Bridging HPC and AI/ML: Integrating Slurm with MLOps Platforms

The Convergence Challenge

Over the past decade, enterprises have invested heavily in High Performance Computing (HPC) infrastructure to tackle complex scientific problems. These organizations have built sophisticated systems using Slurm to schedule massively parallel jobs across large clusters equipped with accelerated hardware. Now, as AI/ML workloads demand similar computational resources for deep learning model training, enterprises are seeking ways to leverage their existing HPC investments for modern AI development.

The emergence of agile MLOps methodologies has revolutionized how organizations productionize AI/ML models. The key challenge lies in marrying AI/ML development practices with established HPC/Slurm infrastructure—a combination that could significantly accelerate adoption and maximize return on existing investments.

Understanding the Foundation Technologies

High Performance Computing and Slurm

High Performance Computing serves specialized engineering and scientific applications that require systems capable of performing extremely complex operations on massive datasets. A typical HPC system consists of:

• Large numbers of compute nodes (ranging from tens to tens of thousands)
• High-performance storage subsystems
• Ultra-fast network interconnects
• Compute accelerators, particularly GPUs

Slurm has emerged as the leading open-source platform for scheduling compute jobs on large Linux clusters. Its popularity stems from its high scalability and resilience, making it the go-to solution for distributing workloads across HPC clusters. While traditionally focused on Linux processes, Slurm has evolved to support containerized workloads, with Singularity containers becoming increasingly popular due to their ability to convert Docker containers for HPC environments.

The investment scale is substantial—enterprises and research institutions have committed hundreds of millions of dollars to building Slurm-based HPC infrastructures and associated software ecosystems.

AI/ML and the Kubernetes Ecosystem

Modern enterprises across industries are adopting AI-based deep learning methodologies to address diverse challenges including autonomous driving, drug discovery, and process automation. Deep learning infrastructure requirements mirror those of HPC: GPU-accelerated compute nodes, large-scale storage, and high-speed networking.

Kubernetes has become the de facto standard for running AI/ML workloads at scale. Open-source platforms like Kubeflow, along with various commercial offerings, are predominantly built on Kubernetes foundations. These platforms increasingly incorporate MLOps methodologies to accelerate model productionization.

The Integration Imperative

Enterprises using HPC for traditional scientific computing are rapidly expanding into AI/ML and deep learning domains. While the hardware infrastructure requirements are remarkably similar—GPU-accelerated networked compute nodes with large storage—the domains differ significantly in toolsets, management approaches, orchestration methods, and development frameworks.

Organizations running both HPC and AI workloads would benefit tremendously from a unified infrastructure, especially given their substantial HPC investments. The ideal solution would combine the benefits of MLOps through Kubernetes-based AI/ML platforms with the scale and resilience of HPC/Slurm systems.

Two Paths to Integration

Option 1: Slurm/Kubernetes Operator Integration

This approach creates tight coupling between Slurm and Kubernetes clusters, making the Slurm cluster appear as an extension of Kubernetes nodes.

Advantages:

• Seamless integration allowing most Kubernetes tools to function normally
• Support for any Kubernetes workload scheduling
• Familiar Kubernetes administration interface

Disadvantages:

• Difficulty supporting on-demand usage models
• Complex requirement for Slurm to support Kubernetes semantics
• Potential compromise of Slurm's inherent scale and resilience
• Administrative complexity from the Kubernetes layer

Example implementation: The Sylabs slurm-operator project (github.com/sylabs/slurm-operator)

Option 2: MLOps Controller Plugin Integration

This approach employs a hub-and-spoke model where an MLOps controller serves as the hub, connecting to HPC/Slurm clusters as spokes through controller plugins. This loose integration allows enterprises to optimize both traditional HPC and AI/ML workloads while preserving existing infrastructure investments.

Advantages:

• Simplified integration model through loose coupling
• Support for on-demand, compute-intensive AI/ML workloads like large-scale model training
• Independent cluster operation with separate administration domains
• Preservation of traditional HPC/Slurm environments
• Minimal disruption to existing workflows

Disadvantages:

• Limited scope focused primarily on AI/ML workloads
• Restricted to job-related activities such as automated model training

The Recommended Path Forward

We believe the second option—MLOps controller plugin integration—offers the most practical solution for real-world implementations. The fundamental differences in tools, frameworks, and workflows between HPC and AI/ML domains reflect the distinct organizations, user communities, and methodologies driving their development.

Attempting to maintain synchronization between these divergent approaches presents ongoing compatibility challenges that are often impractical to resolve. The plugin-based approach allows each domain to evolve independently while maintaining communication through a relatively thin software layer.

This architectural choice offers several strategic advantages:

• Domain Independence: Each field can progress according to its specific requirements and constraints
• Compatibility Assurance: Integration challenges are managed through focused plugin development
• Investment Protection: Existing HPC infrastructure remains fully functional while adding AI/ML capabilities
• Operational Flexibility: Organizations can optimize workflows for each domain without compromising either

Conclusion

The convergence of HPC and AI/ML represents a significant opportunity for enterprises to maximize their computational investments. By choosing an integration approach that respects the unique characteristics of each domain while enabling productive collaboration, organizations can build robust, scalable platforms that serve both traditional scientific computing and modern AI development needs.

The plugin-based integration model offers the most sustainable path forward, enabling enterprises to harness the power of both Slurm-managed HPC clusters and Kubernetes-based MLOps platforms without compromising the strengths of either approach.