This video delves into the step-by-step process of containerizing a simple AI/ML application with Docker.
Docker is a powerful platform that has revolutionized the development and distribution of applications by utilizing containerization, a lightweight alternative to full machine virtualization. Containerization involves encapsulating an application and its environment—dependencies, libraries, and configuration files—into a container, which is a portable and consistent unit of software. This approach ensures that the application runs uniformly and consistently across any infrastructure, from a developer’s laptop to a high-compute cloud-based server.
You need Docker desktop that can be easily installed on your computer from the official website:
You also need a Docker Hub account which you can easily create for free from here:
The application is a simple one that uses OpenAI api key and responds back to a user query.
Note: There is a simple app that can’t actually be considered as an AI application, but the main aim of this video to let AI/ML engineers know how to use Docker.
Once the application is ready, write a Dockerfile. A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image.
We will build the image using docker build command.
We will tag the image with a name using docker tag command.
Then, we will push the image as a package using the docker push command.
Once you do these steps, your application as a package gets pushed to Docker Hub as a container image.
The whole idea is to package our application so anybody can access it anywhere on the earth without having to separately installing the dependencies and libraries required to run the application.
They can do that by pulling the image from Docker Hub.
Here is a reference article from me to learn more:
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