Quick Start

Quick Start#

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

Prerequisites#

  • Hardware: Supported AMD GPU or APU — select your device in the Installation section below. Examples:

    • Radeon PRO: AI PRO R9700/R9600D

    • Radeon: RX 9070/9060 series

    • Ryzen AI: Max+ PRO 395, Max PRO 390/385/380, Max+ 395, Max 390/385, 9 HX 375/370, 9 365

  • Memory: 32GB+ RAM (64GB recommended)

  • Storage: 500GB+ SSD

  • OS: Ubuntu 24.04.3 LTS

  • Docker: Install Docker and configure for non-root access (see below; skip if already installed)

Package dependencies#

Install build tools (required for building container images):

sudo apt install build-essential

Install Docker#

Install Docker — skip if already installed

If Docker is already installed and your user is in the docker group, skip this section.

# 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

# Verify installation
docker --version

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

Installation#

Select your AMD device family and GPU below. The install commands update to use the correct GPU_TYPE for your selection.

AMD device family
Radeon PRO GPU
Radeon GPU
Ryzen AI APU 
git clone https://github.com/AMDResearch/aup-learning-cloud.git
cd aup-learning-cloud && chmod +x auplc-installer

sudo ./auplc-installer install --gpu=rdna4

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

Uninstall#

To remove all components (K3s, JupyterHub, and related resources):

sudo ./auplc-installer uninstall

See also

For all other commands (upgrade, runtime-only install/remove, image build/pull, mirror configuration, etc.), see the Single-Node Deployment guide.

Next Steps#

After installation:

  1. Access JupyterHub at http://localhost:30890

  2. Review the JupyterHub Configuration guide

  3. Set up Authentication if needed

  4. Configure User Management for your environment

  5. Explore the available learning toolkits

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