Full Platform Setup

This guide walks you through deploying the complete Skyulf platform — backend API, Celery workers, Redis, and the web UI.

Just want the Python library? See Installation to pip install skyulf-core and skip this page.

Prerequisites

Requirement	Notes
Docker Desktop (recommended)	Docker Compose launches everything in containers — no host Python or Redis needed.
Python 3.10+ (manual path)	Only if you prefer running services directly on your machine.
Redis (manual path)	Required for Celery background tasks. Install natively, via WSL, or run `docker run -d -p 6379:6379 redis:7`.
Git	To clone the repository.

1. Clone the Repository

git clone https://github.com/flyingriverhorse/Skyulf.git
cd Skyulf

2. Configure Environment Variables

Copy the example environment file and review the settings:

cp .env.example .env

Key variables in .env:

Variable	Default	Description
`DB_TYPE`	`sqlite`	Database backend (`sqlite` or `postgres`)
`USE_CELERY`	`true`	Set `false` to run background tasks in-process (no Redis needed)
`CELERY_BROKER_URL`	`redis://localhost:6379/1`	Redis URL for Celery message broker
`REDIS_URL`	`redis://localhost:6379/0`	Redis URL for caching
`SECRET_KEY`	(placeholder)	Change this in production
`DEBUG`	`true`	Set `false` in production

Minimal setup: For local development with SQLite and no Celery, the only change needed is USE_CELERY=false. No Redis required.

3A. Docker Compose (Recommended)

From the repository root:

docker compose up --build

This starts three containers:

Service	Port	Purpose
`api`	`8000`	FastAPI backend + static frontend
`redis`	`6379`	Message broker for Celery
`worker`	—	Celery worker for background training jobs

Verify it's running:

curl http://127.0.0.1:8000/health
# Expected: {"status": "healthy", ...}

Useful commands:

# Run in background
docker compose up --build -d

# Follow API logs
docker compose logs -f api

# Stop everything
docker compose down

Tip: Docker Compose mounts your working tree, so local code edits are picked up on reload.

3B. Manual Setup (No Docker)

Create a virtual environment

python -m venv .venv

# Windows PowerShell
.\.venv\Scripts\Activate.ps1

# macOS / Linux
source .venv/bin/activate

Install dependencies

pip install --upgrade pip
pip install -r requirements-fastapi.txt

Start Redis (if `USE_CELERY=true`)

# Option 1: Docker (simplest)
docker run --name skyulf-redis -p 6379:6379 -d redis:7

# Option 2: Native install (Linux)
sudo apt install redis-server && sudo systemctl start redis

# Option 3: Skip Redis entirely
# Set USE_CELERY=false in your .env file

Launch the API server

python run_skyulf.py

Or with uvicorn directly:

python -m uvicorn backend.main:app --reload --host 127.0.0.1 --port 8000

Start the Celery worker (optional)

In a second terminal with the virtualenv activated:

python -m celery -A celery_worker.celery_app worker --pool=solo --loglevel=info --queues mlops-training

4. Verify the Stack

Once the server is running, check these URLs:

URL	What it is
http://127.0.0.1:8000	Web dashboard (main UI)
http://127.0.0.1:8000/docs	Swagger UI — interactive API explorer
http://127.0.0.1:8000/redoc	ReDoc — API reference documentation
http://127.0.0.1:8000/health	Health check endpoint
http://127.0.0.1:8000/health/detailed	Detailed diagnostics (DB, Redis, Celery status)

5. Default Credentials

Skyulf ships with fallback admin credentials for local development:

Field	Value
Username	`admin`
Password	`admin123`

These are defined in backend/config.py (AUTH_FALLBACK_* settings) and are enabled by default in development mode.

Security: Change AUTH_FALLBACK_ENABLED to false and SECRET_KEY to a strong random value before deploying to any network-accessible environment.

6. First Steps After Setup

Sign in at http://127.0.0.1:8000 using the default credentials above.
Upload data: Navigate to Data Sources and upload a CSV file.
Explore your data: Open EDA to see automated profiling, distributions, and outlier alerts.
Build a pipeline: Open the ML Canvas, drag nodes, connect them, and hit Execute.
Deploy a model: Register a trained model and test it from the Deployments page.

For a detailed step-by-step walkthrough, see Platform Walkthrough.

Troubleshooting

Problem	Fix
`Connection refused` on port 8000	Is the API running? Check `docker compose logs api` or your terminal.
Celery tasks stuck in "pending"	Verify Redis is running: `redis-cli ping` should return `PONG`.
`ModuleNotFoundError` on startup	Activate your virtualenv and run `pip install -r requirements-fastapi.txt`.
Database errors after update	Delete `mlops_database.db` (SQLite) to start fresh — it auto-recreates on boot.
CORS errors in browser	Add your frontend URL to `CORS_ORIGINS` in `.env`. Default allows `localhost:3000` and `localhost:8080`.

For more issues, see Troubleshooting.