Docker Custom Examples

Some minor fixes working with git project

	#https://github.com/Soluto/python-flask-sklearn-docker-template
	Building the image
	=====================
	docker build . -t prodexample -f .
	docker run -p 5014:5003 -d prodexample
	Dockerfile
	===========
	#Had to change base image to use this
	FROM python:3.6
	WORKDIR /app/
	COPY requirements.txt /app/
	COPY . /app/
	RUN pip install -r ./requirements.txt
	#ENV ENVIRONMENT production
	COPY main.py __init__.py /app/
	RUN pip install -r requirements.txt
	#Added below two lines to run the app with base image changes
	ENTRYPOINT ["python"]
	CMD ["main.py"]
	Checking Logs
	===============
	docker logs 'container_id'
	ModuleNotFoundError: No module named 'sklearn.linear_model._base'
	Kill all running containers
	=============================
	docker container kill $(docker container ls -q)
	Modified Code
	=============
	#!flask/bin/python
	import os
	from flask import Flask
	from flask import request
	import pandas as pd
	from sklearn import linear_model
	import pickle
	# creating and saving some model
	reg_model = linear_model.LinearRegression()
	reg_model.fit([[1.,1.,5.], [2.,2.,5.], [3.,3.,1.]], [0.,0.,1.])
	pickle.dump(reg_model, open('some_model.pkl', 'wb'))
	app = Flask(__name__)
	@app.route('/isAlive')
	def index():
	return "true"
	@app.route('/prediction/api/v1.0/some_prediction', methods=['GET'])
	def get_prediction():
	feature1 = float(request.args.get('f1'))
	feature2 = float(request.args.get('f2'))
	feature3 = float(request.args.get('f3'))
	loaded_model = pickle.load(open('some_model.pkl', 'rb'))
	prediction = loaded_model.predict([[feature1, feature2, feature3]])
	return str(prediction)
	if __name__ == '__main__':
	#if os.environ['ENVIRONMENT'] == 'production':
	# app.run(port=80,host='0.0.0.0')
	#if os.environ['ENVIRONMENT'] == 'local':
	app.run(port=5003,host='0.0.0.0')
	commands
	===========
	docker build . -t prodexample -f .
	docker run -p 5015:5005 -d prodexample
	docker run -d -p 5013:5003 lr
	http://0.0.0.0:5015/isAlive
	http://0.0.0.0:5015/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4

view raw dockerlessons.txt hosted with ❤ by GitHub

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Best practice for container-based deployment system 

• Use a container-orchestration system such as Kubernetes or Docker Swarm to manage and deploy your containers.
• Use a container registry such as Docker Hub or Quay to store and manage your container images.
• Use a continuous integration system such as Jenkins or Travis CI to automate the build and deployment of your containers.
• Monitor your containers and applications using tools such as Prometheus or Grafana to ensure they are running optimally.
• Use a service mesh such as Istio or Linkerd to manage the communication between your services.
• Use a logging and monitoring system such as ELK or Splunk to track the performance of your containers and applications.
• Use a security scanning tool such as Twistlock or Aqua Security to ensure your containers are secure.
• Use a configuration management system such as Ansible or Chef to manage the configuration of your containers.
• Use a deployment automation tool such as Helm or Terraform to automate the deployment of your containers.
• Use a cloud provider such as Amazon Web Services or Google Cloud Platform to host your containers.

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