Chapter 3. Technology Preview features

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 AI 2.8 as a Technology Preview feature. This feature was first introduced in OpenShift AI 2.4.

Red Hat OpenShift AI now includes the code-server notebook image. See code-server in GitHub for more information.

With the code-server workbench image, you can customize your workbench environment by using a variety of extensions to add new languages, themes, debuggers, and connect to additional services. You can also enhance the efficiency of your data science work with syntax highlighting, auto-indentation, and bracket matching.