About
This site contains documentation for HPE Ezmeral Runtime Enterprise, including installation, configuration, administration, and reference content, and information about related solutions. Examples of related solutions include HPE Ezmeral ML Ops and HPE Ezmeral Runtime Analytics for Apache Spark.
5.7 Reference
- HPE Ezmeral Runtime Enterprise 5.7
- Software Versions
- Kubernetes Bundles
  Kubernetes Bundles are software packages that can contain software to support newer Kubernetes versions, updated add-ons, and software fixes. Kubernetes Bundles enable you to update your deployment without requiring you to upgrade to a newer version of HPE Ezmeral Runtime Enterprise.
- Quick Links
- What's New in Version 5.7.x
  This topic summarizes the new features and important changes in HPE Ezmeral Runtime Enterprise 5.7.x compared to HPE Ezmeral Runtime Enterprise 5.6.5.
- Prepackaged Applications
- On-Premises, Hybrid, and Multi-Cloud Deployments
- Third-Party Licenses
- Universal Concepts
- Accessing HPE Ezmeral Runtime Enterprise Applications and Services
- Navigating the GUI
  Describes the screen layout of the HPE Ezmeral Runtime Enterprise graphical user interface (GUI).
- HPE Ezmeral ML Ops
  The topics in this section provide information about machine learning operations (ML Ops/MLOps) using HPE Ezmeral ML Ops in HPE Ezmeral Runtime Enterprise. (Not available with HPE Ezmeral Runtime Enterprise Essentials.)
  - AI and ML Project Workflow
    This topic describes getting started with the AI and ML workflows in HPE Ezmeral Runtime Enterprise deployments that implement HPE Ezmeral ML Ops.
  - Installing HPE Ezmeral ML Ops
    This topic describes how to install HPE Ezmeral ML Ops on Kubernetes clusters in HPE Ezmeral Runtime Enterprise.
  - Toolbar & Main Menu - ML Ops Project Member
    Describes the toolbar and navigation sidebar available to users with Tenant Member access rights to an ML Ops project in HPE Ezmeral Runtime Enterprise.
  - ML Ops Tasks
    This topic describes Project Administrator and Project Member Tasks in HPE Ezmeral Runtime Enterprise deployments that implement HPE Ezmeral ML Ops.
    - Data Sources
      The topics in this section describe using data sources. Data sources are available for use in both HPE Ezmeral ML Ops projects and non-HPE Ezmeral ML Ops projects.
    - Source Control Configurations
      The topics in this section describe using source control configurations. Source control is available for use in both HPE Ezmeral ML Ops projects and non-HPE Ezmeral ML Ops projects.
    - Notebook Servers
      The topics in this section describe using Notebook Servers in HPE Ezmeral ML Ops.
    - Experiments
      The topics in this section describe using Experiments in HPE Ezmeral ML Ops.
    - Models
      The topics in this section describe using Models in HPE Ezmeral ML Ops.
  - Model Management APIs
    This topic describes Model Management APIs in HPE Ezmeral ML Ops.
  - Notebook ezmllib Functions
    HPE Ezmeral ML Ops on Kubernetes in HPE Ezmeral Runtime Enterprise provides a specialized function library to streamline various Machine Learning (ML) and Spark pipeline operations in Jupyter notebooks. This library, ezmllib, has several modules.
  - ezmllib
  - Notebook Magic Functions
    Jupyter notebook magic functions, also known as magics, are special commands that provide notebook functions that might not be easy for you to program using Python. HPE Ezmeral ML Ops on Kubernetes in HPE Ezmeral Runtime Enterprise supports line magics and cell magics.
  - Tutorials for HPE Ezmeral ML Ops on Kubernetes
  - Kubeflow Tutorials
- Spark on Kubernetes
  The topics in this section provide information about Apache Spark on Kubernetes in HPE Ezmeral Runtime Enterprise. (Not available with HPE Ezmeral Runtime Enterprise Essentials.)
- Kubernetes
- Platform Administration
  Tasks and reference information for Platform Adminstrators (Site Administrators) managing the HPE Ezmeral Runtime Enterprise deployment.
- HPE Ezmeral Data Fabric Introduction
  HPE Ezmeral Data Fabric is a platform for data-driven analytics, ML, and AI workloads. The patented file-system architecture was designed and built for performance, reliability, and scalability. HPE Ezmeral Runtime Enterprise supports multiple implementations of HPE Ezmeral Data Fabric.
- HPE Ezmeral Data Fabric on Kubernetes Administration
  You administer HPE Ezmeral Data Fabric on Kubernetes and Embedded Data Fabric as part of your HPE Ezmeral Runtime Enterprise environment. The external "bare metal" implementation of HPE Ezmeral Data Fabric is administered through its own tools and has its own documentation. (Not available in HPE Ezmeral Runtime Enterprise Essentials.)
- GPU and MIG Support
  This topic provides information about support for NVIDIA GPU and MIG devices on HPE Ezmeral Runtime Enterprise.
- Licensing
- Global Settings
- Planning the Deployment
  A high-level overview of the items to consider when planning an HPE Ezmeral Runtime Enterprise deployment.
- System Requirements
- Deploying the Platform
  The topics in this section describe deploying HPE Ezmeral Runtime Enterprise. Deployment is divided into phases.
- Upgrading to HPE Ezmeral Runtime Enterprise 5.7.x
  This article describes the process to upgrade to the latest 5.7.x version of HPE Ezmeral Runtime Enterprise.
- Upgrading from HPE Ezmeral Runtime Enterprise Essentials
  Upgrade from HPE Ezmeral Runtime Enterprise Essentials to the full-featured HPE Ezmeral Runtime Enterprise or to HPE Ezmeral ML Ops by uploading a license. No additional steps are required.
- Manually Restarting HPE Ezmeral Runtime Enterprise Services
  This topic describes restarting HPE Ezmeral Runtime Enterprise services in non-Kubernetes hosts.
- Uninstalling and Reinstalling HPE Ezmeral Runtime Enterprise
- Support and Troubleshooting

ML Ops Tasks

This topic describes Project Administrator and Project Member Tasks in HPE Ezmeral Runtime Enterprise deployments that implement HPE Ezmeral ML Ops.

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HPE Ezmeral Runtime Enterprise 5.7 Documentation
Abstract	HPE Ezmeral Container Platform is a unified container platform built on open source Kubernetes and designed for both cloud-native applications and non-cloud-native applications running on any infrastructure either on-premises, in multiple public clouds, in a hybrid model, or at the edge.
Published	November 2024
Edition	5.7.0