Release 2.0.12

• Add default parameters for get_load_status
• Add ipython as a dependency in setup.py (Link to PT)
• Add parameters in load_status for details, errors, page, and errorsPerPage

Release 2.0.10

• Print execution time when running %load command (Link to PR)

Release 2.0.9

• New datasets and notebooks for Sample applications in Gremlin including:
  • Fraud Graph
  • Knowledge Graph
  • Identity Graph

Release 2.0.7

• Added What’s Next sections to 01-Getting-Started notebooks to point users to next suggested notebook tutorials after finishing one notebook.

Release 2.0.6

• Add missing __init__ to notebook directories to they get installed correctly
• Update list of available magics in notebook documentation

Release 2.0.5

Gremlin Visualization

• Enhanced Gremlin Visualization output to group vertices and color-code them based on groups. When not specified it will group by the label (if it exists). You can also specify the property to groupby using the switch --groupby or -g followed by the property name
• Added the functionality to sort the values in the details box by key
• Updated Air-Routes-Visualization notebook to discuss the group by functionality

NeptuneML

• Add tutorial notebooks for NeptuneML functionality

Release 2.0.3

• Integration with NeptuneML feature set in AWS Neptune
• Add helper library to perform Sigv4 signing for %neptune_ml export ..., we will move our other signing at a later date.
• Swap how credentials are obtained for ROLE iam credentials provider such that it uses a botocore session now instead of calling the ec2 metadata service. This should make the module more usable outside of Sagemaker.
• Add sub-configuration for sparql to allow specifying path to sparql endpoint

New Line magics:

• %neptune_ml export status
• %neptune_ml dataprocessing start
• %neptune_ml dataprocessing status
• %neptune_ml training start
• %neptune_ml training status
• %neptune_ml endpoint create
• %neptune_ml endpoint status

New Cell magics:

• %%neptune_ml export start
• %%neptune_ml dataprocessing start
• %%neptune_ml training start
• %%neptune_ml endpoint create

NOTE: If a cell magic is used, its line inputs for specifying parts of the command will be ignore such as --job-id as a line-param.

Inject variable as cell input:
Currently this will only work for our new cell magic commands details above. You can now specify a variable to use as the cell input received by our neptune_ml magics using the syntax ${var_name}. For example...

# in one notebook cell:
foo = {'foo', 'bar'}

# in another notebook cell:
%%neptune_ml export start

${foo}

NOTE: The above will only work if it is the sole content of the cell body. You cannot inline multiple variables at this time.

Release 2.0.1

• Fix bug in argparser for load_status and cancel_load line magics
• Expand loader status values that terminate load line magic

Release 2.0.0

• Add support for storing query results to a variable for use in other notebook cells
• Remove %query_mode magic in favor of query parameterization

Release 1.33.0

• Fix Windows Compatibility by using path join instead of building paths using strings
• Hooks to install nbextensions using the jupyter nbextension ... syntax
• Fix bug preventing gremlin results which have a datetime object in them from being displayed