René's URL Explorer Experiment


Title: Encrypt GPU workload data in use with Confidential GKE Nodes  |  GKE AI/ML  |  Google Cloud Documentation

Open Graph Title: Encrypt GPU workload data in use with Confidential GKE Nodes  |  GKE AI/ML  |  Google Cloud Documentation

Description: Configure nodes that use Confidential Computing technologies to run GPU workloads.

Open Graph Description: Configure nodes that use Confidential Computing technologies to run GPU workloads.

Opengraph URL: https://docs.cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes

direct link

Domain: cloud.google.com


Hey, it has json ld scripts:
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Simplified autoscaling concepts for AI/ML workloads in GKEhttps://cloud.google.com/kubernetes-engine/docs/concepts/machine-learning/understand-autoscaling
Quickstart: Serve your first AI model on GKEhttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-open-models-terraform
About AI/ML model inference on GKEhttps://cloud.google.com/kubernetes-engine/docs/concepts/machine-learning/inference
Analyze model serving performance and costs with GKE Inference Quickstarthttps://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/inference/inference-quickstart
Expose AI applications with GKE Inference Gatewayhttps://cloud.google.com/kubernetes-engine/docs/concepts/about-gke-inference-gateway
Overviewhttps://cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference
Choose a load balancing strategy for inferencehttps://cloud.google.com/kubernetes-engine/docs/concepts/machine-learning/choose-lb-strategy
Autoscale inference workloads on GPUshttps://cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference/autoscaling
Autoscale LLM inference workloads on TPUshttps://cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference/autoscaling-tpu
Optimize LLM inference workloads on GPUshttps://cloud.google.com/kubernetes-engine/docs/best-practices/machine-learning/inference/llm-optimization
Serve Gemma open models using GPUs with vLLMhttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-gemma-gpu-vllm
Serve LLMs like DeepSeek-R1 671B or Llama 3.1 405Bhttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-multihost-gpu
Serve an LLM with GKE Inference Gatewayhttps://cloud.google.com/kubernetes-engine/docs/how-to/serve-with-gke-inference-gateway
Serve an LLM with multiple GPUshttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-multiple-gpu
Serve T5 with Torch Servehttps://cloud.google.com/kubernetes-engine/docs/tutorials/scalable-ml-models-torchserve
Fine-tune Gemma open models using multiple GPUshttps://cloud.google.com/kubernetes-engine/docs/tutorials/finetune-gemma-gpu
Serve Llama on TPUs with vLLMhttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-vllm-tpu
Serve LLMs using multi-host TPUs with JetStream and Pathwayshttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-multihost-tpu-jetstream
Serve Stable Diffusion XL on TPUs with MaxDiffusionhttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-sdxl-tpu
Serve open models on TPUs with Terraformhttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-open-models-tpu-terraform
Train large-scale models with Multi-tier Checkpointinghttps://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/training/multi-tier-checkpointing
Train a model with GPUs on GKE Standard modehttps://cloud.google.com/kubernetes-engine/docs/quickstarts/train-model-gpus-standard
Train a model with GPUs on GKE Autopilot modehttps://cloud.google.com/kubernetes-engine/docs/quickstarts/train-model-gpus-autopilot
Train a Llama model on GPUs with Megatron-LMhttps://cloud.google.com/kubernetes-engine/docs/quickstarts/training-megatron-llama-workload
Deploy AI agents with the ADK and Vertex AI APIhttps://cloud.google.com/kubernetes-engine/docs/tutorials/agentic-adk-vertex
