# Skypilot Multi Cloud Orchestration

Multi-cloud orchestration for ML workloads with automatic cost optimization.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill skypilot-multi-cloud-orchestration -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill skypilot-multi-cloud-orchestration -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill skypilot-multi-cloud-orchestration -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill skypilot-multi-cloud-orchestration -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

# Terminate
sky down hello
```

## Core concepts

### Task YAML structure

```yaml
# Task name (optional)
name: my-task

# Resource requirements
resources:
  cloud: aws              # Optional: auto-select if omitted
  region: us-west-2       # Optional: auto-select if omitted
  accelerators: A100:4    # GPU type and count
  cpus: 8+                # Minimum CPUs
  memory: 32+             # Minimum memory (GB)
  use_spot: true          # Use spot instances
  disk_size: 256          # Disk size (GB)

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/09-infrastructure/skypilot/SKILL.md