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DLab 2.2 Release
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@viravit viravit released this 25 Nov 14:59
· 0 commits to v2.2-RC1 since this release
2.2-RC2
16172b9

DLab is Self-service, Fail-safe Exploratory Environment for Collaborative Data Science Workflow

New features in v2.2

All Cloud platforms:

  • added concept of Projects into DLab. Now users can unite under Projects and collaborate
  • for ease of use we've added web terminal for all DLab Notebooks
  • updated versions of installed software:
    • angular 8.2.7

GCP:

  • added billing report to monitor Cloud resources usage into DLab, including ability to manage billing quotas
  • updated versions of installed software:
    • Dataproc 1.3

Improvements in v2.2

All Cloud platforms:

  • implemented login via KeyCloak to support integration with multiple SAML and OAUTH2 identity providers
  • added DLab version into WebUI
  • augmented ‘Environment management’ page
  • added possibility to tag Notebook from UI
  • added possibility to terminate computational resources via scheduler

GCP:

  • added possibility to create Notebook/Data Engine from an AMI image

AWS and GCP:

  • UnGit tool now allows working with remote repositories over ssh
  • implemented possibility to view Data Engine Service version on UI after creation

Bug fixes in v2.2

All Cloud platforms:

  • fixed sparklyr library (r package) installation on RStudio, RStudio with TensorFlow notebooks

GCP:

  • fixed a bug when Data Engine creation fails for DeepLearning template
  • fixed a bug when Jupyter does not start successfully after Data Engine Service creation (create Jupyter -> create Data Engine -> stop Jupyter -> Jupyter fails)
  • fixed a bug when DeepLearning creation was failing

Known issues in v2.2

All Cloud platforms:

  • Notebook name should be unique per project for different users in another case it is impossible to operate Notebook with the same name after the first instance creation

Microsoft Azure:

  • DLab deployment is unavailable if Data Lake is enabled
  • custom image creation from Notebook fails and deletes existed Notebook

Refer to the following link in order to view the other major/minor issues in v2.2:

Apache DLab: known issues

Known issues caused by cloud provider limitations in v2.2

Microsoft Azure:

  • resource name length should not exceed 80 chars
  • TensorFlow templates are not supported for Red Hat Enterprise Linux
  • low priority Virtual Machines are not supported yet

GCP:

  • resource name length should not exceed 64 chars
  • billing data is not available
  • NOTE: DLab has not been tested on GCP for Red Hat Enterprise Linux
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