packet-gpu-cli

packet-gpu-cli

Command-line interface for packet.ai GPU cloud.

Installation

npm install -g packet-gpu-cli

Quick Start

# Login to your packet.ai account
packet login

# List available GPU types
packet gpus

# Launch a GPU with VS Code pre-installed
packet launch --gpu rtx-pro-6000 --setup vscode

# Launch a bare GPU and wait for SSH
packet launch --gpu h100 --wait

# List your running instances
packet ps

# SSH into an instance
packet ssh <instance-id>

# View instance logs
packet logs <instance-id>

# Terminate an instance
packet terminate <instance-id>

Commands

Authentication

Command	Description
packet login	Authenticate with your API key
packet logout	Remove stored credentials
packet whoami	Show current account and balance

GPU Management

Command	Description
packet gpus	List available GPU types and pricing
packet launch --gpu <type>	Launch a new GPU instance
packet ps	List your running instances
packet ssh <id>	SSH into an instance
packet logs <id>	View instance status and info
packet terminate <id>	Terminate an instance

Auto-Setup

Command	Description
packet setup list	List available setup presets
packet setup <preset> <id>	Run a setup preset on an existing instance
packet launch --setup <preset>	Launch with auto-setup

packet launch

Launch a new GPU instance.

Options:

• -g, --gpu <type> - GPU type (e.g., rtx-pro-6000, h100)
• -n, --name <name> - Instance name
• -s, --setup <preset> - Auto-setup preset (see below)
• --gpus <count> - Number of GPUs (default: 1)
• -w, --wait - Wait for instance to be ready

packet setup

Auto-setup apps on GPU instances. Available presets:

Preset	Description	Port
vscode	VS Code in Browser (code-server)	8080
jupyter	Jupyter Lab with data science packages	8888
jupyter-torch	Jupyter Lab with PyTorch and CUDA	8888
workspace	Persistent workspace linking	-
full-dev	VS Code + Jupyter + Persistence	8080, 8888

Launch with auto-setup:

packet launch --gpu rtx-pro-6000 --setup vscode
packet launch --gpu h100 --setup full-dev --name "my-dev-box"

Setup an existing instance:

packet setup vscode 12345
packet setup jupyter-torch 12345

List presets:

packet setup list

packet ssh <instance-id>

SSH into a running instance. Automatically uses the correct credentials.

Options:

• -c, --command <cmd> - Run a command instead of interactive shell
• --copy - Print the SSH command without connecting

packet logs <instance-id>

View instance status and connection info.

packet terminate <instance-id>

Terminate a running instance.

Options:

• -f, --force - Skip confirmation prompt

Configuration

Credentials are stored securely in your system's config directory:

• macOS: ~/Library/Preferences/packet-cli-nodejs/
• Linux: ~/.config/packet-cli-nodejs/
• Windows: %APPDATA%/packet-cli-nodejs/

Requirements

• Node.js 18.0.0 or higher
• An account at packet.ai
• sshpass for packet setup on existing instances (install with brew install sshpass or apt install sshpass)

Examples

Launch with VS Code pre-installed

packet launch --gpu rtx-pro-6000 --setup vscode --name "dev-server"

Launch with full dev environment

packet launch --gpu h100 --setup full-dev
# Includes VS Code (port 8080), Jupyter (port 8888), and persistent workspace

Setup Jupyter on an existing instance

# Get your instance ID
packet ps

# Install Jupyter + PyTorch
packet setup jupyter-torch 12345

CI/CD pipeline

# In GitHub Actions or similar
packet login --key $PACKET_API_KEY
INSTANCE=$(packet launch --gpu h100 --wait | grep "Instance ID" | awk '{print $3}')
packet ssh $INSTANCE -c "cd /workspace && python train.py"
packet terminate $INSTANCE -f

Support

• Website: packet.ai
• Documentation: packet.ai/cli
• Email: [e-mail]

License

MIT