ABOUT

AI teams running diverse workloads — long-running model serving endpoints, batch training jobs, data pipeline workers, and GPU-accelerated tasks — either maintain separate orchestrators for each workload type or force everything through Kubernetes, which adds significant operational complexity. Nomad solves this with a single lightweight orchestrator that handles containers, standalone binaries, Java JARs, and raw executables using a declarative job specification. It integrates natively with HashiCorp Consul for service discovery and Vault for secrets management, providing a simpler alternative to Kubernetes for ML workloads that don't need its full container orchestration surface.

INTEGRATION GUIDE

1. Schedule batch ML training jobs across a heterogeneous cluster of GPU and CPU nodes with resource-aware placement 2. Deploy long-running model inference microservices with health checking, automatic restart, and rolling updates 3. Run data pipeline workers as batch jobs that execute on schedule or on-demand with parameterized job specifications 4. Co-locate GPU-accelerated training workloads with supporting services on the same cluster using a single orchestrator 5. Orchestrate distributed hyperparameter sweeps and multi-node model evaluation tasks with Nomad parameterized jobs

NOMAD

ABOUT

INTEGRATION GUIDE

TAGS