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For Pytorch and Tensorflow, there are tags which differentiate devel and runtime, I am not quite sure what are the difference between these two, can someone help me understand that better?
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A runtime image is the Docker image for the container that will run your step(s). JFrog provides a set of base runtime images stored in the Artifactory instance releases.jfrog.io in the docker repository . When Pipelines is installed using default settings, Pipelines draws runtime images from this registry.
Images can exist without containers, whereas a container needs to run an image to exist. Therefore, containers are dependent on images and use them to construct a run-time environment and run an application. The two concepts exist as essential components (or rather phases) in the process of running a Docker container.
A Docker image executes code in a Docker container. You add a writable layer of core functionalities on a Docker image to create a running container. Think of a Docker container as a running image instance. You can create many containers from the same image, each with its own unique data and state.
Copy from nvidia-docker:
CUDA images come in three flavors and are available through the NVIDIA public hub repository.
base: starting from CUDA 9.0, contains the bare minimum (libcudart) to deploy a pre-built CUDA application. Use this image if you want to manually select which CUDA packages you want to install.runtime: extends the base image by adding all the shared libraries from the CUDA toolkit. Use this image if you have a pre-built application using multiple CUDA libraries.devel: extends the runtime image by adding the compiler toolchain, the debugging tools, the headers and the static libraries. Use this image to compile a CUDA application from sources.
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