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Merge pull request #12799 from fitzthum/fixup-nv-doc-1
docs: update flow for setting nvidia devices to ready
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@@ -213,12 +213,10 @@ API and kernel drivers, interacting with the pass-through GPU device.
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An additional step is exercised in our CI samples: when using images from an
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authenticated registry, the guest-pull mechanism triggers attestation using
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Trustee's Key Broker Service (KBS) for secure release of the NGC API
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authentication key used to access the NVCR container registry. As part of
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this, the attestation agent exercises composite attestation and transitions
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the GPU into `Ready` state (without this, the GPU has to explicitly be
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transitioned into `Ready` state by passing the `nvrc.smi.srs=1` kernel
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parameter via the shim config, causing NVRC to transition the GPU into the
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`Ready` state).
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authentication key used to access the NVCR container registry.
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In this flow the CPU and all additional devices are attested.
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GPUs will automatically be set to ready by NVRC per the NVRC configuration
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flag in the default kernel command line.
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## Deployment Guidance
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@@ -465,8 +463,6 @@ kind: Pod
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metadata:
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name: cuda-vectoradd-kata
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namespace: default
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annotations:
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io.katacontainers.config.hypervisor.kernel_params: "nvrc.smi.srs=1"
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spec:
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runtimeClassName: ${GPU_RUNTIME_CLASS_NAME}
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restartPolicy: Never
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@@ -613,12 +609,7 @@ You can author pod manifests leveraging your own containers, for instance,
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containers built using the CUDA container toolkit. We recommend to start
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with a CUDA base container.
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The GPU is transitioned into the `Ready` state via attestation, for instance,
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when pulling authenticated images. If your deployment scenario does not use
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attestation, please refer back to the CUDA vectorAdd pod manifest. In this
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manifest, we ensure that NVRC sets the GPU to `Ready` state by adding the
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following annotation in the manifest:
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`io.katacontainers.config.hypervisor.kernel_params: "nvrc.smi.srs=1"`
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When using the GPU runtime classes, the GPUs will automatically be set to ready.
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> **Notes:**
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>
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