Theory

Kubeflow is an open-source platform used to deploy and manage machine learning (ML) workflows on Kubernetes. It offers tools for the complete ML lifecycle—data preparation, model training, deployment, and monitoring—and also supports GPU acceleration for computationally intensive operations. SLURM, which is a widely used workload manager in high-performance computing (HPC), schedules and runs jobs across cluster resources, including GPUs, and thus is best suited for complex ML workloads. Both environments support GPU-boosted ML operations such as model training. While high-performance GPUs such as the NVIDIA A100 are ideal, the demo employs substitute GPUs owing to realities, with principles transferable to advanced configurations.