Kubernetes 1.35 Native Gang Scheduling - Complete Demo and Workload API Setup

Explore Kubernetes v1.35's revolutionary native Gang Scheduling feature through a comprehensive 13-minute tutorial that demonstrates the new Workload API for all-or-nothing scheduling in AI, ML, HPC, and distributed training environments. Build a Kubernetes 1.35 cluster from source using a kind cluster, then enable the Workload API and Gang Scheduling feature gates to implement coordinated pod scheduling. Learn to deploy Workloads with PodGroups and schedule pods as a gang using workloadRef to ensure all pods in a group schedule together or none do at all. Discover how Gang Scheduling prevents deadlocks in distributed jobs, eliminates resource waste from partially scheduled pods, and improves cluster utilization for multi-pod workloads including PyTorch, TensorFlow, Spark, and Ray frameworks. Master the technical implementation of Kubernetes v1.35.0-beta.0, Workload Aware Scheduling, the scheduling.k8s.io/v1alpha1 Workload API, and Opportunistic Batching in beta status while working with kind clusters on Ubuntu/Linux systems. Follow along with the provided GitHub repository containing gang-scheduling-demo code to implement these advanced scheduling capabilities in your own Kubernetes environments for distributed computing workloads.