> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/ai-workbench/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/ai-workbench/_mcp/server.

> 'AI Workbench Walkthrough: Customize Your Environment'

# Walkthrough: Customize Your Environment

Use this documentation to learn how to customize your container environment by adding a package to your to [NVIDIA AI Workbench](/ai-workbench/overview/introduction) project.
For the full list, see [Quickstart Guides](/ai-workbench/quickstart/overview).

The base container that you choose when you create your AI Workbench project might not have all the features that you need.
You can customize your project by installing additional packages, customizing mounts, and defining new environment variables.

In this walkthrough, you use the AI Workbench Desktop Application to perform the following tasks:

- [Open AI Workbench and Start Your Project Environment](/ai-workbench/quickstart/quickstart-environment#open-ai-workbench-and-start-your-project-environment)
- [Customize Your Project by Installing a Package](/ai-workbench/quickstart/quickstart-environment#customize-your-project-by-installing-a-package)
- [Rebuild Your Project Environment](/ai-workbench/quickstart/quickstart-environment#rebuild-your-project-environment)
- [Test by Using Your New Package in Code](/ai-workbench/quickstart/quickstart-environment#test-by-using-your-new-package-in-code)

<Note>

To create your own fully custom container, see [Advanced Walkthrough: Use Your Own Container](/ai-workbench/quickstart/quickstart-byoc) instead.

</Note>

## Prerequisites

Before you complete the steps in this walkthrough, you need the following:

- NVIDIA AI Workbench is installed on your local system.
- A basic test project. If you don't have one, see [Basic Quickstart](/ai-workbench/quickstart/quickstart-basic).

## Open AI Workbench and Start Your Project Environment

1. Start AI Workbench by double clicking the icon on your desktop, or by searching for the program and pressing enter. The icon has the NVIDIA logo in it.
   AI Workbench starts and the **My Locations** page appears.

2. On the **My Locations** page, for **Select a location**, click **Local**. AI Workbench connects to the location and the **My Projects** page appears.

3. Click the project that you want to use for this walkthrough.

   The project opens.

4. Click **Environment**, and then click **Start Environment**.

## Customize Your Project by Installing a Package

Use the following procedure to add a package to your AI Workbench project.

1. Near the top of the AI Workbench window, click **Open JupyterLab**.

   JupyterLab opens in your default browser ready for you to start working.

2. In JupyterLab, in the file browser, double-click *requirements.txt* to open it.

   The file opens and you should see an entry for `jupyterlab`.

3. Move to the first empty line in the file, type `numpy`, and then save the file.

4. In JupyterLab, choose **File**, and then click **Shut Down**. Confirm shut down and close the browser tab.

## Rebuild Your Project Environment

Whenever you change your project environment, you must rebuild the project before you can access the changes. Use the following procedure to rebuild your environment.

1. In AI Workbench, on the **Environment** page, if your environment is running, click **Stop Environment**.

   Wait until you see **READY** next to **Environment**.

2. Click **Start Build**.

   The project builds. Wait until you see **Build Ready** in the status bar.

3. Scroll to the **Packages** section and verify that you see `numpy` in the list.

4. Click **Start Environment**.

## Test by Using Your New Package in Code

After you add the package to your project and rebuild the container environment, you can use the new package in your code.

1. Near the top of the AI Workbench window, click **Open JupyterLab**.

   JupyterLab opens in your default browser ready for you to start working.

2. In JupyterLab, navigate to the *code* folder. If your project already has a notebook, click to open it. Otherwise; create a new notebook by choosing Python 3 (ipykernel).

3. In your notebook, enter the following code and then click run.

   ```python
   import numpy as np
   ```

4. Enter the following code and then click run.

   ```python
   print(np.e)
   ```

   You should see the following output which is the value of `e`.

   ```python
   2.718281828459045
   ```

5. Save your notebook.

6. In JupyterLab, choose **File**, and then click **Shut Down**. Confirm shut down and close the browser tab.

7. In AI Workbench, wait until you see **0 apps running** in the status bar.

8. Click **Environment**, and then click **Stop Environment**.

## Related Topics

- [Basic Quickstart (CLI)](/ai-workbench/quickstart/quickstart-cli)
- [Walkthrough: Customize Your Environment (CLI)](/ai-workbench/quickstart/quickstart-environment-cli)
- [Walkthrough: Add a Custom Application](/ai-workbench/quickstart/quickstart-custom-app)
- [Advanced Walkthrough: Hybrid RAG](/ai-workbench/quickstart/quickstart-hybrid-rag)
- Explore [NVIDIA AI Workbench Example Projects](/ai-workbench/quickstart/example-projects)
- Explore the [NVIDIA Developer Forum](https://forums.developer.nvidia.com/c/ai-data-science/nvidia-ai-workbench/671)

**Learn the Concepts**

- [How to Work with AI Workbench Projects](/ai-workbench/how-to/projects/overview)
- [Add a Brev Instance](/ai-workbench/how-to/locations/add-brev)
- [Integrations and Credentials](/ai-workbench/reference/faq#integrations-and-credentials)
- [Applications](/ai-workbench/concepts/application-concept)