The following notes are provided to help you understand the major changes made between releases, and therefore may not include minor bug fixes and updates. If you are experiencing issues using Anaconda Enterprise, consider reviewing the known issues documented here to find workarounds.
Anaconda Enterprise 5.3.1¶
Released: July 17, 2019
Added support for using on-premises versions of Bitbucket and GitLab, and removed the previous requirement to connect to your repository endpoint over SSL.
Added support for installing the Anaconda Enterprise cluster on RHEL/CentOS 7.6.
Added support for NVIDIA CUDA 10.x drivers on GPU worker nodes.
Added the ability to set global environment variables via a new configuration file, making them available across all containers. This method can be used to address the issue where values in custom
.condarcfiles could be overwritten if the file was placed in a directory of “lower priority” that the user’s home directory.
Patched JupyterLab and Jupyter Notebook to address “session timeout” and “failed to fetch” issues. Users may still see an error, but if they reload their notebook, they can continue working without losing any work.
Fixed issue where users were being asked to confirm the environment when creating a project from the Hadoop-Spark template.
Fixed issue where the UI makes it appear that changes made by collaborators on a project have not been committed, when they have been, leading the user to believe that an error has occurred.
Improved the usability of the Schedules UI.
Backend improvements (non-visible changes)
Upgraded Jupyter Notebook to version 5.7.8 to address CVEs.
Anaconda Enterprise 5.3.0¶
Released: March 22, 2019
Increased the minimum and recommended disk space requirements for the master node.
Added recommendation to setup partitions on the master node using Logical Volume Management (LVM) to accommodate easier future expansion.
noarchto the default platforms in the
anaconda.yamlmirror config file.
Added a bootstrap executable that you can run to install conda to the Anaconda Enterprise installer.
Changed the process for installing and configuring the Anaconda Enterprise cli and cas-mirror slightly.
Added ability to deploy projects to user-supplied, static URLs.
Improved UI notifications on behind-the-scenes processes, and added a Notification Center.
Optimized database operations and made other performance improvements.
Added a sample project for connecting to an S3 bucket.
Fixed issue where users couldn’t use Kerberos authentication (kinit) to access a Spark/Hadoop cluster from within a notebook.
Fixed issue where incorrect default kernels were being used for projects created from the Hadoop-Spark template.
Improved error message handling to clarify errors and provide instructions on how to workaround or recover from them.
Added usability improvements related to scheduling deployment runs, audit trail logging, and session initialization.
Anaconda Enterprise 5.2.4¶
Released: January 21, 2019
Fixed issue where custom resource profiles weren’t being captured during in-place upgrades.
Added security fixes.
Anaconda Enterprise 5.2.3¶
Released: January 2, 2019
Included fix to address a vulnerability in Kubernetes which allowed for permission escalation. You can learn more about the vulnerability here.
Added ability for users to store secrets that can be used to access file systems, data stores and other enterprise resources from within sessions and deployments. Any secrets added to the platform will be available across all projects associated with the user’s account. For more information, see Storing secrets.
Fixed issue that required users to modify the
anaconda-project.ymlfile to make the Hadoop-Spark environment template work properly.
Added ability to view each project’s owner, and sort the list of projects based on this column.
Fixed various issues to improve project and session performance.
Anaconda Enterprise 5.2.2¶
Released: October 10, 2018
Added ability to configure an external Git repository (instead of the internal Git repository) to store projects containing version-controlled notebooks, code, and other files. Supported external Git version control systems include Atlassian BitBucket, GitHub and GitHub Enterprise, and GitLab.
Administrators can optionally configure GPU worker nodes to be used only for sessions and deployments that require a GPU (by preventing CPU-only sessions and deployments from accessing GPU resources).
In-place upgrades can now be performed from AE 5.2.x to AE 5.2.2.
Improved functionality in backup script related to backup location and disk capacity requirements.
Implemented multiple security enhancements related to cache control headers, HTTP strict transport security, and default ciphers and protocols across all services.
Administrators no longer need to generate separate TLS/SSL certificates for the Operations Center.
Improved validation of custom TLS/SSL certificates in the Administrator Console.
Administrators can now disable access to
sudo yumoperations in sessions across the platform.
Fixed an issue related to orphaned clients for sessions and deployments not being removed from Authentication Center.
Tokens for user notebook sessions and deployments are now stored in encrypted format.
Renamed platform-wide conda settings to
ssl_verifysettings in the
condasection of configmap to be consistent with
Administrators can now specify the channel priority order when creating environments/installers.
Fixed an issue related to sorting of package versions when creating environments/installers.
Fixed an issue with download links for custom Anaconda parcels.
Improved behavior of package mirroring tool to only remove existing packages when clean mode is active.
Fixed an issue related to mirroring pip packages from PyPI repository.
Added support for
noarchpackages in package mirroring tool.
Improved logging and error handling in package mirroring tool.
Fixed an issue related to projects failing to be created due to special characters in usernames.
