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 .

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "gpu-cli" with this command: npx skills add gpu-cli/gpu/gpu-cli-gpu-gpu-cli

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

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

  • Setup: gpu login then gpu init

  • Run job: gpu run python train.py --epochs 10

  • With specific GPU: gpu run --gpu-type "RTX 4090" python train.py

  • Detached job: gpu run -d python long_training.py then gpu status --json

  • Check status: gpu status --json

  • View logs: gpu logs --json

  • Stop pods: gpu stop -y

  • LLM inference: gpu llm run --ollama --model llama3 -y

  • ComfyUI: gpu comfyui run flux_schnell

  • 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

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Open in GitHubOpen in ClawHub

Related Skills

Related by shared tags or category signals.

Coding

gpu

No summary provided by upstream source.

Repository SourceNeeds Review
6-gpu-cli
Coding

Vast Ai

Provision and manage on-demand GPUs on VAST.ai, including search by GPU and price, renting containers, retrieving SSH, and checking account balance.

Registry SourceRecently Updated
1.1K1Profile unavailable
Coding

research-spike

No summary provided by upstream source.

Repository SourceNeeds Review
4-gpu-cli
Coding

tmux-cli-test

No summary provided by upstream source.

Repository SourceNeeds Review
4-gpu-cli