Chapter 3. Technology Preview features
Important
This section describes Technology Preview features in Red Hat OpenShift Data Science 2.4. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.
For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.
- Accelerator profiles
- An accelerator is a specialized hardware component that increases the efficiency, speed, and scalability of compute-intensive tasks. For Red Hat OpenShift Data Science, the supported accelerators are NVIDIA graphics processing units (GPUs) and Habana Gaudi devices (Gaudi 1 and Gaudi 2). An accelerator profile defines the specification of an accelerator. Before you can use an accelerator in OpenShift Data Science, your OpenShift instance must contain the associated accelerator profile. Administrators can configure Red Hat OpenShift Data Science to enable users to select a specific type of accelerator that is most appropriate for a workload, in addition to specifying the preferred number of accelerators. For information on how to manually create an accelerator profile, see Working with accelerator profiles. The OpenShift Data Science upgrade process automatically creates an accelerator profile for existing NVIDIA GPUs. This profile can be modified after the upgrade. The accelerator profiles feature is currently available in Red Hat OpenShift Data Science 2.4 as a Technology Preview feature. This feature was first introduced in OpenShift Data Science 2.4.
- HabanaAI notebook image
- The HabanaAI notebook image optimizes high-performance deep learning (DL) with Habana Gaudi devices. Habana Gaudi devices accelerate DL training workloads and maximize training throughput and efficiency. Notebook images available on Red Hat OpenShift Data Science are pre-built and ready for you to use immediately after you install or upgrade OpenShift Data Science. You can use this feature with HabanaAI Operator 1.10 only. The HabanaAI notebook image feature is currently available in Red Hat OpenShift Data Science 2.4 as a Technology Preview feature. This feature was first introduced in OpenShift Data Science 2.4.
- Distributed workloads
- Distributed workloads enable data scientists to use multiple cluster nodes in parallel for faster, more efficient data processing and model training. The CodeFlare framework simplifies task orchestration and monitoring, and offers seamless integration for automated resource scaling and optimal node utilization with advanced GPU support. Designed for data scientists, the CodeFlare framework enables direct workload configuration from Jupyter Notebooks or Python code, ensuring a low barrier of adoption, and streamlined, uninterrupted workflows. Distributed workloads significantly reduce task completion time, and enable the use of larger datasets and more complex models. The distributed workloads feature is currently available in Red Hat OpenShift Data Science 2.4 as a Technology Preview feature. This feature was first introduced in OpenShift Data Science 2.4.
