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Installation Guide

This guide will walk you through installing and setting up Synthetic Data Studio on your local machine.

Prerequisites​

System Requirements​

  • Python: 3.9 or higher
  • RAM: Minimum 4GB, recommended 8GB+
  • Disk Space: 2GB free space
  • Operating System: Windows 10+, macOS 10.15+, or Linux

Required Software​

  • Git: For cloning the repository
  • Python: Version 3.9 or higher
  • pip: Python package installer (comes with Python)

Installation Steps​

Step 1: Clone the Repository​

# Clone the repository
git clone https://github.com/Urz1/synthetic-data-studio.git

# Navigate to the backend directory
cd synthetic-data-studio/backend

Step 2: Create Virtual Environment​

# Windows
python -m venv .venv
.venv\Scripts\activate

# Linux/macOS
python -m venv .venv
source .venv/bin/activate

Step 3: Install Dependencies​

# Install Python dependencies
pip install -r requirements.txt

# Optional: Install development dependencies
pip install -r requirements-dev.txt

Step 4: Set Up Environment Variables​

Create a .env file in the backend directory:

# Copy the example file
cp .env.example .env

Edit .env with your configuration:

# Database Configuration
DATABASE_URL=sqlite:///./synth_studio.db

# Security Settings
SECRET_KEY=your-super-secret-key-here-change-this-in-production
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

# File Upload Settings
UPLOAD_DIR=./uploads
MAX_FILE_SIZE=100MB

# Optional: External Services
# OPENAI_API_KEY=your-openai-key
# AWS_ACCESS_KEY_ID=your-aws-key
# AWS_SECRET_ACCESS_KEY=your-aws-secret
# S3_BUCKET=your-s3-bucket

Step 5: Initialize Database​

# Create database tables
python -m app.database.create_tables

Step 6: Verify Installation​

# Start the development server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Visit http://localhost:8000/docs to see the API documentation.

� Docker Installation (Alternative)​

If you prefer using Docker:

# Build the Docker image
docker build -t synth-studio-backend .

# Run the container
docker run -p 8000:8000 -v $(pwd)/uploads:/app/uploads synth-studio-backend

Troubleshooting​

Common Issues​

Python Version Issues​

# Check Python version
python --version

# If you have multiple Python versions, use python3
python3 -m venv .venv
source .venv/bin/activate  # Linux/macOS

Database Connection Issues​

# For PostgreSQL, ensure you have the driver
pip install psycopg2-binary

# Update DATABASE_URL in .env
DATABASE_URL=postgresql://user:password@localhost:5432/synth_studio

Port Already in Use​

# Use a different port
uvicorn app.main:app --reload --host 0.0.0.0 --port 8001

Dependency Issues​

CUDA/GPU Support​

If you have CUDA-compatible GPU:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Memory Issues​

For systems with limited RAM:

# Use smaller batch sizes in configuration
# Set environment variable to limit memory usage
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512

Testing Installation​

Run Basic Tests​

# Run all tests
pytest

# Run specific test categories
pytest tests/test_auth.py
pytest tests/test_datasets.py

# Run with verbose output
pytest -v

Health Check​

# Check if server is responding
curl http://localhost:8000/health

# Should return: {"status": "healthy", "service": "synthetic-data-studio-backend"}

Project Structure​

After installation, your project should look like this:

synthetic-data-studio/
├── backend/
│   ├── app/                 # Main application code
│   ├── docs/                # Documentation
│   ├── tests/               # Test files
│   ├── uploads/             # Uploaded datasets
│   ├── .env                 # Environment configuration
│   ├── requirements.txt     # Python dependencies
│   └── synth_studio.db      # SQLite database (if using SQLite)
├── docker/
└── README.md

Next Steps​

Now that you have Synthetic Data Studio installed:

  1. Quick Start: Follow the Quick Start Tutorial
  2. Upload Data: Learn how to upload your first dataset
  3. Generate Data: Try generating synthetic data
  4. API Documentation: Explore the full API at http://localhost:8000/docs

Support​

If you encounter issues during installation:

Updating​

To update to the latest version:

# Pull latest changes
git pull origin main

# Update dependencies
pip install -r requirements.txt --upgrade

# Run database migrations if needed
python -m app.database.create_tables

# Restart the server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Installation complete! Ready to generate your first synthetic dataset?