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

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:

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

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

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:

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

Microsoft Azure:

GCP: