Containerized Python Development – Part 1

Anca Iordache

Jul 15 2020

Developing Python projects in local environments can get pretty challenging if more than one project is being developed at the same time. Bootstrapping a project may take time as we need to manage versions, set up dependencies and configurations for it. Before, we used to install all project requirements directly in our local environment and then focus on writing the code. But having several projects in progress in the same environment becomes quickly a problem as we may get into configuration or dependency conflicts. Moreover, when sharing a project with teammates we would need to also coordinate our environments. For this we have to define our project environment in such a way that makes it easily shareable. 

A good way to do this is to create isolated development environments for each project. This can be easily done by using containers and  Docker Compose to manage them.  We cover this in a series of blog posts, each one with a specific focus.

This first part covers how to containerize a Python service/tool and the best practices for it.

Requirements

To easily exercise what we discuss in this blog post series, we need to install a minimal set of tools required to manage containerized environments locally:

Containerize a Python service

We show how to do this with a simple Flask service such that we can run it standalone without needing  to set up other components.

server.py

from flask import Flask
server = Flask(__name__)

@server.route("/")
 def hello():
    return "Hello World!"

if __name__ == "__main__":
   server.run(host='0.0.0.0')

In order to run this program, we need to make sure we have all the required dependencies installed first. One way to manage dependencies is by using a package installer such as pip. For this we need to create a requirements.txt file and write the dependencies in it. An example of such a file for our simple server.py is the following:

requirements.txt

Flask==1.1.1

We have now the following structure:

app
├─── requirements.txt
└─── src
     └─── server.py

We create a dedicated directory for the source code to isolate it from other configuration files. We will see later why we do this.

To execute our Python program, all is left to do is to install a Python interpreter and run it. 

We could run this program locally. But, this goes against the purpose of containerizing our development which is to keep a clean standard development environment that allows us to easily switch between projects with different conflicting requirements.

Let’s have a look next on how we can easily containerize this Python service.

Dockerfile 

The way to get our Python code running in a container is to pack it as a Docker image and then run a container based on it. The steps are sketched below.

To generate a Docker image we need to create a Dockerfile which contains instructions needed to build the image. The Dockerfile is then processed by the Docker builder which generates the Docker image. Then, with a simple docker run command, we create and run a container with the Python service.

Analysis of a Dockerfile

An example of a Dockerfile containing instructions for assembling a Docker image for our hello world Python service is the following:

Dockerfile

# set base image (host OS)
FROM python:3.8

# set the working directory in the container
WORKDIR /code

# copy the dependencies file to the working directory
COPY requirements.txt .

# install dependencies
RUN pip install -r requirements.txt

# copy the content of the local src directory to the working directory
COPY src/ .

# command to run on container start
CMD [ "python", "./server.py" ]

For each instruction or command from the Dockerfile, the Docker builder generates an image layer and stacks it upon the previous ones. Therefore, the Docker image resulting from the process is simply a read-only stack of different layers.

We can also observe in the output of the build command the Dockerfile instructions being executed as steps.

$ docker build -t myimage .
Sending build context to Docker daemon 6.144kB
Step 1/6 : FROM python:3.8
3.8.3-alpine: Pulling from library/python

Status: Downloaded newer image for python:3.8.3-alpine
---> 8ecf5a48c789
Step 2/6 : WORKDIR /code
---> Running in 9313cd5d834d
Removing intermediate container 9313cd5d834d
---> c852f099c2f9
Step 3/6 : COPY requirements.txt .
---> 2c375052ccd6
Step 4/6 : RUN pip install -r requirements.txt
---> Running in 3ee13f767d05

Removing intermediate container 3ee13f767d05
---> 8dd7f46dddf0
Step 5/6 : COPY ./src .
---> 6ab2d97e4aa1
Step 6/6 : CMD python server.py
---> Running in fbbbb21349be
Removing intermediate container fbbbb21349be
---> 27084556702b
Successfully built 70a92e92f3b5
Successfully tagged myimage:latest

Then, we can check the image is in the local image store:

$ docker images
REPOSITORY    TAG       IMAGE ID        CREATED          SIZE
myimage       latest    70a92e92f3b5    8 seconds ago    991MB

During development, we may need to rebuild the image for our Python service multiple times and we want this to take as little time as possible. We analyze next some best practices that may help us with this.

Development Best Practices for Dockerfiles

We focus now on best practices for speeding up the development cycle. For production-focused ones, this blog post and the docs cover them in more details.

