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

New features in v2.3

All Cloud platforms:

  • Added support for multi-Cloud orchestration for AWS, Azure and GCP. Now, a single DLab instance can connect to the above Clouds, by means of respective set of API's, deployed on cloud endpoints;
  • Added JupyterLab v.0.35.6 template
  • Updated versions of installed software:
    • Jupyter notebook v.6.0.2;
    • Apache Zeppelin v.0.8.2;
    • RStudio v.1.2.5033;
    • Apache Spark v.2.4.4 for standalone cluster;

AWS:

  • Added support of new version of Data Engine Service (EMR) v.5.28.0;

GCP:

  • Added support of new version of Data Engine Service (Dataproc) v.1.4;
  • Added new template Superset v.0.35.1;

Improvements in v2.3

All Cloud platforms:

  • Grouped project management actions in single Edit project menu for ease of use;
  • Introduced new "project admin" role;
  • SSO now also works for Notebooks;
  • Implemented ability to filter installed libraries;
  • Added possibility to sort by project/user/charges in 'Billing report' page;
  • Added test option for remote endpoint;

Bug fixes in v2.3

All Cloud platforms:

  • Fixed a bug when Notebook name should be unique per project for different users, since it was impossible to operate Notebook with the same name after the first instance creation;
  • Fixed a bug when administrator could not stop/terminate Notebook/computational resources created by another user;
  • Fixed a bug when shell interpreter was not showing up for Apache Zeppelin;
  • Fixed a bug when scheduler by start time was not triggered for Data Engine;
  • Fixed a bug when it was possible to start Notebook if project quota was exceeded;
  • Fixed a bug when scheduler for stopping was not triggered after total quota depletion;

AWS:

  • Fixed a bug when Notebook image/snapshot were still available after SSN termination;

Microsoft Azure:

  • Fixed a bug when custom image creation from Notebook failed and deleted the existing Notebook of another user;
  • Fixed a bug when detailed billing was not available;
  • Fixed a bug when spark reconfiguration failed on Data Engine;
  • Fixed a bug when billing data was not available after calendar filter usage;

Known issues in v2.3

GCP:

  • SSO is not available for Superset;

Microsoft Azure:

  • Notebook creation fails on RedHat;
  • Web terminal is not working for Notebooks only for remote endpoint;

Refer to the following link in order to view the other major/minor issues in v2.3:

Apache DLab: known issues

Known issues caused by cloud provider limitations in v2.3

Microsoft Azure:

  • Resource name length should not exceed 80 chars;
  • TensorFlow templates are not supported for RedHat Enterprise Linux;
  • Low priority Virtual Machines are not supported yet;

GCP:

  • Resource name length should not exceed 64 chars;
  • NOTE: DLab has not been tested on GCP for RedHat Enterprise Linux;