Agent skill

gpu-cli

Run ML training, LLM inference, and ComfyUI workflows on remote NVIDIA GPUs (A100, H100, RTX 4090). Cloud GPU compute with smart file sync — prefix any command with 'gpu' to run it remotely.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/gpu-cli/gpu/tree/main/skills/gpu

Metadata

Additional technical details for this skill

openclaw 
{
    "tags": [
        "gpu",
        "machine-learning",
        "cloud-compute",
        "remote-execution",
        "llm",
        "comfyui",
        "nvidia",
        "cuda",
        "training",
        "inference",
        "runpod",
        "pytorch",
        "stable-diffusion"
    ],
    "install": {
        "brew": "gpu-cli/tap/gpu",
        "script": "curl -fsSL https://gpu-cli.sh/install | sh"
    },
    "homepage": "https://gpu-cli.sh",
    "requires": {
        "bins": [
            "gpu"
        ]
    }
}

SKILL.md

GPU CLI

GPU CLI runs local commands on remote NVIDIA GPUs by prefixing with gpu. It provisions a pod, syncs your code, streams logs, and syncs outputs back: uv run python train.py becomes gpu run uv run python train.py.

Quick diagnostics

bash
gpu doctor --json       # Check if setup is healthy (daemon, auth, provider keys)
gpu status --json       # See running pods and costs
gpu inventory --json    # See available GPUs and pricing

Command families

Getting started

Command Purpose
gpu login Browser-based authentication
gpu logout [-y] Remove session
gpu init [--gpu-type T] [--force] Initialize project config
gpu upgrade Open subscription upgrade page

Running code

Command Purpose
gpu run <command> Execute on remote GPU (main command)
gpu run -d <command> Run detached (background)
gpu run -a <job_id> Reattach to running job
gpu run --cancel <job_id> Cancel a running job
gpu status [--json] Show project status, pods, costs
gpu logs [-j JOB] [-f] [--tail N] [--json] View job output
gpu attach <job_id> Reattach to job output stream
gpu stop [POD_ID] [-y] Stop active pod

Key gpu run flags: --gpu-type, --gpu-count <1-8>, --min-vram, --rebuild, -o/--output, --no-output, --sync, -p/--publish <PORT>, -e <KEY=VALUE>, -i/--interactive.

GPU inventory

Command Purpose
gpu inventory [--available] [--min-vram N] [--max-price P] [--json] List GPUs with pricing

Volumes

Command Purpose
gpu volume list [--detailed] [--json] List network volumes
gpu volume create [--name N] [--size GB] [--datacenter DC] Create volume
gpu volume delete <VOL> [--force] Delete volume
gpu volume extend <VOL> --size <GB> Increase size
gpu volume set-global <VOL> Set default volume
gpu volume status [--volume V] [--json] Volume usage
gpu volume migrate <VOL> --to <DC> Migrate to datacenter
gpu volume sync <SRC> <DEST> Sync between volumes

Vault (encrypted storage)

Command Purpose
gpu vault list [--json] List encrypted output files
gpu vault export <PATH> <DEST> Export decrypted file
gpu vault stats [--json] Storage usage stats

Configuration

Command Purpose
gpu config show [--json] Show merged config
gpu config validate Validate against schema
gpu config schema Print JSON schema
gpu config set <KEY> <VALUE> Set global config option
gpu config get <KEY> Get global config value

Authentication

Command Purpose
gpu auth login [--profile P] Authenticate with cloud provider
gpu auth logout Remove credentials
gpu auth status Show auth status
gpu auth add <HUB> Add hub credentials (hf, civitai)
gpu auth remove <HUB> Remove hub credentials
gpu auth hubs List configured hubs

Organizations

Command Purpose
gpu org list List organizations
gpu org create <NAME> Create organization
gpu org switch [SLUG] Set active org context
gpu org invite <EMAIL> Invite member
gpu org service-account create --name N Create service token
gpu org service-account list List service accounts
gpu org service-account revoke <ID> Revoke token

