What Is the JAX Deep Learning Framework?

JAX is a machine learning framework developed by Google that has been gaining traction in deep learning research due to its seamless integration with Python's numpy and its ability to run efficiently on GPUs. JAX is renowned for its four main function transformations: grad, which automatically differentiates functions for backpropagation; jit, which optimizes functions for efficient execution; vmap, which maps functions across dimensions; and pmap, which allows for parallel processing across multiple processors. While JAX shares similarities with PyTorch in terms of being "numpy-esque," PyTorch offers a broader range of libraries and utilities, making it more suitable for application development. Compared to TensorFlow, another Google product, JAX is perceived as easier to develop with but lacks the extensive infrastructure and higher-level abstractions that TensorFlow offers. JAX is particularly appealing for research projects, but for application development, PyTorch and TensorFlow might be more advantageous due to their comprehensive features and deployment capabilities.