skypilot-multi-cloud-orchestration

Multi-cloud ML orchestration optimizes cloud costs by automatically selecting the cheapest provider and region for your workloads. It leverages spot instances with auto-recovery to maximize savings without interrupting distributed training jobs.

Yes, spot instances can be used for distributed training with automatic checkpointing and auto-recovery. This ensures robust job management and fault tolerance, allowing multi-node training jobs to resume seamlessly even if spot capacity is reclaimed.

Deploying machine learning models with autoscaling is handled using Sky Serve within a multi-cloud orchestration setup. This approach manages resource allocation across providers and implements fault tolerance to ensure efficient model serving.

Yes, SkyPilot supports gang scheduling to manage multi-node distributed training jobs effectively. This ensures all required nodes are allocated simultaneously across your chosen cloud providers, maintaining robust execution and fault tolerance.

Kubernetes is supported but not required, as the orchestration works across AWS, GCP, Azure, Lambda, RunPod, and other providers directly. You only need SkyPilot installed to manage the environments and leverage cloud provider APIs for resource allocation.