Administrative information


Title Hardware and software frameworks for deep learning
Duration 60 min
Module B
Lesson Type Lecture
Focus Technical - Deep Learning
Topic

Computational Graphs

 

Keywords


deep learning, software, hardware, GPU infrastructure,

 

Learning Goals


 

Expected Preparation


Learning Events to be Completed Before

Obligatory for Students

None.

Optional for Students

None.

References and background for students:

None.

Recommended for Teachers

None.

Lesson Materials


 

The materials of this learning event are available under CC BY-NC-SA 4.0.

 

Instructions for Teachers


The purpose of this lecture is to show students what hardware and software architectures help train and deploy deep learning solutions. We must acknowledge that these hardware and software components are brilliant technical solutions that enable us to scale training and inference. Apart from the hardware, NVIDIA GPUs and Google TPUs are the best choices today because they implement optimized deep learning algorithms with high-quality and fast drivers.

For deep learning, we use much more software than deep learning frameworks alone. We use configuration, scheduling, orchestration, and many other tools. The short introduction in this lecture only scratches the surface.

At the last part of the lecture, we show how multi-GPU training can be realized with Horovod. The aim is not to have a deep dive into multi-GPU trainings, but to show, that it is not so hard to implement a basic solution.

Opening the web sites of the hardware manufacturers and of the software providers might help the students to have some hands-on experience.

Outline

  • hardware solutions - from desktop to server grade
  • deep learning software frameworks
  • additional softwares for deep learning solutions
Time schedule
Duration (Min) Description
5 The need of parallel computing in deep learning
15 Hardware solutions
5 How to compare different hardware for deep learning
10 Deep learning software architecture
10 Deep learning frameworks
10 Additional software components

 

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

 

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities

HCAIM Consortium