Major features and updates for the Cloudera Machine Learning service on Private Cloud.
April 11, 2022
CML on Private Cloud, version 1.3.4, has the following updates.
- ML Runtimes - ML Runtimes are now supported in CML Private Cloud. For more information, see Managing ML Runtimes.
- Cloudera Data Visualization - Cloudera Data Visualization is now available in the default runtime.
- GPU Taint support - GPU taints, which affect node scheduling, are now supported for both OCP and ECS clusters. For more information, see GPU node setup.
January 13, 2022
CML on Private Cloud, version 1.3.3, has the following updates.
- Business User Experience - A new user role, ML BusinessUser, provides restricted access to view Applications created in CML.
- API v2 - A new API for operations on projects, jobs, models, and applications is now generally available.
- Upgrade - It is not possible to upgrade an existing ML workspace on an ECS cluster. You have to provision a new workspace.
- Upgrade - After upgrading the ECS control plane, model and experiment building in an ML workspace might fail. See the Known Issue for more information.
- Engines - Engine version 15-cml-2021.09-2 has been patched for CVE-2021-44228, the Apache Log4j2 vulnerability. You should use engine version 15-cml-2021.09-2 instead of version 15-cml-2021.09-1 wherever possible.
October 29, 2021
CML on Private Cloud, version 1.3.2, has the following updates.
- Upgrade - Fixed an issue so that upgrading a CML workspace with ML Governance enabled works.
October 4, 2021
CML on Private Cloud, version 1.3.1, has the following new features and updates.
- Embedded Container Service (ECS) is now supported.
- Installation - If ECS is installed using Cloudera Docker Registries, then CML Workspace Model and Experiment building is not supported.
- Upgrade - Upgrading a CML workspace with ML Governance enabled fails.
April 27, 2021
CML on Private Cloud, version 1.2, has the following new features and updates.
- Support for OCP 4.6 and upgrading from PVC 1.1.
- Improved non-transparent proxy support for air-gapped environments.
- Introduced Applied ML Prototypes (AMPs).
- Added NFS support:
- NFS versions v3 and v4.x are supported.
- External NFS security improvements -
no_root_squashexport option has been removed.
- Support added for custom service principals (Beta).
- Monitoring now uses CDP centralized Grafana. Added database metrics and improved alerts.
- DSE-12037 - Fixed an issue with the seamless login for Grafana.
- DSE-14891 - Fixed an issue with broken Engine and Session log links.
- Various security fixes.
December 16, 2020
CML on Private Cloud, version 1.1, has the following new features and updates.
- MLOPS-216 - Production ML Support
Model Metrics track machine model serving performance metrics. Model Governance use Apache Atlas to track builds, experiments and deployment of machine learning models.
- DSE-10777 - UMS Integration
MLUser and MLAdmin resource roles are now available and assignable through Environment settings.
- DSE-12955 - Self Signed Private CA certs For custom container registries
Customers can now use Container registries that are using self signed or private CA signed certificates. There is an option to upload the self signed or private CA signed certificates certificate during Private Cloud installation.
- DSE-10759 - GPU support
The OpenShift Nvidia operator is now supported for use with CML workloads.
August 17, 2020
This is the first release of CML on Private Cloud, version 1.0.
- Run Machine Learning workloads on OpenShift clusters in your own data center.
- Easily onboard a new tenant and provision an ML workspace in a shared OpenShift environment.
- Enable data scientists to access shared data on CDP Private Cloud Base and CDW.
- Leverage Spark-on-K8s to spin up and down Spark clusters on demand.
- Take advantage of most CML features on public cloud, including Teams, Projects, Experiments, Models, and Applications.