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Docker 101

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Early adopters report "easier replication, faster deployment and lower configuration and operating costs" of applications that involve Docker containers - an open platform that allows developers and sysadmins to build, ship and execute distributed applications.

Not surprisingly then, a groundswell of organizations are interested in evaluating Docker containers in proof-of-concept initiatives and/or pilot projects. The transition to production use, however, introduces additional requirements as Docker containers need to be incorporated into existing IT infrastructures and (ultimately) integrated into application workflows.

In answering the 5 Ws and one H, the aim of this webinar is to provide a technical overview and demonstration of Docker and to frame its use within the context of High Performance Computing and Big Data Analytics.

Learn all about Docker.


• What are Docker containers - relative to physical machines, VMs and other containers?
• Who is responsible for Docker containers?
• Why and when were Docker containers created?
• What is the container ecosystem?
• Where is use of containers appropriate and not appropriate?
     ▸ HPC applications?
     ▸ Big Data Analytics? Specifically, Spark-based applications?
     ▸ On premise and in the cloud?
     ▸ Is running Docker different in HPC versus microservice-based applications?
• How can I make use of Docker containers?
     ▸ How can I containerize my application?
     ▸ How can I create, or make use of, a Docker image?
     ▸ How can I run Docker containers as I do other types of workloads?
• Getting Started and Next Steps

Ian Lumb, System Architect, Univa Corporation.

As an HPC specialist, Ian Lumb has spent about two decades at the global intersection of IT and science. Ian received his B.Sc. from Montreal's McGill University, and then an M.Sc. from York University in Toronto. Although his undergraduate and graduate studies emphasized geophysics, Ian's current interests include workload orchestration and container optimization for HPC to Big Data Analytics in clusters and clouds.

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