![]() You can use the Docker command line to build image. Please also check it too see if it meets your requirement. It contains the file and the folder to be excluded from the image. Please review the Dockerfile it generated and see if there is anything need to modify. You can run below command in the terminal to create the requirements.txt file:Īfter the file is generated, please add “gunicorn” in the requirements.txt if there is no "gunicorn" as the Dockerfile use it to launch your application for Flask application. Therefore, it is recommended to have the requirements.txt file before you create the Dockerfile. However, it DOES NOT contain all the modules you installed for this project. If you do not have requirements.txt file in the project, the Docker extension will create one for you. I select no as it is not multi-container. I select 80.įinally, you will be asked if Docker Compose file is included. Secondly, you will be asked the port your application listens on. I select “Python Flask”.įirstly, you will be asked to select the entry point file. You will be required to select your programming languages and framework(It also supports other language such as node.js, java, node). Then select “Docker: Add Docker Files to Workspace” To do this, enter ctrl+shift+P and search "Dockerfile" in your Visual Studio Code. You can use Docker extension to create the Dockerfile for you automatically. To create a docker image, you need to have a Dockerfile for your application. Generate a Dockerfile for your application Then run below command to install the modules required in the requirement.txt:ĥ. ![]() If you open a new terminal, you also find the prompt shows that you are now in new python environment as well: Then if you open the app.py file, you can see it used the newly created python environment as your python environment: Then after you open the folder, you will be able to see some folders are created in your project: To setup your Python Environment in your project, you need to run below commands in the terminal: It makes your runtime and modules easy to be managed. It is recommended to create your own project python environment. ![]() I use a quick simple Flask application here for example, so I run below command to clone its git project:Īfter you install Python 3 and create project folder. Then you should see a terminal for your WSL. To deploy your container to Azure in Visual Studio Code, you also need to have Azure Tools installed.Ĭlick "Terminal" in menu, and click "New Terminal": Please search and install via Visual Studio Code Extension. The Docker extension can help you create Dockerfile automatically and highlight the syntax of Dockerfile. Install some extensions for Visual Studio Codeīelow two extensions have to be installed after you connect Visual Studio Code to WSL. Then select “Connect to WSL” or “Connect to WSL using Distro”:Ģ. To develop your project in Visual Studio Code in WSL, you need to click the bottom left blue button: Download the installer from below Docker website and run the downloaded file to install it. To create an image for your application in WSL, you'll need Docker Desktop for Windows. For Ubuntu, please refer to below official documentation: You'll need to install Docker in your Linux environment. Then run following commands to install python 3.10 (if you use Python 3.5 or a lower version, you may need to install venv by yourself): You'll also need to install the WSL extension in your Visual Studio Code. To install WSL 2, open PowerShell or Windows Command Prompt in administrator mode, enter below command: We recommend installing WSL 2 as it has better support with Docker. WSL provides a great way to develop your Linux application on a Windows machine, without worrying about compatibility issues when running in a Linux environment. In this tutorial, we will use a Python Flask application as an example, but the steps should be similar for other languages such as Node.js.īefore you begin, you'll need to have the following prerequisites set up: This focus on the UI is what makes the process graceful and user-friendly. While there are a few steps that require the use of command lines, the majority of tasks can be completed using the UI. This tutorial emphasizes using the user interface to complete most of the steps, making the process more reliable and understandable. This tutorial outlines a graceful process for developing and deploying a Linux Docker container on your Windows PC, making it easy to deploy to Azure resources. Creating and deploying Docker containers to Azure resources manually can be a complicated and time-consuming process.
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