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.

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Anaconda Enterprise 5.5.1

Released: August 23, 2021 Expanded support for Bring Your Own Kubernetes
  • Updated internal Kubernetes API support to include versions up to 1.21
    • Previous versions of AE5 were limited to Kubernetes 1.15 and earlier, which are no longer available in most environments.
    • Verified on multiple Kubernetes offerings
      • Google Kubernetes Engine (GKE)
      • Amazon Elastic Kubernetes Service (EKS)
      • OpenShift (up to OCP 4.7)
    • Tested with Anthos/GKE and OpenShift on-prem
  • Expanded support for Managed Persistence volumes
    • AE 5.5.1 allows the administrator to specify a custom group ID (GIDs) for the mounted volume, in order to support a wider variety of attached storage providers
User-facing changes
  • Improved upon the “session failed” notification: when a session is started on the project grid page, a spinner now shows the session is working in the background.
  • Collaborator can be successfully removed from a project (from the UI), and a project with at least one collaborator can be successfully deleted.
  • If you use an internal proxy for accessing external resources, you can (and should) set the global proxy in anaconda-enterprise-env-var-config and restart the workspace pod. This will enable the session to start successfully.
  • Changed the value of kubernetes.run_as_root from false to true in the configmap, so that sessions, deployments, and jobs will run as UID from the start. This enables features like authenticated NFS and the sparkconfig script. This also resolved the issue with sessions taking a long time to open.
  • Values you put in conda.other_variables appear in the log list and are available in sessions, deployments, and job.
  • JupyterLab and Jupyter Notebook both notify the user that the session is ready instead of the “failed to start session” message.
  • Corrected an issue in the UI pod where, when a user deletes a session, the UI will still ask for file changes (e.g. the git commit UI) before it completely cleans out references to that session, causing a traceback.
  • Corrected the traceback that occured whenever a user closed or navigated away from a window with JupyterLab running.
  • Fixed the dnf issue related to the new repo added for sssd.
    • Launch a session
    • Open a terminal
    • Run sudo dnf list , which should not cause errors:
      • sudo dnf install --installroot=/opt vim 2
      • Sudo dnf list
  • Ability to successfully commit, push and/or revert the commit from cli.
  • Resolved an issue with JupyterLab staying on screen when a session is stopped. Users are unlikely to have two JupyterLab windows open, but even when a user does, and they stop the underlying session in one window, it should be understood that the other window will lose its connection as well. The main window will go back to the “No open sessions” page.
  • Resolved an issue around config file secrets, where run-once and scheduled jobs were not getting the spark-config config file secret. As a result, the sssd.conf was not set up properly for authenticated NFS on jobs.
  • Modified the startup logic so that 1) all project sessions can share a common package cache, and 2) deployments and jobs get their own separate package cache.
  • A proper validation message will now be displayed when saving web certificates to inform the user which field the error is coming from.
  • Ability to allow /opt/continuum/project to persist, allowing the existing volume mount support to apply to /opt/continuum/project and not lose uncommitted data. This will ensure any changes the user has made to the conda environment will be properly encapsulated into changes to anaconda-project.yml.
  • Documented a new volume configuration syntax: Mounting an external file share.
  • Clarified steps for migrating projects to or from Bitbucket repository in the instructions for migrating projects between repositories. If a user wants to migrate to or from Bitbucket repository, they must use their Bitbucket account ID instead of Bitbucket username in the user mappings file.
  • In sessions and deployments, you now have the ability to comment out environment variables or add a digit in the name of the global variable within env-var-config.yml.

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Anaconda Enterprise 5.5.0

Released: April 14, 2021 Administrator-facing changes
  • Support for installation on customer-supplied Kubernetes platforms
    • Initial release: support for OpenShift 4.2 and GKE with k8s 1.15 or earlier
    • Additional platforms and OCP/k8s versions to come in a fast-follow release
  • Managed Persistence: leverages a persistent, shared volume, allowing administrators to easily customize:
    • Sample and template projects
    • Pre-baked conda environments
User-facing changes
  • Managed Persistence: Users benefit from persistent storage of relevant content, with appropriate user-level and project-level access controls:
    • Project session code, data, and conda environments
    • Server and database credentials, software preferences
  • Worked on several key areas to ensure the release meets the requirements outlined by customers:
    • Error and warning notification
    • Scheduling workflow
    • Log workflow
    • User Interface
    • Improved experience with projects, search, authentication
    • Improved support and updates which include validation of sample projects along with template environments & lab_launch environment

