Applications

Applications are software or user interfaces available to use in, or with, a Workbench Project.

Some Example Applications

  • JupyterLab: The default base images for NVIDIA AI Workbench have JupyterLab installed.

  • Visual Studio Code: If VS Code is installed on your local system, you can connect it to the Project container, similar to Dev Containers.

  • Gradio Apps: Some Projects have simple web apps like chat apps or RAG apps installed.

Types of Applications

  • Web App: an application that runs in the Project container with a browser-based UI.

    • A typical example is JupyterLab.

    • Web apps are installed in the Project container, and that is where they run.

  • Process: a command or script that runs to perform a task without a UI or exposing any network access.

    • A typical example is a shell script that downloads a model.

    • Processes usually rely on some code in the Project repository, and they run in the Project container.

  • Native App: an application launched outside of the Project container on your machine.

    • VS Code is the only supported native app at the moment.

    • Native apps run on your local system. They may not have access to the Project container.

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Each Project has its own applications, and you can start and stop them on the Project window in the AI Workbench UI or using the CLI.

Default Applications and Built-In Applications

  • When you create a new Project in AI Workbench, each of the Default Bases has JupyterLab installed and configured. The Pytorch and Tensorflow bases also have Tensorboard installed.

  • In addition, if you are working on a Project that doesn’t have JupyterLab installed, AI Workbench has a feature that will install it for you.

Note

You can also install and configure applications yourself. You can also use NVIDIA’s example projects on GitHub as a reference.

Installing and Configuring a Web App

  • You install a web app by installing any dependencies in the container and making sure any relevant code is set up in the Project.

  • Basically, if you know how to run the application manually then you should be able to get it running in the Project.

  • In addition to installing it, you need to configure AI Workbench to manage the application. This involves some simple metadata and commands.

  • For example, configuring a web app requires information like

    • Application name, e.g. JupyterLab

    • Start and stop commands

    • A health check command

    • Port the app is available on

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