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