hopsworks

What it is and what it does

Hopsworks is a Python client library for the Hopsworks platform, an MLOps and DataOps system centered on a feature store. It lets you connect to a Hopsworks cluster (cloud-hosted or self-managed) to manage features, training datasets, ML models, and real-time serving. The library supports two operational modes: Spark mode for large-scale data engineering jobs that write features into the store, and Python mode for data science workflows that read features, build training datasets, and deploy models for inference.

The package wraps three main APIs: the Feature Store API (create feature groups, insert dataframes, build training datasets, serve real-time feature vectors), the Model Registry API (register and version ML models), and the Model Serving API (deploy and manage model endpoints). It depends on 24 runtime packages including pandas, numpy, boto3, grpcio, and opensearch-py to handle data processing, cloud storage, gRPC communication, and search operations. Most workflows begin with `hopsworks.login()` to authenticate against your cluster, then branch into feature store, model registry, or serving operations.

Use it for:

• Build and manage a centralized feature repository: create feature groups from dataframes, version them, and share features across multiple ML projects.
• Generate training datasets: query feature groups, join them, and export as CSV, Parquet, or TFRecord for model training pipelines.
• Serve real-time predictions: fetch online feature vectors on demand (combining stored features with request-time context) to feed into deployed models.
• Register and deploy ML models: version TensorFlow, PyTorch, or other models in the registry, then deploy them as HTTP endpoints for inference.
• Integrate with cloud storage: use storage connectors to ingest data from S3, GCS, or other sources into the feature store.
• Monitor and manage Hopsworks infrastructure: on self-hosted clusters, manage environments, Kafka topics, and job executions via the API.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Python SDK for connecting to Hopsworks clusters to manage feature stores, model registries, and model serving deployments.

Yes, if you are building ML systems on Hopsworks. The package is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive Apache-2.0 license. Its 24 dependencies are substantial but necessary for the breadth of integrations (cloud storage, databases, gRPC, search). Suitable for teams already committed to the Hopsworks platform; not a general-purpose feature store library for other backends.

Install

hopsworks on PyPI

pip install hopsworks

uv add hopsworks

poetry add hopsworks

Installing hopsworks

Before you install

Low install friction with a pure-wheel distribution. Active maintenance with a release 9 days old; last commit 2026-08-14. Supports Python 3.10–3.13.

License in practice

Apache-2.0 (permissive): you can use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license and state significant changes.

Quickstart

pip install "hopsworks[python]"

import hopsworks
project = hopsworks.login()
fs = project.get_feature_store()
fg = fs.get_or_create_feature_group(name="my_features", version=1)
fg.insert(my_dataframe)

Requires a Hopsworks cluster (SaaS or self-hosted) and an API key to authenticate; Python mode works with just a Python interpreter but still needs network access to the cluster.

Verify before relying

• Whether the 24 runtime dependencies (including boto3, pandas, grpcio, protobuf, opensearch-py) are all required for basic Feature Store operations or if some are optional for specific integrations.
• Performance characteristics and latency for real-time feature vector serving at scale.
• Whether Spark mode (mentioned in description) requires additional setup beyond the Python package itself.

Package facts

Evidence: hopsworks-5.0.4-py3-none-any.whl

Keywords: Hopsworks, Feature Store, hsfs, Spark, Machine Learning, MLOps, DataOps, Model Registry, hsml, Models, ML, Machine Learning Models, TensorFlow, PyTorch

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