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5. Kits Reference

In this section, we cover kits, integrations with various data science libraries and infrastructure provided by Data Workspaces.

Jupyter

.. automodule:: dataworkspaces.kits.jupyter
   :no-undoc-members:
   :members: NotebookLineageBuilder, is_notebook, get_step_name_for_notebook

Magics

This module also provides a collection of IPython magics (macros) to simplify interactions with your data workspace when develping in a Jupyter Notebook.

Limitations

Currently these magics are only supported in interactive Jupyter Notebooks. They do not run properly within JupyterLab (we are currently working on an extension specific to JupyterLab), the nbconvert command, or if you run the entire notebook with "Run All Cells".

To develop a notebook interactively using the DWS magic commands and then run the same notebook in batch mode, you can set the variable DWS_MAGIC_DISABLE in your notebook, ahead of the call to load the magics (%load_ext). If you set it to True, the commands will be loaded in a disabled state and will run with no effect. Setting DWS_MAGIC_DISABLE to False will load the magics in the enabled state and run all commands normally.

Loading the magics

To load the magics, run the following in an interactive cell of your Jupyter Notebook:

import dataworkspaces.kits.jupyter
%load_ext dataworkspaces.kits.jupyter

If the load runs correctly, you should see output like this in your cell:

Ran DWS initialization. The following magic commands have been added to your notebook:

  • %dws_info - print information about your dws environment
  • %dws_history - print a history of snapshots in this workspace
  • %dws_snapshot - save and create a new snapshot
  • %dws_lineage_table - show a table of lineage for the workspace resources
  • %dws_lineage_graph - show a graph of lineage for a resource
  • %dws_results - show results from a run (results.json file)

Run any command with the --help option to see a list of options for that command. The variable DWS_JUPYTER_NOTEBOOK has been added to your variables, for use in future DWS calls.

If you want to disable the DWS magic commands (e.g. when running in a batch context), set the variable DWS_MAGIC_DISABLE to True ahead of the %load_ext call.

Magic Command reference

We now describe the command options for the individual magics.

%dws_info

usage: dws_info [-h]

Print some information about this workspace

optional arguments:
-h, --help show this help message and exit

%dws_history

usage:
dws_history [-h] [--max-count MAX_COUNT] [--tail] [--baseline TAG_OR_HASH]
[--heatmap] [maximize-metrics METRICS] [--minimize-metrics METRICS]

Print a history of snapshots in this workspace

optional arguments:
-h, --help show this help message and exit
--max-count MAX_COUNT
 Maximum number of snapshots to show
--tail Just show the last 10 entries in reverse order
--baseline TAG_OR_HASH
 Snapshot tag or hash to use as a basis for metrics comparison.
--heatmap Show a heatmap for metrics columns
--maximize-metrics METRICS
 Metrics where larger values are better (e.g. accuracy)
--minimize-metrics METRICS
 Metrics where smaller values are better (e.g. loss)

For easy visualization of results, the %dws_history command supports two styles of color coding. The --heatmap option will color code the background of metrics cells from dark red (worst results) to dark green (best results). For common metrics, like accuracy and loss, DWS knows the directionality of the metric (higher is better vs. lower is better). For less common or custom metrics, you can use the --maximize-metrics and --minimize-metrics options to specify this. Here is an example heatmap:

_static/snapshot-history-heatmap.png

The second style of coloring takes a baseline snapshot. Any metric values better than the baseline have their text colored green, any metric values close to the baseline are bold black text, and any metric values worse than the baseline are colored red. The --baseline=SNAPSHOT option enables this display mode. Here is an example:

_static/snapshot-history-baseline.png

%dws_snapshot

usage: dws_snapshot [-h] [-m MESSAGE] [-t TAG]

Save the notebook and create a new snapshot

optional arguments:
-h, --help show this help message and exit
-m MESSAGE, --message MESSAGE
 Message describing the snapshot
-t TAG, --tag TAG
 Tag for the snapshot. Note that a given tag can only be used once (without deleting the old one).

%dws_lineage_table

usage: dws_lineage_table [-h] [--snapshot SNAPSHOT]

Show a table of lineage for the workspace's resources

optional arguments:
-h, --help show this help message and exit
--snapshot SNAPSHOT
 If specified, print lineage as of the specified snapshot hash or tag

%dws_lineage_graph

usage: dws_lineage_table [-h] [--resource RESOURCE] [--snapshot SNAPSHOT]

Show a graph of lineage for a resource

optional arguments:
-h, --help show this help message and exit
--resource RESOURCE
 Graph lineage from this resource. Defaults to the results resource. Error if not specified and there is more than one.
--snapshot SNAPSHOT
 If specified, graph lineage as of the specified snapshot hash or tag

%dws_results

usage: dws_results [-h] [--resource RESOURCE] [--snapshot SNAPSHOT]

Show results from a run (results.json file)

optional arguments:
-h, --help show this help message and exit
--resource RESOURCE
 Look for the results.json file in this resource. Otherwise, will look in all results resources and return the first match.
--snapshot SNAPSHOT
 If specified, get results as of the specified snapshot or tag. Otherwise, looks at current workspace and then most recent snapshot.

Scikit-learn

.. automodule:: dataworkspaces.kits.scikit_learn
   :no-undoc-members:
   :members: load_dataset_from_resource,LineagePredictor,Metrics,BinaryClassificationMetrics,MulticlassClassificationMetrics

TensorFlow

.. automodule:: dataworkspaces.kits.tensorflow
   :no-undoc-members:
   :members: DwsModelCheckpoint,CheckpointConfig,add_lineage_to_keras_model_class