Skip to content

The NERC's OpenShift AI dashboard Overview

In the NERC's RHOAI dashboard, you can see multiple links on your left hand side.

RHOAI Dashboard

  1. Data Science Projects: View your existing projects. This will show different projects corresponding to your NERC-OCP (OpenShift) resource allocations. Here, you can choose specific projects corresponding to the appropriate allocation where you want to work. Within these projects, you can create workbenches, deploy various development environments (such as Jupyter Notebooks, VS Code, RStudio, etc.), create connections, or serve models.

    More information about Data Science Projects (DSP) can be found here.

    What are Workbenches?

    Workbenches are development environments. They can be based on JupyterLab, but also on other types of IDEs, like VS Code or RStudio. You can create as many workbenches as you want, and they can run concurrently.

  2. Models: Manage and view the health and performance of your deployed models across different projects corresponding to your NERC-OCP (OpenShift) resource allocations.

    • Model registry: RHOAI provides a central place to view and manage registered models, helping data scientists share, version, deploy and track predictive and gen AI models, metadata and model artifacts. Model registries provide a structured and organized way to store, share, version, deploy, and track models. Users can select a model registry to view and manage your registered models.

      Important Note

      This feature is not yet available to users.

    • Model deployments: Manage and view the health and performance of your deployed models. Also, you can "Deploy Model" to a specific project selected from the dropdown menu here. Models enable you to quickly serve trained models for real-time inference. Each data science project can have multiple model servers, and each server can host models. More information about Model Serving in NERC RHOAI can be found here.

  3. Data Science Pipelines:

    • Pipelines: Manage your pipelines for a specific project selected from the dropdown menu.

    • Runs: Manage and view your runs for a specific project selected from the dropdown menu.

  4. Experiments: An Experiment in RHOAI is a logical grouping of multiple pipeline runs that share a common objective or context. For example, you might create an experiment to test different hyperparameter configurations, preprocessing strategies, or model architectures for the same dataset.

  5. Distributed workloads: Distributed workloads help teams accelerate data processing and the full ML lifecycle-training, tuning, and serving - by prioritizing and distributing jobs for optimal node utilization. Advanced GPU support ensures the performance and scalability required for foundation-model workloads.

    Important Note

    This feature is not yet available to users.

  6. Applications:

    • Enabled: Launch your enabled applications, view documentation, or get started with quick start instructions and tasks.

    • Explore: View optional applications for your RHOAI instance.

    Important Note

    Most of Applications are disabled by default on NERC RHOAI right now.

  7. Resources: Access all learning resources that Resources showcases various tutorials or demos helping your onboarding to the RHOAI platform.