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Merge pull request #1533 from tkatila/gpu-docs-max-sriov
gpu docs update and xpum-sidecar deployment version bump
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cmd/gpu_plugin/README.md

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* [Verify Plugin Registration](#verify-plugin-registration)
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* [Testing and Demos](#testing-and-demos)
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* [Labels created by GPU plugin](#labels-created-by-gpu-plugin)
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* [SR-IOV use with the plugin](#sr-iov-use-with-the-plugin)
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* [Issues with media workloads on multi-GPU setups](#issues-with-media-workloads-on-multi-gpu-setups)
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* [Workaround for QSV and VA-API](#workaround-for-qsv-and-va-api)
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## Introduction
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Intel GPU plugin facilitates Kubernetes workload offloading by providing access to
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discrete (including Intel® Data Center GPU Flex Series) and integrated Intel GPU devices
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discrete (including Intel® Data Center GPU Flex & Max Series) and integrated Intel GPU devices
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supported by the host kernel.
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Use cases include, but are not limited to:
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If installed with NFD and started with resource-management, plugin will export a set of labels for the node. For detailed info, see [labeling documentation](./labels.md).
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## SR-IOV use with the plugin
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GPU plugin does __not__ setup SR-IOV. It has to be configured by the cluster admin.
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GPU plugin does however support provisioning Virtual Functions (VFs) to containers for a SR-IOV enabled GPU. When the plugin detects a GPU with SR-IOV VFs configured, it will only provision the VFs and leaves the PF device on the host.
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## Issues with media workloads on multi-GPU setups
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OneVPL media API, 3D and compute APIs provide device discovery

deployments/xpumanager_sidecar/kustomization.yaml

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# XeLink topology information is only available from >= 1.x.y release
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resources:
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- https://raw.githubusercontent.com/intel/xpumanager/v1.2.0_golden/deployment/kubernetes/daemonset-intel-xpum.yaml
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- https://raw.githubusercontent.com/intel/xpumanager/V1.2.18/deployment/kubernetes/daemonset-intel-xpum.yaml
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namespace: monitoring
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apiVersion: kustomize.config.k8s.io/v1beta1
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kind: Kustomization

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