skypilot-multi-cloud-orchestration

Multi-cloud GPU orchestration runs distributed training across AWS, GCP, Azure, and Kubernetes through a unified interface. You provide a SkyPilot YAML declaring resources and run commands, and it schedules multi-node jobs without requiring separate infrastructure code for each cloud.

Spot instances support automatic recovery for ML training preemptions via checkpoint resumption. The orchestrator enables spot execution with auto-recovery, allowing long-running workloads to tolerate preemptions and automatically resume from saved checkpoints.

Cost optimization for GPU compute is achieved by automatically selecting the cheapest available cloud and region options. The tool evaluates resource constraints and fallbacks declared in your task YAML to choose the most cost-effective provider for fine-tuning workloads.

SkyPilot supports multi-node managed jobs with built-in checkpoint and failover patterns. It handles distributed training execution, manages recovery behavior for spot instances, and optionally deploys scalable model serving via Sky Serve.

A SkyPilot entry task configuration is required to launch batch workloads. You must provide a task YAML that declares accelerators, optional cloud and region constraints or fallbacks, spot and recovery options, plus run and setup commands consistent with SkyPilot semantics.

Multi-cloud orchestration is not suited for workloads lacking checkpoint mechanisms if running on spot instances, as preemption recovery depends on resuming from saved states. Additionally, single-cloud jobs with strict data residency requirements may not benefit from cross-provider fallbacks.