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Workload Identity and Secrets

A running model service needs permission to read weights and emit telemetry. Separate the workload identity from its credentials, scope access to specific operations, and test expiry, revocation and cross-tenant denial.

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RELATED CONCEPTS

Multi-Tenancy, Quotas and Fair Share Control Plane and API Design for GPU Platforms Model Onboarding: From Hugging Face to Production Containers, Images and GPU Cold Starts

LESSONS THAT TEACH THIS

From Model to ServiceGive the service an identity and a reproducible home12 min→From Model to ServicePromote a tested release and keep a way back12 min→From Model to ServiceChange the corpus without leaking old access12 min→

PRACTICE THIS IN REAL QUESTIONS

Behavioral & OwnershipTell me about an isolation or access problem you found before anyone else did.→Hardware, Cabling & Cluster Build-OutDesign the physical layer for a multi-tenant GPU cloud. What changes versus a single-tenant cluster?→Hardware, Cabling & Cluster Build-OutDesign the out-of-band management network for a 512-GPU cluster. What connects to it?→Kubernetes, Slurm & GPU SchedulingWalk me through what happens when a pod asks Kubernetes for four GPUs, from the manifest to the container seeing them.→Kubernetes, Slurm & GPU SchedulingMIG, time-slicing and MPS all let several jobs share one GPU. What is the difference, and when would you pick each?→Kubernetes, Slurm & GPU SchedulingSlurm or Kubernetes for a 2,000-GPU training cluster? Make the case, and tell me what you lose either way.→