Metadata-Version: 2.1
Name: hsml
Version: 2.5.0
Summary: HSML: An environment independent client to interact with the Hopsworks Model Registry
Home-page: https://github.com/logicalclocks/machine-learning-api
Author: Logical Clocks AB
Author-email: robin@logicalclocks.com
License: Apache License 2.0
Download-URL: https://github.com/logicalclocks/machine-learning-api/releases/tag/2.5.0
Description: # Hopsworks Model Registry
        
        <p align="center">
          <a href="https://community.hopsworks.ai"><img
            src="https://img.shields.io/discourse/users?label=Hopsworks%20Community&server=https%3A%2F%2Fcommunity.hopsworks.ai"
            alt="Hopsworks Community"
          /></a>
            <a href="https://docs.hopsworks.ai"><img
            src="https://img.shields.io/badge/docs-HSML-orange"
            alt="Hopsworks Model Registry Documentation"
          /></a>
          <a href="https://pypi.org/project/hsml/"><img
            src="https://img.shields.io/pypi/v/hsml?color=blue"
            alt="PyPiStatus"
          /></a>
          <a href="https://archiva.hops.works/#artifact/com.logicalclocks/hsml"><img
            src="https://img.shields.io/badge/java-HSML-green"
            alt="Scala/Java Artifacts"
          /></a>
          <a href="https://pepy.tech/project/hsml/month"><img
            src="https://pepy.tech/badge/hsml/month"
            alt="Downloads"
          /></a>
          <a href="https://github.com/psf/black"><img
            src="https://img.shields.io/badge/code%20style-black-000000.svg"
            alt="CodeStyle"
          /></a>
          <a><img
            src="https://img.shields.io/pypi/l/hsml?color=green"
            alt="License"
          /></a>
        </p>
        
        HSML is the library to interact with the Hopsworks Model Registry. The library makes it easy to export and manage models.
        
        The library automatically configures itself based on the environment it is run.
        However, to connect from an external Python environment additional connection information, such as host and port, is required. For more information about the setup from external environments, see the setup section.
        
        ## Getting Started On Hopsworks
        
        Instantiate a connection and get the project model registry handle
        ```python
        import hsml
        
        # Create a connection
        connection = hsml.connection()
        
        # Get the model registry handle for the project's model registry
        mr = connection.get_model_registry()
        ```
        
        Create a new model
        ```python
        mnist_model_meta = mr.tensorflow.create_model(name="mnist",
                                                      version=1,
                                                      metrics={"accuracy": 0.94},
                                                      description="mnist model description")
        mnist_model_meta.save("/tmp/model_directory")
        ```
        
        Download a model
        ```python
        mnist_model_meta = mr.get_model("name", version=1)
        
        model_path = mnist_model_meta.download()
        ```
        
        Delete a model
        ```python
        mnist_model_meta.delete()
        ```
        
        Get best performing model
        ```python
        mnist_model_meta = mr.get_best_model('mnist', 'accuracy', 'max')
        
        ```
        
        You can find more examples on how to use the library in [examples.hopsworks.ai](https://examples.hopsworks.ai).
        
        ## Documentation
        
        Documentation is available at [Hopsworks Model Registry Documentation](https://docs.hopsworks.ai/).
        
        ## Issues
        
        For general questions about the usage of Hopsworks Machine Learning please open a topic on [Hopsworks Community](https://community.hopsworks.ai/).
        
        Please report any issue using [Github issue tracking](https://github.com/logicalclocks/machine-learning-api/issues).
        
        
        ## Contributing
        
        If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md).
        
Keywords: Hopsworks,ML,Models,Machine Learning Models,Model Registry,TensorFlow,PyTorch,Machine Learning,MLOps,DataOps
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Developers
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: docs
