SemiAnalysis 测试 Vultr GPU 集群暴露多项问题

The images that Vultr built for our testing had many libraries with applicable CVEs, including CUDA, DCGM, Docker, runc, and ConnectX firmware. Our Slinky login pod did not mount `/shared` and lacked the Pyxis plugstack config, so we had to orchestrate from a worker pod until the Vultr support team reprovisioned the login pod mid-campaign, fixing both issues. There was another basic problem with the storage visible from the K8s layer: the default storage configuration was incompatible with our bare-metal cluster. The default StorageClass, `vultr-block-storage`, provisioned and bound without complaint, then the pod sat in `ContainerCreating` forever. However, the Lustre tier, which came attached to the worker nodes, was excellent, achieving 91.7 GB/s aggregate read across 32 clients. Grafana was configured but only partially correct, as NVLink read 0 due to unconfigured `DCGM_FI_PROF_NVLINK_*` fields. (2/3)