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averseyaversey
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Fix circular import error in docgen
The fix removes return type of get_feature_store. It should be done differently in the long term.
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‎docs/CONTRIBUTING.md

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pip install -e ".[dev]"
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```
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- Install [pre-commit](https://pre-commit.com/) and then activate its hooks. pre-commit is a framework for managing and maintaining multi-language pre-commit hooks. The Feature Store uses pre-commit to ensure code-style and code formatting through [ruff](https://docs.astral.sh/ruff/). Run the following commands from the `python` directory:
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- Install [pre-commit](https://pre-commit.com/) and then activate its hooks. pre-commit is a framework for managing and maintaining multi-language pre-commit hooks. The library uses pre-commit to ensure code-style and code formatting through [ruff](https://docs.astral.sh/ruff/). Run the following commands from the `python` directory:
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```bash
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cd python

‎docs/index.md

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# Hopsworks Client
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<p align="center">
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<a href="https://community.hopsworks.ai"><img
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src="https://img.shields.io/discourse/users?label=Hopsworks%20Community&server=https%3A%2F%2Fcommunity.hopsworks.ai"
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alt="Hopsworks Community"
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/></a>
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<a href="https://docs.hopsworks.ai"><img
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src="https://img.shields.io/badge/docs-HOPSWORKS-orange"
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alt="Hopsworks Documentation"
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/></a>
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<a><img
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src="https://img.shields.io/badge/python-3.8+-blue"
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alt="python"
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/></a>
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<a href="https://pypi.org/project/hopsworks/"><img
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src="https://img.shields.io/pypi/v/hopsworks?color=blue"
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alt="PyPiStatus"
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/></a>
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<a href="https://pepy.tech/project/hopsworks/month"><img
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src="https://pepy.tech/badge/hopsworks/month"
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alt="Downloads"
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/></a>
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<a href=https://github.com/astral-sh/ruff><img
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src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json"
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alt="Ruff"
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/></a>
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<a><img
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src="https://img.shields.io/pypi/l/hopsworks?color=green"
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alt="License"
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/></a>
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</p>
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*hopsworks* is the python API for interacting with a Hopsworks cluster. Don't have a Hopsworks cluster just yet? Register an account on [Hopsworks Serverless](https://app.hopsworks.ai/) and get started for free. Once connected to your project, you can:
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- Insert dataframes into the online or offline Store, create training datasets or *serve real-time* feature vectors in the Feature Store via the [Feature Store API](https://github.com/logicalclocks/feature-store-api). Already have data somewhere you want to import, checkout our [Storage Connectors](https://docs.hopsworks.ai/latest/user_guides/fs/storage_connector/) documentation.
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- register ML models in the model registry and *deploy* them via model serving via the [Machine Learning API](https://gitub.com/logicalclocks/machine-learning-api).
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- manage environments, executions, kafka topics and more once you deploy your own Hopsworks cluster, either on-prem or in the cloud. Hopsworks is open-source and has its own [Community Edition](https://github.com/logicalclocks/hopsworks).
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Our [tutorials](https://github.com/logicalclocks/hopsworks-tutorials) cover a wide range of use cases and example of what *you* can build using Hopsworks.
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## Getting Started On Hopsworks
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Once you created a project on [Hopsworks Serverless](https://app.hopsworks.ai) and created a new [Api Key](https://docs.hopsworks.ai/latest/user_guides/projects/api_key/create_api_key/), just use your favourite virtualenv and package manager to install the library:
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```bash
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pip install hopsworks
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```
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Fire up a notebook and connect to your project, you will be prompted to enter your newly created API key:
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```python
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import hopsworks
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project = hopsworks.login()
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```
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Access the Feature Store of your project to use as a central repository for your feature data. Use *your* favourite data engineering library (pandas, polars, Spark, etc...) to insert data into the Feature Store, create training datasets or serve real-time feature vectors. Want to predict likelyhood of e-scooter accidents in real-time? Here's how you can do it:
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```python
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fs = project.get_feature_store()
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# Write to Feature Groups
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bike_ride_fg = fs.get_or_create_feature_group(
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name="bike_rides",
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version=1,
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primary_key=["ride_id"],
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event_time="activation_time",
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online_enabled=True,
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)
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fg.insert(bike_rides_df)
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# Read from Feature Views
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profile_fg = fs.get_feature_group("user_profile", version=1)
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bike_ride_fv = fs.get_or_create_feature_view(
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name="bike_rides_view",
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version=1,
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query=bike_ride_fg.select_except(["ride_id"]).join(profile_fg.select(["age", "has_license"]), on="user_id")
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)
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bike_rides_Q1_2021_df = bike_ride_fv.get_batch_data(
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start_date="2021-01-01",
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end_date="2021-01-31"
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)
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# Create a training dataset
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version, job = bike_ride_fv.create_train_test_split(
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test_size=0.2,
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description='Description of a dataset',
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# you can have different data formats such as csv, tsv, tfrecord, parquet and others
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data_format='csv'
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)
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# Predict the probability of accident in real-time using new data + context data
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bike_ride_fv.init_serving()
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while True:
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new_ride_vector = poll_ride_queue()
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feature_vector = bike_ride_fv.get_online_feature_vector(
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{"user_id": new_ride_vector["user_id"]},
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passed_features=new_ride_vector
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)
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accident_probability = model.predict(feature_vector)
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```
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Or you can use the Machine Learning API to register models and deploy them for serving:
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```python
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mr = project.get_model_registry()
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# or
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ms = project.get_model_serving()
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```
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## Tutorials
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Need more inspiration or want to learn more about the Hopsworks platform? Check out our [tutorials](https://github.com/logicalclocks/hopsworks-tutorials).
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## Documentation
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Documentation is available at [Hopsworks Documentation](https://docs.hopsworks.ai/).
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## Issues
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For general questions about the usage of Hopsworks and the Feature Store please open a topic on [Hopsworks Community](https://community.hopsworks.ai/).
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Please report any issue using [Github issue tracking](https://github.com/logicalclocks/hopsworks-api/issues).
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## Contributing
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If you would like to contribute to this library, please see the [Contribution Guidelines](CONTRIBUTING.md).
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‎python/hopsworks/project.py

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from __future__ import annotations
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import json
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from typing import TYPE_CHECKING
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import humps
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from hopsworks import client, constants, util
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)
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if TYPE_CHECKING:
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from hsfs import feature_store
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class Project:
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def __init__(
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self,
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"""Timestamp when the project was created"""
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return self._created
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def get_feature_store(self, name: str = None, engine: str = None) -> feature_store.FeatureStore:
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def get_feature_store(self, name: str = None, engine: str = None):
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"""Connect to Project's Feature Store.
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Defaulting to the project name of default feature store. To get a

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