Chapter 2. Set up your working environment

Red Hat AI includes a maintained Python package index that provides secure and reliable access to supported libraries, with full support for disconnected environments.

For a list of packages in an index for a specific OpenShift AI release and runtime, go to the Red Hat AI Components Indexes page.

For details about Red Hat support for the Python package index, see Support philosophy: A secure platform.

If you are using a disconnected environment, use the following code example to access the Red Hat Python index content and copy it locally. You can then upload the packages into your own internal hosting service:

#!/bin/bash -x

URL=https://packages.redhat.com/api/pypi/public-rhai/rhoai/3.4/cuda12.9-ubi9/simple/
 wget \
     --verbose \                 # Show detailed progress
     --mirror \                  # Mirror the directory structure
     --continue \                # Resume partial downloads
     --no-host-directories \     # Don't create host-named directories
     --cut-dirs=4 \              # Remove first 4 path segments
     $URL

To ensure reliable and secure access to supported libraries, start your model customization workflow with a JupyterLab workbench that is pre-configured with theRed Hat Python index.

When you use one of the images, both pip and uv commands are pre-configured to use the Red Hat Python index and system trust store for HTTPS.

When you run a pip install command, it installs the package version referenced in the Red Hat Python index, ensuring that you are installing a version of the library that is secure and reliably accessible.

For example, use the following commands to install the model customization libraries:

To get started with customizing your models, you can run provided example notebooks and scripts. The following table lists the Git repositories that provide example notebooks for each model customization component.

For a comprehensive tutorial that demonstrates an AI/ML workflow, see the Knowledge Tuning example on the Red Hat AI examples site.

The Knowledge Tuning tutorial is a curated collection of Jupyter notebooks that includes examples of using Docling to process data, Training Hub to fine-tune a model on that data, and KServe to deploy the final model for a Question and Answer application.