title: Feast Joins the PyTorch Ecosystem

description: We're excited to announce that Feast has officially joined the PyTorch ecosystem, bringing feature stores to the broader ML community.

date: 2025-01-26

authors: ["Francisco Javier Arceo", "Hao Xu", "Shuchu Han"]

<div class="hero-image">

<img src="/images/blog/rocket.png" alt="Feast Joins PyTorch Ecosystem" loading="lazy">

</div>

We're thrilled to announce that Feast has officially joined the [PyTorch ecosystem](https://pytorch.org/blog/feast-joins-the-pytorch-ecosystem/)! This is a significant milestone for the Feast community and represents our commitment to making feature stores accessible to the broader machine learning community.

By joining the PyTorch ecosystem, Feast becomes part of a vibrant community of tools and libraries that power modern machine learning workflows. PyTorch has become the de facto standard for ML research and production, and we're excited to bring feature store capabilities to PyTorch users.

Feature stores solve critical challenges that ML engineers face when moving from model development to production:

1. **Consistent Feature Engineering**: Ensure that features computed during training match those used in production

2. **Low-Latency Feature Serving**: Serve features with millisecond latency for real-time inference

3. **Feature Reusability**: Share features across teams and models to accelerate development

4. **Point-in-Time Correctness**: Prevent data leakage by ensuring training data reflects what would have been available at prediction time

5. **Data Infrastructure Abstraction**: Decouple your ML code from underlying data infrastructure

Feast integrates seamlessly with PyTorch workflows, whether you're training models locally or deploying them at scale. Here's a typical workflow:

from feast import FeatureStore

import torch

from torch.utils.data import Dataset, DataLoader

import pandas as pd

from datetime import datetime

store = FeatureStore(repo_path=".")

entity_df = pd.DataFrame({

"user_id": [1001, 1002, 1003],

"event_timestamp": [datetime(2025, 1, 1), datetime(2025, 1, 2), datetime(2025, 1, 3)]

})

training_df = store.get_historical_features(

entity_df=entity_df,

features=[

"user_features:age",

"user_features:activity_score",

"item_features:popularity"

]

).to_df()

class FeatureDataset(Dataset):

def __init__(self, df, feature_cols):

self.features = torch.tensor(df[feature_cols].values, dtype=torch.float32)

self.labels = torch.tensor(df['label'].values, dtype=torch.float32)

def __len__(self):

return len(self.features)

def __getitem__(self, idx):

return self.features[idx], self.labels[idx]

feature_cols = ['age', 'activity_score', 'popularity']

dataset = FeatureDataset(training_df, feature_cols)

dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

model = torch.nn.Sequential(

torch.nn.Linear(len(feature_cols), 64),

torch.nn.ReLU(),

torch.nn.Linear(64, 1)

)

Once your PyTorch model is trained, Feast makes it easy to serve features in production:

online_features = store.get_online_features(

entity_rows=[{"user_id": 1001}],

features=[

"user_features:age",

"user_features:activity_score",

"item_features:popularity"

]

).to_dict()

feature_values = [

online_features['age'][0],

online_features['activity_score'][0],

online_features['popularity'][0]

]

feature_tensor = torch.tensor(feature_values, dtype=torch.float32)

with torch.no_grad():

prediction = model(feature_tensor)

Feast helps you move from notebook experiments to production-ready feature pipelines. Define your features once and use them consistently across training and serving.

Feast supports a wide range of data sources including:

- BigQuery, Snowflake, Redshift for batch features

- Redis, DynamoDB, PostgreSQL for online features

- Parquet files for local development

Whether you're training on a laptop or serving millions of predictions per second, Feast scales with your needs.

Feast is fully open source with a pluggable architecture, making it easy to extend and customize for your specific use cases.

Ready to use Feast with PyTorch? Here's how to get started:

pip install feast

feast init my_feature_repo

cd my_feature_repo

feast apply

feast serve

Check out our [documentation](https://docs.feast.dev) for comprehensive guides and examples.

We're excited to be part of the PyTorch ecosystem and look forward to collaborating with the community. Here's how you can get involved:

- **GitHub**: Star us at [github.com/feast-dev/feast](https://github.com/feast-dev/feast)

- **Slack**: Join our [community Slack](https://slack.feast.dev)

- **Documentation**: Explore our [docs](https://docs.feast.dev)

- **Examples**: Check out [example projects](https://github.com/feast-dev/feast/tree/master/examples)

We're committed to making Feast the best feature store for PyTorch users. Some of our upcoming priorities include:

- Enhanced PyTorch integration examples

- Performance optimizations for large-scale deployments

- Additional connectors for popular data sources

- Improved developer experience

Thank you to the PyTorch team for welcoming us to the ecosystem, and to the Feast community for your continued support!

- [PyTorch Blog Announcement](https://pytorch.org/blog/feast-joins-the-pytorch-ecosystem/)

- [Feast Documentation](https://docs.feast.dev)

- [Feast GitHub Repository](https://github.com/feast-dev/feast)

- [What is a Feature Store?](./what-is-a-feature-store)

Have questions or feedback? Reach out to us on [Slack](https://slack.feast.dev) or [GitHub Discussions](https://github.com/feast-dev/feast/discussions).