Release notes
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.
What’s New
- The Anaconda Assistant is available to install and use with your Jupyter Notebooks.
- You can now create branches within projects in Anaconda Enterprise for additional utility in collaborating on work.
- You can add tags to your projects and locate them quickly using the project tag filter.
- Collaborators can now be added to projects and deployments when they are created.
- Additional UI has been added to include MLFlow in the left-hand navigation once installed.
- Projects can be configured to have reloadable MLFlow model endpoints so that the model can be retrained without having to restart the deployment.
- System pods have been updated to ubi9.
- Keycloak has been updated to version 22.0.5.
- Redis has been removed from the system images.
- Postgres has been updated to 12.17
- Fixed a bug that prevented scheduled jobs from converting back to run-once jobs.
- The Manage Resources button has been removed from BYOK8s installations.
- The UI pod recovers more gracefully if Redis is down.
- Various updates have been made to UI notification messages for consistency and clarity.
What’s New
- MLFlow can now be installed as an optional component.
- Package license information is now available in the UI.
- You can now filter projects by owner.
- Keycloak has been configured to apply the ‘secure’ flag to its cookies to prevent them from being sent over insecure HTTP connections.
-
Several system images have been updated:
- Python 3.11
- Kubernetes 1.26
- Keycloak 21.1.1
- Angular 13.4.0
- NodeJS 16.13.1
- Postgres 12.15
- Redis 7.0.11
- Various UI bugs have been fixed.
- Fixed a bug that prevented collaborators from being removed from deployments/projects correctly.
- Fixed a bug that prevented users from saving changes to their projects from Jupyter lab sessions.
What’s New
-
Introducing: Project filtering!
- Use the newly added filter on the project dashboard to sort your projects by owner and/or show only projects with active sessions.
- Applied filters will also limit the results returned from using the Search field.
- Applied filters persist as you navigate Anaconda Enterprise, and as you create and delete projects!
- Hide the filter if you don’t need it! You will still be informed if filters are being applied to your view.
-
Projects dashboard improvements:
- Newly created/uploaded/added projects are moved to the top left of the projects dashboard for 60 seconds. Once you refresh the dashboard, the project will be sorted into the “active sessions” group at the top of the dashboard, alphanumerically, according to its title.
- Whenever you start a session for an existing project it is sorted into the “active sessions” group at the top of the dashboard, alphanumerically, according to its title. You will be returned to the top of the dashboard after starting the session.
-
Improved integration with Anaconda Server!
- Use the same credentials to sign in to both Anaconda Server and Anaconda Enterprise!
- Create secure channels by applying filters to mirrors in Anaconda Server, then use those channels with projects in Anaconda Enterprise!
-
Several system images have been updated:
- Base image to ubi8.7-1054
- Postgres to 12.13
- Redis to 7.0.8
- Keycloak to 20.0.3
- Groups with an external version control system are no longer available when adding collaborators to a project from the project’s Share page.
- The “Oil and Gas” sample project can now be deployed successfully.
- External version control credentials are now properly saved when entered.
What’s New
- Keycloak has been upgraded to version 18.0.2 to resolve known vulnerabilities in the vendor-supplied containers for Keycloak. We’ve also eliminated AE5’s direct access to the postgres database storing Keycloak’s data. This information is now retrieved using Keycloak API calls.
-
Documentation Updates:
- Added instructions on how to use RStudio as an in browser IDE.
- Updated instructions on how to wrap your python functions using
tranquilizerand deploy them as REST API endpoints within AE5. - Updated instructions for upgrading between versions of AE5.
- JupyterLab has been updated to version 3.4.
- Added a dashboard to view all of a user’s scheduled jobs.
- A Python 3.10 environment has been added to the sample projects dashboard.
- Updated
openssl,ca-certificates,certifi, and the Python minor version in each existing environment. - UI improvements have been made for an overall better experience when deploying a project or scheduling a project for deployment.
