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

Bug fixes in v2.0.3

• upgraded jackson library to eliminate https://nvd.nist.gov/vuln/detail/CVE-2018-7489 bug

Known issues in v2.0.3

All Cloud platforms:

• remote kernel list for Data Engine is not updated after stop/start Data Engine
• following links can be opened via tunnel for Data Engine/Data Engine: service: worker/application ID, application detail UI, event timeline, logs for Data Engine

AWS

• can not open master application URL on resource manager page, issue known for Data Engine Service v.5.12.0

GCP:

• storage permissions aren't differentiated by users via Dataproc permissions (all users have R/W access to other users buckets)
• Data Engine Service creation is failing after environment has been recreated
• it is temporarily not possible to run playbooks using remote kernel of Data Engine (dependencies issue)
• DeepLearning creation fails

Microsoft Azure:

• creation of Zeppelin from custom image fails on the step when cluster kernels are removing
• start Notebook by scheduler does not work when Data Lake is enabled

Known issues caused by cloud provider limitations in v2.0.3

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
• occasionally billing data is not available for Notebook secondary disk

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