MSCCL++: A GPU-driven communication stack for scalable AI applications

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Making large AI models cheaper, faster and more accessible

🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

Source for https://fullstackdeeplearning.com

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Medical Imaging Deep Learning library to train and deploy models on Azure Machine Learning

The pytest framework makes it easy to write small tests, yet scales to support complex functional testing

Example models using DeepSpeed

A fast MoE impl for PyTorch

Read Google Cloud Storage, Azure Blobs, and local paths with the same interface. Clone of tensorflow.io.gfile

Ongoing research training transformer language models at scale, including: BERT & GPT-2

code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503

Development repository for the Triton language and compiler

Playing around with GitHub pages.

Deep Learning Examples