bdep | build2 project dependency manager | Build Tool library
kandi X-RAY | bdep Summary
kandi X-RAY | bdep Summary
This package contains the build2 project dependency manager.
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QUESTION
I am a relatively seasoned AI practitioner however, I am a complete newbie when it comes to deployment of these models. I followed an online tutorial that deployed the model locally using Docker Desktop. It created a stack of containers for frontend and backend. I installed Tensorflow in each of these containers to run the AI model (RUN pip3 install tensorflow in the Dockerfile). However, I can't deploy it on Kubernetes.
I hecked the option which allowed Docker Stacks to be sent to Kubernetes. I can see both frontend and backend images when I run docker images
. The next step that I did was that I created a GCP project and created cluster in it. Then I pushed these images after tagging them in the specific format gcr.io/project/name:tag
to both front end and back end. Then I deployed both and then exposed them as well as fdep (frontend) and bdep (backend). Both of them are running correctly as seen here:
[![enter image description here][1]][1]
However, when I go to front end external ip and run the model, nothing happens. As if the back end is not outputting anything. Here is what I get when I use postman to post a request on backend external IP: [![enter image description here][2]][2]
Any help here. What am I doing wrong?
...ANSWER
Answered 2020-Apr-30 at 00:37Since this multi-container docker app that was not originally developed for kubernetes, make sure specify a name when generating the service for your backend,
kubectl expose deployment bdep --port 8081 --name (name-that-the-front-end-apps-expect)
In your case, without the option --name, the service name defaults to the deployment name "bdep", but the front end apps are expecting the name "backend".
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