Metadata-Version: 2.4
Name: loaderx
Version: 0.1.2
Summary: Minimal data loader for Flax
Author-email: Ben0i0d <ben0i0d@foxmail.com>
License: MIT License
        
        Copyright (c) 2025 EOELAB AI Research
        
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Project-URL: Homepage, https://github.com/eoeair/loaderx
Project-URL: Documentation, https://github.com/eoeair/loaderx
Project-URL: Source, https://github.com/eoeair/loaderx
Project-URL: Bug Tracker, https://github.com/eoeair/loaderx/issues
Keywords: flax,python,dataloader
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Dynamic: license-file

# loaderx
A Minimal Data Loader for Flax

## Why Create loaderx?

While Flax supports multiple data-loading backends—including PyTorch, TensorFlow, Grain, and jax_dataloader—each comes with notable drawbacks:

1. Installing large frameworks like PyTorch or TensorFlow *just* for data loading is often undesirable.
2. Grain provides a clean API, but its real-world performance can be suboptimal.
3. jax_dataloader defaults to using GPU memory, which may lead to inefficient memory utilization in some workflows.

## Design Philosophy

loaderx is built around several core principles:

1. A pragmatic approach that prioritizes minimal memory overhead and minimal dependencies.
2. A strong focus on single-machine training workflows.
3. A NumPy-based implementation for excellent compatibility with JAX.
4. An **immortal (endless) step-based data loader**, rather than the traditional epoch-based design—better aligned with modern ML training practices.

## Current Limitations

Currently, loaderx only supports single-host environments and does not yet support multi-host training.

## Integrating with Flax

For practical integration examples, please refer to the **Data2Latent** repository:
**[https://github.com/eoeair/Data2Latent](https://github.com/eoeair/Data2Latent)**
