skypilot-multi-cloud-orchestration

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.

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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-outputs
    store: s3
    mode: MOUNT_CACHED

Storage modes

Mode	Description	Best For
`MOUNT`	Stream from cloud	Large datasets, read-heavy
`COPY`	Pre-fetch to disk	Small files, random access
`MOUNT_CACHED`	Cache with async upload	Checkpoints, outputs

Sky Serve (Model Serving)

Basic service

# service.yaml
service:
  readiness_probe: /health
  replica_policy:
    min_replicas: 1
    max_replicas: 10
    target_qps_per_replica: 2.0

resources:
  accelerators: A100:1

run: |
  python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --port 8000

# Deploy
sky serve up -n my-service service.yaml

# Check status
sky serve status

# Get endpoint
sky serve status my-service

Autoscaling policies

service:
  replica_policy:
    min_replicas: 1
    max_replicas: 10
    target_qps_per_replica: 2.0
    upscale_delay_seconds: 60
    downscale_delay_seconds: 300
  load_balancing_policy: round_robin

Cost optimization

Automatic cloud selection

# SkyPilot finds cheapest option
resources:
  accelerators: A100:8
  # No cloud specified - auto-select cheapest

# Show optimizer decision
sky launch task.yaml --dryrun

Cloud preferences

resources:
  accelerators: A100:8
  any_of:
    - cloud: gcp
      region: us-central1
    - cloud: aws
      region: us-east-1
    - cloud: azure

Environment variables

envs:
  HF_TOKEN: $HF_TOKEN  # Inherited from local env
  WANDB_API_KEY: $WANDB_API_KEY

# Or use secrets
secrets:
  - HF_TOKEN
  - WANDB_API_KEY

Common workflows

Workflow 1: Fine-tuning with checkpoints

name: llm-finetune

file_mounts:
  /checkpoints:
    name: finetune-checkpoints
    store: s3
    mode: MOUNT_CACHED

resources:
  accelerators: A100:8
  use_spot: true

setup: |
  pip install transformers accelerate

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

Workflow 2: Hyperparameter sweep

name: hp-sweep-${RUN_ID}

envs:
  RUN_ID: 0
  LEARNING_RATE: 1e-4
  BATCH_SIZE: 32

resources:
  accelerators: A100:1
  use_spot: true

run: |
  python train.py \
    --lr $LEARNING_RATE \
    --batch-size $BATCH_SIZE \
    --run-id $RUN_ID

# Launch multiple jobs
for i in {1..10}; do
  sky jobs launch sweep.yaml \
    --env RUN_ID=$i \
    --env LEARNING_RATE=$(python -c "import random; print(10**random.uniform(-5,-3))")
done

Debugging

# SSH to cluster
ssh mycluster

# View logs
sky logs mycluster

# Check job queue
sky queue mycluster

# View managed job logs
sky jobs logs my-job

Common issues

Issue	Solution
Quota exceeded	Request quota increase, try different region
Spot preemption	Use `sky jobs launch` for auto-recovery
Slow file sync	Use `MOUNT_CACHED` mode for outputs
GPU not available	Use `any_of` for fallback clouds

References

Advanced Usage - Multi-cloud, optimization, production patterns
Troubleshooting - Common issues and solutions

Resources

Documentation: https://docs.skypilot.co
GitHub: https://github.com/skypilot-org/skypilot
Slack: https://slack.skypilot.co
Examples: https://github.com/skypilot-org/skypilot/tree/master/examples

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/skypilot-multi-cloud-orchestration/SKILL.md
Commit: f627ab5

Last updated: about 13 hours ago
First committed: 5 months ago

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Orchestra-Research/AI-Research-SKILLs

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since Jul 24, 2026

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