- European date format is now accepted when committing to a job.
- Java has been removed from the editor image.
-
Several system images have been updated:
- Base image to ubi8.6-855
- Postgres to 12.12
- Redis to 6.2.7
- Keycloak to 18.0.2
- Users can now edit and delete jobs with an empty schedule (a.k.a. run once jobs). When a job is edited, it can be converted into a cronjob.
- User now has the ability to tag a commit in the UI after they have already committed all the code changes.
- Deleting a project now deletes all its job run records as well.
- Updated the error message that displays when a user attempts to use an invalid certificate.
What’s New
- Added
libnsl.so.1to the user container to enable database connections. - Added Docker-free RStudio installation method by mounting a shared volume at
/usr/lib/rstudio-server, writable (temporarily) by a standard Anaconda Enterprise user. - Ensured that all of the following repo configurations can persist:
anaconda-client,conda-repo-cli, andconda-token. - Eliminated the isolated package cache for deployments and jobs.
- Deployments and jobs get their own package cache, and therefore do not reuse the shared package cache users rely on for deployments, which slows down the conda install process for deployments.
- Added clear instructions for platform administrators to add projects to samples/templates, with the ability to rebuild index.json to include customer-driven indexing of the sample projects.
- Properly initialize storage and persistence subdirectories via Helm.
- The Helm install succeeds with managed persistence enabled, with empty storage and persistence directories.
- Once the pods stabilize all subdirectories should be properly created and permissioned:
- storage:
git,object,pgdata - persistence:
projects,environments,gallery
- storage:
- Ability to update the helm chart without forcing certs to be modified.
- Allows existing secrets to be preserved; eliminated
kube-systemssl secret - Added three different certificate generation modes:
generateor booleanTrue: use our generateCerts helperloador booleanFalse: load certificates from the certs/ subdirectory of the helm chart- any other string: do nothing
- Allows existing secrets to be preserved; eliminated
- Updated “concurrencyPolicy” for cronjobs to prevent a cronjob from scheduling a new job if one is still running, by setting the “concurrencyPolicy” to “Forbid” for scheduled jobs.
- This will alleviate a scheduled job that had inadvertently used a deployment command instead of a batch job command, resulting in the job creating a new pod for each run of the job, all of which just stayed running eventually causing the total requests for these jobs to fill available memory.
- CVE-2020-36242 - Upgraded package
cryptographyto version 3.3.2 or above to address vulnerability. - Zeppelin has been updated to version 0.9.0 and made an installable tool
- https://zeppelin.apache.org/download
- Note that Zeppelin seems not to support Python 3.8 or later yet.
- Updated “Run stopped” notification when a run is deleted, notification is now “Run Deleted successfully”
- Improved behavior of the UI when the operation-controller API fails, so that when the UI is assembling the project grid it queries the operation-controller for the status of created jobs.
- If the operation-controller pod is restored to health, the full project list comes back and all is well.
- If an error is detected, the user is notified that there was an error querying the project creation queue.
- Quickly deleted projects will no longer show up in the project grid.
- The operation controller will omit from the list the jobs that are completed and removed from the project grid.
- Removed the free channel from the default
condarc. - Updated the project creation jobs to respect affinity settings, which are now present in the yaml file.
- Corrected job scheduling with Kubernetes - Since Kubernetes behaves according to its default behavior, a job that exits with a non-zero exit code causes Kubernetes to retry the exact same task over again.
- Currently, AE5 looks at the job and only runs it once (success or failure) since our backend monitors the execution of jobs, and if a re-attempt is launched, AE5 actually detects that and shuts it down.
- This can cause an issue since there’s a chance that user code is run twice and the user may be unaware of this.
- Setting the Kubernetes “backoffLimit” parameter to
0will ensure that the retries are fully suppressed.
