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PREFACE CHAPTER 1. USING DATA SCIENCE PROJECTS 1.1. CREATING A DATA SCIENCE PROJECT 1.2. UPDATING A DATA SCIENCE PROJECT 1.3. DELETING A DATA SCIENCE PROJECT CHAPTER 2. USING PROJECT WORKBENCHES 2.1. CREATING A WORKBENCH AND SELECTING AN IDE 2.1.1. About workbench images 2.1.2. Creating a workbench 2.2. STARTING A WORKBENCH 2.3. UPDATING A PROJECT WORKBENCH 2.4. DELETING A WORKBENCH FROM A DATA SCIENCE PROJECT CHAPTER 3. USING CONNECTIONS 3.1. ADDING A CONNECTION TO YOUR DATA SCIENCE PROJECT 3.2. UPDATING A CONNECTION 3.3. DELETING A CONNECTION CHAPTER 4. CONFIGURING CLUSTER STORAGE 4.1. ABOUT PERSISTENT STORAGE 4.1.1. Storage classes in OpenShift AI 4.1.2. Access modes 4.1.2.1. Using shared storage (RWX) 4.2. ADDING CLUSTER STORAGE TO YOUR DATA SCIENCE PROJECT 4.3. UPDATING CLUSTER STORAGE 4.4. CHANGING THE STORAGE CLASS FOR AN EXISTING CLUSTER STORAGE INSTANCE 4.5. DELETING CLUSTER STORAGE FROM A DATA SCIENCE PROJECT CHAPTER 5. MANAGING ACCESS TO DATA SCIENCE PROJECTS 5.1. GRANTING ACCESS TO A DATA SCIENCE PROJECT 5.2. UPDATING ACCESS TO A DATA SCIENCE PROJECT 5.3. REMOVING ACCESS TO A DATA SCIENCE PROJECT CHAPTER 6. CREATING PROJECT-SCOPED RESOURCES FOR YOUR PROJECT 3 4 4 5 5 7 7 7 10 15 15 16 17 17 18 19 20 20 20 20 21 21 22 24 26 28 28 29 30 32 Table of Contents
PREFACE As a data scientist, you can organize your data science work into a single project. A data science project in OpenShift AI can consist of the following components: Workbenches Creating a workbench allows you to work with models in your preferred IDE, such as JupyterLab. Cluster storage For data science projects that require data retention, you can add cluster storage to the project. Connections Adding a connection to your project allows you to connect data inputs to your workbenches. Pipelines Standardize and automate machine learning workflows to enable you to further enhance and deploy your data science models. Models and model servers Deploy a trained data science model to serve intelligent applications. Your model is deployed with an endpoint that allows applications to send requests to the model. PREFACE
CHAPTER 1. USING DATA SCIENCE PROJECTS 1.1. CREATING A DATA SCIENCE PROJECT To implement a data science workflow, you must create a project. In OpenShift, a project is a Kubernetes namespace with additional annotations, and is the main way that you can manage user access to resources. A project organizes your data science work in one place and also allows you to collaborate with other developers and data scientists in your organization. Within a project, you can add the following functionality: Connections so that you can access data without having to hardcode information like endpoints or credentials. Workbenches for working with and processing data, and for developing models. Deployed models so that you can test them and then integrate them into intelligent applications. Deploying a model makes it available as a service that you can access by using an API. Pipelines for automating your ML workflow. Prerequisites You have logged in to Red Hat OpenShift AI. You have the appropriate roles and permissions to create projects. Procedure
CHAPTER 2. USING PROJECT WORKBENCHES 2.1. CREATING A WORKBENCH AND SELECTING AN IDE A workbench is an isolated area where you can examine and work with ML models. You can also work with data and run programs, for example to prepare and clean data. While a workbench is not required if, for example, you only want to service an existing model, one is needed for most data science workflow tasks, such as writing code to process data or training a model. When you create a workbench, you specify an image (an IDE, packages, and other dependencies). Supported IDEs include JupyterLab, code-server, and RStudio (Technology Preview). The IDEs are based on a server-client architecture. Each IDE provides a server that runs in a container on the OpenShift cluster, while the user interface (the client) is displayed in your web browser. For example, the Jupyter workbench runs in a container on the Red Hat OpenShift cluster. The client is the JupyterLab interface that opens