Unreal Engine for Remote Visualization and Machine Learning (Trainingskurs, hybrid)
Dirk Helmrich
(Kurs-Nr. 1662023 im Trainingsprogramm 2023 des Forschungszentrums Jülich)
Der Kurs ist als hybride Veranstaltung geplant. Er findet statt am Montag, 5. Juni, und Mittwoch, 7. Juni. Der Link zur Online-Plattform wird nur den registrierten Teilnehmer:innen bekannt gegeben.
Inhalt:
The Unreal Engine is one of the state-of-the-art 3D rendering engines, mainly used for game development. In recent years, however, its use in industry and science has been steadily increasing, which is further supported by new features from the producer Epic Games Inc. This course gives an in-depth training to using Unreal Engine as a data generator – by gaining measurements from virtual worlds. Using the ground truth data generated with a realistic rendering engine, projects gain more robust AI pipelines, insight into AI performance on quantifiable data, as well as measurements from virtual scenes with environmental conditions that can be manipulated. At the end of the course, participants have setup their own pipeline with UE and a simple ML workflow in one of the leading supercomputing centres.
Ablauf:
- Visualization pipelines with Unreal Engine
- Scalability, Generalization, Domain Visualization
- Using Pixel Streaming for Remote Visualization
- Introduction into WebRTC concepts, connectivity, and HPC usability
- Building an AI/ML pipeline from WebRTC
- Preparing the frameworks
- Parsing and using data
- Best practices
Voraussetzungen:
- Basic knowledge of Machine Learning frameworks – We assume that the participants are familiar with general concepts of machine learning and deep learning. For an introduction to these topics, we refer to open resources:
- The MIT introduction to Deep Learning Course (http://introtodeeplearning.com/)
- The Machine Learning course and Deep Learning specialization by Andrew Ng et al. at Stanford (https://cs230.stanford.edu/) and on Coursera (www.coursera.org)
- The notebook-based courses of fast.ai (www.fast.ai) and of Master Datascience Paris Saclay (https://github.com/m2dsupsdlclass/lectures-labs)
- Deep Learning, MIT Press book, Ian Goodfellow, YOshua Bengio, Aaron Courville (https://www.deeplearningbook.org/)
- This course should be visited with previous knowledge of Unreal Engine. While it provides some insight into how Unreal Engine work, it is not to be understood as an introduction. We refer to our in-house course “Introduction to Unreal Engine”, which typically takes place in March each year.
Zielgruppe:
Scientists who want more robust computer vision pipelines or a concrete method of evaluation CV algorithms. We also aim at developers of analysis pipelines working on streaming data as well as developers who are creating and managing digital twin models.
Sprache:
Der Kurs wird auf Englisch gehalten.
Dauer:
2 Tage
Zeit:
5. Juni 2023, jeweils 09:00-12:00 und 13:00-16:00
7. Juni 2023, jeweils 09:00-12:00 und 13:00-16:00
Ort:
Hybrid: Jülich Supercomputing Centre, Geb. 16.3, R. 213a (Ausbildungsraum 1)
Online via Zoom
Vorbereitung:
To be well prepared, we ask you to install the Unreal Engine 5.1.* and a C++ Development IDE such as Visual Studio 2022 upfront. Details are available on https://go.fzj.de/unrealcourse, where we are updating information as problems and questions arise.
Teilnehmerzahl:
mindestens 5, maximal 30
Referent:
Dirk Helmrich, JSC
Kontakt:
- Institute for Advanced Simulation (IAS)
- Jülich Supercomputing Centre (JSC)
Raum 307
Anmeldung:
Bitte melden Sie sich bis zum 15. Mai über das Anmeldeformular an.