Note: (was: With FLINK-17044, an external resource framework has
been introduced to support requesting various types of resources from the
underlying resource management systems (e.g., Kubernetes), and supply
information needed for using these resources to the operators. Different
resource types can be supported. You can either leverage built-in plugins
provided by Flink (currently only for GPU support), or implement your own
plugins for custom resource types.
More details, please refer to Flink user documents.)
> FLIP-108: Add GPU support in Flink
> ----------------------------------
>
> Key: FLINK-17044
> URL: https://issues.apache.org/jira/browse/FLINK-17044
> Project: Flink
> Issue Type: New Feature
> Components: Runtime / Coordination
> Reporter: Yangze Guo
> Assignee: Yangze Guo
> Priority: Major
> Fix For: 1.11.0
>
>
> With widespread advances in machine learning (or deep learning), more and
> more enterprises are beginning to incorporate ML models across a number of
> products. Supporting the ML scenarios is one of Flink’s roadmap targets. GPU
> is widely used as the accelerator by people from the ML community. It is
> necessary to add GPU support.
> Currently, Flink only supports to request GPU resource in Mesos integration
> while most users and enterprises deploying Flink on Yarn/Kubernetes or
> Standalone mode. Thus, we propose to add GPU support in Flink. As a first
> step, we propose to:
> - Enable user to configure the GPU cores per task executor and forward such
> requirements to the external resource managers (for Kubernetes/Yarn/Mesos
> setups).
> - Provide information of available GPU resources to operators.
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