in your web browser on your local computer. All of the commands that you enter in JupyterLab are executed by the workbench. Similarly, other IDEs like code-server or RStudio Server provide a server that runs in a container on the OpenShift cluster, while the user interface is displayed in your web browser. This architecture allows you to interact through your local computer in a browser environment, while all processing occurs on the cluster. The cluster provides the benefits of larger available resources and security because the data being processed never leaves the cluster. In a workbench, you can also configure connections (to access external data for training models and to save models so that you can deploy them) and cluster storage (for persisting data). Workbenches within the same project can share models and data through object storage with the data science pipelines and model servers. For data science projects that require data retention, you can add container storage to the workbench you are creating. Within a project, you can create multiple workbenches. When to create a new workbench depends on considerations, such as the following: The workbench configuration (for example, CPU, RAM, or IDE). If you want to avoid editing the configuration of an existing workbench’s configuration to accommodate a new task, you can create a new workbench instead. Separation of tasks or activities. For example, you might want to use one workbench for your Large Language Models (LLM) experimentation activities, another workbench dedicated to a demo, and another workbench for testing.
A workbench image is preinstalled with the tools and libraries that you need for model development. You can use the provided workbench images or an OpenShift AI administrator can create custom workbench images adapted to your needs. To provide a consistent, stable platform for your model development, many provided workbench images contain the same version of Python. Most workbench images available on OpenShift AI are pre-built and ready for you to use immediately after OpenShift AI is installed or upgraded. For information about Red Hat support of workbench images and packages, see Red Hat OpenShift AI: Supported Configurations. CHAPTER 2. USING PROJECT WORKBENCHES
code-server With the code-server workbench image, you can customize your workbench environment to meet your needs using a variety of extensions to add new languages, themes, debuggers, and connect to additional services. Enhance the efficiency of your data science work with syntax highlighting, auto-indentation, and bracket matching, as well as an automatic task runner for seamless automation. For more information, see code-server in GitHub. NOTE: Elyra-based pipelines are not available with the code-server workbench image. RStudio Server (Technology preview) Use the RStudio Server workbench image to access the RStudio IDE, an integrated development environment for R, a programming language for statistical computing and graphics. For more information, see the RStudio Server site. To use the RStudio Server workbench 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, seeBuilding the RStudio Server workbench images.
Disclaimer: Red Hat supports managing workbenches in OpenShift AI. However, Red Hat does not provide support for the RStudio software. RStudio Server is available through https://rstudio.org/ and is subject to RStudio licensing terms. Review the licensing terms before you use this sample workbench. Image name Description CHAPTER 2. USING PROJECT WORKBENCHES
CUDA - RStudio Server (Technology Preview) Use the CUDA - RStudio Server workbench image to access the RStudio IDE and NVIDIA CUDA Toolkit. RStudio is an integrated development environment for R, a programming language for statistical computing and graphics. With the NVIDIA CUDA toolkit, you can optimize your work using GPU-accelerated libraries and optimization tools. For more information, see the RStudio Server site. To use the CUDA - RStudio Server workbench 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 cuda-rstudio-rhel9 image stream. For more information, seeBuilding the RStudio Server workbench images.
