kaggle-gpu-ide 0.1.0

Run Ollama coding models on your own Kaggle GPU instance

KaggleLLM-IDE

Run frontier coding models on your own free Kaggle GPU — no shared server, no VRAM limits on your laptop.

Each user brings their own Kaggle account. The app spins up a private GPU kernel, installs Ollama, pulls your chosen model, and tunnels the inference endpoint directly to your browser via ngrok. Your prompts never touch anyone else's server.

You → Setup form → Kaggle API → Your GPU kernel → ngrok tunnel → Chat UI

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Why this exists

Most coding LLMs worth running (Qwen2.5-Coder 32B, DeepSeek-Coder-V2) need 16–20 GB of VRAM. Most laptops have 4–8 GB. Kaggle gives every verified account 30 free GPU hours per week on P100/T4 instances. This app is the bridge.

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Supported models

Model	VRAM	Best for
qwen2.5-coder:7b	~5 GB	Fast completions, everyday coding
qwen2.5-coder:14b	~9 GB	Balanced speed + quality
qwen2.5-coder:32b	~20 GB	Best quality (P100 only)
deepseek-coder-v2:16b	~10 GB	Strong reasoning + code
codellama:13b	~8 GB	General purpose

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Prerequisites

You need three things before starting:

1. Kaggle account + API key

1. Sign up at kaggle.com
2. Go to Settings → API → Create New Token — this downloads kaggle.json
3. Your username and key are inside that file

2. Phone verification (required for GPU)

Kaggle requires phone verification to unlock GPU kernels. Complete it at: kaggle.com/settings/phone-verification

3. ngrok auth token

1. Sign up at ngrok.com (free)
2. Go to Dashboard → Your Authtoken
3. Copy the token

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Getting started

Backend

cd backend
pip install -r requirements.txt
uvicorn main:app --reload
# Runs on http://localhost:8000

Frontend

cd frontend
npm install
npm run dev
# Runs on http://localhost:5173

Open http://localhost:5173, fill in your credentials, pick a model, and hit Launch GPU Kernel.

First launch takes 5–8 minutes (Ollama install + model download). Subsequent launches of the same model are faster since Kaggle caches the environment.

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Project structure

kaggle-gpu-ide/
├── backend/
│   ├── main.py              # FastAPI app — session lifecycle endpoints
│   ├── kaggle_runner.py     # Kaggle API client — push, poll, stop kernels
│   ├── auth.py              # Credential + GPU access verification
│   └── requirements.txt
├── frontend/
│   ├── index.html
│   ├── vite.config.js
│   ├── package.json
│   └── src/
│       ├── App.jsx          # Setup / launching / chat screens
│       ├── index.css
│       ├── main.jsx
│       ├── hooks/
│       │   ├── useSession.js   # Session state + polling
│       │   └── useChat.js      # Message history + inference
│       └── lib/
│           └── api.js          # Backend + tunnel API clients
├── kernel/
│   └── kernel_bootstrap.py  # Runs ON Kaggle GPU — installs Ollama + ngrok
└── pyproject.toml

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How it works

1. You enter Kaggle credentials + ngrok token in the setup form

2. Backend calls POST /auth/verify
   ├── Checks API key is valid
   └── Confirms GPU access is enabled on your account

3. Backend calls Kaggle API to push kernel_bootstrap.py
   to your Kaggle account with GPU + internet enabled

4. kernel_bootstrap.py runs on Kaggle's GPU:
   ├── Installs Ollama
   ├── Pulls your chosen model
   ├── Starts a FastAPI inference server on port 8000
   └── Opens an ngrok tunnel → prints [TUNNEL_URL]https://...[/TUNNEL_URL]

5. Backend polls the kernel log every 8s until the URL appears

6. Frontend receives the tunnel URL → all prompts go DIRECTLY
   to your Kaggle kernel — no inference traffic through any shared server

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API reference

Backend (http://localhost:8000)

Method	Path	Description
POST	/auth/verify	Validate credentials + GPU access
POST	/session/start	Push kernel + begin polling
GET	/session/{id}/status	launching / ready / error
DELETE	/session/{id}	Stop kernel + clear session
GET	/health	Liveness check

Kernel tunnel (https://<ngrok-url>)

Method	Path	Description
GET	/health	Kernel liveness
GET	/models	List pulled Ollama models
POST	/generate	Run inference

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Environment variables

Create a .env file in backend/ (see .env.example):

# Not strictly required — credentials are passed per-request from the frontend.
# Set these if you want to hardcode a single-user deployment.
KAGGLE_USERNAME=
KAGGLE_KEY=
NGROK_AUTHTOKEN=
SECRET_KEY=

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Kaggle GPU quota

• 30 hours/week per verified account
• Resets every Sunday
• Kernels auto-terminate after 12 hours of continuous runtime
• GPU types: Tesla P100 (16 GB VRAM) or Tesla T4 (15 GB VRAM) — assigned automatically

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Contributing

# Fork → clone → create a feature branch
git checkout -b feat/your-feature

# Make changes, then open a PR against dev (not main)

Please keep PRs focused. One feature or fix per PR.

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License

MIT — see LICENSE.