Ray on a Mac
Distributed compute for AI (training/serving/tuning) · AI Clusters
Ray scales Python/AI workloads across a cluster — Ray Train, Tune, Serve and RLlib, with autoscaling and GPU scheduling. pip install 'ray[default]', then ray start --head (dashboard on :8265, GCS on :6379) and join workers with ray start --address=…. Monitor via the dashboard or ray status / the Jobs & State REST API (:8265). Pairs with vLLM (Ray Serve) and KubeRay on Kubernetes.
Run Ray with FrontierStack
FrontierStack lists Ray in its AI Clusters catalog. Install or connect it from one place, then monitor its status, ports and certificate, secure it with the firewall and Malware Audit, and back it up.
Run it all from one Mac app.
FrontierStack installs, monitors and secures the whole stack — locally and across your fleet — from a single native macOS app.
Download FrontierStack