This repository contains a labs to help you get started with Creating and deploying Azure machine learning module.

VS Code Remote - Containers

Follow these steps to open this sample in a container using the VS Code Remote - Containers extension:

1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in the getting started steps.

2. To use this repository, you can either open the repository in an isolated Docker volume:

   • Press F1 and select the Remote-Containers: Try a Sample... command.
   • Choose the "Python" sample, wait for the container to start, and try things out!

     Note: Under the hood, this will use the Remote-Containers: Clone Repository in Container Volume... command to clone the source code in a Docker volume instead of the local filesystem. Volumes are the preferred mechanism for persisting container data.

   Or open a locally cloned copy of the code:

   • Clone this repository to your local filesystem.
   • Press F1 and select the Remote-Containers: Open Folder in Container... command.
   • Select the cloned copy of this folder, wait for the container to start, and try things out!

3. Rebuild or update your container

   You may want to make changes to your container, such as installing a different version of a software or forwarding a new port. You'll rebuild your container for your changes to take effect.

   Open browser automatically: As an example change, let's update the portsAttributes in the .devcontainer/devcontainer.json file to open a browser when our port is automatically forwarded.

   • Open the .devcontainer/devcontainer.json file.
   • Modify the "onAutoForward" attribute in your portsAttributes from "notify" to "openBrowser".
   • Press F1 and select the Remote-Containers: Rebuild Container or Codespaces: Rebuild Container command so the modifications are picked up.

Labs

After you've completed the setup steps above, you can use your Visual Studio Online environment to complete the labs.

Note: Labs that involve running code include all of the code you'll need - you'll just need to copy and paste a few values and run the code that is provided, so don't worry if you're not a programmer! We've used Python code in the labs, because that can be runs interactively in the notebooks themselves.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Legal Notices

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