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

Table 2.1 lists the images that are configured to use the Red Hat Python index.

Notes:

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://console.redhat.com/api/pypi/public-rhai/rhoai/3.2/cuda12.9-ubi9/simple/

wget \
--verbose \
--mirror \
--continue \
--no-host-directories \
--cut-dirs=4 \
$URL

To ensure reliable and secure access to supported libraries, start your model customization workflow by creating a workbench image that is based on a Red Hat base image that is configured to use the Red Hat Python index. These base images are listed in Table 2.1.

Note: When you create a custom workbench image that is based on one of the images listed in Table 2.1, install JupyterLab. You can use JupyterLab to run the example model customization notebooks.

For guidance on custom workbenches, see Creating a custom workbench image from your own image.

When you use one of the images listed in Table 2.1 as a base image, 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. Table 2.2 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.