AI-LAB

Researchers

Indicates if the platform is accessible for researchers (e.g., PhD students, postdocs, faculty) for research purposes.

Students

Indicates if the platform is accessible to students for educational purposes (e.g., coursework, projects, thesis).

Lecturers

Indicates if the platform is accessible to lecturers for teaching purposes.

Sensitive Data

Whether the platform supports processing and storing sensitive or confidential data

CPU processing

Indicates if the platform supports computational tasks that only require CPU resources.

GPU processing

Indicates if the platform supports computational tasks that require GPU resources for acceleration (e.g., deep learning).

Unlimited compute

Whether the platform allows unrestricted compute usage, without limitations on the amount of usage time.

Terminal interface

The method used to access the platform.

Pre-installed apps

Indicates if the platform comes with pre-installed applications or frameworks for convenience (e.g., Ansys, PyTorch, TensorFlow).

Collaboration friendly

Indicates if the platform supports collaborative work (e.g., sharing resources, co-editing, team projects).

Working interactively

Indicates if the platform supports interactive workflows where users can interact with running processes (e.g., Jupyter notebooks).

Possible to add GUI

Whether it is possible to run graphical user interfaces (GUIs) on the platform (e.g., remote desktops, JupyterLab).

Introduction

AI-LAB is designed exclusively for students at Aalborg University, offering high-performance computing (HPC) right at your fingertips. Think of it as a mini supercomputer, packed with GPUs, making it a perfect playground for training deep learning models, running simulations, and performing high-speed data analysis.

Getting Started

How to access

Learn how to access AI-LAB

Guides for AI-LAB

Learn the basics on how to use AI-LAB

Terms and Conditions

Get an overview of the Terms and Conditions for AI-LAB

Key Features

Access powerful GPUs

Access powerful GPUs for training deep learning models, simulations, and large-scale data analysis.

Deep Learning Capabilities

Integrated frameworks like PyTorch and TensorFlow, enabling fast experimentation and neural network training.

Efficient Batch Processing

AI-LAB uses Slurm for seamless job scheduling, enabling easy batch processing and background task management.

Common Use Cases

Training ML models for projects

GPU access for deep learning

Course and educational purposes

Pilot testing and exploration

Fine-tuning large language models

Molecular dynamics simulations

Group and semester projects

AI model development and testing

AI-driven research and innovation

Important Information

Not for confidential or sensitive data

With AI-LAB you are only allowed to work with public or internal information according to AAU’s data classification model (classified as levels 0 and 1, respectively). If you would like to work with confidential or sensitive data (classified as levels 2 and 3), then we support another HPC platform called UCloud.

Not suitable for CPU-only computational tasks

The powerful GPU processors allow users to process large datasets much more efficiently than would be the case with pure CPU processing - given that your application can be parallelised in a GPU compatible manner. At the same time, the AI-LAB platform is not designed for CPU-only computational tasks, and we have alternative recommended platforms, such as UCloud for those needs.