I have been using the J-link debugger software using the LPC-Link 2 card but wanted to integrate it with visual studio code. When prompted, select the Azure Machine Learning Deployment: Docker Debug configuration. From your development environment, you monitor the logs created by the training process to find the IP address where the script is running. If you're satisfied with the settings, select, An Azure Virtual Machine in the virtual network, A Compute instance of Notebook VM in the virtual network. For more information on using Docker, see the Docker Documentation. So you are someone who likes developing in C/C++, and prefers to work on Linux with GCC. Although you can work with Azure Machine Learning resources that are not behind a virtual network, using a virtual network is recommended. Note that the screenshot below is on macOS. You attach the debugger and interactively step through the script. An Azure Machine Learning workspace that is configured to use an Azure Virtual Network. This method of debugging does not work when using Model.deploy() and LocalWebservice.deploy_configuration to deploy a model locally. To configure VS Code to communicate with the Azure Machine Learning compute that is running the debugger, create a new debug configuration: From VS Code, select the Debug menu and then select Open configurations. If you want to learn more about navigation in the debug process, refer to [5]. To start a Docker container using the image, use the following command: This attaches your score.py locally to the one in the container. Your ML pipeline steps run Python scripts. In some cases, you may need to interactively debug the Python code contained in your model deployment. In the launch.json file, find the line that contains "configurations": [, and insert the following text after it. Click on the debugger symbol on the left-side panel. Genesis code, can help you to create a configuration for remote debugging with GDB. Note that when working with compute instances, Docker is already installed. You tell VS Code the IP address to connect the debugger to by using a launch.json file. If everything went as expected, you will see a debug navigation menu along with a cursor pointing to the line in the file where you set your breakpoint. Installation. You can also select the debug icon from the side bar, the Azure Machine Learning: remote debug entry from the Debug dropdown menu, and then use the green arrow to attach the debugger. 2. Bundled with this installation comes GDB, a classic debugger for C/C++ [2]. If you are trying to debug a program running on a machine that cannot run GDB in the usual way, it is often useful to use remote debugging. You compile your target. To install debugpy on your local VS Code development environment, use the following command: To configure VS Code to communicate with the Docker image, create a new debug configuration: In the launch.json file, find the line that contains "configurations": [, and insert the following text after it: This section attaches to the Docker container using port 5678. An Azure Machine Learning pipeline that uses Python scripts as part of the pipeline steps. If the log displays an entry stating Debugger attached = False, then the timeout has expired and the script continued without the debugger. Note that the “preLaunchTask” specifies “build”. Set breakpoints where you want the script to stop once you've attached. Yes! Specifying a debug build type using the CMake option is necessary for the debugger to be able to find the breakpoints (no symbol file will be created otherwise).[3]. These arguments allow you to enable the debugger as needed, and set the timeout for attaching the debugger: Add the following statements. Let’s see how to configure it to debug with GDB. and set pip_packages=['debugpy', 'azureml-sdk==']. Into the launch.json in your ‘.vscode’ directory, paste the following. Docker images that use the same dependencies defined in your environment are reused between runs. So now you have a C/C++ compiler and a debugger. Start VS Code, open the local copy of score.py, set a breakpoint, and have it ready to go before using the steps in this section. Debug. Change the "host": "" entry to the IP address returned in your logs from the previous section. It also demonstrates how to use the environment created earlier by setting runconfig=run_config: When the pipeline runs, each step creates a child run. You can now step through the code as it runs, view variables, etc. Otherwise, select No. Doing so attaches the VS Code debugger to the container running your experiment. 8. To install debugpy on your VS Code development environment, use the following command: For more information on using debugpy with VS Code, see Remote Debugging. You can follow the following steps to set up a debugger in your visual studio code : 2. To stop the container, use the following command: Now that you've set up VS Code Remote, you can use a compute instance as remote compute from VS Code to interactively debug your code. If at any point you want to cancel your run, right-click your run node and select Cancel run. NOTE: Some versions of Blastem, can crash when is used with vscode debugging session; this is a unimplemented features in Blastem for the GDB protocol.