Chapter 3. Technology Preview features

Hardware profiles are now available as a Technology Preview. The hardware profiles feature enables users to target specific worker nodes for workbenches or model-serving workloads. It allows users to target specific accelerator types or CPU-only nodes.

This feature replaces the current accelerator profiles feature and container size selector field, offering a broader set of capabilities for targeting different hardware configurations. While accelerator profiles, taints, and tolerations provide some capabilities for matching workloads to hardware, they do not ensure that workloads land on specific nodes, especially if some nodes lack the appropriate taints.

The hardware profiles feature supports both accelerator and CPU-only configurations, along with node selectors, to enhance targeting capabilities for specific worker nodes. Administrators can configure hardware profiles in the settings menu. Users can select the enabled profiles using the UI for workbenches, model serving, and Data Science Pipelines where applicable.

InstructLab is an open-source project for enhancing large language models (LLMs) in generative artificial intelligence (gen AI) applications. It fine-tunes models using synthetic data generation (SDG) techniques and a structured taxonomy to create diverse, high-quality training datasets.

The InstructLab pipeline is now available as a Technology Preview feature, enabling you to run the full InstructLab workflow through a data science pipeline in OpenShift AI. For prerequisites and setup instructions to run this pipeline, see InstructLab on Red Hat OpenShift AI.

Cluster administrators can use the Validating Admission Policy feature to enforce the mandatory labeling of Ray cluster resources with Kueue local-queue identifiers. This labeling ensures that workloads are properly categorized and routed based on queue management policies, which prevents resource contention and enhances operational efficiency.

When the local-queue labeling policy is enforced, Ray clusters are created only if they are configured to use a local queue, and the Ray cluster resources are then managed by Kueue. The local-queue labeling policy is enforced for all projects by default, but can be disabled for some or all projects. For more information about the local-queue labeling policy, see Enforcing the use of local queues.

With the RStudio Server notebook image, you can access the RStudio IDE, an integrated development environment for R. The R programming language is used for statistical computing and graphics to support data analysis and predictions.

To use the RStudio Server notebook image, you must first build it by creating a secret and triggering the BuildConfig, and then enable it in the OpenShift AI UI by editing the rstudio-rhel9 image stream. For more information, see Building the RStudio Server workbench images.

With the CUDA - RStudio Server notebook image, you can access the RStudio IDE and NVIDIA CUDA Toolkit. The RStudio IDE is an integrated development environment for the R programming language for statistical computing and graphics. With the NVIDIA CUDA toolkit, you can enhance your work by using GPU-accelerated libraries and optimization tools.

To use the CUDA - RStudio Server notebook image, you must first build it by creating a secret and triggering the BuildConfig, and then enable it in the OpenShift AI UI by editing the rstudio-rhel9 image stream. For more information, see Building the RStudio Server workbench images.

The optional Guardrails Orchestrator configurations are now available as a Technology Preview feature: