HPC/AI Deep Dive Training Experiences with Containers and JupyterLab

High Performance Computing (HPC) and Artificial Intelligence (AI) are currently the two main pillars of research and development for various academics and professionals in science and engineering, and effective use of both technologies requires training. We conducted several HPC/AI deep dive trainings in two different ways: (1) for a single institute and organization in both online and offline mode, and (2) for a broader set of institutes and organizations in online mode. This work presents three different types of HPC/AI deep dive training with learning objectives and content that are beneficial for science and engineering academics, researchers, undergraduate and postgraduate students, as well as professionals. We describe the portability, reproducibility, and interactivity of deep dive training for HPC/AI by eliminating the need to set up the necessary prerequisites for the labs with containers and using the JupyterLab interface for a userfriendly and understandable environment. We evaluate the HPC/AI deep dive training feedback from participants and discuss the outcomes.