Disclaimer: Red Hat supports managing workbenches in OpenShift AI. However, Red Hat does not provide support for the RStudio software. RStudio Server is available through https://rstudio.org/ and is subject to RStudio licensing terms. Review the licensing terms before you use this sample workbench. The CUDA - RStudio Server workbench image contains NVIDIA CUDA technology. CUDA licensing information is available at https://docs.nvidia.com/cuda/. Review the licensing terms before you use this sample workbench. ROCm Use the ROCm workbench image to run AI and machine learning workloads on AMD GPUs in OpenShift AI. It includes ROCm libraries and tools optimized for high-performance GPU acceleration, supporting custom AI workflows and data processing tasks. Use this image integrating additional frameworks or dependencies tailored to your specific AI development needs. ROCm- PyTorch Use the ROCm-PyTorch workbench image to run PyTorch workloads on AMD GPUs in OpenShift AI. It includes ROCm-accelerated PyTorch libraries, enabling efficient deep learning training, inference, and experimentation. This image is designed for data scientists working with PyTorch-based workflows, offering integration with GPU scheduling. ROCm- TensorFlow Use the ROCm-TensorFlow workbench image to run TensorFlow workloads on AMD GPUs in OpenShift AI. It includes ROCm-accelerated TensorFlow libraries to support high- performance deep learning model training and inference. This image simplifies TensorFlow development on AMD GPUs and integrates with OpenShift AI for resource scaling and management. Image name Description
When you create a workbench, you specify an image (an IDE, packages, and other dependencies). You can also configure connections, cluster storage, and add container storage. Prerequisites You have logged in to Red Hat OpenShift AI. Red Hat OpenShift AI Self-Managed 2.25 Working on data science projects
You can change the workbench image after you create the workbench.
The hardware profiles feature is currently available in Red Hat OpenShift AI 2. as a Technology Preview feature. 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. If the hardware profiles feature is not enabled: a. From the Container size list, select the appropriate size for the size of the model that you want to train or tune. For example, to run the example fine-tuning job described in Fine-tuning a model by using Kubeflow Training, select Medium. b. From the Accelerator list, select a suitable accelerator profile for your workbench. If project-scoped accelerator profiles exist, the Accelerator list includes subheadings to distinguish between global accelerator profiles and project-scoped accelerator profiles. If the hardware profiles feature is enabled: a. From the Hardware profile list, select a suitable hardware profile for your workbench. If project-scoped hardware profiles exist, the Hardware profile list includes subheadings to distinguish between global hardware profiles and project-scoped hardware profiles. The hardware profile specifies the number of CPUs and the amount of memory allocated to the container, setting the guaranteed minimum (request) and maximum (limit) for both. b. If you want to change the default values, click Customize resource requests and limit and enter new minimum (request) and maximum (limit) values.
Red Hat OpenShift AI Self-Managed 2.25 Working on data science projects
By default, the hardware profiles feature is not enabled: hardware profiles are not shown in the dashboard navigation menu or elsewhere in the user interface. In addition, user interface components associated with the deprecated accelerator profiles functionality are still displayed. To show the Settings → Hardware profiles option in the dashboard navigation menu, and the user interface components associated with hardware profiles, set the disableHardwareProfiles value to false in the OdhDashboardConfig custom resource (CR) in OpenShift. For more information about setting dashboard configuration options, see Customizing the dashboard.
You cannot change the storage class after you add the cluster storage to the workbench. d. For storage classes that support multiple access modes, select an Access mode to define how the volume can be accessed. For more information, see About persistent storage. Only the access modes that have been enabled for the storage class by your cluster and OpenShift AI administrators are visible. e. Under Persistent storage size, enter a new size in gibibytes or mebibytes. Use existing persistent storage to reuse existing storage and select the storage from the Persistent storage list. CHAPTER 2. USING PROJECT WORKBENCHES
Any cluster storage that you associated with the workbench during the creation process is displayed on the Cluster storage tab for the project. The Status column on the Workbenches tab displays a status of Starting when the workbench server is starting, and Running when the workbench has successfully started. Optional: Click the open icon ( ) to open the IDE in a new window. 2.2. STARTING A WORKBENCH You can manually start a data science project’s workbench from the Workbenches tab on the project details page. By default, workbenches start immediately after you create them. Prerequisites You have logged in to Red Hat OpenShift AI. You have created a data science project that contains a workbench. Procedure
