skypilot-multi-cloud-orchestration

# SkyPilot Multi-Cloud Orchestration Comprehensive guide to running ML workloads across clouds with automatic cost optimization using SkyPilot. ## When to use SkyPilot **Use SkyPilot when:** - Running ML workloads across multiple clouds (AWS, GCP, Azure, etc.) - Need cost optimization with automatic cloud/region selection - Running long jobs on spot instances with auto-recovery - Managing distributed multi-node training - Want unified interface for 20+ cloud providers - Need to avoid vendor lock-in **Key features:** - **Multi-cloud**: AWS, GCP, Azure, Kubernetes, Lambda, RunPod, 20+ providers - **Cost optimization**: Automatic cheapest cloud/region selection - **Spot instances**: 3-6x cost savings with automatic recovery - **Distributed training**: Multi-node jobs with gang scheduling - **Managed jobs**: Auto-recovery, checkpointing, fault tolerance - **Sky Serve**: Model serving with autoscaling **Use alternatives instead:** - **Modal**: For simpler serverless GPU with Python-native API - **RunPod**: For single-cloud persistent pods - **Kubernetes**: For existing K8s infrastructure - **Ray**: For pure Ray-based orchestration ## Quick start ### Installation ```bash pip install "skypilot[aws,gcp,azure,kubernetes]" # Verify cloud credentials sky check ``` ### Hello World Create `hello.yaml`: ```yaml resources: accelerators: T4:1 run: | nvidia-smi echo "Hello from SkyPilot!" ``` Launch: ```bash sky launch -c hello hello.yaml # SSH to cluster ssh hello # Term...