'CUDA VISIBLE DEVICES' is essential for GPU affinity. It allows you to specify which GPUs are visible to a particular process. Without it, all processes might try to use the same GPU, leading to performance bottlenecks. controls the order in which GPUs are enumerated. specifies the path to shared libraries. is hypothetical. forces synchronous CUDA calls.

Spine-Leaf architecture is designed to handle high-bandwidth, low-latency traffic patterns characteristic of AI training. It provides a non-blocking fabric with equal cost paths between any two servers, making it ideal for east-west communication. Three-Tier is more suited for traditional applications with north-south traffic. Ring and Bus are less scalable and perform poorly under heavy load. Mesh is complex and expensive for large-scale deployments.

Increased ScudaMemcpy' times indicate a bottleneck in data transfer between the CPU and GPIJs or within the GPU memory itself. While PCle saturation or failing NVLink connections could contribute, a heavily loaded CPU is often the primary culprit. CPU-bound preprocessing tasks or general system load can create contention for system memory, slowing down data movement to the GPUs. The NVLink being used is less likely because 'nvidia-smi' would likely report errors or lower speeds between the GPUs

Cordoning the node prevents new pods from being scheduled on it. After upgrading the driver and rebooting, uncordoning the node allows Kubernetes to resume scheduling workloads. Draining the node before upgrading can cause unnecessary downtime if pods are migrated before the upgrade process starts. The NVIDIA container toolkit must be compatible to the NVIDIA driver, but the upgrade sequence follows Option C steps.