Enabling PyTorch on XLA Devices (e.g. Google TPU)

Multi-dimensional arrays (tensors) and numerical definitions for Elixir

JAX - A curated list of resources https://github.com/google/jax

The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

Any model. Any hardware. Zero compromise. Built with @ziglang / @openxla / MLIR / @bazelbuild

Elegant and Performant Deep Learning

GoMLX: An Accelerated Machine Learning Framework For Go

ALBERT model Pretraining and Fine Tuning using TF2.0

Felafax is building AI infra for non-NVIDIA GPUs

Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python ⚡

S + Autograd + XLA :: S-parameter based frequency domain circuit simulations and optimizations using JAX.

Extending JAX with custom C++ and CUDA code

Julia on TPUs

Official scala pool repository

基于tensorflow1.x的预训练模型调用，支持单机多卡、梯度累积，XLA加速，混合精度。可灵活训练、验证、预测。

TensorFlow wheels built for latest CUDA/CuDNN and enabled performance flags: SSE, AVX, FMA; XLA

XLA integration of Open Neural Network Exchange (ONNX)

PyTorch distributed training acceleration framework

Simple and efficient RevNet-Library for PyTorch with XLA and DeepSpeed support and parameter offload

A fast transfer matrix method written in jax for modelling optical multilayer thin films