Images were incorrectly updated after upgrading OpenShift Data ScienceĢ.60. Uninstall process failed to complete when both OpenShift Data Science and OpenShift API Management were installedĢ.59. Pachyderm now compatible with OpenShift Dedicated 4.10 clustersĢ.58. GPU selection persisted when GPU nodes were unavailableĢ.57. GPU tutorial did not appear on dashboardĢ.56. Red Hat OpenShift API Management 1.15.2 add-on installation did not successfully completeĢ.55. Changing alert notification emails required pod restartĢ.54.
#GPU COMPARE WORKBENCH DOWNLOAD#
Starburst Galaxy quick start did not provide download link in the instruction stepsĢ.53. The OpenVINO notebook image failed to build successfullyĢ.52. Cluster settings were reset on operator restartĢ.51. PVC usage limit alerts were not sent when usage exceeded 90% and 100%Ģ.50. Jupyter was unable to display images when the NVIDIA GPU add-on was installedĢ.49. Incorrect headings were displayed in the Notebook Images pageĢ.48. A non-standard check box displayed after disabling usage data collectionĢ.47. Jupyter failed to start a notebook server using the OpenVINO notebook imageĢ.46. Error occurred while fetching the generated images in the sample Pachyderm notebookĢ.45. Excessive "missing x-forwarded-access-token header" error messages displayed in dashboard logĢ.44. Old Minimal Python notebook image persisted after upgradeĢ.43. Group role bindings were not applied to cluster administratorsĢ.42. Admin users could add invalid tolerations to notebook podsĢ.41. Incorrect package version displayed during notebook selectionĢ.40. Cluster admin did not get administrator access if it was the only user present in the cluster.Ģ.39.
The Number of GPUs drop-down was only visible if there were GPUs availableĢ.38. Environment variable names were not validated when starting a notebook serverĢ.37. PyTorch and TensorFlow images were unavailable when upgradingĢ.36.
The notebook Administration page did not provide administrator access to a user’s notebook serverĢ.35. Data connection configuration details were overwrittenĢ.34. Data science projects were not visible to users in Red Hat OpenShift Data ScienceĢ.33. When multiple persistent volumes were mounted to the same directory, workbenches failed to startĢ.32. Incorrect number of available GPUs was displayed in JupyterĢ.31. ISV icons did not render when using a browser other than Google ChromeĢ.30. Workbench event log was not clearly visibleĢ.29. Administrators were unable to stop all notebook serversĢ.28. Returning to the Hub Control Panel dashboard from the data science workbench failedĢ.27. Error message was not displayed if a data science notebook was stuck in "pending" statusĢ.26. Admin users were not warned when usage exceeded 90% and 100% for PVCs created by data science projects.Ģ.25.
#GPU COMPARE WORKBENCH PROFESSIONAL#
Anaconda Professional Edition could not be enabled in OpenShift Data ScienceĢ.24. Models failed to be served after upgrading from OpenShift Data Science 1.20 to OpenShift Data Science 1.21Ģ.23. Workbenches failed to receive the latest tolerationĢ.22. A workbench’s data connection was incorrectly updated when creating a duplicated data connectionĢ.21. Deleted users stayed logged in until dashboard was refreshedĢ.20. OpenShift Data Science administrators could not access Settings page if an admin group was deleted from clusterĢ.19. Data connections could not be created or connected to when creating a workbenchĢ.18. Idle notebook culler did not take active terminals into accountĢ.17. An error was displayed when creating a workbench with the Intel OpenVINO or Anaconda Professional Edition imagesĢ.16. Uploading a secret file containing environment variables resulted in double-encoded valuesĢ.15.
Workbenches could have multiple data connectionsĢ.14. Environment variables uploaded as ConfigMap were stored in Secret insteadĢ.13. Changing the host project when creating a pipeline ran resulted in an inaccurate list of available pipelinesĢ.12. OpenVINO Model Server runtime did not have the required flag to force GPU usageĢ.11. Users with "Edit" permission could not create a Model ServerĢ.10. When sharing a project with another user, the OpenShift Data Science user interface text was misleadingĢ.9. Newly created data connections were not detected when you attempted to create a pipeline serverĢ.8. Data science pipeline graphs did not display node edges for running pipelinesĢ.7. When editing the details of a shared project, the user interface remained in a loading state without reporting an errorĢ.6. Exporting an Elyra pipeline exposed S3 storage credentials in plain textĢ.5. After upgrade, the Data Science Pipelines tab was not enabled on the OpenShift Data Science dashboardĢ.4. Deploying a custom model-serving runtime could result in an error messageĢ.3. Pipelines with non-unique names did not appear in the data science project user interfaceĢ.2.
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