skypilot-multi-cloud-orchestration

Multi-cloud orchestration for distributed training is managed by provisioning compute nodes across AWS, GCP, and Azure through a unified interface. This simplifies executing machine learning workloads seamlessly across different providers while optimizing for cost and performance.

GPU computing costs are reduced by automatically selecting the cheapest available cloud provider and leveraging spot instances. This optimization can yield up to 6x cost savings for your compute infrastructure without sacrificing workload execution.

Multi-cloud orchestration supports auto-recovery for preempted spot instances by providing managed jobs with checkpointing and fault tolerance. This ensures long-running tasks like model training automatically resume if instances are interrupted.

You need to install SkyPilot to manage cloud instances, as it is the required dependency for provisioning and managing compute infrastructure. It abstracts away cloud-specific complexities for tasks like model training and batch inference.

The best way to run batch inference across AWS and GCP is using a unified orchestration layer that automatically selects the cheapest resources. This manages diverse cloud resources seamlessly while optimizing for cost and performance.