


Download Docker
Docker Images
- Docker Images are the templates (blueprint) of the project
- Images can not be updated it's read-only
- It has Source code, Libraries, external dependencies, tools etc.
Docker Containers
- Containers are the running instance of the Image
- It runs independently on computer hence we can call it, it's an isolated process
Docker base Image or Parent Image
- Example Python programming image which we can download from official website of Docker hub
How do we create Docker image
- To create a Docker image, we write the details in a Dockerfile that contains instructions for building the image. When we build this Dockerfile, an image is generated, which we can then run as a container.
Now let's proceed with setup and run
After downloading docker desktop
search Python base image in docker hub pull it using Terminal or Docker Desktop
-
run using Terminal or Docker Desktop
docker run -it --name rajnish_python python /bin/bash
now go to Container in docker desktop and see if it's running.
- Open Container and explore it more by checking python version
let's create a basic Python Flask web-app and run it through docker
- either you can create a new web app or just clone my repository productivity
git clone https://github.com/rajnishspandey/productivity.git
Here I have created a project and it's in my local I want to create a new repository on github and push it from my Terminal
git init
in case you want to remove the git initialised you can run below command and do git init again to add.
rm -rf git
git add .
git commit -m 'Initial Commit'
- I created a repository called productivity on github and will link it with my local/remote git
git remote set-url origin https://github.com/rajnishspandey/productivity.git
git push -u origin master
- now let's build the app and copy all the files of our application to our container > docker build -t productivity-app .
command to check how many images we have in docker
run docker images in Terminal
we can see now new images is created in the docker
Now we have to run it through container
- click on ports 5500:5000
it will redirect you to the browser and you should see the app running
Docker Command list from official site
some useful docker commands
- docker images to check all the images
-
docker build -t
-app . to build an images from your application -
docker image rm
- to delete image which is not in use -
docker run -it --name
/bin/bash to create a new container and run it from base image. (here above we had python as base image) -
docker image rm
-f delete image which is in use forcefully - docker ps -a to see all the containers running
-
docker container rm
to delete container which is not running -
docker container rm
-f to delete container forcefully which is running - docker system prune -a to delete all containers, images and caches.
- docker compose up to run docker compose file and created image
The above is the detailed content of From Setup to Deployment: Running a Flask App in Docker on Mac. For more information, please follow other related articles on the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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