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The fix removes return type of get_feature_store. It should be done differently in the long term.
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# Hopsworks Client | ||
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<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-HOPSWORKS-orange" | ||
alt="Hopsworks Documentation" | ||
/></a> | ||
<a><img | ||
src="https://img.shields.io/badge/python-3.8+-blue" | ||
alt="python" | ||
/></a> | ||
<a href="https://pypi.org/project/hopsworks/"><img | ||
src="https://img.shields.io/pypi/v/hopsworks?color=blue" | ||
alt="PyPiStatus" | ||
/></a> | ||
<a href="https://pepy.tech/project/hopsworks/month"><img | ||
src="https://pepy.tech/badge/hopsworks/month" | ||
alt="Downloads" | ||
/></a> | ||
<a href=https://github.com/astral-sh/ruff><img | ||
src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json" | ||
alt="Ruff" | ||
/></a> | ||
<a><img | ||
src="https://img.shields.io/pypi/l/hopsworks?color=green" | ||
alt="License" | ||
/></a> | ||
</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: | ||
- 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. | ||
- 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). | ||
- 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 | ||
pip install hopsworks | ||
``` | ||
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Fire up a notebook and connect to your project, you will be prompted to enter your newly created API key: | ||
```python | ||
import hopsworks | ||
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project = hopsworks.login() | ||
``` | ||
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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 | ||
fs = project.get_feature_store() | ||
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# Write to Feature Groups | ||
bike_ride_fg = fs.get_or_create_feature_group( | ||
name="bike_rides", | ||
version=1, | ||
primary_key=["ride_id"], | ||
event_time="activation_time", | ||
online_enabled=True, | ||
) | ||
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fg.insert(bike_rides_df) | ||
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# Read from Feature Views | ||
profile_fg = fs.get_feature_group("user_profile", version=1) | ||
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bike_ride_fv = fs.get_or_create_feature_view( | ||
name="bike_rides_view", | ||
version=1, | ||
query=bike_ride_fg.select_except(["ride_id"]).join(profile_fg.select(["age", "has_license"]), on="user_id") | ||
) | ||
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bike_rides_Q1_2021_df = bike_ride_fv.get_batch_data( | ||
start_date="2021-01-01", | ||
end_date="2021-01-31" | ||
) | ||
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# Create a training dataset | ||
version, job = bike_ride_fv.create_train_test_split( | ||
test_size=0.2, | ||
description='Description of a dataset', | ||
# you can have different data formats such as csv, tsv, tfrecord, parquet and others | ||
data_format='csv' | ||
) | ||
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# Predict the probability of accident in real-time using new data + context data | ||
bike_ride_fv.init_serving() | ||
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while True: | ||
new_ride_vector = poll_ride_queue() | ||
feature_vector = bike_ride_fv.get_online_feature_vector( | ||
{"user_id": new_ride_vector["user_id"]}, | ||
passed_features=new_ride_vector | ||
) | ||
accident_probability = model.predict(feature_vector) | ||
``` | ||
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Or you can use the Machine Learning API to register models and deploy them for serving: | ||
```python | ||
mr = project.get_model_registry() | ||
# or | ||
ms = project.get_model_serving() | ||
``` | ||
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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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