Popular GitHub Repositories for Xla

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

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

GoMLX: An Accelerated Machine Learning Framework For Go

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.

Felafax is building AI infra for non-NVIDIA GPUs

Extending JAX with custom C++ and CUDA code

ALBERT model Pretraining and Fine Tuning using TF2.0

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.

XLA integration of Open Neural Network Exchange (ONNX)

Fast and easy distributed model training examples.

Provides code to serialize the different models involved in Stable Diffusion as SavedModels and to compile them with XLA.

Compiling JAX to WebAssembly for exploring client-side machine learning

Classification of multilingual dataset trained only on English training data using pre-trained models. Model is trained on TPUs using PyTorch and torch_xla library.

deep learning inference perf analysis

As the quality of large language models increases, so do our expectations of what they can do. Since the release of OpenAI's GPT-2, text generation capabilities have received attention. And for good reason - these models can be used for summarization, translation, and even real-time learning in some language tasks.