Network Bachelor

Learn how to validate NVIDIA GPU workloads on Kubernetes using CUDA VectorAdd, PyTorch, NCCL, gpu-burn and vLLM inference tests.

Validate an NVIDIA GPU Kubernetes cluster before running AI workloads. Check GPU health, RDMA networking, storage, scheduling, resources and observability.

Learn what runs on an NVIDIA GPU node in Kubernetes, from GPU drivers and container toolkit to device plugins, DCGM monitoring, and troubleshooting.

Learn how to add NVIDIA GPU worker nodes to Kubernetes, install containerd and kubeadm, deploy GPU Operator components, validate GPUs, and verify RDMA networking.

Part 3 of the Falcon AI workbook series. Follow along with real commands — this post has no new infrastructure to build. Instead we go hands-on inside the operators installed in Part 2, so you know exactly what “idle and watching” actually means before we hand it real GPU hardware in Part 4. Prerequisite You’ve

Learn Kubernetes GPU scheduling with NVIDIA RuntimeClass, taints and tolerations, node affinity, topology-aware placement and Volcano gang scheduling.

Build a Kubernetes cluster for NVIDIA GPU workloads from bare metal. Configure containerd, kubeadm, HA control planes, CNI, and validate cluster readiness.

Learn how Kubernetes CNI networking works, from pod IPs and CNI plugins to IPAM, Flannel, VXLAN, host-gw, and a hands-on CNI networking lab.

Learn Kubernetes networking fundamentals, including OSI layers, overlay networks, VXLAN, network namespaces, veth pairs, Linux bridges, and pod networking.

Learn how Kubernetes handles AI/ML workloads with GPU scheduling, NVIDIA GPU Operator, Kubeflow, MLflow, Kueue, Volcano, multi-tenancy and day-2 operations.