Overview#
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.
What is AUP Learning Cloud?#
AUP Learning Cloud provides a multi-user Jupyter notebook environment optimized for AI and machine learning education on AMD hardware platforms.
Key Features#
Hardware Acceleration#
AMD GPU: Leverage ROCm on AMD Ryzen™ AI iGPUs (Strix Halo, Strix) and AMD Radeon™ RDNA 4 discrete GPUs (RX 9070 XT, AI Pro R9700) for high-performance deep learning and AI workloads
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, and produce offline install bundles for air-gapped environments.
Custom URL Launcher#
We provide a basic ROCm + PyTorch environment; you can clone your own Git repository into this environment at server start (via URL and branch or by selecting a repo from your GitHub account). Private repositories are supported via a GitHub App or a pre-configured default access token. Your code is then available in the workspace so you can run it immediately.
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
GitHub Teams → Groups sync: GitHub team membership is mapped to JupyterHub groups for resource access control
Batch user management: CSV/Excel-based bulk operations via scripts
Admin Dashboard#
A dedicated React-based admin dashboard (under /hub/admin/) provides user/group management and a live usage view — daily active users, active sessions (via Server-Sent Events), pending spawns, idle-session warnings, and course/accelerator usage breakdowns. Quotas can be applied and reset in batch.
Observability#
Optional Prometheus + Grafana integration exposes Hub and single-user metrics. Two preset Grafana dashboards ship with the chart (grafana-dashboard-aup-hub-ops.json, grafana-dashboard-aup-hub-resources.json).
Storage Management and Security#
Dynamic NFS provisioning ensures scalable and persistent storage for user data, while Traefik ingress with automated TLS certificate management guarantees secure and reliable communication.
Learning Solutions#
AUP Learning Cloud offers the following Learning Toolkits:
Computer Vision - 10 hands-on labs covering common computer vision concepts and techniques
Deep Learning - 12 hands-on labs covering common deep learning concepts and techniques
Large Language Model from Scratch - 9 hands-on labs designed to teach LLM development from scratch
Physics Simulation - Hands-on labs for physics simulation with GPU acceleration
Available Notebook Environments#
Environment |
Image |
Hardware |
|---|---|---|
Base CPU |
|
CPU |
GPU Base |
|
GPU |
CV Course |
|
GPU |
DL Course |
|
GPU |
LLM Course |
|
GPU |
PhySim Course |
|
GPU |