Deploy AI agents with the ADK and a self-hosted LLMhttps://cloud.google.com/kubernetes-engine/docs/tutorials/agentic-adk-vllm
Isolate AI code execution with Agent Sandboxhttps://cloud.google.com/kubernetes-engine/docs/how-to/agent-sandbox
Save and restore Agent Sandbox environmentshttps://cloud.google.com/kubernetes-engine/docs/how-to/agent-sandbox-pod-snapshots
About Ray on GKEhttps://cloud.google.com/kubernetes-engine/docs/add-on/ray-on-gke/concepts/overview
Quickstart: Deploy your first Ray application on GKEhttps://cloud.google.com/kubernetes-engine/docs/add-on/ray-on-gke/quickstarts/ray-gpu-cluster
Enable managed KubeRay with the Ray Operator add-onhttps://cloud.google.com/kubernetes-engine/docs/add-on/ray-on-gke/how-to/enable-ray-on-gke
Serve an LLM on GPUs with Ray Servehttps://cloud.google.com/kubernetes-engine/docs/how-to/serve-llm-l4-ray
Serve an LLM on TPUs with Ray Servehttps://cloud.google.com/kubernetes-engine/docs/tutorials/serve-llm-tpu-ray
Serve a diffusion model on GPUs with Rayhttps://cloud.google.com/kubernetes-engine/docs/add-on/ray-on-gke/tutorials/deploy-ray-serve-stable-diffusion
Serve a diffusion model on TPUs with Rayhttps://cloud.google.com/kubernetes-engine/docs/add-on/ray-on-gke/tutorials/deploy-ray-serve-stable-diffusion-tpu
Train with PyTorch, Ray, and GKEhttps://cloud.google.com/kubernetes-engine/docs/add-on/ray-on-gke/tutorials/train-model-ray-pytorch
Train an LLM using Jax and Ray Train on TPUs with GKEhttps://cloud.google.com/kubernetes-engine/docs/tutorials/distributed-training-tpu
Set up Ray on GKE with TPU Trilliumhttps://cloud.google.com/kubernetes-engine/docs/tutorials/kuberay-trillium
View logs for the Ray Operator on GKEhttps://cloud.google.com/kubernetes-engine/docs/add-on/ray-on-gke/how-to/view-ray-operator-logs
View logs and metrics for Ray clusters on GKEhttps://cloud.google.com/kubernetes-engine/docs/add-on/ray-on-gke/how-to/collect-view-logs-metrics
About GPUshttps://cloud.google.com/kubernetes-engine/docs/concepts/gpus
Configure A3 or A4 VMs with AI Hypercomputerhttps://cloud.google.com/ai-hypercomputer/docs/create/gke-ai-hypercompute-custom
Deploy GPU workloads in Standard clustershttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus
Deploy GPU workloads in Autopilot clustershttps://cloud.google.com/kubernetes-engine/docs/how-to/autopilot-gpus
Configure autoscaling for LLM workloads on GPUshttps://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/inference/autoscaling
Manage the GPU stack with the NVIDIA GPU Operatorhttps://cloud.google.com/kubernetes-engine/docs/how-to/gpu-operator
Encrypt GPU workloads in-placehttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes
Deploy AI Hypercomputer clusters (A3, A4 VMs)https://cloud.google.com/ai-hypercomputer/docs/create/gke-ai-hypercompute
About GPU sharing strategies on GKEhttps://cloud.google.com/kubernetes-engine/docs/concepts/timesharing-gpus
Use multi-instance GPUshttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-multi
Configure timesharing GPUshttps://cloud.google.com/kubernetes-engine/docs/how-to/timesharing-gpus
Use NVIDIA MPShttps://cloud.google.com/kubernetes-engine/docs/how-to/nvidia-mps-gpus
Overviewhttps://cloud.google.com/kubernetes-engine/docs/concepts/dws
Run a large-scale workload with flex-starthttps://cloud.google.com/kubernetes-engine/docs/how-to/provisioningrequest
Run a small batch workload with GPUs and flex-starthttps://cloud.google.com/kubernetes-engine/docs/how-to/dws-flex-start-training
About TPUshttps://cloud.google.com/kubernetes-engine/docs/concepts/tpus
Ironwood (TPU7x)https://cloud.google.com/kubernetes-engine/docs/concepts/tpu-ironwood
Plan TPUs on GKEhttps://cloud.google.com/kubernetes-engine/docs/concepts/plan-tpus
Deploy TPU workloads on Standard clustershttps://cloud.google.com/kubernetes-engine/docs/how-to/tpus
Deploy TPU workloads on Autopilot clustershttps://cloud.google.com/kubernetes-engine/docs/how-to/tpus-autopilot
