Character.ai has unveiled a system named Slonk, which integrates SLURM with Kubernetes to enhance the efficiency of GPU research clusters. Although not a fully supported open-source project, the company is sharing details about the architecture and tools behind Slonk, aimed at resolving a significant challenge in the machine learning infrastructure landscape: balancing the productivity benefits of traditional High-Performance Computing (HPC) environments with the operational advantages of Kubernetes.
The development of Slonk addresses a common issue faced by research teams when scaling training infrastructure. Researchers preferred the reliability of SLURM—a scheduler known for its fair queue management and gang scheduling—while the infrastructure team sought the orchestration capabilities and health management features offered by Kubernetes. Slonk serves both needs, providing a familiar user experience with SLURM commands while leveraging Kubernetes for its robust control plane functionalities.
The system offers a range of features that merge the two environments. Researchers can utilize SLURM commands such as sbatch and squeue, while Kubernetes manages the underlying resources, ensuring stability and effective GPU sharing. The daily workflow for researchers remains akin to traditional HPC practices: accessing a login node via SSH, editing code on a shared NFS home, submitting jobs, and reviewing logs. Slonk’s controller handles resource allocation and scheduling, with results returning to the same volume.
At its architectural core, Slonk treats SLURM nodes as long-running Kubernetes pods, consisting of three StatefulSets: controller, worker, and login. Each SLURM node corresponds to a specific Kubernetes pod, facilitating a seamless integration of workloads. The controller pods operate slurmctld; worker pods manage slurmd; and login pods offer SSH access. This arrangement allows other workloads to coexist on the same physical machines while ensuring the system remains efficient.
Several aspects contribute to Slonk’s functionality. Each pod includes a lightweight initialization layer that hosts SLURM daemons and SSH services, with configurations managed through ConfigMaps. A persistent NFS volume provides shared access to the /home directory, while scripts for pre- and post-job processes are distributed via git-sync. Authentication integrates with Single Sign-On (SSO) and Unix accounts through LDAP, with unique Kubernetes Services assigned to each node to manage service discovery challenges.
Monitoring and health check mechanisms are key to Slonk’s operation. The system employs a suite of checks for GPU, network, and storage health before, during, and after job execution, enabling automatic draining and recycling of faulty nodes. The topology-aware scheduler in SLURM ensures that large jobs are allocated GPUs situated close together within the network, optimizing performance.
Despite its advantages, Slonk faced several technical challenges, including reconciling the differing resource views between SLURM and Kubernetes, which necessitated the development of alignment utilities. Maintaining health checks at scale posed further hurdles, as issues like faulty GPUs or misconfigured network interfaces could disrupt large training runs. The integration of a Kubernetes operator that enforces a goal state for every node simplifies the management of machine lifecycles, while observability systems maintain logs of faulty nodes for future analysis.
Slonk’s architecture allows for dynamic shifts in GPU resources between training and inference tasks, thereby enhancing operational flexibility. Researchers continue to utilize familiar SLURM commands while benefit from the resilience and automation offered by Kubernetes. This dual management system aims to provide reliability without compromising user experience.
The recent release includes Helm charts and container specifications for the controller, login, and worker StatefulSets, alongside health-check scripts and cluster utilities. Character.ai positions this as a reference implementation, encouraging other developers to adapt and build upon it. The company is also actively hiring machine learning infrastructure engineers interested in the intersection of HPC and cloud technologies, aiming to create systems that support scalable model training and inference.
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