google/grain
Library for reading and processing ML training data.
This library helps machine learning engineers efficiently prepare data for training and evaluating JAX models. It takes raw datasets and transforms them through steps like shuffling, mapping, and batching, outputting ready-to-use data batches for model ingestion. It's designed for ML practitioners working with JAX who need flexible, fast, and deterministic data pipelines.
691 stars. Used by 3 other packages. Actively maintained with 20 commits in the last 30 days. Available on PyPI.
Use this if you are a machine learning engineer working with JAX models and need a reliable way to define and execute complex data preprocessing steps before model training.
Not ideal if you are not working with machine learning models or require GPU/TPU acceleration for the data transformation steps themselves.
Stars
691
Forks
69
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
20
Dependencies
6
Reverse dependents
3
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