claude-colab

Give Claude Code GPU access via Google Colab.

AI coding agents can read files, run bash, and edit code — but they have zero GPU access. claude-colab bridges that gap using Colab's free T4 GPU.

How It Works

Colab (T4 GPU)                              Your Mac / PC
┌─────────────────────────┐                 ┌──────────────────────┐
│ Flask API               │                 │ claude-colab CLI     │
│  /exec, /python,        │◄── HTTPS ──────►│  or                  │
│  /upload, /download     │  (cloudflared)  │ MCP Server           │
│                         │  E2E encrypted  │  (Claude Code tools) │
│ Bearer token + Fernet   │                 │                      │
└─────────────────────────┘                 └──────────────────────┘

Open one Colab notebook, hit "Run All"
Copy the connection string
Claude Code gains GPU access

Quick Start

Install

pip install claude-colab

Start the Colab server

Open notebooks/claude_colab_server.ipynb in Google Colab. Set runtime to T4 GPU. Run all cells. Copy the connection string.

Connect

claude-colab connect cc://TOKEN:KEY@your-tunnel.trycloudflare.com

Use it

claude-colab status                          # GPU info
claude-colab exec "nvidia-smi"               # Run shell commands
claude-colab python -c "import torch; print(torch.cuda.get_device_name(0))"
claude-colab upload model.py /content/model.py
claude-colab download /content/results.csv ./results.csv

MCP Server (Claude Code Integration)

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "claude-colab": {
      "command": "claude-colab",
      "args": ["mcp-serve"]
    }
  }
}

Claude Code now has 5 GPU tools:

Tool	What it does
`colab_status`	GPU info, VRAM, disk, uptime
`colab_exec`	Run shell commands
`colab_python`	Execute Python code
`colab_upload`	Upload files to Colab
`colab_download`	Download files from Colab

Example

You: "Benchmark this model on the GPU"

Claude:
1. colab_status → Tesla T4, 15GB VRAM
2. colab_upload → sends model.py
3. colab_exec → "pip install torch transformers"
4. colab_exec → "python model.py"
5. colab_download → fetches results.json

Connection Safety

The connection string contains your auth token and encryption key. Three ways to connect:

# Direct (convenient, visible in shell history)
claude-colab connect cc://TOKEN:KEY@host

# Interactive prompt (nothing in history)
claude-colab connect

# Pipe from clipboard (nothing in ps aux or history)
pbpaste | claude-colab connect -

Security

E2E Encryption

All request and response bodies are encrypted with Fernet (AES-128-CBC + HMAC-SHA256). The encryption key is generated per Colab session and is separate from the bearer token.

Actor	Can see	Cannot see
Random user	Nothing (no token)	Everything
Cloudflare	URL paths, timing, token	Request/response bodies
Google (Colab)	Everything on the VM	Your local files

What we don't protect against

Google seeing data on the Colab VM — it's their hardware
Arbitrary code execution on Colab — intentional, it's your session
Local machine compromise exposing ~/.claude-colab.json

Limitations

Session timeout: Free Colab dies after 90min idle / 12hr max. You'll need to restart and reconnect.
No streaming: Long-running commands return nothing until completion. Redirect to file: colab_exec "python train.py > output.log 2>&1"
50MB file limit: For larger files, use colab_exec with wget or gdown.
No persistent state: Each /python call runs in a fresh namespace.

License

MIT