Metadata-Version: 2.1
Name: hsfs
Version: 2.3.3
Summary: HSFS: An environment independent client to interact with the Hopsworks Featurestore
Home-page: https://github.com/logicalclocks/feature-store-api
Author: Logical Clocks AB
Author-email: moritz@logicalclocks.com
License: Apache License 2.0
Download-URL: https://github.com/logicalclocks/feature-store-api/releases/tag/2.3.3
Description: # Hopsworks Feature Store
        
        <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-HSFS-orange"
            alt="Hopsworks Feature Store Documentation"
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            src="https://img.shields.io/pypi/v/hsfs?color=blue"
            alt="PyPiStatus"
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          <a href="https://archiva.hops.works/#artifact/com.logicalclocks/hsfs"><img
            src="https://img.shields.io/badge/java-HSFS-green"
            alt="Scala/Java Artifacts"
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          <a href="https://pepy.tech/project/hsfs/month"><img
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            alt="Downloads"
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            alt="CodeStyle"
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          <a><img
            src="https://img.shields.io/pypi/l/hsfs?color=green"
            alt="License"
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        </p>
        
        HSFS is the library to interact with the Hopsworks Feature Store. The library makes creating new features, feature groups and training datasets easy.
        
        The library is environment independent and can be used in two modes:
        
        - Spark mode: For data engineering jobs that create and write features into the feature store or generate training datasets. It requires a Spark environment such as the one provided in the Hopsworks platform or Databricks. In Spark mode, HSFS provides bindings both for Python and JVM languages.
        
        - Python mode: For data science jobs to explore the features available in the feature store, generate training datasets and feed them in a training pipeline. Python mode requires just a Python interpreter and can be used both in Hopsworks from Python Jobs/Jupyter Kernels, Amazon SageMaker or KubeFlow.
        
        The library automatically configures itself based on the environment it is run.
        However, to connect from an external environment such as Databricks or AWS Sagemaker,
        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 feature store handler
        ```python
        import hsfs
        
        connection = hsfs.connection()
        fs = connection.get_feature_store()
        ```
        
        Create a new feature group
        ```python
        fg = fs.create_feature_group("rain",
                                version=1,
                                description="Rain features",
                                primary_key=['date', 'location_id'],
                                online_enabled=True)
        
        fg.save(dataframe)
        ```
        
        Upsert new data in to the feature group with `time_travel_format="HUDI"`".
        ```python
        fg.insert(upsert_df)
        ```
        
        Retrieve commit timeline metdata of the feature group with `time_travel_format="HUDI"`".
        ```python
        fg.commit_details()
        ```
        
        "Reading feature group as of specific point in time".
        ```python
        fg = fs.get_feature_group("rain", 1)
        fg.read("2020-10-20 07:34:11").show()
        ```
        
        Read updates  that occurred between specified points in time.
        ```python
        fg = fs.get_feature_group("rain", 1)
        fg.read_changes("2020-10-20 07:31:38", "2020-10-20 07:34:11").show()
        ```
        
        Join features together
        ```python
        feature_join = rain_fg.select_all()
                            .join(temperature_fg.select_all(), on=["date", "location_id"])
                            .join(location_fg.select_all())
        feature_join.show(5)
        ```
        
        join feature groups that correspond to specific point in time
        ```python
        feature_join = rain_fg.select_all()
                            .join(temperature_fg.select_all(), on=["date", "location_id"])
                            .join(location_fg.select_all())
                            .as_of("2020-10-31")
        feature_join.show(5)
        ```
        
        join feature groups that correspond to different time
        ```python
        rain_fg_q = rain_fg.select_all().as_of("2020-10-20 07:41:43")
        temperature_fg_q = temperature_fg.select_all().as_of("2020-10-20 07:32:33")
        location_fg_q = location_fg.select_all().as_of("2020-10-20 07:33:08")
        joined_features_q = rain_fg_q.join(temperature_fg_q).join(location_fg_q)
        ```
        
        Use the query object to create a training dataset:
        ```python
        td = fs.create_training_dataset("rain_dataset",
                                        version=1,
                                        data_format="tfrecords",
                                        description="A test training dataset saved in TfRecords format",
                                        splits={'train': 0.7, 'test': 0.2, 'validate': 0.1})
        
        td.save(feature_join)
        ```
        
        Feed the training dataset to a TensorFlow model:
        ```python
        tf_data_object = training_dataset.tf_data(target_name="label",
                                                  split="train",
                                                  is_training=True)
        train_input = tf_data_object.tf_record_dataset(batch_size=32,
                                                       num_epochs=5,
                                                       process=True)
        ```
        
        A short introduction to the Scala API:
        ```scala
        import com.logicalclocks.hsfs._
        val connection = HopsworksConnection.builder().build()
        val fs = connection.getFeatureStore();
        val attendances_features_fg = fs.getFeatureGroup("games_features", 1);
        attendances_features_fg.show(1)
        ```
        
        You can find more examples on how to use the library in our [hops-examples](https://github.com/logicalclocks/hops-examples) repository.
        
        ## Documentation
        
        Documentation is available at [Hopsworks Feature Store Documentation](https://docs.hopsworks.ai/).
        
        ## Issues
        
        For general questions about the usage of Hopsworks and the Feature Store please open a topic on [Hopsworks Community](https://community.hopsworks.ai/).
        
        Please report any issue using [Github issue tracking](https://github.com/logicalclocks/feature-store-api/issues).
        
        
        ## Contributing
        
        If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md).
        
Keywords: Hopsworks,Feature Store,Spark,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
Provides-Extra: hive
