In the following examples, we are assuming that you chose the latter option. Alternatively, you can download this article’s source code. First, we need to install three extensions:Īfter installation, we can open our project (created in the previous article). We’ll use it to run and debug our Python service. You can find plenty of tutorials at the official website. Thanks to its extensibility, it supports an endless list of languages and technologies. It is available for Windows, macOS, and Linux.
We are assuming that you are familiar with Visual Studio Code, a very light and flexible code editor. You are welcome to download the code used in this article. In this article, we’ll use Visual Studio Code to debug our service running in the Docker container. It allowed us to run our NLP models in a web browser. In the previous article of the series, we have exposed inference NLP models via Rest API using Fast API and Gunicorn with Uvicorn worker.
This series assumes that you are familiar with AI/ML, containerization in general, and Docker in particular. In this series of articles, we explore Docker usage in Machine Learning (ML) scenarios. You can attach the Visual Studio debugger to a process running in a Windows Docker container on your local machine using the Attach to Process dialog box.Container technologies, such as Docker, simplify dependency management and improve portability of your software. Select a running container to attach to after connecting to the daemon successfully and hit OK.Ĭhoose the corresponding container process from the list of Available processes and select Attach to start debugging your C# container process in Visual Studio!Īttach to a process running on a Windows Docker container.
via TCP, IP, etc.) under Docker host (Optional) and click the refresh link. To set the target to a remote container running a process via a Docker daemon
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