1.2.0
Welcome to the 1.2.0 release of HPE Machine Learning Inferencing Software (MLIS).
Highlights #
This release includes the following features:
Model Caching (PV/PVC) #
As an admin, you can now enable model caching when installing MLIS. Model caching uses ReadWriteMany (RWX) PersistentVolumeClaims (PVCs) to improve inference service startup times and performance.
- Automatically managed by the controller
- Efficient access to cached models across multiple namespaces
- Configurable caching behavior and storage options
- Support for NFS and compatible storage classes
- Ability to enable/disable caching for specific models and deployments
- Tools for managing and cleaning up cached models
To enable, set modelsCacheStorage.enabled: true in Helm values during installation.
For full details, see the Model Caching documentation.
Model Registries & Storage #
Added support for a new registry type: HPE Machine Learning Data Management PFS repositories. Models can now be pulled using the pfs:// protocol. See the PFS Registry Setup Guide and Add Registry Guide for more information.
Manageable Auto Scaling & Resource Templates #
MLIS now offers enhanced customization of auto scaling and resource templates. While default templates are provided, you can create and manage custom templates through the Settings page in the UI. Key features include:
- Custom resource template creation for packaged models
- Custom auto scaling template creation for deployments
Enhancements #
- Added tooltip to user icon displaying logged-in username on hover
Bug Fixes #
- Fixed UI issue preventing confirmation prompt closure when deleting registries, packaged models, or deployments
- Corrected display of
imagefield requirement based on packaged model type - Ensured proper saving of environment variables without values for packaged models and deployments
- Fixed persistence of environment variable changes on paused deployments
- Stabilized display of packaged models list from NGC registry in UI