README.md

Explore Our Open Source AI Portfolio

Develop, train, and deploy your AI solutions quickly with performance- and productivity-optimized tools from Intel.

Documentation

Labs for Intel® Certified Developer Program — MLOps Professional

• https://github.com/intel/certified-developer

Drivers

• Android Neural Networks HAL with OpenVINO

  • https://github.com/intel/nn-hal

• OpenCL and L0 Compute Drivers, ocloc compiler

  • https://github.com/intel/compute-runtime

Tools and utilities

Compilers

• Intel® Graphics Compiler
  • https://github.com/intel/intel-graphics-compiler
• Intel® oneAPI DPC++/C++ Compiler
  • https://github.com/intel/llvm

Setup tools

• Intel® Graphic Drivers and environment setup for OpenVINO
  • https://github.com/intel/edge-insights-vision

Benchmarks

• Intel® Edge AI Performance Evaluation Toolkit
  • https://github.com/intel/Intel-Edge-AI-Performance-Evaluation-Toolkit

Extensions, plugins and modules

For PyTorch

• https://github.com/intel/intel-extension-for-pytorch
• https://github.com/intel/torch-ccl

For TensorFlow

• https://github.com/intel/intel-extension-for-tensorflow

For Triton

• https://github.com/intel/intel-xpu-backend-for-triton

For Chainer

• https://github.com/intel/ideep

For Deepspeed

• https://github.com/intel/intel-extension-for-deepspeed

For OpenXLA

• https://github.com/intel/intel-extension-for-openxla

For HuggingFace Transformers

• https://github.com/intel/intel-extension-for-transformers

For Horovod

• https://github.com/intel/intel-optimization-for-horovod

For MLIR

• https://github.com/intel/mlir-extensions

For SciKit-Learn

• https://github.com/intel/scikit-learn-intelex

Based on OpenVINO

• For Blender
  • https://github.com/intel/dream-textures-openvino
• For Audacity
  • https://github.com/intel/openvino-plugins-ai-audacity
• For Gimp
  • https://github.com/intel/openvino-ai-plugins-gimp
• For OBS Project
  • https://github.com/intel/openvino-plugins-for-obs-studio
• For Rust
  • https://github.com/intel/openvino-rs

Frameworks and libraries

• XPU Operators for PyTorch
  • https://github.com/intel/torch-xpu-ops
• Intel® Xe Super Sampling library
  • https://github.com/intel/xess
• Optimization library for LLMs
  • https://github.com/intel/xFasterTransformer
• Intel® Neural Compressor (library for model compression)
  • https://github.com/intel/neural-compressor
• Neural Speed (library for efficient LLMs inference)
  • https://github.com/intel/neural-speed
• Robot Operating System framework for OpenVINO based inference
  • https://github.com/intel/ros_openvino_toolkit
  • https://github.com/intel/ros2_openvino_toolkit
• Data Flow Facilitator for Machine Learning
  • https://github.com/intel/dffml
• Intel® End-to-End AI Optimization Kit
  • https://github.com/intel/e2eAIOK

Containers and microservices

IPEX and ITEX base containers

• https://github.com/intel/ai-containers

AI Connect for Scientific Data – set of microservices

• https://github.com/intel/AiCSD

Models

• AutoRound - Advanced Weight-Only Quantization Algorithm for LLMs
  • https://github.com/intel/auto-round
• NN-Based Cost Model for NPU Devices
  • https://github.com/intel/npu-nn-cost-model
• $\rho$-Diffusion - A diffusion-based density estimation model for computational physics
  • https://github.com/intel/rho-diffusion
• Text-Visual Prompting Model
  • https://github.com/intel/TVP
• Intel® solution for RecSys challenge 2023  
  • https://github.com/intel/recsys2023-intel-submission

Reference Designs

Intel® Cloud Optimization Modules

• For AWS
  • https://github.com/intel/intel-cloud-optimizations-aws
• For Microsoft Azure
  • https://github.com/intel/intel-cloud-optimizations-azure
• For Google Cloud Platform (GCP)
  • https://github.com/intel/intel-cloud-optimizations-gcp
• For https://www.terraform.io/
  • For Databricks on AWS
    • https://github.com/intel/terraform-intel-aws-databricks-workspace
  • For AWS Sagemaker
    • https://github.com/intel/terraform-intel-aws-sagemaker-endpoint
  • For Virtual Machines on AWS
    • https://github.com/intel/terraform-intel-aws-vm
  • For Azure Data Explorer
    • https://github.com/intel/terraform-intel-azure-adx
  • For Databricks Microsoft Azure
    • https://github.com/intel/terraform-intel-azure-databricks-workspace
  • For Databricks cluster
    • https://github.com/intel/terraform-intel-databricks-cluster
  • For Virtual Machines on Google Cloud Platform (GCP)
    • https://github.com/intel/terraform-intel-gcp-vm

Examples (demos and samples)

For BigDL

• https://github.com/intel/BigDL-Distributed-Training-and-Inference-Workflow
• https://github.com/intel/BigDL-Privacy-Preserving-Machine-Learning-Toolkit
• https://github.com/intel/BigDL-Recommender-System-Toolkit
• https://github.com/intel/BigDL-Time-Series-Toolkit

Cloud Examples

• https://github.com/intel/cloud-client-ai-service-framework
• https://github.com/intel/cloud-native-ai-pipeline
• https://github.com/intel/credit-card-fraud-detection

Multi-modal disease prediction

• https://github.com/intel/disease-prediction

Documents analysis (OCR, search, sentiment extraction, etc.)

• https://github.com/intel/document-automation
• https://github.com/intel/document-level-sentiment-analysis

Forks and staging repos

• https://github.com/intel/onnxruntime

Other AI-related Intel orgs on GitHub

• https://github.com/HabanaAI
• https://github.com/openvinotoolkit
• https://github.com/intel-analytics