research-skypilot

# 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 - **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