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Setting up your Python project for GitHub Codespaces

Get started with your Python project in GitHub Codespaces by creating a custom dev container.

GitHub Codespaces 可用于使用 GitHub Team 或 GitHub Enterprise Cloud 的组织。 GitHub Codespaces 也可作为受限的 beta 版本提供给使用 GitHub Free 和 GitHub Pro 计划的个人用户。 有关详细信息,请参阅“GitHub 的产品”。

Introduction

This guide shows you how to set up your Python project in GitHub Codespaces. It will take you through an example of opening your project in a codespace, and adding and modifying a dev container configuration from a template.

Prerequisites

  • You should have an existing Python project in a repository on GitHub.com. If you don't have a project, you can try this tutorial with the following example: https://github.com/2percentsilk/python-quickstart.
  • You must have GitHub Codespaces enabled for your organization.

Step 1: Open your project in a codespace

  1. Under the repository name, use the Code drop-down menu, and in the Codespaces tab, click Create codespace on main.

    New codespace button

    If you don’t see this option, GitHub Codespaces isn't available for your project. See Access to GitHub Codespaces for more information.

When you create a codespace, your project is created on a remote VM that is dedicated to you. By default, the container for your codespace has many languages and runtimes including Node.js, JavaScript, Typescript, nvm, npm, and yarn. It also includes a common set of tools like git, wget, rsync, openssh, and nano.

You can customize your codespace by adjusting the amount of vCPUs and RAM, adding dotfiles to personalize your environment, or by modifying the tools and scripts installed. For more information, see "Customizing your codespace."

GitHub Codespaces uses a file called devcontainer.json to configure the development container that you use when you work in a codespace. Each repository can contain one or more devcontainer.json files, to give you exactly the development environment you need to work on your code in a codespace.

On launch, GitHub Codespaces uses a devcontainer.json file, and any dependent files that make up the dev container configuration, to install tools and runtimes, and perform other setup tasks that the project requires. For more information, see "Introduction to dev containers."

Step 2: Add a dev container configuration to your repository from a template

The default development container, or "dev container," for GitHub Codespaces comes with the latest Python version, package managers (pip, Miniconda), and other common tools preinstalled. However, we recommend that you configure your own dev container to include all of the tools and scripts that your project needs. This will ensure a fully reproducible environment for all GitHub Codespaces users in your repository.

若要将存储库设置为使用自定义开发容器,需要创建一个或多个 devcontainer.json 文件。 可以在 Visual Studio Code 中从模板中添加这些文件,也可以自行编写。 有关开发容器配置的详细信息,请参阅“开发容器简介”。

  1. 访问 Visual Studio Code Command Palette (Shift+Command+P (Mac) / Ctrl+Shift+P (Windows/Linux)),然后开始键入“开发容器”。 选择“代码空间: 添加开发容器配置文件...”。

    Visual Studio Code Command Palette 中的“代码空间: 添加开发容器配置文件...”

  2. For this example, click Python 3. If you need additional features you can select any container that’s specific to Python or a combination of tools such as Python 3 and PostgreSQL. Select Python option from the list

  3. Click the recommended version of Python. Python version selection

  4. Accept the default option to add Node.js to your customization. Add Node.js selection

  5. Access the VS Code Command Palette (Shift+Command+P (Mac) / Ctrl+Shift+P (Windows/Linux)), then start typing "rebuild". Select Codespaces: Rebuild Container.

    Rebuild container option

Anatomy of your dev container

Adding the Python dev container template adds a .devcontainer directory to the root of your project's repository with the following files:

  • devcontainer.json
  • Dockerfile

The newly added devcontainer.json file defines a few properties that are described after the sample.

devcontainer.json

{
	"name": "Python 3",
	"build": {
		"dockerfile": "Dockerfile",
		"context": "..",
		"args": {
			// Update 'VARIANT' to pick a Python version: 3, 3.6, 3.7, 3.8, 3.9
			"VARIANT": "3",
			// Options
			"INSTALL_NODE": "true",
			"NODE_VERSION": "lts/*"
		}
	},

	// Set *default* container specific settings.json values on container create.
	"settings": {
		"terminal.integrated.shell.linux": "/bin/bash",
		"python.pythonPath": "/usr/local/bin/python",
		"python.linting.enabled": true,
		"python.linting.pylintEnabled": true,
		"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
		"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
		"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
		"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
		"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
		"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
		"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
		"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
		"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint"
	},

	// Add the IDs of extensions you want installed when the container is created.
	"extensions": [
		"ms-python.python"
	],