Fixed an issue related to authorization center errors when syncing large number of users from external identity providers.
Added logout functionality to
Apache Zeppelin is now available as a notebook editor for projects (in addition to Jupyter Notebooks and JupyterLab). Apache Zeppelin is a web-based notebook that enables data-driven, interactive data analytics and collaborative documents with interpreters for Python, R, Spark, Hive, HDFS, SQL, and more.
Conda channels in the repository can be made publicly available (default), or access can be restricted to specific authenticated users or groups.
A single notebook kernel (associated with the active conda environment used within a project) is now displayed by default in Jupyter Notebooks and JupyterLab.
Collaborators can now select a different default editor for projects that have been shared with them.
Implemented various fixes to configuration parameters for scheduled jobs within a project.
Improved input/form validation related to projects, deployments, packages, and settings across the platform.
Improved error messaging/handling across the platform, along with the ability to view errors and logs from underlying services.
Improved notifications for tasks such as uploading projects and copying sample projects.
Users are now prompted to delete all related sessions, deployments, jobs, and runs (including those used by collaborators) when deleting a project.
Fixed an issue that caused numerous erroneous job runs to be spawned based on the default job scheduling parameters.
Anaconda Enterprise 5.2.1¶
Released: August 30, 2018
Fixed issue with loading spinner appearing on top of notebook sessions
Fixed issue related to missing projects and copying sample projects when upgrading from AE 5.1.x
Improved visual feedback when loading notebook sessions/deployments and performing actions such as creating/copying projects
Anaconda Enterprise 5.2.0¶
Released: July 27, 2018
New administrative console with workflows for managing channels and packages, creating installers, and other distinct administrator tasks
Added ability to mirror pip packages from PyPI repository
Added ability to define custom hardware resource profiles based on CPU, RAM, and GPU for user sessions and deployments
Added support for GPU worker nodes that can be defined in resource profiles
Added ability to explicitly install different types of master nodes for high availability
Added ability to specify NFS file shares that users can access within sessions and deployments
Significantly reduced the amount of time required for backup/restore operations
Added channel and package management tasks to UI, including downloading/uploading packages, creating/sharing channels, and more
Anaconda Livy is now included in the Anaconda Enterprise installer to enable remote Spark connectivity
All network traffic for services is now routed on standard HTTPS port 443, which reduces the number of external ports that need to be configured and accessed by end users
Notebook/editor sessions are now accessed via subdomains for security and isolation
Reworked documentation for administrator workflows, including managing cluster resources, configuring authentication, generating custom installers, and more
Reduced verbosity of console output from anaconda-enterprise-cli
Suppressed superfluous database errors/warnings
Added support for selecting GPU hardware in project sessions and deployments, to accelerate model training and other computations with GPU-enabled packages
Added ability to select custom hardware resource profiles based on CPU, RAM, and GPU for individual sessions and deployments
Added support for scheduled and batch jobs, which can be used for recurring tasks such as model training or ETL pipelines
Added support for connecting to external Git repositories in a project session or deployment using account-wide credentials (SSH keys or API tokens)
New, responsive user interface, redesigned for data science workflows
Added ability to share deployments with unauthenticated users outside of Anaconda Enterprise
Changed the default editor in project sessions to Jupyter Notebooks (formerly JupyterLab)
Added ability to specify default editor on a per-project basis, including Jupyter Notebooks and JupyterLab
Added ability to work with data in mounted NFS file shares within sessions and deployments
Added ability to export/download projects from Anaconda Enterprise to local machine
Added package and channel management tasks to UI, including uploading/downloading packages, creating/sharing channels, and more
Reworked documentation for data science workflows, including working with projects/deployments/packages, using project templates, machine learning workflows, and more
Added ability to force delete a project with running sessions, shared collaborators, etc.
Improved messaging when a session or deployment cannot be scheduled due to limited cluster resources
The last modified date/time for projects now accounts for commits to the project
Unique names are now enforced for projects and deployments
Fixed bug in which project creator role was not being enforced
Backend improvements (non-visible changes)
Updated to Kubernetes 1.9.6
Added RHEL/CentOS 7.5 to supported platforms
Added support for SELinux passive mode
Anaconda Enterprise now uses the Helm package manager to manage and upgrade releases
New version (v2) of backend APIs with more comprehensive information around projects, deployments, packages, channels, credentials and more
Fixed various bugs related to custom Anaconda installer builds
Fixed issue with
Anaconda Enterprise 5.1.3¶
Released: June 4, 2018
Backend improvements (non-visible changes)
Fixed issue when generating custom Anaconda installers that contain packages with duplicate files
Fixed multiple issues related to memory errors, file size limits, and network transfer limits that affected the generation of large custom Anaconda installers
Improved logging when generating custom Anaconda installers
Anaconda Enterprise 5.1.2¶
Released: March 16, 2018
Fixed issue with image/version tags when upgrading AE
Backend improvements (non-visible changes)
Updated to Kubernetes 1.7.14
Anaconda Enterprise 5.1.1¶
Released: March 12, 2018
Ability to specify custom UID for service account at install-time (default UID: 1000)
Added pre-flight checks for kernel modules, kernel settings, and filesystem options when installing or adding nodes
Improved initial startup time of project creation, sessions, and deployments after installation. Note that all services will be in the
ContainerCreatingstate for 5 to 10 minutes while all AE images are being pre-pulled, after which the AE user interface will become available.
