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What is Python Docker? Know New Ways to Use Python with Docker



You might be wondering what this Docker is all about. So let me tell you that Docker, which is a source code for the Core Docker project, is nothing but an infrastructure management platform that is required for running as well as the deployment of software. The main motive behind using Docker is that containers of an OS system level are used as an abstraction layer on top of the application and deployment operations.

What is the Importance of Docker?

The best part of Docker is that it can package up applications along with all the required operating system dependencies for flexible deployment across environments. Moreover, it has also been noticed that in the long run, Docker has the potential to act as an abstraction layer. With this, it can easily run on any of the servers irrespective of the server on which it is about to be hosted.

In addition, to all this, you will be amazed to know that within just five years of release, Docker has gained tremendous popularity by the user. As per the Docker official report, there are more than three and half million apps that have been containerized with the help of this technology. And also, there are more than fifteen thousand Docker-related jobs which are listed on LinkedIn.

Talking about its benefits, you will find that Docker is highly efficient. The reason behind it is that Docker makes use of the shared operating system, and thus it leaves behind unnecessary padding that is associated with the virtual machines. This all helps to make Docker a cost-effective as well as faster application. As per the user experience, Docker works best with a web application such as Flask or Django. 

Ways by which you can use Docker with Python

You might be wondering how Docker can be used with the python application tool. So the first thing which we need to do is write an application that is just similar to Docker’s welcome world. It can be said that the main motive behind this program is to print the welcome world message. This should only be displayed when we try to execute the container with the help of an app that is completely based on Python with Docker.

In order to do this, the first thing which we need to do is create a new project. This could 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 which 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 most 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. 

What can be used for Docker?

As mentioned earlier, 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 the 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 which, Docker Python is used for pushing the application into the test environment. There manual or automated tests are performed where bugs are encountered and fixed so that they could 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 delivery of the application.

Docker offers Responsive Deployment and Scaling

You will be astonished to know that Docker allows an individual for a higher portable workload. In addition to this,  Docker can be executed on the local laptop of the developers or on any virtual machine. Thus, you can easily manage your workload with it. If you have any intention of scaling up or tearing down the application, then it has been made easy with Docker.   

Moreover, Docker Python also provides a platform that helps to manage the lifecycle of a container.

  • All that you need to do is develop your application and all its supporting components with the use of the container.
  • Then the container is the only thing that becomes the unit for testing as well as distributing your application.
  • The moment you find that you are completely ready after the bug fixes and all then you will have to deploy your code to the production environment as a container. Here, you need to keep one thing in your mind that the process works the same irrespective of the fact that you are using a production environment, local data center, or a hybrid model of both. 

For suppose your team is developing a website using javascript and are using SQL server 15 to create a database to store data.  Your developers can push the code on to docker to share their work with their colleagues or testing team. By doing this the testing team does not require the total environment to be set up on their local system but just that they can execute manual and automated systems. By using Python Docker, a lot of time can be saved and resources can be used efficiently.