	// Use 'forwardPorts' to make a list of ports inside the container available locally.
	// "forwardPorts": [],

	// Use 'postCreateCommand' to run commands after the container is created.
	// "postCreateCommand": "pip3 install --user -r requirements.txt",

	// Comment out connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
	"remoteUser": "vscode"
}
  • name - You can name our dev container anything, this is just the default.
  • build - The build properties.
    • dockerfile - In the build object, dockerfile contains the path to the Dockerfile that was also added from the template.
    • args
      • variant: This file only contains one build argument, which is the node variant we want to use that is passed into the Dockerfile.
  • settings - These are Visual Studio Code settings.
    • terminal.integrated.shell.linux - While bash is the default here, you could use other terminal shells by modifying this.
  • extensions - These are extensions included by default.
    • ms-python.python - The Microsoft Python extension provides rich support for the Python language (for all actively supported versions of the language: >=3.6), including features such as IntelliSense, linting, debugging, code navigation, code formatting, refactoring, variable explorer, test explorer, and more.
  • forwardPorts - Any ports listed here will be forwarded automatically. For more information, see "Forwarding ports in your codespace."
  • postCreateCommand - Use this to run commands that aren't defined in the Dockerfile, like pip3 install -r requirements, after your codespace is created.
  • remoteUser - By default, you’re running as the vscode user, but you can optionally set this to root.

Dockerfile

# [Choice] Python version: 3, 3.9, 3.8, 3.7, 3.6
ARG VARIANT="3"
FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT}

# [Option] Install Node.js
ARG INSTALL_NODE="true"
ARG NODE_VERSION="lts/*"
RUN if [ "${INSTALL_NODE}" = "true" ]; then su vscode -c "umask 0002 && . /usr/local/share/nvm/nvm.sh && nvm install ${NODE_VERSION} 2>&1"; fi

# [Optional] If your pip requirements rarely change, uncomment this section to add them to the image.
# COPY requirements.txt /tmp/pip-tmp/
# RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
#    && rm -rf /tmp/pip-tmp

# [Optional] Uncomment this section to install additional OS packages.
# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
#     && apt-get -y install --no-install-recommends <your-package-list-here>

# [Optional] Uncomment this line to install global node packages.
# RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1

You can use the Dockerfile to add additional container layers to specify OS packages, node versions, or global packages we want included in our container.

Step 3: Modify your devcontainer.json file

With your dev container configuration added and a basic understanding of what everything does, you can now make changes to customize your environment further. In this example, you'll add properties to install extensions and your project dependencies when your codespace launches.

  1. In the Explorer, expand the .devcontainer folder and select the devcontainer.json file from the tree to open it.

    devcontainer.json file in the Explorer

  2. Update the extensions list in your devcontainer.json file to add a few extensions that are useful when working with your project.

    JSON
    "extensions": [
    	  "ms-python.python",
    	  "cstrap.flask-snippets",
    	  "streetsidesoftware.code-spell-checker"
      ],
  3. Uncomment the postCreateCommand to auto-install requirements as part of the codespaces setup process.

    JSON
    // Use 'postCreateCommand' to run commands after the container is created.
    "postCreateCommand": "pip3 install --user -r requirements.txt",
  4. Access the VS Code Command Palette (Shift+Command+P (Mac) / Ctrl+Shift+P (Windows/Linux)), then start typing "rebuild". Select Codespaces: Rebuild Container.

    Rebuild container option

    在代码空间内进行重建可确保在将更改提交到仓库之前,更改能够按预期工作。 如果某些问题导致了故障,您将进入带有恢复容器的代码空间中,您可以从该容器进行重建以继续调整容器。

  5. Check your changes were successfully applied by verifying the Code Spell Checker and Flask Snippet extensions were installed.

    Extensions list

Step 4: Run your application

In the previous section, you used the postCreateCommand to install a set of packages via pip3. With your dependencies now installed, you can run your application.

  1. Run your application by pressing F5 or entering python -m flask run in the codespace terminal.

  2. When your project starts, you should see a toast in the bottom right corner with a prompt to connect to the port your project uses.

    Port forwarding toast

Step 5: Commit your changes

在对代码空间进行更改(无论是添加新代码还是更改配置)之后,您需要提交更改。 将更改提交到仓库可确保从此仓库创建代码空间的其他任何人都具有相同的配置。 这也意味着你所做的任何自定义,例如添加 VS Code 扩展,都会显示给所有用户。

有关详细信息,请参阅“在 codespace 中使用源代码管理”。

Next steps

You should now be ready start developing your Python project in GitHub Codespaces. Here are some additional resources for more advanced scenarios.