google/jax: GPU- and TPU-backed NumPy with differentiation and JIT compilation.
JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivative
Beyond Numpy Arrays in Python
Beyond Numpy Arrays in Python Preparing the ecosystem for GPU, distributed, and sparse arrays Executive Summary In recent years Python’s array computing ecosystem has grown organically to support GPUs, sparse, and distributed arrays. This is wonderful and a great example of the growth that can occur in decentralized open source development. However to solidify this growth and apply it across the e
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