Category
Leverage Kubernetes native features and GPU hardware to run ML workloads at scale. From node affinity to MIG partitioning.
Start with K8s built-in features, then dive into GPU-specific configuration.
Node affinity and anti-affinity for workload placement, taints and tolerations for dedicated GPU nodes, persistent volumes for datasets, K8s Jobs for training, and resource quotas to prevent runaway GPU spend.
Kubernetes for MLThe complete NVIDIA stack: kernel driver installation, device plugin operation, GPU Operator for automated management, MIG partitioning on A100/H100, and DCGM monitoring with Prometheus and Grafana.