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
sudo ./auplc-installer install

After installation completes, open http://localhost:30890 in your browser. No login credentials are required - you will be automatically logged in. The installer uses Docker as the default container runtime (K3S_USE_DOCKER=1), see more at link

Uninstall

sudo ./auplc-installer uninstall

💡 Tip: For mirror configuration (registries, PyPI, npm), see Mirror Configuration.

Cluster Installation

For multi-node cluster installation or need more control over the deployment process:

• Multi-Node Cluster Deployment - Production deployment with Ansible playbooks

Learning Solution

AUP Learning Cloud offers the following Learning Toolkits:

• 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.

• Physical Simulation
  Hands-on labs for physics simulation and robotics using Genesis.

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 configured via custom.resources.images in runtime/values.yaml. These settings should be consistent with prePuller.extraImages.

Environment	Image	Hardware
Base CPU	ghcr.io/amdresearch/auplc-default	CPU
GPU Base	ghcr.io/amdresearch/auplc-base	GPU
CV COURSE	ghcr.io/amdresearch/auplc-cv	GPU
DL COURSE	ghcr.io/amdresearch/auplc-dl	GPU
LLM COURSE	ghcr.io/amdresearch/auplc-llm	GPU
PhySim COURSE	ghcr.io/amdresearch/auplc-physim	GPU

Documentation

Full documentation is available at: https://amdresearch.github.io/aup-learning-cloud/

• Deployment Guide - Single-node and multi-node deployment
• Configuration Reference - runtime/values.yaml field reference
• Authentication Guide - GitHub OAuth and native authentication
• User Management Guide - Batch user operations with scripts
• User Quota System - Resource usage tracking and quota management

Contributing

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

Acknowledgment

AUP would like to thank the following universities and professors. This learning solution was made possible through the joint efforts of these partners.

University	Professors and Labs	Toolkits
National Taiwan University	Prof. Chun-Yi Lee, ELSA Lab	DL, CV
Nanjing University	Prof. Jingwei Xu, NJUDeepEngine	LLM

The following repositories and icons are used in AUP Learning Cloud, either in close to original form or as an inspiration:

• Genesis

• Flaticon: deployment (Prashanth Rapolu 15, Freepik), team & user (Freepik), machine learning (Becris).