setup.md

Setup and Installation

This guide covers the steps to set up the Real-Time ML Multicloud Platform for local development.

Prerequisites

Ensure you have the following installed:

• Docker & Docker Compose: For running services.
• Python 3.11+: For local development.
• Poetry: For dependency management.
• Git: For version control.
• Hardware: Minimum 8GB RAM recommended.

Installation

1. Clone the Repository

git clone <repository-url>
cd rt-ml-multicloud-platform

2. Install Python Dependencies

We use Poetry for dependency management.

# Install Poetry if you haven't already
pip install poetry

# Install project dependencies
poetry install

# Activate the virtual environment
poetry shell

3. Environment Configuration

Copy the example environment file:

cp .env.example .env

Review the .env file and adjust settings if necessary. For local development, the defaults usually work fine.

Starting Services

The platform uses Docker Compose to run infrastructure services (Redis, PostgreSQL, MLflow, MinIO, etc.).

# Start all services in detached mode
docker-compose up -d

Wait for about 30-60 seconds for all services to initialize.

Verification

Check if the services are running correctly:

# Check API health
curl http://localhost:8000/health

# Expected response:
# {"status":"healthy", ...}

You can also access the following interfaces:

• API Documentation: http://localhost:8000/docs
• MLflow UI: http://localhost:5000
• Grafana: http://localhost:3001 (Default credentials: admin / admin123)
• Prometheus: http://localhost:9090
• MinIO Console: http://localhost:9001 (Default credentials: minioadmin / minioadmin123)

Troubleshooting

Services fail to start

Check the logs for specific services:

docker-compose logs -f model-api
docker-compose logs -f mlflow-server

Port conflicts

Ensure ports 8000, 5000, 6379, 5432, 9090, 3000 are free. You can change ports in docker-compose.yml and .env if needed.