Add nvidia gpu operator

What does this MR do and why?

This MR adds a new sylva unit encapsulating NVIDIA GPU Operator that contains all components needed to provision Nvidia GPUs: driver, container runtime, device plugin and monitoring support. It allows:

• Allocation of an entire NVIDIA GPU to a pod in bare-metal workload clusters.
• Collection of GPU metrics (notably utilization, power, temperature) by Prometheus if monitoring is enabled within the cluster

Details

• The operator is installing by default a Node Feature Discovery component, which may be redundant with the one provided by SRIOV. Hence test is done to install it only if necessary
• Since the container socket path is required, depends on the provider and is used in 2 units, a code factorization enables to define it only in one place (.Values.internal.container_runtime_settings)

Related reference(s)

Closes #1029 (closed)

Test coverage

• Operator own tests once it is deployed

• Deployment on a capm3 platform with GPU

  Test / platform	RKE2 with ubuntu	RKE2 with opensuse
  Simple CUDA operation	✅	✅
  vLLM installation with a 7b model	Not tested	✅ (privileged mode only)

CI configuration

Below you can choose test deployment variants to run in this MR's CI.

Click to open to CI configuration

Legend:

Icon	Meaning	Available values
☁️	Infra Provider	capd, capo, capm3
🚀	Bootstrap Provider	kubeadm (alias kadm), rke2
🐧	Node OS	ubuntu, suse
🛠️	Deployment Options	light-deploy, dev-sources, ha, misc, maxsurge-0, logging, no-logging
🎬	Pipeline Scenarios	Available scenario list and description

• 🎬 preview ☁️ capd 🚀 kadm 🐧 ubuntu
• 🎬 preview ☁️ capo 🚀 rke2 🐧 suse
• 🎬 preview ☁️ capm3 🚀 rke2 🐧 ubuntu 🟢 wkld:nvidia-gpu-operator
• ☁️ capd 🚀 kadm 🛠️ light-deploy 🐧 ubuntu
• ☁️ capd 🚀 rke2 🛠️ light-deploy 🐧 suse 🟢 wkld:nvidia-gpu-operator
• ☁️ capo 🚀 rke2 🐧 suse
• ☁️ capo 🚀 kadm 🐧 ubuntu 🟢 wkld:nvidia-gpu-operator
• ☁️ capo 🚀 rke2 🎬 rolling-update 🛠️ ha 🐧 ubuntu
• ☁️ capo 🚀 kadm 🎬 wkld-k8s-upgrade 🐧 ubuntu
• ☁️ capo 🚀 rke2 🎬 rolling-update-no-wkld 🛠️ ha 🐧 suse
• ☁️ capo 🚀 rke2 🎬 sylva-upgrade-from-1.4.x 🛠️ ha 🐧 ubuntu
• ☁️ capo 🚀 rke2 🎬 sylva-upgrade-from-1.4.x 🛠️ ha,misc 🐧 ubuntu
• ☁️ capo 🚀 rke2 🛠️ ha,misc 🐧 ubuntu
• ☁️ capm3 🚀 rke2 🐧 suse 🟢 wkld:nvidia-gpu-operator
• ☁️ capm3 🚀 kadm 🐧 ubuntu 🟢 wkld:nvidia-gpu-operator
• ☁️ capm3 🚀 kadm 🎬 rolling-update-no-wkld 🛠️ ha,misc 🐧 ubuntu
• ☁️ capm3 🚀 rke2 🎬 wkld-k8s-upgrade 🛠️ ha 🐧 suse
• ☁️ capm3 🚀 kadm 🎬 rolling-update 🛠️ ha 🐧 ubuntu
• ☁️ capm3 🚀 rke2 🎬 sylva-upgrade-from-1.4.x 🛠️ ha 🐧 suse
• ☁️ capm3 🚀 rke2 🛠️ misc,ha 🐧 suse
• ☁️ capm3 🚀 rke2 🎬 sylva-upgrade-from-1.4.x 🛠️ ha,misc 🐧 suse
• ☁️ capm3 🚀 kadm 🎬 rolling-update 🛠️ ha 🐧 suse
• ☁️ capm3 🚀 ck8s 🎬 no-wkld 🛠️ light-deploy 🐧 ubuntu

Global config for deployment pipelines

• autorun pipelines

• allow failure on pipelines

• record sylvactl events

Notes:

• Enabling autorun will make deployment pipelines to be run automatically without human interaction
• Disabling allow failure will make deployment pipelines mandatory for pipeline success.
• if both autorun and allow failure are disabled, deployment pipelines will need manual triggering but will be blocking the pipeline

Be aware: after configuration change, pipeline is not triggered automatically. Please run it manually (by clicking the run pipeline button in Pipelines tab) or push new code.