Metadata-Version: 1.0
Name: faculty-models
Version: 0.2.0
Summary: Python library for retrieving models from Faculty platform.
Home-page: https://faculty.ai/products-services/platform/
Author: Faculty
Author-email: opensource@faculty.ai
License: Apache Software License
Description: faculty-models
        ==============
        
        ``faculty-models`` is a tool to help you use models from the model registry in
        Faculty Platform.
        
        .. warning::
        
            This library's API is subject to change as new functionality is added to
            the model registry feature in Faculty Platform.
        
        Installation
        ------------
        
        ``faculty-models`` comes preinstalled in Python environments available in
        Faculty platform. To use it externally, install it from PyPI with ``pip``:
        
        .. code-block:: bash
        
            pip install faculty-models
        
        If you've not already done so on the computer you're using, you'll also need to
        generate and store CLI credentials for the Platform. You can do this with
        `the Faculty CLI
        <https://docs.faculty.ai/user-guide/command_line_interface.html#initialising-faculty>`_.
        
        Usage
        -----
        
        The model registry in Faculty Platform includes a feature that helps you
        generate the snippets you need. It will help you get the project and model IDs
        you need to use ``faculty-models``.
        
        If your model is in the `MLmodel format
        <https://mlflow.org/docs/latest/models.html>`_ (likely because you used `MLflow
        <https://mlflow.org/>`_ to store it), you can load it directly back into Python
        with:
        
        .. code-block:: python
        
            import faculty_models
        
            model = faculty_models.load_mlmodel(
                project_id="998328c3-23df-4225-a3ee-0a53d1409fbd",
                model_id="c998fca9-e093-47ea-9896-8f75db695b91"
            )
        
        Otherwise, you can use the following to download the contents of the model to
        the local filesystem. ``download`` returns the path of the downloaded model
        files:
        
        .. code-block:: python
        
            import faculty_models
        
            path = faculty_models.download(
                project_id="998328c3-23df-4225-a3ee-0a53d1409fbd",
                model_id="c998fca9-e093-47ea-9896-8f75db695b91"
            )
        
        The above examples always download the latest version of a model. To get a
        specific verion, pass the version number when calling either function:
        
        .. code-block:: python
        
            import faculty_models
        
            model = faculty_models.load_mlmodel(
                project_id="998328c3-23df-4225-a3ee-0a53d1409fbd",
                model_id="c998fca9-e093-47ea-9896-8f75db695b91",
                version=4
            )
        
        If you only wish to download part of the model, or if you wish to load an
        MLmodel that is in a subdirectory of the model, pass the path argument to
        either function:
        
        .. code-block:: python
        
            import faculty_models
        
            model = faculty_models.load_mlmodel(
                project_id="998328c3-23df-4225-a3ee-0a53d1409fbd",
                model_id="c998fca9-e093-47ea-9896-8f75db695b91",
                path="sub/path"
            )
        
Platform: UNKNOWN
