AUP Learning Cloud

✨ New UI is coming! 🚀 Try it now on https://tpe.aupcloud.io/ 👀

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: Supported Ryzen AI 300 series and above APUs and Radeon 9000 series PCIe GPUs.
• Memory: 32GB+ RAM (64GB recommended)
• Storage: 500GB+ SSD
• OS: Ubuntu 24.04.4 LTS
• Docker: Install Docker and configure for non-root access
• TUI deps: python3-questionary and python3-prompt-toolkit (apt) for the recommended interactive installer; conda/venv users use pip install questionary prompt_toolkit

# Ryzen AI APU only: OEM kernel for ROCm on Ubuntu 24.04 (reboot required)
sudo apt update && sudo apt install linux-image-6.14.0-1018-oem

# 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

# TUI dependencies (required for the recommended interactive install)
sudo apt install python3-questionary python3-prompt-toolkit

Kernel note (Ryzen AI APU only): The OEM kernel package follows AMD ROCm's Ryzen APU installation guidance for Ubuntu 24.04. See the ROCm 7.13.0 preview installation guide for Ryzen APUs for details. Radeon dGPU systems typically use the stock Ubuntu kernel—check ROCm docs for your GPU.

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

TUI note: System Python (apt): install python3-questionary and python3-prompt-toolkit as shown above. Conda or virtualenv users: use pip install questionary prompt_toolkit inside your active environment instead of the apt packages. These are required for the interactive TUI; non-interactive ./auplc-installer install does not need them.

Installation

Interactive (recommended):

git clone https://github.com/AMDResearch/aup-learning-cloud.git
cd aup-learning-cloud
./auplc-installer                      # pick Install, accept defaults, set Image tag to develop

Non-interactive:

git clone https://github.com/AMDResearch/aup-learning-cloud.git
cd aup-learning-cloud
./auplc-installer install

A successful install looks like this:

This operation needs root privileges. Requesting sudo password...
  ✓ [1/8] Detecting GPU  (0.2s)
  ✓ [2/8] Generating values overlay (initial)  (0.0s)
  ✓ [3/8] Installing helm + k9s  (0.0s)
  ✓ [4/8] Installing K3s (single-node)  (3.8s)
  ✓ [5/8] Pulling custom + external images  (25.0s)
  ✓ [6/8] Deploying ROCm GPU device plugin + node labeller  (0.2s)
  ✓ [7/8] Refreshing values overlay from node labels  (0.2s)
  ✓ [8/8] Deploying JupyterHub runtime (helm install + wait)  (9.2s)

   _    _   _ ____    _                          _                  ____ _                 _
  / \  | | | |  _ \  | |    ___  __ _ _ __ _ __ (_)_ __   __ _     / ___| | ___  _   _  __| |
 / _ \ | | | | |_) | | |   / _ \/ _` | '__| '_ \| | '_ \ / _` |   | |   | |/ _ \| | | |/ _` |
/ ___ \| |_| |  __/  | |__|  __/ (_| | |  | | | | | | | | (_| |   | |___| | (_) | |_| | (_| |
/_/   \_\___/|_|     |_____\___|\__,_|_|  |_| |_|_|_| |_|\__, |    \____|_|\___/ \__,_|\__,_|
                                                         |___/
    You have successfully installed AUP Learning Cloud!

    Open in your browser: http://localhost:30890
    (auto-logged-in as 'student' — no login needed)

    kubectl is configured at $HOME/.kube/config; try `kubectl get nodes`

See the full guide at Quick Start and Single-Node Deployment.

Uninstall

./auplc-installer uninstall

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 and Coding 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
Code CPU	ghcr.io/amdresearch/auplc-code-cpu	CPU
Code GPU	ghcr.io/amdresearch/auplc-code-gpu	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

The auplc-default, auplc-base, and Course-* images remain notebook and course focused. Browser-based coding is provided by generic code-server images instead of per-course VS Code image variants. Resources launch code-server when their custom.resources.metadata.<resource>.launchMode is set to code-server; the default configuration uses code-cpu for CPU-only coding workspaces and code-gpu for GPU-accelerated coding workspaces.

Build the images:

./auplc-installer img build base-rocm --gpu=strix

The code-server container starts on port 8888 with code-server --auth none. This is safe only when the user pod is reachable exclusively through JupyterHub and the JupyterHub proxy authentication boundary. Do not expose the code-server pod port directly through a NodePort, LoadBalancer, ingress, or other unauthenticated route.

The code images install the built-in extension list from dockerfiles/Code/extensions.txt plus local .vsix packages such as the AUPLC Back-to-Hub extension. Before adding or distributing additional VS Code, OpenVSX, or Marketplace extensions, confirm their licenses and marketplace terms are compatible with your deployment and redistribution model.

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).