https://github.com/into-ai/kerasltisubmission

Submit your trained keras models to a kerasltiprovider that proxies any LTI conforming learning platform
https://github.com/into-ai/kerasltisubmission

assignment hand-in keras lti-tool pip python tensorflow

Last synced: 26 days ago
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Submit your trained keras models to a kerasltiprovider that proxies any LTI conforming learning platform

• Host: GitHub
• URL: https://github.com/into-ai/kerasltisubmission
• Owner: into-ai
• License: mit
• Created: 2020-03-09T17:30:03.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-02-05T23:45:39.000Z (9 months ago)
• Last Synced: 2024-09-30T10:02:37.144Z (about 1 month ago)
• Topics: assignment, hand-in, keras, lti-tool, pip, python, tensorflow
• Language: Python
• Homepage:
• Size: 518 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE

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README

        
===============================

kerasltisubmission

===============================

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""""""""

This python package allows you to submit your trained ``keras`` models to a

``kerasltiprovider`` (see `here `_)

that proxies any

`LTI `_

conforming learning platform for grading.

.. code-block:: console

    $ pip install kerasltisubmission

.. code-block:: python

    import kerasltisubmission

    # Looking for the provider?

    # See https://github.com/into-ai/kerasltiprovider

    provider = LTIProvider(

        input_api_endpoint="http://localhost:8080",

        submission_api_endpoint="http://localhost:8080/submit",

        user_token="7dd7367c-40c2-43cb-a052-bb04e1d0a858",

    )

    # Submit your keras model

    submission = Submission(assignment_id=12, model=model)

    provider.submit(submission)

For a complete example, see `example.py `_.

Also see the `official documentation`_ as well as the

`documentation `_ of

the ``kerasltiprovider``.

.. _official documentation: https://kerasltisubmission.readthedocs.io

Development

-----------

For detailed instructions see `CONTRIBUTING `_.

Tests

~~~~~~~

You can run tests with

.. code-block:: console

    $ invoke test

    $ invoke test --min-coverage=90     # Fail when code coverage is below 90%

    $ invoke type-check                 # Run mypy type checks

Linting and formatting

~~~~~~~~~~~~~~~~~~~~~~~~

Lint and format the code with

.. code-block:: console

    $ invoke format

    $ invoke lint

All of this happens when you run ``invoke pre-commit``.

Note

-----

This project is still in the alpha stage and should not be considered production ready.