Containerized ML applications inside a VM

The vSphere Integrated Containers architecture gives two container deployment models:

  1. Virtual container hosts: vSphere Integrated Containers leverages the native constructs of vSphere to provision containers. It extends the availability and performance capabilities (DRS, HA, vMotion ) of vSphere to containerized workloads. A container image can be used as a virtual machine, and developers can also consume it as a Docker API.
  2. Docker container hosts: Developers can self-provision Docker container hosts on demand and use them as a development sandbox to repackage apps. This architecture complements agile development practices and DevOps methodologies such as continuous integration (CI) and continuous deployment (CD).

It will be costly and time consuming to re-architect an in-house application that is tightly coupled to its data and other application components/logic, so it cuts costs to repackage the application in a container without changing the application’s design. The learning curve for repackaging an application is small.

vSphere Integrated Containers gives an option to instantiate a Docker image by using the Docker command-line interface and then deploying the container image as a VM instead of as a container on top of a Docker host, so we can get the benefits of packaging the application as a container without re-architecting it. This way, we keep the isolation of VMs. vSphere Integrated Containers is the ideal solution for application repackaging without any new infrastructure/dedicated hardware or the need to implement new tools.

The repackaged containerized application can run alongside other virtual machines running traditional or containerized applications. vSphere Integrated Containers have high availability at the infrastructure level without developer intervention to support the repackaged container. We can also utilize core vSphere features such as vSphere high availability and vSphere vMotion.

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