![]() ![]() Workbenches fail to receive the latest tolerationĥ.6. Unclear error message displays when using invalid characters to create a data science projectĥ.5. Anaconda Professional Edition cannot be validated and enabled in OpenShift Data Scienceĥ.4. Unable to scale down a workbench’s GPUs when all GPUs in the cluster are being usedĥ.3. Attempting to increase the size of a Persistent Volume (PV) fails when it is not connected to a workbenchĥ.2. Ten minute wait after notebook server start failsĥ.1. Missing step in Getting Started with OpenShift Streams for Apache KafkaĤ.44. Incorrect Python versions displayed during notebook selectionĤ.43. Quick start links did not display for enabled applicationsĤ.42. Incorrect TensorFlow and TensorBoard versions displayed during notebook selectionĤ.41. Images were incorrectly updated after upgrading OpenShift Data ScienceĤ.40. Uninstall process failed to complete when both OpenShift Data Science and OpenShift API Management were installedĤ.39. Pachyderm now compatible with OpenShift Dedicated 4.10 clustersĤ.38. GPU selection persisted when GPU nodes were unavailableĤ.37. GPU tutorial did not appear on dashboardĤ.36. Red Hat OpenShift API Management 1.15.2 add-on installation did not successfully completeĤ.35. Changing alert notification emails required pod restartĤ.34. Starburst Galaxy quick start did not provide download link in the instruction stepsĤ.33. Incorrect package versions were displayed during notebook selectionĤ.32. The OpenVINO notebook image failed to build successfullyĤ.31. Cluster settings were reset on operator restartĤ.30. PVC usage limit alerts were not sent when usage exceeded 90% and 100%Ĥ.29. Jupyter was unable to display images when the NVIDIA GPU add-on was installedĤ.28. Incorrect headings were displayed in the Notebook Images pageĤ.27. A non-standard check box displayed after disabling usage data collectionĤ.26. Jupyter failed to start a notebook server using the OpenVINO notebook imageĤ.25. Error occurred while fetching the generated images in the sample Pachyderm notebookĤ.24. Excessive "missing x-forwarded-access-token header" error messages displayed in dashboard logĤ.23. Old Minimal Python notebook image persisted after upgradeĤ.22. ![]() ![]() Group role bindings were not applied to cluster administratorsĤ.21. Admin users could add invalid tolerations to notebook podsĤ.20. Incorrect package version displayed during notebook selectionĤ.19. Cluster admin did not get administrator access if it was the only user present in the cluster.Ĥ.18. The Number of GPUs drop-down was only visible if there were GPUs availableĤ.17. Environment variable names were not validated when starting a notebook serverĤ.16. PyTorch and TensorFlow images were unavailable when upgradingĤ.15. The notebook Administration page did not provide administrator access to a user’s notebook serverĤ.14. Data connection configuration details were overwrittenĤ.13. Data science projects were not visible to users in Red Hat OpenShift Data ScienceĤ.12. When multiple persistent volumes were mounted to the same directory, workbenches failed to startĤ.11. Incorrect number of available GPUs was displayed in JupyterĤ.10. ISV icons did not render when using a browser other than Google ChromeĤ.9. Workbench event log was not clearly visibleĤ.8. Administrators were unable to stop all notebook serversĤ.7. Returning to the Hub Control Panel dashboard from the data science workbench failedĤ.6. Error message was not displayed if a data science notebook was stuck in "pending" statusĤ.5. Admin users were not warned when usage exceeded 90% and 100% for PVCs created by data science projects.Ĥ.4. Anaconda Professional Edition could not be enabled in OpenShift Data ScienceĤ.3. Models failed to be served after upgrading from OpenShift Data Science 1.20 to OpenShift Data Science 1.21Ĥ.2. Features for IT Operations administratorsĤ.1. Providing feedback on Red Hat documentationģ.2. Makes working with jupyterlab in conjunction with vscode a lot easier.1. You will be able to access all of your other conda environments using this jupyterlab You don’t have to do the ssh server -L xxxx:localhost:xxxx with the extra port In addition, the permissions are weird so some python code doesn’t play nice when you want to do execute commands using python (and sometimes the terminal - need sudo).Ī simple way to open jupyterlab with your vscode ide So you have to play games with the directories which is a pain in the butt. This is convenient but the biggest problem with this is that it’s not in your home directory. Another thing people do is they use the Notebook Instances from the GCP webpage. It’s not good enough and it’s quite slow compared to JupyterLab. Some people try to use the built-in jupyter notebook support from VSCode. If you don’t you probably should… But it’s a bit annoying when we need both. So most people like to use a combination of a dedicated IDE as well as JupyterLab. ![]()
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