infrastructure-skypilot

Topics in the source

• SkyPilot Multi-Cloud Orchestration
• When to use SkyPilot
• Quick start
• Installation
• Hello World
• Core concepts
• Task YAML structure
• Key commands

Comprehensive guide to running ML workloads across clouds with automatic cost optimization using 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 Create hello.yaml: Launch: | Command | Purpose | |---------|---------| | sky launch | Launch cluster and run task | | sky exec | Run task on existing cluster | | sky status | Show cluster status | | sk

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