Cloudera Docs

October 31, 2025

Release notes and fixed issues for version 2.0.53-b241.

New Features / Improvements

Model Hub
  • Improved model information display by surfacing attributes like MaxTokens, Parameters, and Dimensions for Embedding Models, enabling better decision-making before importing models.
Cloudera AI Inference service
  • Added the ability for users to start and stop deployed model endpoints, providing greater control over resource management and cost optimization. This feature allows you to pause inactive endpoints to save resources while maintaining the ability to quickly restart them when needed.
  • Improved user experience by enabling customers to open model endpoints in different browser tabs, allowing for better multitasking and simultaneous monitoring of multiple endpoints.
  • Enhanced API accessibility by providing customers access to the Swagger UI for invoking AI Inference APIs, enabling interactive API testing and documentation exploration.
  • Added visibility to the underlying compute cluster through a direct link in the UI for AI Inference instances, providing seamless navigation to cluster details and infrastructure monitoring.
  • Implemented validation for root volume size when adding new nodes to AI Inference deployments, preventing configuration errors and ensuring adequate storage capacity.
  • Enhanced node group management by displaying GPU types and enabling filtering based on GPU type when searching through available nodes, improving resource selection efficiency.
  • Upgraded KServe from version 0.12 to 0.15, enhancing the underlying model serving infrastructure.
Cloudera AI Platform
  • Enhanced support for self-signed certificates in public cloud deployments, resolving installation and update pain points.
  • Re-enabled cadence workflow for workbench upgrades, fixing version compatibility issues.
Cloudera AI Workbench
  • Optimized the loading speed of the Site Administration overview and the Projects pages by improving cluster-wide resource usage data collection, ensuring quick loading even in environments with 1,000+ user namespaces.
  • The Job Retry feature introduces automated recovery for failed, timed out, or skipped jobs. Administrators can now set concurrent retry limits and customize the retry behavior to enhance job resilience and eliminate the need for manual failure intervention.

Fixed Issues

Cloudera AI Registry
  • Fixed Events & Logs rendering during the creation of new registries in the UI. (DSE-48101)
Cloudera AI Inference service
  • Resolved inconsistencies in the tooltips displayed on the Model Endpoint Details page, ensuring accurate and helpful information is shown to users. (DSE-45042)
  • The API service can now inspect the metadata of MLflow models from the Cloudera AI Registry to correctly identify the Hugging Face transformer flavor. This ensures the model is automatically routed to the correct runtime (i.e. HuggingFace runtime with vLLM backend unlike defaulting to Triton + ONNX earlier), enabling successful deployment of MLflow transformer models. (DSE-46462)
Cloudera AI Workbench
  • Removed the rigid requirement for every team to have an Administrator, allowing Team Administrators (or Site Administrators if none are designated) to manage team membership and roles. (DSE-41700)
  • You can now customize the model build process with two new options: model_root_dir allows setting a custom source directory, and build_script_path enables specifying a custom location for the build script. (DSE-45166)
  • Resolved a page layout issue that occurred after horizontal window resizing. The fix ensures the browser view restores correctly without requiring a manual refresh. (DSE-40393).
MLRuntimes
  • Fixed an issue where system messages were hidden or suppressed when a Workload (such as a Session, Job, or Application) failed to start up while using a PBJ Workbench Runtime. (DSE-46126)

Behavioral Change

MLRuntimes
  • The R kernel output mechanism has been updated to improve visibility of interactive commands. The output of R commands (for example, install.packages("sparklyr")) is now mirrored to both the Session tab and the Logs tab by default, rather than appearing solely in the Logs. This ensures immediate visibility of important messages like installation progress, compilation output, and errors. Users can revert to the previous behavior by setting the environment variable JUPYTER_KERNEL_OUTPUT_FILTER_REGEX to "DISABLED" or "^$". (DSE-35335)
  • Clearing a session now permanently removes all content from the session history, ensuring that subsequent PDF exports and email reports contain only current, relevant results and no stale data from previous runs. (DSE-12565)
Cloudera AI Inference service
  • MLFlow integration with transformer models is now complete, enabling models saved using the workbench (via MLFlow) in the Hugging Face transformer format to be successfully deployed on Cloudera AI Inference service, including both regular and finetuned models. (DSE-43840)