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