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What is Python Docker? Importance and Ways to Use Python with Docker

Updated: Jan 17


Docker is an open-source tool that automates the deployment of an application inside a software container. The easiest way to grasp the idea behind Docker is to compare it to, well… standard shipping containers. The main motive for using Docker is that containers if an OS System Level are used as an abstraction layer on top of the application and deployment operations.

Importance of Docker:

Below are some of the Importance of Dockers:

1. Faster Development Process:

Docker gives the ability to run different versions of the same applications at the same time without any 3rd party apps. It is quite complex to keep two applications of the same version. But the Docker is the perfect solution for such applications.

2. Handy Application Encapsulation:

Docker gives you a unified image format to distribute your applications in different host systems and cloud services. Because of this, you can deliver the application in one piece (encapsulated) with all the requirements (images ) ready to run.

3. Easy Monitoring:

You don't need to remember all the specific paths where the applications and their dependencies are stored and write custom hooks to handle this. Instead, you can integrate an external logging driver and monitor your app log files in one place.

4. Easy to Scale:

In Docker, we have some core principles such as Configuration over environment variables, Communication over TCP/UDP Ports, etc. which you have to follow. If the application is correct then it will be ready for the scaling.

5. Supported Platform:

Docker's Platform is Linux, as it is based on the features provided by the Linux kernel. It allows easy adding Docker on Ubuntu for Python Projects. You can also run it on MacOS and Windows.

Ways to Use Python with Docker

To do this, the first thing we need to do is create a new project. This can only be done when you create a new file that is named and then write the main code to it. Here, our main code is to display a welcome world. After the coding part is done then the next thing that needs to be done is the creation of a Docker file.

For this, there is a set of code that you need to enter; the code explains the version of Python that you are using, followed by the RUN, work directory, copy, and the entry point. Let me explain in brief all the points.

  • RUN — You will have to decide which base image you want to use for your project. There are two ways by which you can look for images one is in the Docker hub, and the next one is in the official Python image.

  • Work Directory — After the run is done then we will have to create a directory inside the Docker image so that we can save our project for future use.

  • Copy — In this stage, you will have to copy your complete project.

  • EntryPoint — This is the final part where we will have to set an entry point. You might be thinking about what this entry point is. So, you must know that it is a command as well as the parameter that is going to be executed the moment we are going to run the Docker image.

Python with Docker streamlines the development lifecycles just by enabling the developers to work in a standardized environment with the help of a local container that provides services and applications. Moreover, it has also been noted that containers are great for the continuous integration and delivery of workflows. For instance, just think of a scenario where you will write a code locally and then share the same with your colleague with the use of a Python Docker Container.

After that, Docker Python is used for pushing the application into the test environment. Manual or automated tests are performed where bugs are encountered and fixed so that they can be deployed again to the test environment and thereafter to the higher environment. Thus, it can be said that with Docker, there is a faster way of delivering the application.


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