TensorFlow Serving: deployment and service VS GPU example

[KikiCS]

Hi everybody,
this issue is just some feedback on the documentation, while I'm studying it to hopefully make it work in my lab.
This page says that serving a model will require a service and a deployment, which can be generated respectively with the commands:
ks generate tf-serving-service servicename
and
ks generate tf-serving-deployment-gcp ${MODEL_COMPONENT}

Then it says that if I want to use GPU, I should specify the parameter (under the deployment parameters I guess) in this way: ks param set ${MODEL_COMPONENT} numGpus X where X is the number of GPU I want to use, in case I want more than 1, right?

This paragraph links to the readme of a github example where a model is served with GPU.
In this example, the command to generate the model is ks generate tf-serving model1 --name=coco: this is neither a service or a deployment. I don't recognize the other parameters as their names are different from the ones in the documentation, except for the numGpus.
I find this confusing because I am new to the field. Maybe the documentation is written describing GCP only?
Thank you for your help.

[issue-label-bot]

Issue-Label Bot is automatically applying the label improvement/enhancement to this issue, with a confidence of 0.56. Please mark this comment with 👍 or 👎 to give our bot feedback!

Links: app homepage, dashboard and code for this bot.

[KikiCS]

The issue #958 already addresses the problem related to what I was pointing out, so I will close this.