Metadata-Version: 2.1
Name: model-card-toolkit
Version: 0.1.2
Summary: Model Card Toolkit
Home-page: https://github.com/tensorflow/model-card-toolkit
Author: Google LLC
Author-email: tensorflow-extended-dev@googlegroups.com
License: Apache 2.0
Description: # Model Card Toolkit
        
        The Model Card Toolkit (MCT) streamlines and automates generation of [Model Cards](https://modelcards.withgoogle.com/about) [1], machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables the sharing model metadata and metrics with researchers, developers, reporters, and more.
        
        Some use cases of model cards include:
        
        * Facilitating the exchange of information between model builders and product developers.
        * Informing users of ML models to make better-informed decisions about how to use them (or how not to use them).
        * Providing model information required for effective public oversight and accountability.
        
        ![Generated model card image](https://raw.githubusercontent.com/tensorflow/model-card-toolkit/master/model_card_toolkit/documentation/guide/images/model_card.png)
        
        ## Installation
        
        The Model Card Toolkit is hosted on [PyPI](https://pypi.org/project/model-card-toolkit/), and can be installed with `pip install model-card-toolkit` (or `pip install model-card-toolkit
        --use-deprecated=legacy-resolver` for pip20.3). See [the installation guide](model_card_toolkit/documentation/guide/install.md) for more details.
        
        ## Getting Started
        
            import model_card_toolkit
        
            # Initialize the Model Card Toolkit with a path to store generate assets
            model_card_output_path = ...
            mct = model_card_toolkit.ModelCardToolkit(model_card_output_path)
        
            # Initialize the model_card_toolkit.ModelCard, which can be freely populated
            model_card = mct.scaffold_assets()
            model_card.model_details.name = 'My Model'
        
            # Write the model card data to a JSON file
            mct.update_model_card_json(model_card)
        
            # Return the model card document as an HTML page
            html = mct.export_format()
        
        ## Automatic Model Card Generation
        
        If your machine learning pipeline uses the [TensorFlow Extended (TFX)](https://www.tensorflow.org/tfx) platform or [ML Metadata](https://www.tensorflow.org/tfx/guide/mlmd), you can automate model card generation. See [this demo notebook](model_card_toolkit/documentation/examples/MLMD_Model_Card_Toolkit_Demo.ipynb) for a demonstration of how to integrate the MCT into your pipeline.
        
        ## Schema
        
        Model cards are stored in JSON as an intermediate format. You can see the model card JSON schema in the `schema` directory. Note that this is not a finalized path and may be hosted elsewhere in the future.
        
        ## References
        
        [1] https://arxiv.org/abs/1810.03993
        
Keywords: model card toolkit ml metadata machine learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6,<4
Description-Content-Type: text/markdown
