Developer Tools

GPU cloud from your terminal

Launch, manage, and SSH into GPU instances without leaving your terminal. The way developers are meant to use cloud GPUs.

$ npm install -g packet-gpu-cli

Quick Start Guide Get API Key →

# Authenticate with your API key
$ packet login
✓ Logged in as developer@company.com
# See available GPUs and pricing
$ packet gpus
┌────────────────┬──────┬──────────┬───────────┐
│ GPU            │ VRAM │ Price/hr │ Status    │
├────────────────┼──────┼──────────┼───────────┤
│ RTX PRO 6000   │ 96GB │ $1.29    │ available │
│ H100           │ 80GB │ $2.49    │ available │
│ B200           │180GB │ $4.99    │ available │
└────────────────┴──────┴──────────┴───────────┘
# Launch a GPU with VS Code pre-installed
$ packet launch --gpu rtx-pro-6000 --setup vscode
⠋ Launching RTX PRO 6000 with VS Code in Browser...
✓ Launched RTX PRO 6000
  Instance ID: 12847
  Setup:       💻 VS Code in Browser
✓ Instance is ready!
  SSH: ssh ubuntu@gpu-12847.packet.ai -p 30122
# Or setup Jupyter on an existing instance
$ packet setup jupyter-torch 12847
⠋ Running 🔥 Jupyter + PyTorch setup (~5 min)...
✓ 🔥 Jupyter + PyTorch is ready!
  jupyter    port 8888  (token: packet)
# SSH directly into your instance
$ packet ssh 12847
ubuntu@gpu-12847:~$ nvidia-smi
# When you're done
$ packet terminate 12847
✓ Instance 12847 terminated

Built for developers

Everything you need to manage GPU instances without leaving your workflow.

⚡

Launch in Seconds

One command to provision a GPU. No clicking through dashboards.

🔐

Secure Auth

API key stored locally. Supports env vars for CI/CD pipelines.

🚀

Auto-Setup

Launch with VS Code, Jupyter, or PyTorch pre-installed. One flag: --setup vscode

📜

Scriptable

Use in bash scripts, Makefiles, or CI/CD. JSON output available.

🖥️

Direct SSH

packet ssh connects instantly. No config files needed.

💰

Cost Tracking

See pricing and runtime costs directly in your terminal.

🌍

Cross-Platform

Works on macOS, Linux, and Windows. Node.js 18+ required.

Quick Start

Install the CLI

npm install -g packet-gpu-cli

Authenticate

Run packet login and enter your API key. Get one from your account settings.

packet login --key your-api-key

Launch a GPU

packet launch --gpu rtx-pro-6000 --wait

Connect via SSH

packet ssh <instance-id>

Command Reference

packet loginAuthenticate with your API key

packet logoutRemove stored credentials

packet whoamiShow current account and balance

packet gpusList available GPU types and pricing

packet launch --gpu <type>Launch a new GPU instance

packet launch --setup <preset>Launch with auto-setup (vscode, jupyter, etc.)

packet setup listList available auto-setup presets

packet setup <preset> <id>Run setup on an existing instance

packet psList your running instances

packet ssh <id>SSH into an instance

packet logs <id>View instance status and info

packet terminate <id>Terminate an instance

Use in CI/CD

Run GPU workloads in GitHub Actions, GitLab CI, or any pipeline.

# .github/workflows/train.yml
name: Train Model

on: [push]

jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Install packet CLI
        run: npm install -g packet-gpu-cli

      - name: Launch GPU and train
        env:
          PACKET_API_KEY: ${{ secrets.PACKET_API_KEY }}
        run: |
          packet login --key $PACKET_API_KEY

          # Launch GPU
          INSTANCE=$(packet launch --gpu h100 --wait | grep "Instance ID" | awk '{print $3}')

          # Run training
          packet ssh $INSTANCE -c "cd /workspace && python train.py"

          # Cleanup
          packet terminate $INSTANCE -f

Ready to try it?

Install the CLI and launch your first GPU in under a minute.