What is the difference between tensorflow serving Dockerfile and Dockerfile.devel?
a Dockerfile is a file where your write the configurations to create a docker image. 
The tensorflow/serving cpu and gpu are docker images which means they are already configured to work with tensorflow, tensorflow_model_server and, in the case of gpu, with CUDA. 
If you have a GPU, then you can use a tensorflow/serving gpu version which would reduce the latency of your predictions. If you don't have a GPU, then you can use a tensorflow/serving cpu version which would do exactly the same but will be slower.
Why are there two different docker files for tensorflow serving - Dockerfile & Dockerfile.devel - for both CPU and GPUs?
Which one is necessary for deploying and testing?