Anaconda Enterprise version 5.1.1 released!

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:

Administrator-facing changes

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

User-facing changes

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.