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

pip install "skypilot[aws,gcp,azure,kubernetes]"

# Verify cloud credentials
sky check

Hello World

Create hello.yaml:

resources:
  accelerators: T4:1

run: |
  nvidia-smi
  echo "Hello from SkyPilot!"

Launch:

sky launch -c hello hello.yaml

# SSH to cluster
ssh hello

# Terminate
sky down hello

Core concepts

Task YAML structure

# 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

# NVIDIA GPUs
accelerators: T4:1
accelerators: L4:1
accelerators: A10G:1
accelerators: L40S:1
accelerators: A100:4
accelerators: A100-80GB:8
accelerators: H100:8

# Cloud-specific
accelerators: V100:4         # AWS/GCP
accelerators: TPU-v4-8       # GCP TPUs

GPU fallbacks

resources:
  accelerators:
    H100: 8
    A100-80GB: 8
    A100: 8
  any_of:
    - cloud: gcp
    - cloud: aws
    - cloud: azure

Spot instances

resources:
  accelerators: A100:8
  use_spot: true
  spot_recovery: FAILOVER  # Auto-recover on preemption

Cluster management

Launch and execute

# Launch new cluster
sky launch -c mycluster task.yaml

# Run on existing cluster (skip setup)
sky exec mycluster another_task.yaml

# Interactive SSH
ssh mycluster

# Stream logs
sky logs mycluster

Autostop

resources:
  accelerators: A100:4
  autostop:
    idle_minutes: 30
    down: true  # Terminate instead of stop

# Set autostop via CLI
sky autostop mycluster -i 30 --down

Cluster status

# All clusters
sky status

# Detailed view
sky status -a

Distributed training

Multi-node setup

resources:
  accelerators: A100:8

num_nodes: 4  # 4 nodes × 8 GPUs = 32 GPUs total

setup: |
  pip install torch torchvision

run: |
  torchrun \
    --nnodes=$SKYPILOT_NUM_NODES \
    --nproc_per_node=$SKYPILOT_NUM_GPUS_PER_NODE \
    --node_rank=$SKYPILOT_NODE_RANK \
    --master_addr=$(echo "$SKYPILOT_NODE_IPS" | head -n1) \
    --master_port=12355 \
    train.py

Environment variables

Variable	Description
SKYPILOT_NODE_RANK	Node index (0 to num_nodes-1)
SKYPILOT_NODE_IPS	Newline-separated IP addresses
SKYPILOT_NUM_NODES	Total number of nodes
SKYPILOT_NUM_GPUS_PER_NODE	GPUs per node

Head-node-only execution

run: |
  if [ "${SKYPILOT_NODE_RANK}" == "0" ]; then
    python orchestrate.py
  fi

Managed jobs

Spot recovery

# Launch managed job with spot recovery
sky jobs launch -n my-job train.yaml

Checkpointing

name: training-job

file_mounts:
  /checkpoints:
    name: my-checkpoints
    store: s3
    mode: MOUNT

resources:
  accelerators: A100:8
  use_spot: true

run: |
  python train.py \
    --checkpoint-dir /checkpoints \
    --resume-from-latest

Job management

# List jobs
sky jobs queue

# View logs
sky jobs logs my-job

# Cancel job
sky jobs cancel my-job

File mounts and storage

Local file sync

workdir: ./my-project  # Synced to ~/sky_workdir

file_mounts:
  /data/config.yaml: ./config.yaml
  ~/.vimrc: ~/.vimrc

Cloud storage

file_mounts:
  # Mount S3 bucket
  /datasets:
    source: s3://my-bucket/datasets
    mode: MOUNT  # Stream from S3

  # Copy GCS bucket
  /models:
    source: gs://my-bucket/models
    mode: COPY  # Pre-fetch to disk

  # Cached mount (fast writes)
  /outputs:
    name: my-