Welcome to JAX DataLoader's documentation! ======================================= JAX DataLoader is a high-performance data loading library for JAX that provides efficient data loading and preprocessing capabilities. It is designed to be simple, fast, and memory-efficient, making it perfect for deep learning and data science workflows. Features -------- * **High Performance**: Optimized data loading with minimal overhead * **Memory Efficient**: Smart memory management and data streaming * **Flexible**: Support for various data formats (CSV, JSON, Images) * **Easy to Use**: Simple API with familiar interface * **Type Safe**: Full type hints and static type checking * **Extensible**: Easy to add custom data loaders * **Benchmarking**: Comprehensive performance analysis tools for CPU and multi-device setups * **Performance Optimization**: Advanced tools for analyzing and optimizing data loading performance Quick Start ---------- .. code-block:: python from jax_dataloader import DataLoader import jax.numpy as jnp # Create a simple dataset data = jnp.array([1, 2, 3, 4, 5]) dataset = DataLoader(data, batch_size=2) # Iterate over batches for batch in dataset: print(batch) Installation ----------- .. code-block:: bash pip install jax-dataloaders For development installation: .. code-block:: bash git clone https://github.com/carrycooldude/JAX-Dataloader.git cd JAX-Dataloader pip install -e ".[dev]" Documentation Contents -------------------- .. toctree:: :maxdepth: 2 :caption: Contents: installation usage api examples tutorials changelog Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` Getting Help ----------- If you encounter any issues or have questions: * Open an issue on `GitHub `_ * Check the `examples `_ for common use cases * Join our `Discussions `_ Contributing ----------- We welcome contributions! Please see our `Contributing Guide `_ for details.