Computing and Data Management

Our training for computing encompasses a wide range of topics, including High Performance Computing (HPC), which focuses on harnessing the power of supercomputers and clusters to solve complex scientific and engineering problems efficiently, and Research Data Management (RDM). Our HPC training is provided in close collaboration with Nationales Hochleistungsrechnen (NHR). In our HPC training, we introduce trainees how to leverage state-of-the-art machine learning techniques in HPC environments to gain new insights into data, make predictions, and automate tasks, nowadays essential tasks in artificial intelligence research and data science.

Our training includes data management skills as well as those are crucial in the age of machine learning. Beyond data storage and file management, we introduce our trainees to topics such as copyright and licensing, version control using git and how to write and implement data management plans.

We provide training for big data challenges to enable our trainees to handle and process massive datasets, as well as data storage and management strategies to extract valuable insights from large volumes of information. In the context of training for computing, Jupyter Hub is a popular platform that provides a collaborative and interactive environment for coding and data analysis, making it an invaluable tool for researchers and data scientists.

Trainings

• HPC and Big Data
• Machine Learning on HPC
• R on HPC

Related Living Lab lectures

• OmniOpt: A Tool for Hyper-Parameter Optimization on HPC
• Parallel and Distributed Deep Learning
• Big Data Context Awareness by Drift Detection
• Big Data Performance Analysis

Related resources

• TUD Dresden University of Technology HPC Compendium
• Leipzig University Scientific Computing Knowledge Base
• NHR Training Resources
• NHR Trainings
• Open Science, Sharing & Licensing
• Collaborative Work and Version Control with git
• Data Management