Metadata-Version: 2.1
Name: hsml
Version: 3.1.0rc0
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/3.1.0rc0
Description: # Hopsworks Model Management
        
        <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 Management 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 and Model Serving. The library makes it easy to export, manage and deploy 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 and serving handles
        ```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()
        
        # Get the model serving handle for the current model registry
        ms = connection.get_model_serving()
        ```
        
        Create a new model
        ```python
        model = mr.tensorflow.create_model(name="mnist",
                                           version=1,
                                           metrics={"accuracy": 0.94},
                                           description="mnist model description")
        model.save("/tmp/model_directory") # or /tmp/model_file
        ```
        
        Download a model
        ```python
        model = mr.get_model("mnist", version=1)
        
        model_path = model.download()
        ```
        
        Delete a model
        ```python
        model.delete()
        ```
        
        Get best performing model
        ```python
        best_model = mr.get_best_model('mnist', 'accuracy', 'max')
        
        ```
        
        Deploy a model
        ```python
        deployment = model.deploy()
        ```
        
        Start a deployment
        ```python
        deployment.start()
        ```
        
        Make predictions with a deployed model
        ```python
        data = { "instances": model.input_example }
        
        predictions = deployment.predict(data)
        ```
        
        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 Management 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
