Agent skill

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.

View SKILL.md on GitHub Repository
Stars 1,415
Forks 109

Install this agent skill to your Project

npx add-skill https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/main/bundled/skills/skypilot-multi-cloud-orchestration

SKILL.md

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
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

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

Spot instances

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

Cluster management

Launch and execute

bash
# 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

yaml
resources:
  accelerators: A100:4
  autostop:
    idle_minutes: 30
    down: true  # Terminate instead of stop
bash
# Set autostop via CLI
sky autostop mycluster -i 30 --down

Cluster status

bash
# All clusters
sky status

# Detailed view
sky status -a

Distributed training

Multi-node setup

yaml
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

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

Managed jobs

Spot recovery

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

Checkpointing

yaml
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

bash
# 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

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

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

Cloud storage

yaml
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

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

# Check status
sky serve status

# Get endpoint
sky serve status my-service

Autoscaling policies

yaml
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

yaml
# SkyPilot finds cheapest option
resources:
  accelerators: A100:8
  # No cloud specified - auto-select cheapest
bash
# Show optimizer decision
sky launch task.yaml --dryrun

Cloud preferences

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

Environment variables

yaml
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

yaml
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

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

Debugging

bash
# 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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

foryourhealth111-pixel/Vibe-Skills

pufferlib

This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.

1,415 109
Explore
foryourhealth111-pixel/Vibe-Skills

fluidsim

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

1,415 109
Explore
foryourhealth111-pixel/Vibe-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

1,415 109
Explore
foryourhealth111-pixel/Vibe-Skills

build-error-resolver

Compatibility alias for build-specific error resolution. Use this when VCO routes to build-error-resolver but the upstream agent is unavailable in the current runtime.

1,415 109
Explore
foryourhealth111-pixel/Vibe-Skills

geniml

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

1,415 109
Explore
foryourhealth111-pixel/Vibe-Skills

zinc-database

Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.

1,415 109
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results