Deployment on GENIE.AI

Installation & Configuration Guide

Mon, 01 Jan 0001 00:00:00 +0000

Introduction

Welcome to the GENIE.AI framework. This guide will walk you through the necessary steps to set up, configure, and deploy your own Retrieval-Augmented Generation (RAG) solution. The success of any AI-driven knowledge system lies in the quality and structure of its data. Therefore, the first and most critical phase is to define, curate, and structure the data that will form the backbone of your system’s knowledge. This cannot be over-emphasized. It is the most critical aspect. Our suggestion is that you establish an initial MVP with the framework by simply curating the data, defining the knowledge hierarchy, configuring your quickhelp buttons with prompts and then labeling and ingesting your curated data prior to modifying any code. This way, you will get used to how the framework operates, before you delve into deeper issues and extensions. This approach can also be used to deliver a rapid solution to a RAG problem, without any coding at all (just implementing a knowledge base design and the associated configuration). The application title and theme can also be modified by configuration in JSON without changing code. The suggested approach for this is to utilize something like ChatGPT, Gemini Pro or Grok etc. to build a new configuration for color theme and title etc. This can be done in minutes.

Docker Compose Setup

Mon, 01 Jan 0001 00:00:00 +0000

This guide covers local single-node deployment using docker compose up. For multi-node production deployments with Docker Swarm, see Docker Swarm Deployment Guide.

GENIE.AI uses a single dual-mode docker-compose.yaml at the project root that works with both docker compose up and docker stack deploy.

Prerequisites

Docker Engine 23+ with Compose v2 (docker compose subcommand)
NVIDIA Container Toolkit (only if using OPEA/GPU services)
Hugging Face API token (only if using OPEA/GPU services)

Architecture Overview

For the full system architecture with diagrams (C4 context/container, authentication flows, service auth matrix, token lifecycle, RAG pipeline), see Architecture Overview.

Docker Swarm Setup

Mon, 01 Jan 0001 00:00:00 +0000

This guide covers Docker Swarm deployments (single-node and multi-node). For local single-node development with docker compose up, see Docker Compose Setup Guide.

GENIE.AI uses a single Swarm-compatible docker-compose.yaml at the project root. All services are deployed via docker stack deploy.

Prerequisites

Docker Engine 23+ on all nodes (Compose v2 support required)
NVIDIA Container Toolkit on GPU nodes
Same Docker version on all nodes (recommended)
SSH access to all nodes
Network connectivity between all nodes (ports 2377/tcp, 7946/tcp+udp, 4789/udp)

Architecture Overview

For the full system architecture with diagrams (C4 context/container, authentication flows, service auth matrix, token lifecycle, RAG pipeline), see Architecture Overview.

GPU Deployment

Mon, 01 Jan 0001 00:00:00 +0000

This guide explains how to deploy GENIE.AI on different NVIDIA GPUs.

Prerequisites

NVIDIA Driver installed
NVIDIA Container Toolkit installed
Docker with NVIDIA support
Docker Swarm initialized: docker swarm init
GPU node labeled: docker node update --label-add gpu=true $(hostname)
Created .env file from env template with your secrets

Environment File Loading

Docker Swarm (docker stack deploy) does NOT support --env-file. Instead, source the files before deploying:

# CORRECT - loads both .env and GPU overrides
set -a && source .env && source env.t4 && set +a && docker stack deploy -c docker-compose.yaml genieai

# WRONG - docker stack deploy ignores --env-file
docker stack deploy --env-file .env -c docker-compose.yaml genieai

Available GPU Deployments

1. GPU T4 (16GB VRAM) - Conservative Configuration

Profile: Development and testing GPU: NVIDIA T4 (16GB VRAM)

NVIDIA A40 Install Guide

Mon, 01 Jan 0001 00:00:00 +0000

Guide: NVIDIA Driver Installation on Ubuntu 22.04

This guide covers the process of installing the server-grade NVIDIA drivers on E2E and resolving DKMS build failures caused by manually installed kernel headers. It outlines the specific steps needed to install NVIDIA drivers for an NVIDIA A40 GPU on Ubuntu 22.04, including how to bypass the common “Exit Status 10” error caused by rogue kernel headers. This is provided to help challenge participants install and configure on the specific images provided for the challenge on E2E Neetworks with A40 GPU