Docker containers are a great way of reducing the headaches caused by pre-requisites and software versions when deploying code in general and rt-ai SPEs in particular. So it made sense to add support for SPEs in Docker containers in addition to the existing bare metal SPEs. The screen capture above shows the test design in rtaiDesigner using the Docker containerized version of the existing TensorFlow object detector. It is essentially identical to the bare metal version, just with the object detection SPE replaced with the Dockerized version. The container was based on the TensorFlow GPU image.
SPE code is deployed to nodes as a package that includes start and stop scripts. Normally, the start script is something very simple: a single line kicking off a Python script for example. Docker SPEs use a slightly more complex start script that first tries to pull the required Docker image from a defined registry location and then invokes the container in the required manner (using nvidia-docker if necessary).
No changes were required to the SPE code itself in this case – just customization of the start and stop scripts and I added some files used to build the container and install it in the local registry so that the build and update process is very straightforward. Plus, as this test design shows, bare metal and containerized SPEs can be mixed without limitation as the stream interfaces are identical in all cases.