🛠️ Creating a Workbench

🚀 Launch a Workbench

Once the Data Connection and Pipeline Server are fully created, it’s time to create your workbench! 🎉
Go to Data Science Projects, select your previously created project (userX), and click on Create a workbench

Figure 1. Create Workbench
Make sure it has the following characteristics:
- Choose a name for it, like: My Workbench 🌟
- Image Selection: Minimal Python or Standard Data Science 🐍
- Container Size: Medium 📦
- Accelerator: NVIDIA-GPU 💻
That should look like:

Figure 2. Launch Workbench
Add the created Data Connection by clicking on the Connections section and selecting Attach existing connections. Then, click Attach for the created Minio - models connection. 🔗

Figure 3. Add Data Connection

Figure 4. Attach Data Connection
You should not need to modify any other Workbench settings (such as Storage).
Then, click on Create Workbench and wait for your workbench to be fully started. ⏳
Once it is, click the Open link to connect to it! 🔗

Figure 5. Open Link
Authenticate with the same credentials as earlier. 🔑
You will be asked to accept the following settings:

Figure 6. Accept Settings
Go ahead and do so! 👍
You should now see this:

Figure 7. Jupyter

JupyterLab and Jupyter Notebooks

Workbenches utilize JupyterLab, an interactive development environment you’ll access directly through your web browser. Think of JupyterLab as a workbench that’s excellent for hands-on experimentation, which is especially useful in AI development.

Within JupyterLab, we’ll be using Jupyter Notebooks. A Notebook is like an interactive document where you can:

Write and execute Python code in small, manageable cells.
Instantly see the output from your code.
Easily modify code and vars and re-execute
Combine code with explanatory text (like this!), images, and charts, all in one place. This combination makes Notebooks ideal for learning new concepts and testing ideas step-by-step.

Quick interactive introduction to using and customizing your Workbench (recommended)

(This Interactive demo was recorded using a Virtual Machine Based Jupyter Lab but behaves in exactly the same way.)

🌟 Git-Clone the Common Repo

We will clone the content of our Git repo so that you can access all the materials created as part of our prototyping exercise. 📚

Using the Git UI:
- Open the Git UI in Jupyter:
  
  Figure 8. Git UI
- Enter the URL of the Git repo:
  https://github.com/rhpds/showroom-summit2025-lb2959-neural-magic.git
  Figure 9. Git Clone

At this point, your project is ready for the work we want to do in it. Let’s get started! 🚀