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

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

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

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.

GoMLX: An Accelerated Machine Learning Framework For Go

Felafax is building AI infra for non-NVIDIA GPUs

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

Extending JAX with custom C++ and CUDA code

Julia on TPUs

ALBERT model Pretraining and Fine Tuning using TF2.0

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

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

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

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。

PyTorch distributed training acceleration framework

Presents comprehensive benchmarks of XLA-compatible pre-trained models in Keras.

JAX bindings for the NVIDIA cuDecomp library

Official scala pool repository

XLA integration of Open Neural Network Exchange (ONNX)