@@ -88,65 +88,5 @@ If you're using AMD GPU devices, you can deploy
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Node Labeller is a {{< glossary_tooltip text="controller" term_id="controller" >}} that automatically
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labels your nodes with GPU device properties.
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- At the moment, that controller can add labels for:
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-
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- * Device ID (-device-id)
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- * VRAM Size (-vram)
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- * Number of SIMD (-simd-count)
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- * Number of Compute Unit (-cu-count)
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- * Firmware and Feature Versions (-firmware)
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- * GPU Family, in two letters acronym (-family)
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- * SI - Southern Islands
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- * CI - Sea Islands
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- * KV - Kaveri
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- * VI - Volcanic Islands
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- * CZ - Carrizo
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- * AI - Arctic Islands
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- * RV - Raven
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-
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- ``` shell
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- kubectl describe node cluster-node-23
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- ```
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-
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- ```
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- Name: cluster-node-23
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- Roles: <none>
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- Labels: beta.amd.com/gpu.cu-count.64=1
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- beta.amd.com/gpu.device-id.6860=1
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- beta.amd.com/gpu.family.AI=1
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- beta.amd.com/gpu.simd-count.256=1
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- beta.amd.com/gpu.vram.16G=1
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- kubernetes.io/arch=amd64
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- kubernetes.io/os=linux
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- kubernetes.io/hostname=cluster-node-23
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- Annotations: node.alpha.kubernetes.io/ttl: 0
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- …
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- ```
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-
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- With the Node Labeller in use, you can specify the GPU type in the Pod spec:
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-
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- ``` yaml
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- apiVersion : v1
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- kind : Pod
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- metadata :
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- name : cuda-vector-add
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- spec :
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- restartPolicy : OnFailure
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- containers :
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- - name : cuda-vector-add
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- # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
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- image : " registry.k8s.io/cuda-vector-add:v0.1"
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- resources :
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- limits :
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- nvidia.com/gpu : 1
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- affinity :
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- nodeAffinity :
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- requiredDuringSchedulingIgnoredDuringExecution :
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- nodeSelectorTerms :
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- – matchExpressions :
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- – key : beta.amd.com/gpu.family.AI # Arctic Islands GPU family
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- operator : Exist
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- ` ` `
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-
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- This ensures that the Pod will be scheduled to a node that has the GPU type
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- you specified.
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+ Similar functionality for NVIDIA is provied by
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+ [ GPU feature discovery] ( https://github.com/NVIDIA/gpu-feature-discovery/blob/main/README.md ) .
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