Xla
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A Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and machine learning.
A Python library for composable transformations of numerical programs: automatic differentiation, vectorization, and JIT compilation to GPU/TPU.
A Python library for composable transformations of numerical programs: automatic differentiation, vectorization, and JIT compilation to GPU/TPU.
Multi-dimensional arrays (tensors) and numerical definitions for Elixir, enabling machine learning and scientific computing.
A curated list of awesome libraries, projects, tutorials, and resources for the JAX machine learning ecosystem.
An accelerated machine learning framework for Go, offering a PyTorch/Jax/TensorFlow-like experience with support for CPUs, GPUs, TPUs, and WASM.
A collection of tutorials and resources to help developers learn JAX, Flax, and Haiku for machine learning.
A tutorial demonstrating how to extend JAX with custom C++ and CUDA operations for high-performance computing.
A JAX-powered probabilistic programming library focused on performant sampling methods for Bayesian inference on CPU, GPU, and TPU.
A provable, measurable secure computation device that enables privacy-preserving tensor operations using multi-party computation (MPC).
A JAX-powered reimplementation of MiniGrid offering over 1000x speedup for reinforcement learning experiments.
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