Explore production multi-node job execution with gang scheduling, Kubernetes, GPUs, and RDMA in this conference talk from KubeCon + CloudNativeCon. Dive into the challenges and solutions for running distributed deep learning and machine learning workloads in shared Kubernetes clusters. Learn about distributed TensorFlow, PyTorch, Horovod, and MPI implementations, as well as the use of GPU nodes with NCCL and RDMA for accelerated performance. Discover the end-to-end flow for multi-node jobs in Kubernetes, including gang scheduling, quotas, fairness, and backfilling implemented in a custom GPU scheduler. Gain insights into high-speed networking through RoCE and SR-IOV/Multus CNI, and understand design choices, learnings, and operational experiences, including failure handling, performance optimization, and telemetry in large-scale distributed computing environments.