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aup-learning-cloud

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AUP Learning Cloud is a customized JupyterHub platform that delivers an intuitive, hands‑on AI learning experience with AMD‑accelerated toolkits.

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# AUP Learning Cloud

> **✨ New UI is coming!** 🚀 Try it now on [https://tpe.aupcloud.io/](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.

![Software Architecture](https://raw.githubusercontent.com/AMDResearch/aup-learning-cloud/main/deploy/docs/images/software-stack.png)

## 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`

```bash
# Ryzen AI APU only: OEM kernel for ROCm on Ubuntu 24.04 (reboot required)
sudo apt update && sudo apt install linux-oem-6.14

# 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](https://rocm.docs.amd.com/en/7.13.0-preview/install/rocm.html?fam=ryzen&w=compute&os=ubuntu&ubuntu-ver=24.04&i=pkgman&gpu=max-pro-395&gfx=gfx1151) for details. Radeon dGPU systems typically use the stock Ubuntu kernel—check ROCm docs for your GPU.
>
> **Docker note**: See [Docker Post-installation Steps](https://docs.docker.com/engine/install/linux-postinstall/) and [Install Docker Engine on Ubuntu](https://docs.docker.com/engine/install/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):**

```bash
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:**

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

A successful install looks like this:

```text
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](https://amdresearch.github.io/aup-learning-cloud/installation/quick-start.html) and [Single-Node Deployment](https://amdresearch.github.io/aup-learning-cloud/installation/single-node.html).

### Uninstall

```bash
./auplc-installer uninstall
```

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

- [Multi-Node Cluster Deployment](https://amdresearch.github.io/aup-learning-cloud/installation/multi-node.html) - Production deployment with Ansible playbooks

## Learning Solution

AUP Learning Cloud offers the following Learning Toolkits:

- [**Computer Vision**](projects/CV) \
Includes 10 hands-on labs covering common computer vision concepts and techniques.

- [**Deep Learning**](projects/DL) \
Includes 12 hands-on labs covering common deep learning concepts and techniques.

- [**Large Language Model from Scratch**](projects/LLM) \
Includes 9 hands-on labs designed to teach LLM development from scratch.

- [**Physical Simulation**](projects/PhySim) \
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 App + 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:

```bash
./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](deploy/README.md) - Single-node and multi-node deployment
- [Configuration Reference](https://amdresearch.github.io/aup-learning-cloud/jupyterhub/configuration-reference.html) - `runtime/values.yaml` field reference
- [Authentication Guide](https://amdresearch.github.io/aup-learning-cloud/jupyterhub/authentication-guide.html) - GitHub App and native authentication
- [User Management Guide](https://amdresearch.github.io/aup-learning-cloud/jupyterhub/user-management.html) - Batch user operations with scripts
- [User Quota System](https://amdresearch.github.io/aup-learning-cloud/jupyterhub/quota-system.html) - Resource usage tracking and quota management

## Contributing

Please refer to [CONTRIBUTING.md](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](https://www.csie.ntu.edu.tw/en/member/Faculty/Chun-Yi-Lee-67240464), [ELSA Lab](https://elsalab.ai/) | DL, CV |
| Nanjing University | [Prof. Jingwei Xu](https://njudeepengine.github.io/jingweixu/), [NJUDeepEngine](https://github.com/NJUDeepEngine) | LLM |

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

* [Genesis](https://github.com/Genesis-Embodied-AI/Genesis)

* [Flaticon](https://www.flaticon.com): deployment (Prashanth Rapolu 15, Freepik), team & user (Freepik), machine learning (Becris).

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