skypilot-multi-cloud-orchestration

Multi-cloud ML orchestration runs distributed training across AWS, GCP, and Azure with cost-aware provider selection. It automatically picks the cheapest clouds and manages spot-based, fault-tolerant training jobs through a unified SkyPilot interface.

Yes, spot instance orchestration with auto-recovery is supported for ML workloads. SkyPilot manages spot-based fault-tolerant jobs across clouds, automatically recovering interrupted training and batch processing tasks without manual intervention.

The best way to manage multi-node ML workloads across AWS, GCP, and Azure is through a unified SkyPilot interface. It provides best-practice defaults for launching, monitoring, and managing distributed training jobs with cost-aware region selection.

Yes, SkyPilot supports cost-aware cloud and region selection for batch processing. It evaluates pricing across AWS, GCP, Azure, and more to auto-select the cheapest available resources for your ML workloads.

To launch a multi-cloud SkyPilot task, define your ML training job and let SkyPilot auto-select the cheapest clouds across AWS, GCP, or Azure. It handles spot instance orchestration and manages the fault-tolerant training job automatically.