Tpu
Showing 14 of 14 projects
A Python library for composable transformations of numerical programs: automatic differentiation, vectorization, and JIT compilation to GPU/TPU.
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 fast, differentiable physics engine built with JAX for massively parallel rigid body simulation on accelerator hardware.
A JAX/Flax-based framework for easy and scalable pre-training, fine-tuning, evaluation, and serving of large language models.
A language for distributed deep learning that simplifies model parallelism by specifying tensor computations across hardware meshes.
An accelerated machine learning framework for Go, offering a PyTorch/Jax/TensorFlow-like experience with support for CPUs, GPUs, TPUs, and WASM.
A scalable, hardware-accelerated neuroevolution toolkit built on JAX for parallel training across TPUs/GPUs.
A JAX-based framework for training large language models with a focus on legibility, scalability, and reproducibility.
A JAX-based machine learning framework for configuring and training large-scale models with high efficiency on TPUs and GPUs.
An image processing library built on JAX, designed to be optimized and parallelized with JAX transformations.
A PyTorch frontend for JAX that enables running PyTorch code on TPUs and provides seamless PyTorch-JAX interoperability.
A JAX-based research framework for differentiable and parallelizable acoustic simulations, running on CPU, GPU, and TPU.
GPU/TPU accelerated nonlinear least-squares curve fitting using JAX, designed as a drop-in replacement for SciPy's curve_fit.
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