LLM inference

Command Purpose
gpu llm run [--ollama|--vllm] [--model M] [-y] Launch LLM inference
gpu llm info [MODEL] [--url URL] [--json] Show model info

ComfyUI workflows

Command Purpose
gpu comfyui list [--json] Browse available workflows
gpu comfyui info <WORKFLOW> [--json] Show workflow details
gpu comfyui validate <WORKFLOW> [--json] Pre-flight checks
gpu comfyui run <WORKFLOW> Run workflow on GPU
gpu comfyui generate "<PROMPT>" Text-to-image generation
gpu comfyui stop [WORKFLOW] [--all] Stop ComfyUI pod

Notebooks

Command Purpose
gpu notebook [FILE] [--run] [--new NAME] Run Marimo notebook on GPU

Alias: gpu nb

Serverless endpoints

Command Purpose
gpu serverless deploy [--template T] [--json] Deploy endpoint
gpu serverless status [ENDPOINT] [--json] Endpoint status
gpu serverless logs [ENDPOINT] View request logs
gpu serverless list [--json] List all endpoints
gpu serverless delete [ENDPOINT] Delete endpoint
gpu serverless warm [--cpu|--gpu] Pre-warm endpoint

Templates

Command Purpose
gpu template list [--json] Browse official templates
gpu template clear-cache Clear cached templates

Daemon control

Command Purpose
gpu daemon status [--json] Show daemon health
gpu daemon start Start daemon
gpu daemon stop Stop daemon
gpu daemon restart Restart daemon
gpu daemon logs [-f] [-n N] View daemon logs

Tools and utilities

Command Purpose
gpu dashboard Interactive TUI for pods and jobs
gpu doctor [--json] Diagnostic checks
gpu agent-docs Print agent reference to stdout
gpu update [--check] Update CLI
gpu changelog [VERSION] View release notes
gpu issue ["desc"] Report issue
gpu desktop Desktop app management
gpu support Open community Discord

Common workflows

  1. Setup: gpu login then gpu init
  2. Run job: gpu run python train.py --epochs 10
  3. With specific GPU: gpu run --gpu-type "RTX 4090" python train.py
  4. Detached job: gpu run -d python long_training.py then gpu status --json
  5. Check status: gpu status --json
  6. View logs: gpu logs --json
  7. Stop pods: gpu stop -y
  8. LLM inference: gpu llm run --ollama --model llama3 -y
  9. ComfyUI: gpu comfyui run flux_schnell
  10. Diagnose issues: gpu doctor --json

gpu run is pod-reuse oriented: after a command completes, the next gpu run reuses the existing pod until you gpu stop or cooldown ends.

JSON output

Most commands support --json for machine-readable output. Structured data goes to stdout; human-oriented status and progress messages go to stderr.

Commands with --json: status, logs, doctor, inventory, config show, daemon status, volume list, volume status, vault list, vault stats, comfyui list, comfyui info, comfyui validate, serverless deploy, serverless status, serverless list, template list, llm info.

Exit codes

Code Meaning Recovery
0 Success Proceed
1 General error Read stderr
2 Usage error Fix command syntax
10 Auth required gpu auth login
11 Quota exceeded gpu upgrade or wait
12 Not found Check resource ID
13 Daemon unavailable gpu daemon start, retry
14 Timeout Retry
15 Cancelled Re-run if needed
130 Interrupted Re-run if needed

Configuration

  • Project config: gpu.toml, gpu.jsonc, or pyproject.toml [tool.gpu]
  • Global config: ~/.gpu-cli/config.toml (via gpu config set/get)
  • Sync model: .gitignore controls upload; outputs patterns control download
  • Secrets and credentials must stay in the OS keychain, never plaintext project files
  • CI env vars: GPU_RUNPOD_API_KEY, GPU_SSH_PRIVATE_KEY, GPU_SSH_PUBLIC_KEY

References

  • Project generation and task setup: references/create.md
  • Debugging and common failures: references/debug.md
  • Config schema and field examples: references/config.md
  • Cost and GPU selection guidance: references/optimize.md
  • Persistent storage and volumes: references/volumes.md

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