Containers package entire workflows, including software, libraries, and even data, into a single file. The container can then be run on any compatible hardware that can run the container type, regardless of the underlying operating system.
Containers are finding increased utility in the worlds of scientific computing, deep learning, HPC, machine learning, and artificial intelligence, because they are reproducible, portable (mobility of compute), user friendly (admins don’t have to install everything), and simple, and they isolate resources, reduce complexity (reduction in dependencies), and make it easy to distribute the application and dependencies.
Using containers, you have virtually everything you need in a single file, including a base operating system (OS), the application or workflow (multiple applications), and all of the dependencies. Sometimes the data is also included in the container, although it is not strictly necessary because you can mount filesystems with the data from the container.
To run or execute the container, you just need an OS that accommodates the specific container technology, a container run time, and a compatible hardware system. It does not have to be the same OS as in the container. For example, you could create a container using something like Docker on a Linux Mint 18.2 system and run it on a CentOS 7.2 system. Besides hardware compatibility, the only requirement is that the Linux Mint system have the ability to create a container correctly and that the CentOS 7.2 system be able to run the container correctly.
The creator of the container on the Linux Mint system includes everything needed in the container,…
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