Deploy high-performance TPU workloads with auto-networkinghttps://cloud.google.com/kubernetes-engine/docs/how-to/config-auto-net-for-accelerators
Deploy TPU Multislices on GKEhttps://cloud.google.com/kubernetes-engine/docs/how-to/tpu-multislice
Orchestrate TPU Multislice workloads using JobSet and Kueuehttps://cloud.google.com/kubernetes-engine/docs/tutorials/tpu-multislice-kueue
Configure autoscaling for LLM workloads on TPUshttps://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/inference/autoscaling-tpu
Overviewhttps://cloud.google.com/kubernetes-engine/docs/concepts/dws
Run a small batch workload with TPUs and flex-starthttps://cloud.google.com/kubernetes-engine/docs/how-to/dws-flex-start-training-tpu
Request TPUs with future reservation in calendar modehttps://cloud.google.com/kubernetes-engine/docs/how-to/tpu-calendar-mode
About node disruption for GPUs and TPUshttps://cloud.google.com/kubernetes-engine/docs/concepts/handle-disruption-gpu-tpu
Best practices for running batch workloads on GKEhttps://cloud.google.com/kubernetes-engine/docs/best-practices/batch-platform-on-gke
Deploy a batch system using Kueuehttps://cloud.google.com/kubernetes-engine/docs/tutorials/kueue-intro
Implement a Job queuing system with quota sharinghttps://cloud.google.com/kubernetes-engine/docs/tutorials/kueue-cohort
Optimize resource utilization for mixed AI/ML training and inference workloadshttps://cloud.google.com/kubernetes-engine/docs/tutorials/mixed-workloads
Optimize AI/ML workload prioritizationhttps://cloud.google.com/kubernetes-engine/docs/best-practices/optimize-ai-utilization
Orchestrate TPU Multislice workloads using JobSet and Kueuehttps://cloud.google.com/kubernetes-engine/docs/tutorials/tpu-multislice-kueue
Allocate network resources using GKE managed DRANEThttps://cloud.google.com/kubernetes-engine/docs/how-to/allocate-network-resources-dra
Configure auto-networking for acceleratorshttps://cloud.google.com/kubernetes-engine/docs/how-to/config-auto-net-for-accelerators
About storage for GKE clustershttps://cloud.google.com/kubernetes-engine/docs/concepts/storage-overview
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About Cloud Storage FUSE CSI driver for GKEhttps://cloud.google.com/kubernetes-engine/docs/concepts/cloud-storage-fuse-csi-driver
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Transfer data from Cloud Storage using GKE Volume Populatorhttps://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/volume-populator
Build a RAG chatbot with GKE and Cloud Storagehttps://cloud.google.com/kubernetes-engine/docs/tutorials/build-rag-chatbot
Deploy a Qdrant vector database on GKEhttps://cloud.google.com/kubernetes-engine/docs/tutorials/deploy-qdrant
Deploy automatic application monitoring for AI/ML workloadshttps://cloud.google.com/kubernetes-engine/docs/how-to/configure-automatic-application-monitoring
Troubleshoot GPU issueshttps://cloud.google.com/kubernetes-engine/docs/troubleshooting/gpus
Troubleshoot TPU issueshttps://cloud.google.com/kubernetes-engine/docs/troubleshooting/tpus
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Guides https://docs.cloud.google.com/kubernetes-engine/docs/concepts/machine-learning
Autopilot https://cloud.google.com/kubernetes-engine/docs/concepts/autopilot-overview
Standard https://cloud.google.com/kubernetes-engine/docs/concepts/choose-cluster-mode
Confidential GKE Nodeshttps://cloud.google.com/kubernetes-engine/docs/how-to/confidential-gke-nodes
Confidential VMhttps://cloud.google.com/confidential-computing/confidential-vm/docs/confidential-vm-overview
GPUs in GKEhttps://cloud.google.com/kubernetes-engine/docs/concepts/gpus
Use ComputeClasses to run GPU workloads on Confidential GKE Nodeshttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#use-computeclasses
Manually configure Confidential GKE Nodes in GKE Standardhttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#create-gke-cluster
