New, lightweight and customizable Cloudera ML Runtimes are now generally available in CDP Machine Learning and Cloudera Data Science Workbench 1.9. ML Runtimes have been rebuilt from the ground up to enable greater flexibility in frameworks, processing, and IDEs — powering customizable lightweight deployments without over encumbering the runtime profile. The new profiles are designed to meet the diverse needs of Data Scientists by enabling a variety of ML Runtimes natively and eliminating sizing and versatility issues with previous Cloudera Engine profiles.
This initial release ships with Python 3.6, 3.7, and 3.8 Runtimes as well as both the Workbench editor and with JupyterLab (Tech Preview). New Runtime options will soon follow!
To learn more about our vision and roadmap read the Announcement blog post.
Also in these releases:
CDP Machine Learning:
- Support scaling down to zero CPUs or GPUs on Azure
- Refreshed Data Science Project dashboard experience
CDSW 1.9:
- Availability of Shared Data Experience (SDX for models — enabling model governance and model cataloging on CDP Private Base.
- Refreshed Data Science Project dashboard experience
- LDAP Group Sync enables creating teams that synchronize with an LDAP group for easier user management.
- Applications can now be configured for public, unauthenticated access.
- CDSW is now Certified with SLES12 SP5 and CentOS/RHEL 7.8
As always, we welcome your feedback. Please send your comments and suggestions on our community forums.
