Main

project-ml-microservice-kubernetes/Dockerfile

@@ -2,17 +2,21 @@ FROM python:3.7.3-stretch
 
 ## Step 1:
 # Create a working directory
+WORKDIR /app
 
 ## Step 2:
 # Copy source code to working directory
+COPY . /app
 
 ## Step 3:
 # Install packages from requirements.txt
+RUN pip install --no-cache-dir --trusted-host pypi.python.org -r requirements.txt
 # hadolint ignore=DL3013
 
 ## Step 4:
 # Expose port 80
+EXPOSE 80
 
 ## Step 5:
 # Run app.py at container launch
-
+CMD ["python", "app.py"]

project-ml-microservice-kubernetes/Makefile

@@ -11,6 +11,9 @@ setup:
 	python3 -m venv ~/.devops
 
 install:
+	# Some necessary files for Scipy to work for Ubuntu 
+	# sudo apt install libblas3 liblapack3 liblapack-dev libblas-dev -y
+	# sudo apt install gfortran -y
 	# This should be run from inside a virtualenv
 	pip install --upgrade pip &&\
 		pip install -r requirements.txt
@@ -22,6 +25,8 @@ test:
 
 lint:
 	# See local hadolint install instructions:   https://github.com/hadolint/hadolint
+	wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v2.10.0/hadolint-Linux-x86_64
+	chmod +x /bin/hadolint
 	# This is linter for Dockerfiles
 	hadolint Dockerfile
 	# This is a linter for Python source code linter: https://www.pylint.org/

project-ml-microservice-kubernetes/run_docker.sh

@@ -4,9 +4,13 @@
 
 # Step 1:
 # Build image and add a descriptive tag
+docker build --tag=ml-kub-project .
 
 # Step 2: 
 # List docker images
+docker image ls
 
 # Step 3: 
-# Run flask app
+# Run flask app with logging local and to a file
+docker run -p 8000:80 ml-kub-project
+# docker run --log-driver local --log-opt max-size=10m --log-opt max-file=3 -p 8000:80 ml-kub-project