skypilot

SkyPilot Skill

SkyPilot is a unified framework to run AI workloads on any cloud, Slurm or Kubernetes. It provides a single interface to launch clusters, run jobs, and serve models across 25+ clouds (AWS, GCP, Azure, Coreweave, Nebius, Lambda, Together AI, RunPod, and more), Kubernetes clusters, and Slurm clusters.

When to Use SkyPilot

Use SkyPilot when you need to:

• Manage compute resources on any cloud, Slurm, or Kubernetes cluster
• Launch CPU/GPU/TPU (GB300, GB200, B200, H200, H100, etc.) on any cloud, Kubernetes or Slurm
• Run training, fine-tuning, or batch inference jobs
• Serve models with autoscaling and multi-cloud replicas (SkyServe)
• Run long-running jobs with automatic lifecycle management and recovery (managed jobs)
• Find the cheapest or most available GPU across clouds

Don't use SkyPilot for:

• Local-only workloads (use Docker/conda directly)

Capabilities: When to Use What

SkyPilot has three core abstractions. Use the right one for each stage of your workflow:

1. SkyPilot Clusters (sky launch / sky exec) — Interactive development and debugging

• Use during initial development, debugging, and experimentation
• Launch a cluster, SSH in or connect VSCode/Cursor (code --remote ssh-remote+CLUSTER), iterate quickly
• Cluster stays up until you stop/down it or autostop triggers
• Best for: prototyping, debugging, short experiments

2. Managed Jobs (sky jobs launch) — Long-running training and batch jobs

• Use when submitting long-running jobs that should run unattended
• Manages the full lifecycle: provisioning, execution, recovery, and teardown
• Automatically recovers from spot preemptions, quota limits, and transient failures
• Works across clouds, Kubernetes, and Slurm (handles preemptions and quota)
• Best for: training runs, fine-tuning, hyperparameter sweeps, batch inference

3. SkyServe (sky serve up) — Production model serving

• Use when serving models at scale with autoscaling
• Start with sky launch + open port to test your serving setup, then use sky serve up to scale
• Provides load balancing, autoscaling, and multi-cloud replicas
• Best for: model serving endpoints, API services

Before You Start (Agent Bootstrap)

Bootstrap to confirm SkyPilot is installed, connected to an API server, and has cloud credentials. Once confirmed, skip straight to the user's task.

Step 1: Check installation and API server connectivity

sky api info

Output contains	Meaning	Next action
Server version and status	Server is running and connected	Bootstrap done. Skip to user's task.
No SkyPilot API server is connected	No server connected	Go to "Start or connect a server" below.
Could not connect to SkyPilot API server	Remote server unreachable or auth expired	Tell the user and suggest sky api login --relogin -e <endpoint> to reconnect.
command not found: sky	SkyPilot not installed	Go to "Install SkyPilot" below.

Install SkyPilot (only if sky command not found):

pip install "skypilot[aws,gcp,kubernetes]"  # Pick clouds the user needs

Ask the user which clouds they need if unclear, then re-run sky api info.

Start or connect a server (only if "not running"):

Ask the user:

Do you have an existing SkyPilot API server to connect to, or should I start one locally?

• Connect to existing server: sky api login -e <API_SERVER_URL> — get the URL from the user.
• Start locally: sky api start

After either path, re-run sky api info to confirm the server is reachable.

Step 2: Check cloud credentials (only for fresh setups — skip if the server was already running)

sky check -o json

This shows which clouds are enabled or disabled. If the user's target cloud is not enabled, guide them through credential setup (see Troubleshooting).

Essential Commands

Use -o json with status/query commands to get structured JSON output instead of tables.

Clusters — interactive development and debugging:

Command	Description
sky launch -c NAME task.yaml	Launch a cluster or run a task
sky exec NAME task.yaml	Run task on existing cluster (skips provisioning)
sky status -o json	Show all clusters
sky logs NAME	Stream job logs from a cluster
sky stop NAME / sky start NAME	Stop/restart to save costs (preserves disk)
sky down NAME	Tear down a cluster completely
sky gpus list -o json	List available GPU types across clouds

Managed Jobs — long-running unattended workloads:

Command	Description
sky jobs launch task.yaml	Launch a managed job (auto lifecycle + recovery)
sky jobs queue -o json	Show all managed jobs and their status
sky jobs logs JOB_ID	Stream logs from a managed job
sky jobs cancel JOB_ID	Cancel a managed job

SkyServe — model serving with autoscaling:

Command	Description
sky serve up serve.yaml -n NAME	Start a model serving service
sky serve status NAME	Show service status and endpoint URL
sky serve update NAME new.yaml	Update a running service (rolling)
sky serve down NAME	Tear down a service

For complete CLI reference, see CLI Reference.

