device plugin

Follow a four-GPU request from advertised capacity to concrete devices, and see why free GPUs across nodes cannot satisfy one pod.

Diagnose GPU pod failures by scheduling, container startup and execution boundaries, without treating Allocatable as a health certificate.

Separate discovery labels, GPU resource advertisement and validation, and understand the operator as a reconciler with configurable components.

DRA gives the scheduler device claims and attributes to work with. Compare that with extended-resource counts, then check the deployed driver and controller support before promising sharing, partitioning or gang placement.

A fabric address, an RDMA device and a working GPU-memory registration path are separate requirements. Verify each inside the pod, then test the actual GPU-to-NIC placement and collective behavior.

Trace the job from quota reservation through admission to physical placement. Count eligible node shapes, not just free GPUs, and use controller and scheduler evidence to choose the least disruptive fix.