If you've worked in High Performance Computing (HPC), Machine Learning Operations (MLOps), Data Science (DS), Data Management (DM) or in any scenario that involves vending shared compute resources to a user community, you've probably met one of the available workload management and/or orchestration tools.
In this series of posts, we will explore some of these at a high level, so that you can understand each tool's characteristics at a level that demystifies the "marketing" description without getting into the weeds. As this series is extended, we will update this list so that you can continue to refer to this first post to link to each tool we discuss quickly.
- Slurm – Widely used open-source workload manager in HPC environments.
- ClearML – Open-source MLOps platform enabling experiment tracking, orchestration, and data management for scalable machine learning workflows.
- Run:ai – Commercial (NVIDIA) Kubernetes-based workload orchestration platform optimized for AI/ML workloads, providing dynamic GPU allocation and resource management.
- Ray – Open-source framework for building and scaling distributed applications, widely used for parallelizing Python code and supporting ML workloads.
- Apolo- A comprehensive MLOps and AI‑orchestration platform designed to optimize the utilization of both on-prem (bare metal) and cloud AI infrastructure.
Please watch this page for additional entries.