Quick Start

# Launch a GPU cluster
sky launch -c mycluster --gpus H100 -- nvidia-smi

# Run a task from YAML
sky launch -c mycluster task.yaml

# SSH into cluster
ssh mycluster

# Connect VSCode or Cursor to the cluster for interactive development
code --remote ssh-remote+mycluster /home/user/sky_workdir
# or: cursor --remote ssh-remote+mycluster /home/user/sky_workdir

# Tear down
sky down mycluster

Task YAML Structure

The task YAML is SkyPilot's primary interface. All fields are optional.

# task.yaml
name: my-training-job

# Local directory to sync to remote ~/sky_workdir
workdir: .

# Number of nodes (for distributed training)
num_nodes: 1

resources:
  # GPU/TPU accelerators (SkyPilot auto-selects the cheapest cloud/region)
  accelerators: H200:8
  # Optional: pin to a specific cloud/region/infra
  # infra: aws  # or aws/us-east-1, k8s, ssh/my-pool
  # If infra is left out, SkyPilot automatically fails over across all
  # enabled clouds/regions to find the cheapest available option.
  # Use spot instances for cost savings
  use_spot: false
  # Disk size in GB
  disk_size: 256
  # Open ports for serving
  ports: 8080

# Environment variables (accessible in file_mounts, setup, and run)
envs:
  MODEL_NAME: my-model
  BATCH_SIZE: 32

# Setup: runs once on cluster creation, cached on reuse
setup: |
  pip install torch transformers

# Run: the main command
run: |
  python train.py --model $MODEL_NAME --batch-size $BATCH_SIZE

For complete YAML schema including file mounts, environment variables set by SkyPilot, and advanced fields, see YAML Specification.

GPU and Cloud Selection

IMPORTANT: Let SkyPilot choose the cloud and region. Do NOT manually pick a cloud/region/instance by parsing sky gpus list output. SkyPilot's optimizer automatically selects the cheapest available option across all enabled clouds. Only specify infra: when the user explicitly requests a specific cloud or region.

Default behavior (recommended): Just specify the GPU type. SkyPilot finds the cheapest cloud/region automatically:

resources:
  accelerators: H200:8  # SkyPilot picks the cheapest cloud/region with H200:8

If the user doesn't specify a GPU type, ask them what GPU they need (or what model/workload they're running so you can recommend one). Do NOT run sky gpus list and pick for them — present options and let the user decide, or use any_of to let SkyPilot maximize availability:

# Let SkyPilot choose from multiple acceptable GPU types (cheapest wins)
resources:
  any_of:
    - accelerators: H100:8
    - accelerators: A100-80GB:8
    - accelerators: A100:8

Use ordered only when the user has a strict preference:

# Try H100 first on AWS, fall back to GCP, then A100
resources:
  ordered:
    - infra: aws/us-east-1
      accelerators: H100:8
    - infra: gcp/us-central1
      accelerators: H100:8
    - infra: aws/us-west-2
      accelerators: A100-80GB:8

Only set infra: when the user explicitly says something like "use AWS" or "run on GCP us-central1":

resources:
  infra: aws             # User asked for AWS specifically
  accelerators: H100:8

Cluster Lifecycle

# Launch and run a task
sky launch -c mycluster task.yaml

# Launch with autostop at launch time (preferred: saves cost, no follow-up command needed)
sky launch -c mycluster task.yaml -i 30        # stop after 30 min idle
sky launch -c mycluster task.yaml -i 30 --down # tear down after 30 min idle

# Override or pass environment variables via CLI
sky launch -c mycluster task.yaml --env MODEL_NAME=llama3 --env BATCH_SIZE=64

# Re-run a different task on the same cluster (fast, skips provisioning)
sky exec mycluster another_task.yaml

# Run an inline command
sky exec mycluster -- python train.py --epochs 10

# Set autostop after launch (use if you forgot to set -i at launch time)
sky autostop mycluster -i 30        # stop after 30 min idle, preserving disk (can restart with sky start)
sky autostop mycluster -i 30 --down # tear down after 30 min idle (disk is deleted, cannot restart)

# Stop to save costs, restart later
sky stop mycluster
sky start mycluster

# Tear down completely
sky down mycluster

Managed Jobs

Use sky jobs launch for long-running jobs that should run unattended. SkyPilot manages the full lifecycle — provisioning, execution, recovery from preemptions/quota/failures, and teardown:

# managed-job.yaml
name: training-job

resources:
  accelerators: A100:8

run: |
  python train.py --resume-from-checkpoint

# Launch as managed job
sky jobs launch managed-job.yaml

# Check status
sky jobs queue -o json

# Stream logs
sky jobs logs <job_id>

# Cancel
sky jobs cancel <job_id>

Checkpoint pattern: Your training script should save checkpoints to persistent storage (cloud bucket or volume) and resume from the latest checkpoint on restart. SkyPilot handles the cluster recovery; your script handles the state recovery.

