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We have built-in definitions for Python developers that let you get started with Python 2/3, Python3+Postgres, Miniconda, and Anaconda:

If no container definition exists, you will be prompted to create a new dev container for that workspace.
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devcontainer folder is found in the workspace root, Visual Studio Code will create the dev container use the existing dev container definition. To get started developing in a docker container, run the Remote-Containers: Open Folder in Containers… command and then browse to a folder on your local machine. You can use a Dockerfile to create a single container or a docker-compose.yml for running multiple containers.
#Visual studio remote debugging docker how to#
devcontainer folder and tells Visual Studio Code how to create a Docker environment for that workspace. A dev container is defined by files in a. The “Remote – Containers” extension allows Visual Studio Code to work seamlessly in this development environment using the concept of dev containers. This also allows new team members to reproduce your environment by installing docker and opening your workspace in Visual Studio Code.

#Visual studio remote debugging docker install#
Remote Docker Workspaces and Dev Containersĭocker containers are a popular way to create reproducible development environments without having to install complex dependencies on your local machine. This enables features like auto-completions, debugging, the terminal, source control, extensions you install, almost everything in Visual Studio Code runs seamlessly on the remote machine as if it was your local development workspace. With remote development, we’ve enabled all of these scenarios with remote Python interpreters and more: Visual Studio Code’s UI runs on your local machine and connects to a remote server which hosts your extensions remotely.

We have heard from our Python users many different reasons why they need to work in remote workspaces: in the case of SSH their code needs access to large amounts of data, compute, GPUs, or other resources with Docker they need to be able to create and switch between development environments with complex dependencies and with WSL they may need tools and packages that are only available in a Linux environment. The ability to work with WSL and remote Python interpreters have long been the top requested features on our Python Extension GitHub page.
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To get started download the remote extension pack, check out the Visual Studio Code Remote documentation, and dive right in! Check out the video below for a quick tour and keep on reading to learn more!
