ModelOp Center provides plugins into common data science development tools such as Jupyter, to allow data scientists to interact with ModelOp Center from their preferred development environment.

Table of Contents

What’s New in v2.1:

1. Including “Model Functions” definition feature to “Add Model” wizard.

   1. Inspects the code searching for functions to include as options of the dropdown.

   2. Read “smart comments” and pre-sets functions.

   3. Include help about “Model Functions” and “Smart Tags” in information icon.

2. Editing existing model.

   1. Basic Model Info.

   2. Model Functions (same way).

   3. Platform summary.

   4. Catches unsaved changes in code before saving.

3. ModelOp toolbar update.

   1. Get rid of “Model Details” button (it was for debugging purposes).

   2. The “Asset Details” button lets you edit the asset name and repo info.

4. Include Live Search on the “Load Model” screen.

   1. Allow search by model name

   2. Allow search by tag

5. Support for external parameterized ModelOp Center base URL.

   1. Environment variable setup.

   2. Parameter in NBextensions config.

   3. Enable CORS support to invoke REST API from Jupyter external host.

   4. Enable external libraries compatibility when running on an custom instance.

   5. Add support for Jupyter versions prior to 5.7.0, incompatibility between jquery 3+ and jquery-ui <1.12

6. Validating file extension to source code asset filename (when creating model and when editing asset file name)

7. Visualizing ModelOp Jupyter plugin version in all dialogs

8. Visual feedback on the attachments file upload status

9. Support for asset role definition on the attachments (during model creation and during file upload).

10. Enable plugin for Jupyterhub notebook.

11. Bug Fixes:

    1. When a model was just created the “submit” screen didn’t show the last changes (the initial commit).

    2. Update local cell metadata when saving a model (to avoid keeping outdated data).

    3. Validate to include only code cells in the model creation

    4. Include the model summary in the ‘Save’ screen.

    5. Updating a model overwrote MOC with the current (modified) local copy. That got fixed on “Model Save”, “Model functions editing”, “Asset button edit”

Using ModelOp Center Jupyter plugin

titlev2.1

Overview

The ModelOp Center (MOC) is flexible on how models are registered so that data scientists can leverage their preferred environments for model creation. In other words it is agnostic to the toolkit used to create the model.

Jupyter (http://jupyter.org/) is a popular tool among data scientists and other users of Python and R for building models. It is an evolution of the IPython project, and provides a Notebook interface similar to that of Mathematica or SAGE. For the purposes of the interaction between ModelOp Center and Jupyter we have the Jupyter plugin available which makes it easier to make some of the most useful operations directly in the user’s IDE of choice, in this case, Jupyter notebookThe Jupyer Plugin enables the integration between a Jupyter Notebook and ModelOp Center to allow Data Scientists to register and interact with models directly from the Notebook. This allows the Data Scientists to work from their IDE of choice.

Installation and configuration instructions

ModelOp specific functions available

witdhin Jupyter

Once installed, when the user opens or creates a new Jupyter notebook file, the Jupyter notebook interface is provided with a set of toolbar buttons that interact with ModelOp Center and will be expanded in the sections below.

Registering a new model

Opening an existing model

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Updating an existing model

Submit model version