SkyServe: Model Serving

# serve.yaml
resources:
  accelerators: A100:1
  ports: 8080

run: |
  python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --port 8080

service:
  readiness_probe: /v1/models
  replica_policy:
    min_replicas: 1
    max_replicas: 3
    target_qps_per_replica: 5

# Start service
sky serve up serve.yaml -n my-llm

# Check status / get endpoint
sky serve status my-llm
sky serve status my-llm --endpoint

# Update (rolling)
sky serve update my-llm new-serve.yaml

# Tear down
sky serve down my-llm

Common Workflows

Fine-Tuning Workflow

1. Write task YAML with setup (install deps) and run (training command)
2. Use file_mounts or workdir to sync code
3. sky launch -c train task.yaml to launch
4. sky logs train to monitor
5. sky exec train -- python eval.py to evaluate on same cluster
6. sky down train when done

Hyperparameter Sweep

1. Create parameterized YAML with envs
2. Launch multiple managed jobs:

   for lr in 1e-4 1e-5 1e-6; do
     sky jobs launch sweep.yaml --env LR=$lr --name sweep-lr-$lr
   done
   

3. Monitor with sky jobs queue -o json

Model Serving Deployment

1. Write serve YAML with service: section
2. sky serve up serve.yaml -n my-service
3. Get endpoint: sky serve status my-service --endpoint
4. Update model: sky serve update my-service updated.yaml

Agent Feedback Loop

When using SkyPilot programmatically, follow this loop:

1. Validate: sky launch --dryrun task.yaml (check resource availability/cost)
2. Launch: sky launch -c mycluster task.yaml
3. Monitor: sky status -o json and sky queue mycluster -o json
4. Debug: sky logs mycluster (stream logs) or ssh mycluster (interactive)
5. Iterate: sky exec mycluster updated_task.yaml (run on existing cluster)
6. Cleanup: sky down mycluster

Common Agent Mistakes

Mistake	Why it's wrong	Do this instead
Manually picking cloud/region from sky gpus list output	SkyPilot optimizer does this automatically and better	Just set accelerators: and let SkyPilot choose
Using sky launch for long-running unattended jobs	No recovery if preempted or interrupted	Use sky jobs launch for unattended work
Forgetting sky down or autostop after work is done	Wastes money on idle clusters	Always clean up, or use -i <minutes> --down at launch
Hardcoding infra: aws without user asking	Limits availability and increases cost	Only set infra: when user explicitly requests a cloud
Not using envs: for configurable values	Hard to reuse or override from CLI	Use envs: in YAML + --env KEY=VAL for parameterization
Running sky launch without -c <name>	Creates randomly-named cluster, hard to reference	Always name clusters with -c
Parsing table output from status commands	Table formatting is for humans, fragile to parse	Use -o json for structured output
Using deprecated cloud:/region:/zone: fields	Deprecated in favor of infra:	Use infra: aws/us-east-1 instead

Common Issues Quick Reference

Issue	Solution
GPU not available	Use any_of for fallback, or try different regions/clouds
Setup takes too long	SkyPilot caches setup; use sky exec to skip it on reruns
Task fails silently	Check sky logs <cluster> or ssh <cluster> to debug
Cluster stuck in INIT	sky down <cluster> and relaunch
Preemption/quota	Use sky jobs launch for automatic recovery and lifecycle management
Port not accessible	Ensure ports: is set in resources and security groups allow traffic
File sync slow	Use cloud bucket mounts instead of workdir for large datasets
Credentials error	Run sky check -o json and inspect which clouds are disabled

References

For detailed reference documentation:

• CLI Reference — All commands and flags
• YAML Specification — Complete task YAML schema, file mounts, environment variables
• Python SDK — Programmatic API and SDK usage
• Advanced Patterns — Multi-cloud, distributed training, production patterns
• Troubleshooting — Error diagnosis and solutions
• Examples — Copy-paste task YAML examples