docker containers 
Having heard a lot of positive things about Docker, I decided to write this post to explain what it is, why it matters, and how to start implementing it.
What is Docker?
Docker has become synonymous with containers but is actually various technologies, e.g. Swarm. Docker containers wrap software with everything needed to run it that can be installed on a server: code, runtime, system tools, system libraries, settings, etc.
Unlike VMs, containers share the host system’s kernel with other containers.
Why does Docker matter?
Docker lets:
- software run anywhere, anytime the exact same way, regardless of its environment
- code run faster and use less RAM
- you “sandbox” projects
How to implement?
Containers are typically constructed from a container image that defines all resources and libraries to run the app. This is called a Dockerfile. The Dockerfile is built by running docker build -t [repo name] .
# Use an official Python runtime as a parent image
FROM python:2.7-slim
# Set the working directory to /app
WORKDIR /app
# Copy the current directory contents into the container at /app
ADD . /app
# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt
# Make port 80 available to the world outside this container
EXPOSE 80
# Define environment variable
ENV NAME World
# Run app.py when the container launches
CMD ["python", "app.py"]
Next, starting the Docker container involves the following: docker run -p 4000:80 [repo name]. After pushing the Docker image to a repo, a docker-compose.yml file defines production behavior. Similar to pushing a GitHub repo, Docker images are published which lets the app run on any machine with this command: docker run -p 4000:80 username/repository:tag. To start, reading and writing Dockerfiles and Docker Compose YAML files is highly recommended.
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