Open Source AI Project


A high-efficiency, framework-agnostic data loading library compatible with PyTorch, Jax, or MLX.


The project described is a data loading library designed to offer high efficiency and flexibility across different machine learning frameworks. Its framework-agnostic nature means it can be used in conjunction with popular machine learning tools such as PyTorch, Jax, and MLX, making it versatile for various projects and research.

The primary aim of MLX Data is to streamline the process of loading and processing data, particularly images, at a high throughput—highlighting its capability to handle thousands of images per second. This feature is especially important for training complex machine learning models that require large datasets to improve accuracy and performance.

Moreover, the library supports running arbitrary Python code for transformations on the batches of data it generates. This flexibility is crucial for machine learning workflows, as it allows users to apply custom preprocessing steps, augmentations, or any other transformations required by their specific project needs, directly within the data loading pipeline. This capability ensures that the data fed into the models is exactly as needed, without necessitating separate preprocessing steps or external tools.

In essence, MLX Data is designed to be a high-performance, adaptable solution for data handling in machine learning projects, reducing the complexity and improving the efficiency of working with large datasets across various frameworks.

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