ravisethi21 / udacity_ml_microservices Goto Github PK

Project Overview In this project, I have applied the skills to operationalize a Machine Learning Microservice API.

There are given a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, which was initially taken from Kaggle, on the data source site.

Application:

• app.py application script
• requirements.txt dependencies of app

CI/CD:

• .circleci/config.yml folder with the configuration to CircleCI

Outputs:

• output_txt_files folder with docker and kubernetes outputs files

Screenshots:

• Screenshots folder with with some additional captures

Docker:

• Dockerfile use this file to deploy an image for the app to be runned on a container

Scripts & Makefiles

• Makefile useful commands to make setup, install, test, lint, run_docker, run_kubernetes, upload_docker, all
• run_docker.sh script to build and start container
• run_kubernetes.sh script to run on Kubernetes
• upload_docker.sh script to upload to dockerhub container
• make_prediction.sh script to test application

Setup the Environment

• Run make setup to create a virtual env and activate it
• Run make install to install the necessary dependencies
• Run make lint to perform code lynting

You can run in different ways this app,these are Docker or Kubernetes

1. Run in Docker: ./run_docker.sh

The script will:

• Build an docker image
• List images to verify that this app is dockerized
• Run a container with this specified image and map port 5000 (host) to 80 (container)

You can now access the app on localhost port 5000. http://localhost:5000

2. Run in Kubernetes: ./run_kubernetes.sh

The script will:

• Start to run a container in Kubernetes cluster (make sure to have one ready the best option to locally is use minikube)
• Wait for the pod to be running
• List pod to verify your pod is up
• Forward port 5000 (host) to 80 (container)

You can now access the app on localhost port 5000. http://localhost:5000

You can delete when you've finished the pod with the command kubectl delete pod prediction and if you use minikube to test locally you should clean up your resources and delete the kubernetes cluster with a call to minikube delete to delete it or minikube stop for pause it.

You can test this application by running the script ./make_prediction.sh, remember that depending on the input the model returns a price prediction on a house in Boston. In the script above there is a default json (you can change it) input:

{  
   "CHAS":{  
      "0":0
   },
   "RM":{  
      "0":9.575
   },
   "TAX":{  
      "0":296.0
   },
   "PTRATIO":{  
      "0":15.3
   },
   "B":{  
      "0":396.9
   },
   "LSTAT":{  
      "0":4.98
   }
}