Anaconda, a Python-based tool for encapsulating, running, and reproducing data science projects has released its enterprise version 5.1.1. This release includes some administrator-facing and user-facing changes.
Following are some of the changes included in the Anaconda Enterprise 5.1.1:
- This version includes the ability to specify custom UID for service account at install-time (default UID: 1000)
- An added pre-flight checks for kernel modules, kernel settings, and filesystem options when installing or adding nodes.
- Improved consistency between GUI- and CLI-based installation paths. Also, and improved security and isolation between internal database from user sessions and deployments.
- Added capability to configure a custom trust store and LDAPS certificate validation
- Simplified installer packaging using a single tarball and consistent naming
- Updated documentation for system requirements, including XFS filesystem requirements and kernel modules/settings.
- Added documentation for configuring AE to point to online Anaconda repositories, securing the internal database, and an updated documentation for mirroring packages from channels.
- Other added documentation for configuring RBAC, role mapping, and access control and also for LDAP federation and identity management.
- Includes fixed issues related to deleting related versions of custom Anaconda parcels, default admin role (ae-admin), using special characters with AE Ops Center accounts/passwords, Administrator Console link in menu, and many more. Added command to remove channel permission
- This version includes some improvements to the collaborative workflow such as, added notification on changes made to a project, ability to pull changes, and resolve conflicting changes when saving or pulling changes into a project.
- Additional documentation and examples for connecting to remote data and compute sources: Spark, Hive, Impala, and HDFS
- Optimized startup time for Spark and SAS project templates.
- Improvement in the initial startup time of project creation, sessions, and deployments by pre-pulling images after installation.
- Increased upload limit of projects from 100 MB to 1GB
- Added capability to sudo yum install system packages from within project sessions
- Fixed R kernel in R project template, and issues related to loading sparklyr in Spark Project, displaying kernel names and Spark project icons.
- Improved performance when rendering large number of projects, packages, etc.
- Improved rendering of long version names in environments and projects
- Render full names when sharing projects and deployments with collaborators.
Read more on this, and some other changes on the Anaconda Enterprise Documentation.