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accelerate

🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support

ai

ai-reference-models

Intel® AI Reference Models: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors and Intel® Data Center GPUs

cloud-streaming

compute-runtime

dockers

ffmpeg

Mirror of https://github.com/ffmpeg/ffmpeg (master) for Developer Pre-Submit Intel Media CI checks

gmmlib

gputop

A GPU profiling tool

ideep

Intel® Optimization for Chainer*, a Chainer module providing numpy like API and DNN acceleration using MKL-DNN.

intel-graphics-compiler

intel-xpu-backend-for-triton

OpenAI Triton backend for Intel® GPUs

intel_ffmpeg_plugins

mirror of git://source.ffmpeg.org/ffmpeg.git

langdata_lstm

Data used for LSTM model training

libva

Libva is an implementation for VA-API (VIdeo Acceleration API)

libva-utils

Libva-utils is a collection of tests for VA-API (VIdeo Acceleration API)

libvhal-client

md4c

C Markdown parser. Fast. SAX-like interface. Compliant to CommonMark specification.

media-analytics

media-delivery

This collection of samples demonstrates best practices to achieve optimal video quality and performance on Intel GPUs for content delivery networks. Check out our demo, recommended command lines and quality and performance measuring tools.

media-driver

mediasdk

meson

The Meson Build System

mingw-packages

Package scripts for MinGW-w64 targets to build under MSYS2.

minigbm

oneapi-containers

onednn

oneAPI Deep Neural Network Library (oneDNN)

onevpl

oneAPI Video Processing Library (oneVPL) Core (API, Dispatcher, and common tools)

onevpl-intel-gpu

opencl-clang