Upscaling AI with Containers

Artificial Intelligence (AI) has become a foundational building block of our modern world. Accordingly, a vast effort has been put into bringing AI to researchers and practitioners of a wide range of fields. Nonetheless, the computationally intensive task of training an AI increasingly requires more computational power than what our laptops and PCs can offer. Therefore, the ability to develop and train a neural network on large clusters seems imperative. This workshop teaches us how to scale an AI-powered application in large clusters, i.e., supercomputers.

Prerequisites

Working knowledge of Unix OS is required. In addition, a basic understanding of Neural Networks (NNs) is desirable. Please follow the link to create a username and password on the DockerHub website, as we will use Play-with-Docker (PWD) freely available service. Details of using and access to the cluster are given in Access to Vega section.

20 min	Introduction to Containers
20 min	Introduction to Docker
20 min	Namespaces and cgroups
20 min	Cleaning Up Containers
20 min	Creating your own container images
20 min	Creating More Complex Container Images
20 min	PWD exercises
20 min	Containers in research workflows
20 min	Singularity: Getting started
20 min	Working with Singularity containers
20 min	Building Singularity images
20 min	Running MPI parallel jobs using Singularity containers
20 min	TensorFlow on a single GPU
20 min	Distributed training in TensorFlow
20 min	Intoduction to Horovod

The lesson

Reference

Who is the course for?

About the course

This lesson material is developed by the EuroCC National Competence Center Sweden (ENCCS) and taught in ENCCS workshops. It aims at researchers and developers who have experience working with AI and wish to train their applications on supercomputers. The lesson material is licensed under CC-BY-4.0 and can be reused in any form (with appropriate credit) in other courses and workshops. Instructors who wish to teach this lesson can refer to the Instructor’s guide for practical advice.

Credits

The lesson file structure and browsing layout is inspired by and derived from work by CodeRefinery licensed under the MIT license. We have copied and adapted most of their license text.

Materials from the below references have been used in various parts of this course.

Instructional Material

This instructional material is made available under the Creative Commons Attribution license (CC-BY-4.0). The following is a human-readable summary of (and not a substitute for) the full legal text of the CC-BY-4.0 license. You are free to:

share - copy and redistribute the material in any medium or format
adapt - remix, transform, and build upon the material for any purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow these license terms:

Attribution - You must give appropriate credit (mentioning that your work is derived from work that is Copyright (c) Hossein Ehteshami and individual contributors and, where practical, linking to https://enccs.se), provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

With the understanding that:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

Software

Except where otherwise noted, the example programs and other software provided with this repository are made available under the OSI-approved MIT license.