Base Image

The first instruction from the Dockerfile specifies the base image on which we add new layers for our application. The choice of the base image is pretty important as the features it ships may impact the quality of the layers built on top of it. 

When possible, we should always use official images which are in general frequently updated and may have less security concerns.

The choice of a base image can impact the size of the final one. If we prefer size over other considerations, we can use some of the base images of a very small size and low overhead. These images are usually based on the alpine distribution and are tagged accordingly. However, for Python applications, the slim variant of the official Docker Python image works well for most cases (eg. python:3.8-slim).

Instruction order matters for leveraging build cache

When building an image frequently, we definitely want to use the builder cache mechanism to speed up subsequent builds.  As mentioned previously, the Dockerfile instructions are executed in the order specified. For each instruction, the builder checks first its cache for an image to reuse. When a change in a layer is detected, that layer and all the ones coming after are being rebuilt.

For an efficient use of the caching mechanism , we need to place the instructions for layers that change frequently after the ones that incur less changes.

Let’s check our Dockerfile example to understand how the instruction order impacts caching. The interesting lines are the ones below.

...
# copy the dependencies file to the working directory
COPY requirements.txt .

# install dependencies
RUN pip install -r requirements.txt

# copy the content of the local src directory to the working directory
COPY src/ .
...

During development, our application’s dependencies change less frequently than the Python code. Because of this, we choose to install the dependencies in a layer preceding the code one. Therefore we copy the dependencies file and install them and then we copy the source code. This is the main reason why we isolated the source code to a dedicated directory in our project structure.

Multi-stage builds 

Although this may not be really useful during development time, we cover it quickly as it is interesting for shipping the containerized Python application once development is done. 

What we seek in using multi-stage builds is to strip the final application image of all unnecessary files and software packages and to deliver only the files needed to run our Python code.  A quick example of a multi-stage Dockerfile for our previous example is the following:

# first stage
FROM python:3.8 AS builder
COPY requirements.txt .

# install dependencies to the local user directory (eg. /root/.local)
RUN pip install --user -r requirements.txt

# second unnamed stage
FROM python:3.8-slim
WORKDIR /code

# copy only the dependencies installation from the 1st stage image
COPY --from=builder /root/.local/bin /root/.local
COPY ./src .

# update PATH environment variable
ENV PATH=/root/.local:$PATH

CMD [ "python", "./server.py" ]

Notice that we have a two stage build where we name only the first one as builder. We name a stage by adding an AS <NAME> to the FROM instruction and we use this name in the COPY instruction where we want to copy only the necessary files to the final image.

The result of this is a slimmer final image for our application:

$ docker images
REPOSITORY    TAG      IMAGE ID       CREATED         SIZE
myimage       latest   70a92e92f3b5   2 hours ago     991MB
multistage    latest   e598271edefa   6 minutes ago   197MB

In this example we relied on the pip’s  –user  option to install dependencies to the local user directory and copy that directory to the final image. There are however other solutions available such as virtualenv or building packages as wheels and copy and install them to the final image.

Run the container

After writing the Dockerfile and building the image from it,  we can run the container with our Python service.

$ docker images
REPOSITORY   TAG      IMAGE ID       CREATED       SIZE
myimage      latest   70a92e92f3b5   2 hours ago   991MB
...

$ docker ps
CONTAINER ID   IMAGE   COMMAND   CREATED   STATUS   PORTS   NAMES

$ docker run -d -p 5000:5000 myimage
befb1477c1c7fc31e8e8bb8459fe05bcbdee2df417ae1d7c1d37f371b6fbf77f

We now containerized our hello world server and we can query the port mapped to localhost.

$ docker ps
CONTAINER     ID        IMAGE        COMMAND        PORTS                   ...
befb1477c1c7  myimage   "/bin/sh -c  'python ..."   0.0.0.0:5000->5000/tcp  ...

$ curl http://localhost:5000
"Hello World!"

What’s next?

This post showed how to containerize a Python service for a better development experience. Containerization not only provides deterministic results easily reproducible on other platforms but also avoids dependency conflicts and enables us to keep a clean standard development environment. A containerized development environment is easy to manage and share with other developers as it can be easily deployed without any change to their  standard environment.  

In the next post of this series, we will show how to set up a container-based multi-service project where the Python component is connected to other external ones and how to manage the lifecycle of all these project components with Docker Compose.

Resources