skypilot-multi-cloud-orchestration

Multi-cloud orchestration runs machine learning workloads across AWS, GCP, Azure, and Kubernetes, automatically selecting the cheapest cloud and instance types. It leverages spot instances to reduce infrastructure costs by up to 6x while ensuring reliable execution.

Provisioning 32 A100 GPUs for distributed training is handled by finding the most cost-effective resources across AWS and GCP. It manages multi-node setups and ensures training jobs run reliably even if provisioned spot instances are preempted.

Multi-cloud orchestration supports deploying and scaling ML models through Sky Serve. It provides flexible resource provisioning across AWS, GCP, Azure, and Kubernetes to manage both distributed training and model serving workloads efficiently.

Spot instances are supported for distributed training with automatic cloud selection to handle preemptions. This approach optimizes for cost and performance, ensuring your machine learning training jobs run reliably across multiple cloud providers even if instances are interrupted.

Before orchestrating ML jobs, you need to install SkyPilot and verify your cloud credentials. Run `pip install skypilot[aws,gcp,azure,kubernetes]` followed by the `sky check` command to ensure your environment is ready for multi-cloud workload execution.