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https://github.com/cozystack/cozystack.git
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Aleksei Sviridkin
47a67761aa
* examples/README.md and examples/values-native-talos.yaml header comment still pointed at values-talos.yaml after the rename to values-passthrough.yaml. Update both to the new name. * values-vgpu.yaml: drop the leftover vgpuDeviceManager.config block. The chart only renders the config when vgpuDeviceManager.enabled is true, and we keep it false on Ada+; the orphan config.default key was scaffolding without effect. * docs/gpu-vgpu.md and values.yaml comment: stop grouping B100 under Ada Lovelace. B100 is Blackwell; the SR-IOV vGPU mechanism applies to both architectures but they should be named correctly. Assisted-By: Claude <noreply@anthropic.com> Signed-off-by: Aleksei Sviridkin <f@lex.la>
53 lines
2.1 KiB
YAML
53 lines
2.1 KiB
YAML
# Cozystack Package values for running GPU workloads natively in pods
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# on Talos. This is the counterpart to values-passthrough.yaml, which
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# targets the sandbox (VFIO passthrough) scenario.
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#
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# Prerequisites:
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# - NVIDIA Talos system extension installed on GPU nodes.
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# - examples/nvidia-driver-compat.yaml deployed to stage driver files
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# where the gpu-operator validator looks for them.
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# - examples/dcgm-custom-metrics.yaml applied so DCGM Exporter exports
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# the full set of metrics used by the dashboard and recording rules.
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apiVersion: cozystack.io/v1alpha1
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kind: Package
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metadata:
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name: cozystack.gpu-operator
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spec:
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variant: default
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components:
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gpu-operator:
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values:
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gpu-operator:
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# The compat DaemonSet stages libnvidia-ml.so.1 and nvidia-smi
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# under /var/nvidia-driver. Point the validator at that path.
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hostPaths:
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driverInstallDir: "/var/nvidia-driver"
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# Disable the sandbox path — workloads run in pods, not VMs.
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sandboxWorkloads:
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enabled: false
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vfioManager:
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enabled: false
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# The Talos extension provides the driver and runtime hooks,
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# so the operator's own toolkit and driver components must be
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# switched off to avoid conflicting installations.
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toolkit:
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enabled: false
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devicePlugin:
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enabled: true
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# Export full set of DCGM metrics using the ConfigMap in
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# examples/dcgm-custom-metrics.yaml.
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dcgmExporter:
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serviceMonitor:
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enabled: true
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# Matches the 1m evaluation cadence of GPU recording rules with
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# 4x oversampling headroom. DCGM_FI_PROF_* counters have
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# documented overhead concerns below 1s; 15s is safe.
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interval: "15s"
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honorLabels: true
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relabelings:
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- sourceLabels: [__meta_kubernetes_pod_node_name]
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targetLabel: node
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action: replace
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config:
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name: dcgm-custom-metrics
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