skypilot-multi-cloud-orchestration

Multi-cloud ML orchestration automatically routes distributed training workloads across AWS, GCP, and Azure to find the cheapest available compute. It manages cross-cloud execution automatically while providing spot-instance resilience and fault tolerance for multi-node jobs.

Spot instance resilience during multi-node training is handled through automatic checkpointing and spot-recovery mechanisms. The orchestration layer automatically recovers interrupted workloads across 20+ cloud providers, ensuring distributed experiments continue without losing progress.

SkyPilot supports cost-aware batch job execution across Kubernetes, RunPod, Lambda, and major cloud providers. It automatically selects the cheapest cloud and region for your workloads, enabling scalable ML experiments across over 20 different compute platforms.

Automatic cost optimization for cross-region ML experiments is achieved by continuously evaluating and selecting the cheapest cloud and region combinations. This applies to training, batch jobs, and distributed experiments, maximizing infrastructure savings across cloud boundaries.

To launch a multi-cloud ML task, install SkyPilot (version 0.7.0 or higher) and define your workload in a task.yaml file. The orchestration tool then reads this configuration to provision resources and execute the job across your chosen providers.

Multi-cloud orchestration avoids single-provider lock-in and automatically fetches the cheapest compute resources across AWS, GCP, Azure, and other platforms. It maximizes cost savings and provides spot-recovery fault tolerance that is difficult to coordinate manually across isolated cloud environments.