skypilot-multi-cloud-orchestration

You can run distributed training across multiple cloud providers by orchestrating ML jobs with SkyPilot, which uses a unified interface to automatically select the cheapest cloud and region options for your GPU workloads.

To handle spot instance preemptions for long-running GPU workloads, you can use SkyPilot managed jobs with checkpoint-aware recovery patterns that automatically resume training from saved checkpoints if instances get preempted.

Yes, SkyPilot supports launching multi-node distributed training setups like torchrun across AWS, GCP, and Azure through a single task YAML configuration that coordinates resources and applies multi-cloud fallback logic.

Before launching a cost-optimized batch job, you need to install SkyPilot for your target clouds and write a SkyPilot task YAML specifying your required resources, distributed settings, and spot recovery logic.

SkyPilot supports production-style batch and serving operations by applying managed job patterns with checkpointing and auto-recovery, ensuring reliability during multi-cloud orchestration and automatic failover across regions.

Multi-cloud orchestration helps you avoid wasted GPU spend and vendor lock-in by automatically selecting the cheapest available cloud and region, while coordinating multi-node setups and handling spot interruptions for long-running workloads.