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Anaconda Enterprise 5.4.1

Released: April 15, 2020 Administrator-facing changes
  • Updated minimum and recommended requirements
  • You can now configure size limits for files (Default value of 50MB) being committed into the internal git by changing the related values on the config map flag. This ensures that projects don’t get bogged down by oversized internal storage. We recommend keeping files below 50MB and using external file storage for large data sets. (AENT-5922)
  • You can now set the number of max concurrent queue jobs and enable/disable project creation with a queue using a config map flag. By implementing a queue, Kubernetes jobs for project creation are performed only when resources are available, ensuring that project creation doesn’t fail due to lack of cluster resources. (AENT-5801)
  • Default SSO Timeout increased to 1 day.
User-facing changes
  • You now have the ability to see whether your project is in the queue or actively being created.
  • You will now be alerted when your commits fail, saving time and work.
  • You can now schedule your deployment in multiple timezones via a dropdown in the Scheduler UI. Note that these scheduled deployment times will be displayed in UTC.
  • You can now access public channels and deployments if not added as collaborators.
  • CRON string validation has been added to schedules UI.
Backend improvements (non-visible changes)
  • There was an issue with users trying to create multiple projects at a time, overwhelming the cluster resources and ultimately causing some projects to fail to create. We’ve fixed that by implementing a job queue, limiting the number of simultaneous project creations based on configuration and available system resources.
  • GPU support fixed, built on CUDA 10.x.
  • Job pods automatically clean up upon completion of jobs.

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Anaconda Enterprise 5.4.0

Released: October 31, 2019 Administrator-facing changes User-facing changes
  • New UI look-and-feel
  • New sample gallery projects
  • Fixed JupyterLab and Jupyter Notebook timeout
  • Upgraded Conda to version 4.6.14 and anaconda-project to version 0.8.3. Provide faster package installs and improved error messages
  • NFS mounts now work with scheduled jobs
Backend improvements (non-visible changes)
  • Upgraded nginx to version 1.17.2, which uses nginx-ingress version 1.5.2, to address CVEs.

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Anaconda Enterprise 5.3.1

Released: July 17, 2019 Administrator-facing changes User-facing changes
  • 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.

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Anaconda Enterprise 5.3.0

Released: March 22, 2019 Administrator-facing changes User-facing changes
  • 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.

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Anaconda Enterprise 5.2.4

Released: January 21, 2019 Administrator-facing changes
  • Fixed issue where custom resource profiles weren’t being captured during in-place upgrades.
  • Added security fixes.

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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.
User-facing changes
  • 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.yml file 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.

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Anaconda Enterprise 5.2.2

Released: October 10, 2018 Administrator-facing changes
  • 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 yum operations 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 default_channels, channel_alias, ssl_verify settings in the conda section of configmap to be consistent with conda configuration settings.
  • 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 noarch packages 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 anaconda-enterprise-cli.
User-facing changes
  • 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.

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Anaconda Enterprise 5.2.1

Released: August 30, 2018 User-facing changes
  • 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

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Anaconda Enterprise 5.2.0

Released: July 27, 2018 Administrator-facing changes
  • 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
User-facing changes
  • 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 use plotting/Javascript libraries in JupyterLab
  • 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 kube-router and a CrashLoopBackOff error

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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

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Anaconda Enterprise 5.1.2

Released: March 16, 2018 Administrator-facing changes
  • Fixed issue with image/version tags when upgrading AE
Backend improvements (non-visible changes)
  • Updated to Kubernetes 1.7.14

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Anaconda Enterprise 5.1.1

Released: March 12, 2018 Administrator-facing changes
  • 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 ContainerCreating state 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 verify_ssl setting with anaconda-enterprise-cli
  • Fixed issue related to default admin role (ae-admin)
  • 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
User-facing changes
  • 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 install system 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 sparklyr in 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

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Anaconda Enterprise 5.1.0

Released: January 19, 2018 Administrator-facing changes
  • 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 verify_ssl to ssl_verify throughout AE CLI for consistency with conda
  • Fixed configuration issue with licenses, including field names and online/offline licensing documentation
User changes
  • 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
  • Fixed DiskNodeUnderPressure and 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
Known issues
  • IE 11 compatibility issue when using Bokeh in notebooks (including sample projects)
  • IE 11 compatibility issue when downloading custom installers

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Anaconda Enterprise 5.0.6

Released: November 9, 2017

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Anaconda Enterprise 5.0.5

Released: November 7, 2017

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Anaconda Enterprise 5.0.4

Released: September 12, 2017

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Anaconda Enterprise 5.0.3

Released: August 31, 2017 (General Availability Release)

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Anaconda Enterprise 5.0.2

Released: August 15, 2017 (Early Adopter Release)

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Anaconda Enterprise 5.0.1

Released: March 8, 2017 (Early Adopter Release) Features:
  • 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.