jax-dataloaders 0.1.4

A high-performance data loading library for JAX applications

JAX DataLoader

A high-performance data loading library for JAX, designed for efficient data loading and preprocessing in machine learning workflows.

Features

• Efficient data loading with automatic batching
• Multi-GPU support with automatic batch distribution
• Memory management with automatic batch size tuning
• Support for various data formats (CSV, JSON, Images)
• Progress tracking and statistics
• Data caching and prefetching
• Error handling and recovery

Installation

pip install jax-dataloader

Quick Start

from jax_dataloader import JAXDataLoader, DataLoaderConfig

# Create a DataLoader configuration
config = DataLoaderConfig(
    batch_size=32,
    num_workers=4,
    multi_gpu=True
)

# Load your data
dataloader = JAXDataLoader(
    data_path="path/to/your/data",
    config=config
)

# Iterate over batches
for batch_x, batch_y in dataloader:
    # Process your batch
    ...

Examples

The package includes comprehensive examples demonstrating various features:

# Clone the repository
git clone https://github.com/yourusername/jax-dataloader.git
cd jax-dataloader

# Install example dependencies
pip install -r examples/requirements.txt

# Run the data loading demo
cd examples/data_loading
python demo.py

The examples demonstrate:

• Loading different data formats (CSV, JSON, Images)
• Multi-GPU support
• Memory management
• Progress tracking
• Batch size optimization

For more examples and detailed documentation, visit our documentation.

Documentation

For detailed documentation, including API reference and advanced usage examples, visit our documentation.

Contributing

We welcome contributions! Please see our contributing guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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