A dockerized flask app demonstrating experience in deploying machine learning models
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MongoDB has handled the task of data storage install and run before starting project
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foo@bar:~$ echo "deb [ arch=amd64 ] https://repo.mongodb.org/apt/ubuntu bionic/mongodb-org/4.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-4.0.list
foo@bar:~$ sudo apt-get update
foo@bar:~$ sudo apt-get install -y mongodb-org
foo@bar:~$ sudo systemctl start mongod.
Once the repository has been cloned, change directory to the repository's clone
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foo@bar:~$ cd Horse-Health-Predictor-master/
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Install requirements using pip .
foo@bar:~/Horse-Health-Predictor-master$ sudo pip install -r requirements.txt
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Start running the server
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foo@bar:~/Horse-Health-Predictor-master$ cd Flask/
foo@bar:~/Horse-Health-Predictor/Flask$ python fla.py
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Dockerfile has been generated, an image has been uploaded to the repository to clone and run
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foo@bar:~/Horse-Health-Predictor-master$ docker run -p 4000:5000 prashkurella/horsesurvival:final