Recommendation: Use the NSDT scene editor to quickly build a 3D application scene

Why choose Docker for Data Science?

As a data scientist, it is critical to have a standardized portable analysis and modeling environment. Docker provides an excellent way to create reusable and shareable data science environments. In this article, we'll walk through the steps to set up a basic data science environment using Docker.

Why would we consider using Docker? Docker allows data scientists to create isolated and reproducible environments for their work. Some of the key advantages of using Docker include:

Consistency - The same environment can be replicated on different computers. No more "it works on my machine" questions.
Portability - Docker environments can be easily shared and deployed across multiple platforms.
Isolation - Containers isolate dependencies and libraries required by different projects. No more conflicts!
Scalability - Applications built inside Docker can be easily extended by launching more containers.
Collaboration - Docker enables collaboration by allowing teams to share development environments.

Step 1: Create Dockerfile

The starting point for any Docker environment is the Dockerfile. This text file contains instructions for building a Docker image.

Let's create a basic Dockerfile for the Python data science environment and save it as "Dockerfile" without extension.

# Use official Python image
FROM python:3.9-slim-buster

# Set environment variable
ENV PYTHONUNBUFFERED 1

# Install Python libraries 
RUN pip install numpy pandas matplotlib scikit-learn jupyter

# Run Jupyter by default
CMD ["jupyter", "lab", "--ip='0.0.0.0'", "--allow-root"]

This Dockerfile uses the official Python image and installs some popular data science libraries on it. The last line defines the default command to run Jupyter Lab when starting the container.

Step 2: Build the Docker image

Now we can build the image with the command:docker build

docker build -t ds-python .

This will create an image tagged based on our Dockerfile.ds-python

Building the image may take a few minutes as all dependencies are installed. Once done, we can use the .docker images

Step 3: Run the container

With the image built, we can now start a container:

docker run -p 8888:8888 ds-python

This starts a Jupyter Lab instance and maps port 8888 on the host to 8888 in the container.

We can now navigate to Jupyter in our browser and start running notebooks!localhost:8888

Step 4: Share and deploy the image

A key advantage of Docker is the ability to share and deploy images across environments.

To save an image to a tar archive, run:

docker save -o ds-python.tar ds-python

This tarball can then be loaded onto any other system with Docker installed via:

docker load -i ds-python.tar

We can also push images to Docker registries such as Docker Hub to share with others publicly or privately within the organization.

To push an image to Docker Hub:

Create a Docker Hub account (if you don't already have one)
Log in to Docker Hub from the command line usingdocker login
Tag the image with your Docker Hub username:docker tag ds-python yourusername/ds-python
Push image:docker push yourusername/ds-python

The image is now hosted on Docker Hub. Other users can pull the image by running:ds-python

docker pull yourusername/ds-python

For private repositories, you can create organizations and add users. This allows you to securely share Docker images across your team.

Step 5: Load and run the image

To load and run a Docker image on another system:

Copy the files to the new systemds-python.tar
Load the image usingdocker load -i ds-python.tar
Start the container withdocker run -p 8888:8888 ds-python
Visit Jupyter Labslocalhost:8888

That's it! The ds-python image is now ready to use on the new system.

epilogue

This gives you a quick start in setting up a reproducible data science environment with Docker. Some other best practices to consider:

Use a smaller base image such as Python slim to optimize image size
Data Persistence and Sharing with Docker Volumes
Follow security principles such as avoiding running containers as root
Define and run multi-container applications with Docker Compose

I hope this introduction was helpful to you. Docker offers a plethora of possibilities for simplifying and extending data science workflows.

Original link: Create a simple Docker data science image (mvrlink.com)

Create a simple Docker data science image