Quickstart in the Desktop Application

This guide is a basic tutorial for getting started with NVIDIA AI Workbench. It will walk you through activating the context on your local system, creating a project, and launching a Jupyterlab application. It will also give you an idea of how to modify the environment, manage the container, create versions, and publish local work to GitHub.

Desktop Prerequisites

  • You successfully installed the application on your local system.

  • You have configured your NGC API key and your GitHub credentials.

  • You have set the user.name and user.email in Git.

  • You are familiar with the user docs.

Step 1: Creating a Project and Opening JupyterLab

Here, you create a blank project using an available Default Base Image on NGC, open a Jupyter Notebook running in the container, and print “Hello World!”.

  1. Open the NVIDIA AI Workbench Desktop Application to check the available Contexts on your system. Since we have yet to add any Remote Contexts, the only Context available is the local system.


  2. Click on the Local Context. Since there are no projects populated yet, select the New Project option on the left to create a new project.


  3. Fill in the details specifying the Project Name and Description. The Local Path will auto-populate by default but is editable as well. Select Next.


  4. Select the base container you want to use for your project environment. For this simple hello world project, let’s go for Python Basic. Select Create.


  5. Once the project is built, AI Workbench will automatically redirect you to the new Project page.

  6. To start the JupyterLab development environment, select Open JupyterLab on the top right-hand corner of the window and wait for the browser tab to open.


  7. Navigate to the code directory in the browser, create a new notebook, print “Hello World!” and save the notebook.


Step 2: Modifying the Environment and Rebuilding the Container

Sometimes, the default base container may not be sufficient for your development needs. For example, you may need to install additional packages, customize your mounts, and define certain environment variables. Here, let’s add some Python dependencies and rebuild the container to ensure they are available to your project.

  1. On JupyterLab or on your favorite file editor, add a package to the requirements.txt file. For example, on the Python Basic container there is no Pytorch, so let’s install the torch pip package. Save the requirements.txt file.


  2. Anytime the project environment is changed, a full rebuild is needed to provide the project access to your changes. To rebuild the container, click on Build Ready on the bottom right corner of the AI Workbench Application and select Build.


  3. To restart the container, click on X app(s) running tab on the bottom right corner and toggle it to OFF to stop JupyterLab. Then, select Open JupyterLab again to restart the application.


  4. You will see that you can now import and use the packages you just installed. Play around with it and save your work.


Step 3: Adding Code and Data to the Project

Adding code and data to the project is very easy. The Project folder is available at ~/nvidia-workbench/<project_name>, and you can add code and data directly through your file browser into the code and data subdirectories.

  1. Feel free to add some code and data that will work with the environment you have built through the selection of the Base Environment and Python dependencies you added.


    You can do this by dragging and dropping in the file browser or copying or moving into the code and data folders.

  2. In JupyterLab, adjust any file paths in your added files in order for the code and data to work with the new project directory structure.

  3. Save the changes.

Step 4: Checking Status, Making a Commit and Publishing to GitHub

After Steps 1 through 3, you have created a simple local Project. Now it is time to check the status, commit your work, and publish it to GitHub.

  1. To see the changes you have made to the project state, check under the Commit status on the AI Workbench Application. You should see that the files have been changed.

  2. To commit these changes to a Git server while on the Desktop App, select the Commit button on the top of the window.


  3. Review and edit the commit information. Then select Commit to commit the changes.


  4. To publish this project to a Git server select the Publish button on the top of the window.


  5. Review and edit the information to specify the Git Remote Server, Namespace, and Repository Visibility. Select Publish to confirm.


  6. Now, go to your specified repository to see the newly-published project.

Congrats, you are now ready to get started with NVIDIA AI Workbench!

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