docs/data-science/how-to-contribute/data-scientists/creating-a-new-docker-image-for-c2d.md

Creating a new docker image for C2D

Docker is widely used to run containerized applications with Ocean Compute-to-Data. Ocean Compute-to-Data allows computations to be brought to the data, preserving data privacy, and enabling the use of private data without exposing it. Docker is a crucial part of this infrastructure, allowing applications to run in a secure, isolated manner.

The best way to sell access to models using C2D is by creating a docker image. Docker is an open-source platform designed to automate the deployment, scaling, and management of applications. It uses containerization technology to bundle up an application along with all of its related configuration files, libraries, and dependencies into a single package. This means your applications can run uniformly and consistently on any infrastructure. Docker helps solve the problem of "it works on my machine" by providing a consistent environment from development to production.

Main Value:

• Consistency across multiple development and release cycles, ensuring that your application (and its full environment) can be replicated and reliably moved from one environment to another.
• Rapid deployment of applications. Docker containers are lightweight, featuring fast startup times as they don't include the unnecessary binaries and libraries of full-fledged virtual machines.
• Isolation of applications and resources, allowing for safe testing and effective use of system resources.

Step by Step Guide to Creating a Docker Image

1. Install Docker

   First, you need to install Docker on your machine. Visit Docker's official website for installation instructions based on your operating system.

   • Docker for Windows
   • Docker for Mac
   • Docker for Linux

2. Write a Dockerfile

   Docker images are created using Dockerfiles. A Dockerfile is a text document that contains all the commands needed to assemble an image. Create a new file in your project directory named Dockerfile (no file extension).

3. Configure Your Dockerfile

   Here is a basic example of what a Dockerfile might look like for a simple Python Flask application:

   bashCopy code# Use an official Python runtime as a parent image
   FROM python:3.7-slim
   
   # Set the working directory in the container to /app
   WORKDIR /app
   
   # Add the current directory contents into the container at /app
   ADD . /app
   
   # Install any needed packages specified in requirements.txt
   RUN pip install --no-cache-dir -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"]
   

   For a more detailed explanation of Dockerfile instructions, check the Docker documentation.

4. Build the Docker Image

   Navigate to the directory that houses your Dockerfile in the terminal. Build your Docker image using the docker build command. The -t flag lets you tag your image so it's easier to find later.

   shellCopy codedocker build -t friendlyhello .
   

   The . tells the Docker daemon to look for the Dockerfile in your current directory.

5. Verify Your Docker Image

   Use the docker images command to verify that your image was created correctly.

   shellCopy codedocker images
   

6. Run a Container from Your Image

   Now you can run a Docker container based on your new image:

   shellCopy codedocker run -p 4000:80 friendlyhello
   

   The -p flag maps the port on your machine to the port on the Docker container.

You've just created and run your first Docker image! For more in-depth information about Docker and its various uses, refer to the official Docker documentation.