Enable Google Kubernetes Engine API https://console.cloud.google.com/flows/enableapi?apiid=container.googleapis.com
installhttps://cloud.google.com/sdk/docs/install
initializehttps://cloud.google.com/sdk/docs/initializing
propertyhttps://cloud.google.com/sdk/docs/properties#setting_properties
View supported zoneshttps://cloud.google.com/confidential-computing/confidential-vm/docs/supported-configurations#supported-zones
View and manage quotashttps://cloud.google.com/docs/quotas/view-manage
ComputeClasseshttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#use-computeclasses
flex-starthttps://cloud.google.com/kubernetes-engine/docs/concepts/dws
Previewhttps://cloud.google.com/products#product-launch-stages
Manual configuration in Standard modehttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#create-gke-cluster
Previewhttps://cloud.google.com/products#product-launch-stages
Previewhttps://cloud.google.com/products#product-launch-stages
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ComputeClasshttps://cloud.google.com/kubernetes-engine/docs/concepts/about-custom-compute-classes
View supported zoneshttps://cloud.google.com/confidential-computing/confidential-vm/docs/supported-configurations#supported-zones
View supported zoneshttps://cloud.google.com/confidential-computing/confidential-vm/docs/supported-configurations#supported-zones
Enable Confidential GKE Nodes on Standard clustershttps://cloud.google.com/kubernetes-engine/docs/how-to/confidential-gke-nodes#enabling_in_a_new_cluster
Availabilityhttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#availability
Go to Kubernetes clustershttps://console.cloud.google.com/kubernetes/list
Availabilityhttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#availability
Availabilityhttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#availability
manually install a compatible GPU driverhttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#install-gpu-drivers
configure Flex-start VMs with queued provisioninghttps://cloud.google.com/kubernetes-engine/docs/how-to/provisioningrequest#create-node-pool
View supported zoneshttps://cloud.google.com/confidential-computing/confidential-vm/docs/supported-configurations#supported-zones
manually install a compatible GPU driverhttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus-confidential-nodes#install-gpu-drivers
Run a large-scale workload with flex-start with queued provisioninghttps://cloud.google.com/kubernetes-engine/docs/how-to/provisioningrequest
Update an existing node poolhttps://cloud.google.com/kubernetes-engine/docs/how-to/confidential-gke-nodes#update-existing-node-pool
manual changes that recreate the nodes using a node upgrade strategy without respecting maintenance policieshttps://cloud.google.com/kubernetes-engine/docs/concepts/managing-clusters#manual-changes-strategy-but-no-respect-policies
Planning for node update disruptionshttps://cloud.google.com/kubernetes-engine/docs/concepts/managing-clusters#plan-node-disruption
resource availabilityhttps://cloud.google.com/kubernetes-engine/docs/how-to/node-upgrades-quota
doesn't prevent this changehttps://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-upgrades#disable-only-versions
Manually install NVIDIA GPU drivershttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus#installing_drivers
Troubleshoot GPUs in GKEhttps://cloud.google.com/kubernetes-engine/docs/troubleshooting/gpus#confidential-nodes-gpus
Verify that your GPU nodes use Confidential GKE Nodeshttps://cloud.google.com/kubernetes-engine/docs/how-to/confidential-gke-nodes#verifying_that_are_enabled
Deploy a workload on your GPU nodeshttps://cloud.google.com/kubernetes-engine/docs/how-to/gpus#multiple_gpus
Learn about the methods to run large-scale workloads with GPUshttps://cloud.google.com/kubernetes-engine/docs/concepts/dws#methods
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