- Corrected the api.py loop in
anaconda-enterprise-clito perform retries with backoff in response to “SSLError” or “ConnectionError” exceptions. - Updated
cas-mirrorto correct error when updating an existing channel with new packages via mirroring; “Error DECRYPTION_FAILED_OR_BAD_RECORD_MAC” - Whitelisted a set of environment variables and placed them in
.Renviron. This whitelist currently does not include admin-supplied variables such asHTTPS_PROXY.- RStudio does not pass proxy variables through to the user.
- RStudio Server filters out all of the environment variables before starting a session.
- Corrected corrupt anaconda-platform.yml preventing git commit.
- When the anaconda-platform.yml file within a tagged commit cannot be parsed for any reason, the attempted git commit will fail.
- This is due to an unexpected error in the post-metadata.py file.
- User must remove the offending tags (this must be done even if the corruption is detected and fixed; commits will still fail).
- Corrected the AE5 deployment restart to respect git version tag.
- The version is the same as the original deployment versus changing to latest.
- Corrected issue where deleting a deployment would lead to UI timeout errors; “HTTP 599 Timeout error”
- This is caused because of the
shutil.rmtreecall inside the directory deletion process. - Corrected by discarding the directory and let user session CPU cycles clean them up on background.
- This is caused because of the
- Corrected the ability to edit a scheduled job so that, when you perform a new run, the scheduled job will use the new version you specify.
- Corrected managed persistence GID to be preserved by changing the primary group of the user process from
0to the GID of/opt/continuum/project.- GID
0should still be offered as a supplemental group to ensure that all existing files are accessible.
- GID
- Corrected uploading large size files. The commit should now be successful.
- max-commit-file-size is
50000000.
- max-commit-file-size is
- Updated the openldap library.
- Optionally, you can install openldap 2.4 in the R conda environment to fix the RStudio issue due to broken openldap package.
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
- 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-configand restart the workspace pod. This will enable the session to start successfully. - Changed the value of
kubernetes.run_as_rootfrom 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_variablesappear 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 (for example, the git commit UI) before it completely cleans out references to that session, causing a traceback.
- Corrected the traceback that occurred 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 2Sudo 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/projectto persist, allowing the existing volume mount support to apply to/opt/continuum/projectand not lose uncommitted data. This will ensure any changes the user has made to the conda environment will be properly encapsulated into changes toanaconda-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.
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
- 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_launchenvironment
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. Anaconda recommends 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.
- 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.
- 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.
Administrator-facing changes
- Updated minimum and recommended requirements
- Added support for installing the Anaconda Enterprise cluster on Centos/RHEL 7.7, 8.0
- Upgraded Gravity to version 6.1.9 (and Kubernetes 1.15.05) with updated monitoring dashboards
- Ability to configure external Postgres database
- Authenticated NFS mounts
- Customize Sample Gallery and new project template collection. Requires AE5 Tools.
- 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
- Upgraded nginx to version 1.17.2, which uses nginx-ingress version 1.5.2, to address CVEs.
Administrator-facing changes
- 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 10.0 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.
- Upgraded Jupyter Notebook to version 5.7.8 to address CVEs.
Administrator-facing changes
- 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.
- Added
noarchto the default platforms in theanaconda.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.
Administrator-facing changes
- Fixed issue where custom resource profiles weren’t being captured during in-place upgrades.
- Added security fixes.
- 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.
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 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
default_channels,channel_alias,ssl_verifysettings in thecondasection of configmap to be consistent withcondaconfiguration 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
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
anaconda-enterprise-cli.
- 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.
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
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
- 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
- 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-routerand aCrashLoopBackOfferror
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
Administrator-facing changes
- Fixed issue with image/version tags when upgrading AE
- Updated to Kubernetes 1.7.14
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
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
verify_sslsetting withanaconda-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
- 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
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_ssltossl_verifythroughout AE CLI for consistency withconda - 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 compatibility issues with Microsoft Edge and Internet Explorer 11
- Fixed multiple UI issues with Anaconda Project JupyterLab extension
- 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
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
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.