Slurm Quickstart
Slurm provides a multi-tenant framework for managing compute resources and jobs that span large clusters.
Overview
TensorWave Slurm combines the power of Slurm, the industry-standard workload manager for HPC and AI, with the flexibility of a Kubernetes-native orchestration layer. This integration delivers a modern, multi-tenant environment that scales seamlessly across AMD Instinct GPU clusters—enabling teams to run distributed training, fine-tuning, and simulation workloads without managing the underlying infrastructure.
With TensorWave, you get the familiar Slurm interface running on top of a cloud-native control plane that provides automated scheduling, easy scaling, and container-based execution.
Why Slurm on Kubernetes?
Traditional Slurm deployments were designed for static on-prem clusters. TensorWave modernizes that model by running Slurm inside Kubernetes, unlocking:
Scalable compute pools — resize your slurm cluster within your K8s environment.
Container-native workflows — integrate directly with your existing Docker or Enroot environments.
Multi-tenant isolation — each user or team runs in a secure namespace with defined resource limits.
The result is a unified, cloud-native scheduling experience that bridges HPC scalability with Kubernetes reliability.
Quickstart Example
1. Connect to Your Login Node
Each Slurm environment provides a login node, your interactive entry point for running Slurm commands. Behind the scenes, this login node runs as a managed Kubernetes pod, with the same Slurm interface (srun, sinfo, sbatch), and the benefits of cloud-native orchestration.
ssh <username>@<slurm-login-endpoint>Once connected, you’ll have access to all standard Slurm utilities and your team’s partitions. From here, you can submit jobs, monitor queues, and launch multi-node workloads just as you would on a traditional HPC cluster.
2. Inspect Available Resources
List available partitions and node states:
Example:
Even though these nodes are dynamically managed by Kubernetes, the Slurm CLI remains identical to traditional HPC clusters.
3. Launch a Multi-Node Job
To verify connectivity and RDMA functionality, run a distributed RCCL test across four nodes (32 GPUs total):
Slurm automatically handles:
GPU and node allocation
Network interface binding
MPI coordination
Containers and More Info
Often, users will want to run their HPC payloads in containers. You can learn more about this in the Pyxis Quickstart section.
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