What is the difference between devel and runtime tag for a Docker container?

Question

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?

Lapoint

Regarding tensorflow, the image page on docker hub points to a directory on github where you will find the tooling they use to build their images and the different docker files used for each version. You will see in details what is included in each version. You can probably find the same info for your other image. – Pedo

Answer 1

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.

Heyward

Just to put this into context, I guess you need devel if you want to use TensorRT functionality. Then you don't need to compile the apps yourself, but the library does that for you. Would appreciate, if someone can confirm this idea. – Yardmaster

Answer 2

Basically, development containers have all the stack required for compilation of your own CUDA based application, including headers, static files and more. The end result is having a much larger image, but also one that allows compilation. The base/runtime containers have the bear minimum to resolve runtime dependencies after you application/any other CUDA applications have been compiled. Similiary to how development pacakages and runtime packages work.

Fame