Managing Multiple Slurm Clusters with SkyPilot | SkyPilot

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li]:!relative [&_ul>li]:!pl-4 [&_ul>li]:before:!absolute [&_ul>li]:before:!top-[0.75em] [&_ul>li]:before:!left-0 [&_ul>li]:before:!block [&_ul>li]:before:!h-[5px] [&_ul>li]:before:!w-[5px] [&_ul>li]:before:!-translate-y-1/2 [&_ul>li]:before:!transform [&_ul>li]:before:!rounded-[1px] [&_ul>li]:before:!bg-black [&_ul>li]:before:!content-[''] [&>p:first-child]:!mt-0 prose-td:border-b prose-td:!border-t-0 prose-tr:!border-b-0 prose-td:border-grey-200 [&_code]:before:!content-none [&_code]:after:!content-none prose-p:text-18px prose-ul:text-18px prose-ol:text-18px prose-blockquote:!text-18px [&_h1]:mx-auto [&_h1]:lg:max-w-[780px] [&_h2]:mx-auto [&_h2]:lg:max-w-[780px] [&_h3]:mx-auto [&_h3]:lg:max-w-[780px] [&_p]:mx-auto [&_p]:lg:max-w-[780px] lg:prose-ul:max-w-[780px] prose-ul:mx-auto [&>div]:mx-auto [&>div]:lg:max-w-[780px] [&>blockquote]:mx-auto [&>blockquote]:lg:max-w-[780px] lg:prose-ol:max-w-[780px] prose-ol:mx-auto">p:first-child]:!mt-0">Slurm dominates large-scale computing - 60% of Top500 supercomputers rely on it for workload management. If you’re working in ML research or HPC, having access to multiple GPU clusters sounds like a good problem to have. More resources = more compute, which means faster experiments and shorter iteration cycles.<br>In practice, however, managing multiple Slurm clusters is anything but enjoyable, it’s a manual, error-prone process that becomes a bottleneck as your team scales.<br>Imagine this: your primary cluster is full, so you SSH to a different login node, remember which partition names are different there, tweak your job script, and resubmit. You rinse and repeat across three, four, or five different clusters. You start thinking “there’s gotta be a better way.”<br>What if you could treat all your Slurm clusters as a single resource pool? What if your jobs could automatically find and run on whichever cluster has available GPUs? That’s exactly what SkyPilot enables.<br>The multi-cluster pain points #<br>So what makes managing multiple Slurm clusters difficult?

No unified view of resources<br>Each Slurm cluster is self-contained. To see what GPUs are available across your clusters, you need to:<br>SSH to cluster-a’s login node<br>Run sinfo or check the queue<br>SSH to cluster-b’s login node<br>Run sinfo again<br>Compare in your head (or in a spreadsheet)<br>Decide where to submit<br>This becomes especially time consuming when you’re looking for specific GPU types (e.g. H200s vs H100 vs B300s) or need multi-node allocations. There’s no single command that shows you “here’s what’s available everywhere.”<br>Manual failover when clusters are full<br>When your primary cluster hits capacity, you have to:<br>Notice that your job is stuck in the queue<br>Realize you should try another cluster<br>SSH to a different login node<br>Adapt your submission script (different partition names, different module environments, different paths)<br>Submit again<br>Hope that cluster has capacity<br>For teams running continuous training pipelines or parameter sweeps, this manual intervention breaks the workflow. You can’t just submit 100 jobs and let them find capacity-you have to babysit the process.<br>Inconsistent environments across clusters<br>Different Slurm clusters often have:<br>Different partition naming schemes (gpu vs b300 vs h100-cluster)<br>Different module environments (module load cuda/12.1 vs module load cuda/12.4)<br>Different storage mounts (home directory paths, scratch filesystem locations)<br>Different networking configurations<br>Every time you move a job to a different cluster, you’re effectively debugging environment differences. This is both tedious and it becomes a source of subtle bugs when jobs fail due to environment mismatches.<br>Bad observability = low utilization<br>Without visibility across clusters, you can’t make smart scheduling decisions:<br>Should you wait for H100s on cluster-a, or use available B300s on cluster-b?<br>Should you preempt lower-priority jobs, or fail over to another cluster?<br>Which cluster will give you the fastest queue time?<br>You’ll end up underutilizing your infra based on incomplete information.<br>What about native Slurm multi-cluster support?<br>Slurm does offer multi-cluster operation and federation, but both have limitations. Multi-cluster mode routes jobs to the cluster with the earliest start time at submission, but “Slurm makes no subsequent effort to migrate a job to a different cluster” after that. Federation provides peer-to-peer scheduling with replicated “sibling jobs,” but the docs note it’s “not intended as a high-throughput environment” and suggest configuring fewer clusters if you’re scheduling more than 50,000 jobs a day. It also requires that all compute nodes be reachable from all submission hosts, which is a tall order for clusters in separate data centers.<br>SkyPilot’s unified interface for Slurm #<br>SkyPilot is a control plane that turns fragmented compute into one unified AI compute pool. While SkyPilot supports (single or multiple)...