Improved upgrade process to automatically handle upgrading AE core services
Improved consistency between GUI- and CLI-based installation paths
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
Updated documentation for mirroring packages from channels
Added documentation for configuring AE to point to online Anaconda repositories
Added documentation for securing the internal database
Added documentation for configuring RBAC, role mapping, and access control
Added documentation for LDAP federation and identity management
Improved documentation for backup/restore process
Fixed issue when deleting related versions of custom Anaconda parcels
Added command to remove channel permissions
Fixed issue related to Ops Center user creation in post-install configuration
Silenced warnings when using
Fixed issue related to default admin role (
Fixed issue when generating TLS/SSL certificates with FQDNs greater than 64 characters
Fixed issue when using special characters with AE Ops Center accounts/passwords
Fixed bug related to Administrator Console link in menu
Improvements to collaborative workflow: Added notification when collaborators make changes to a project, ability to pull changes into a project, and ability to 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
Improved initial startup time of project creation, sessions, and deployments by pre-pulling images after installation.
Increased upload limit of projects from 100 MB to 1 GB
Added capability to
sudo yum installsystem packages from within project sessions
Fixed issue when uploading projects that caused them to fail during partial import
Fixed R kernel in R project template
Fixed issue when loading
sparklyrin Spark Project
Fixed issue related to 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
Fixed issue when sorting collaborators and package versions
Fixed issue when saving new environments
Fixed issues when viewing installer logs in IE 11 and Safari
Anaconda Enterprise 5.1.0¶
Released: January 19, 2018
New post-installation administration GUI with automated configuration of TLS/SSL certificates, administrator account, and DNS/FQDN settings; significantly reduces manual steps required during post-installation configuration process
New functionality for administrators to generate custom Anaconda installers, parcels for Cloudera CDH, and management packs for Hortonworks HDP
Improved backup and restore process with included scripts
Switched from groups to roles for role-based access control (RBAC) for Administrator and superuser access to AE services
Clarified system requirements related to system modules and IOPS in documentation
Added ability to specify fractional CPUs/cores in global container resource limits
Fixed consistency of TLS/SSL certificate names in configuration and during creation of self-signed certificates
Changed use of
ssl_verifythroughout AE CLI for consistency with
Fixed configuration issue with licenses, including field names and online/offline licensing documentation
Updated default project environments to Anaconda Distribution 5.0.1
Improved configuration and documentation on using Sparkmagic and Livy with Kerberos to connect to remote Spark clusters
Fixed R environment used in sample projects and project template
Fixed UI rendering issue on package detail view of channels, downloads, and versions
Fix multiple browser compatiblity issues with Microsoft Edge and Internet Explorer 11
Fixed multiple UI issues with Anaconda Project JupyterLab extension
Backend improvements (non-visible changes)
Updated to Kubernetes 1.7.12
Updated to conda 4.3.32
Added SUSE 12 SP2/SP3, and RHEL/CentOS 7.4 to supported platform matrix
Implemented TLS 1.2 as default TLS protocol; added support for configurable TLS protocol versions and ciphers
Fixed default superuser roles for repository service, which is used for initial/internal package configuration step
Implemented secure flag attribute on all session cookies containing session tokens
Fixed issue during upgrade process that failed to vendor updated images
DiskNodeUnderPressureand cluster stability issues
Fixed Quality of Service (QoS) issue with core AE services on under-resourced nodes
Fixed issue when using access token instead of ID token when fetching roles from authentication service
Fixed issue with authentication proxy and session cookies
IE 11 compatibility issue when using Bokeh in notebooks (including sample projects)
IE 11 compatibility issue when downloading custom installers
Anaconda Enterprise 5.0.6¶
Released: November 9, 2017
Anaconda Enterprise 5.0.5¶
Released: November 7, 2017
Anaconda Enterprise 5.0.4¶
Released: September 12, 2017
Anaconda Enterprise 5.0.3¶
Released: August 31, 2017 (General Availability Release)
Anaconda Enterprise 5.0.2¶
Released: August 15, 2017 (Early Adopter Release)
Anaconda Enterprise 5.0.1¶
Released: March 8, 2017 (Early Adopter Release)
Simplified, one-click deployment of data science projects and deployments, including live Python and R notebooks, interactive data visualizations and REST APIs.
End-to-end secure workflows with SSL/TLS encryption.
Seamlessly managed scalability of the entire platform
Industry-grade productionization, encapsulation, and containerization of data science projects and applications.