skypilot-multi-cloud-orchestration

Files (3)

• SKILL.md
• advanced-usage.md · references/
• troubleshooting.md · references/

SKILL.md (first lines)

# 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)

# Number of nodes for distributed training
num_nodes: 2

# Working directory (synced to ~/sky_workdir)
workdir: .

# Setup commands (run once)
setup: |
  pip install -r requirements.txt

# Run commands
run: |
  python train.py
```

### Key commands

| Command | Purpose |
|---------|---------|
| `sky launch` | Launch cluster and run task |
| `sky exec` | Run task on existing cluster |
| `sky status` | Show cluster status |
| `sky stop` | Stop cluster (preserve state) |
| `sky down` | Terminate cluster |
| `sky logs` | View task logs |
| `sky queue` | Show job queue |
| `sky jobs launch` | Launch managed job |
| `sky serve up` | Deploy serving endpoint |

## GPU configuration

### Available accelerators

```yaml
# NVIDIA GPUs
accelerators: T4:1
accelerators: L4:1
accelerators: A10G:1
accelerators: L40S:1

From OpenRaiser/NanoResearch, MIT.

More from OpenRaiser/NanoResearch