Skypilot Multi Cloud Orchestration

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.

⬇ حمّل السكِل (ZIP)المصدر على GitHub ↗

كيف تركّبه؟

١حمّل ملف ZIP من الزر فوق.
٢فكّ الضغط وحطّ المجلد داخل .claude/skills/ في مشروعك (أو ~/.claude/skills/ لو تبيه في كل مشاريعك).
٣شغّل Claude Code من جديد — بيلقط السكِل تلقائياً ويستدعيه وقت ما تحتاجه المهمة.

محتوى 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](references/advanced-usage.md) - Multi-cloud, optimization, production patterns
[Troubleshooting](references/troubleshooting.md) - 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

سكِلات في نفس المجال

Agent Evaluation

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and produc…

Agent Management

Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agen…

Agent Manager Skill

Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.…

Agent Memory Mcp

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, …

Agent Memory Systems

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the a…

Agent Messaging

Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when …

تبي سكِل مفصّل على شغلك أنت؟

نبني لك سكِلات ووكلاء ذكاء اصطناعي يفهمون نظامك ويشتغلون عليه.

كلّمنا