AUP Learning Cloud

AUP Learning Cloud is a tailored JupyterHub deployment designed to provide an intuitive and hands-on AI learning experience. It features a comprehensive suite of AI toolkits running on AMD hardware acceleration, enabling users to learn and experiment with ease.

Quick Start

The simplest way to deploy AUP Learning Cloud on a single machine in a development or demo environment.

Prerequisites

• Hardware: AMD Ryzen™ AI Halo Device (e.g., AI Max+ 395, AI Max 390)
• Memory: 32GB+ RAM (64GB recommended)
• Storage: 500GB+ SSD
• OS: Ubuntu 24.04.3 LTS
• Docker: Install Docker and configure for non-root access

# Install Docker
curl -fsSL https://get.docker.com | sh

# Add current user to docker group
sudo usermod -aG docker $USER

# Apply group changes without logout (or logout/login instead)
newgrp docker

# Install Build Tools
sudo apt install build-essential

Note: See Docker Post-installation Steps and Install Docker Engine on Ubuntu for details.

Installation

git clone https://github.com/AMDResearch/aup-learning-cloud.git
cd aup-learning-cloud/deploy/
sudo ./single-node.sh install

After installation completes, open http://localhost:30890 in your browser. No login credentials are required - you will be automatically logged in.

Script Commands

Command	Description
install	Full installation (K3s, tools, GPU plugin, images, JupyterHub)
uninstall	Complete removal of all components
upgrade-runtime	Upgrade JupyterHub deployment
build-images	Build and import container images
install-tools	Install Helm and K9s only
install-runtime	Deploy JupyterHub only
remove-runtime	Remove JupyterHub only

Example:

# Upgrade JupyterHub after configuration changes
sudo ./single-node.sh upgrade-runtime

# Rebuild images after modifying Dockerfiles
sudo ./single-node.sh build-images

Manual Installation

For users who prefer step-by-step manual installation or need more control over the deployment process:

• Single-Node Manual Deployment - Detailed manual setup for development and demo environments
• Multi-Node Cluster Deployment - Production deployment with Ansible playbooks

Learning Solution

AUP Learning Cloud offers the following Learning Toolkits:

Important

Only Deep Learning and Large Language Model from Scratch are available in the v1.0 release.

• Computer Vision
  Includes 10 hands-on labs covering common computer vision concepts and techniques.

• Deep Learning
  Includes 12 hands-on labs covering common deep learning concepts and techniques.

• Large Language Model from Scratch
  Includes 9 hands-on labs designed to teach LLM development from scratch.

Key Features

Hardware Acceleration

AUP Learning Cloud provides a multi-user Jupyter notebook environment with the following hardware acceleration:

• AMD GPU: Leverage ROCm for high-performance deep learning and AI workloads.
• AMD NPU: Utilize Ryzen™ AI for efficient neural processing unit tasks.
• AMD CPU: Support for general-purpose CPU-based computations.

Flexible Deployment

Kubernetes provides a robust infrastructure for deploying and managing JupyterHub. We support both single-node and multi-node K3s cluster deployments.

Authentication

Seamless integration with GitHub Single Sign-On (SSO) and Native Authenticator for secure and efficient user authentication.

• Auto-admin on install: Initial admin created automatically with random password
• Dual login: GitHub OAuth + Native accounts on single login page
• Batch user management: CSV/Excel-based bulk operations via scripts

Storage Management and Security

Dynamic NFS provisioning ensures scalable and persistent storage for user data, while end-to-end TLS encryption with automated certificate management guarantees secure and reliable communication.

Available Notebook Environments

Current environments are set up as RESOURCE_IMAGES in runtime/jupyterhub/files/hub. These settings should be consistent with Prepullers in runtime/values.yaml.

Environment	Image	Version	Hardware
Base CPU	ghcr.io/amdresearch/auplc-default	v1.0	CPU
CV COURSE	ghcr.io/amdresearch/auplc-cv	v1.0	GPU (Strix-Halo)
DL COURSE	ghcr.io/amdresearch/auplc-dl	v1.0	GPU (Strix-Halo)
LLM COURSE	ghcr.io/amdresearch/auplc-llm	v1.0	GPU (Strix-Halo)

Documentation

• JupyterHub Configuration - Detailed JupyterHub settings
• GitHub OAuth Setup - OAuth configuration
• Maintenance Manual - Operations guide

Contributing

Please refer to CONTRIBUTING.md for details on how to contribute to the project.