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

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Intel® AI Reference Models

This repository contains links to pre-trained models, sample scripts, best practices, and step-by-step tutorials for many popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs.

Containers for running the workloads can be found at Intel® AI Containers.

Intel® AI Reference Models in a Jupyter Notebook is also available for the listed workloads

Purpose of Intel® AI Reference Models

Intel optimizes popular deep learning frameworks such as TensorFlow* and PyTorch* by contributing to the upstream projects. Additional optimizations are built into plugins/extensions such as the Intel Extension for Pytorch* and the Intel Extension for TensorFlow*. Popular neural network models running against common datasets are the target workloads that drive these optimizations.

The purpose of the Intel® AI Reference Models repository (and associated containers) is to quickly replicate the complete software environment that demonstrates the best-known performance of each of these target model/dataset combinations. When executed in optimally-configured hardware environments, these software environments showcase the AI capabilities of Intel platforms.

DISCLAIMER: These scripts are not intended for benchmarking Intel platforms. For any performance and/or benchmarking information on specific Intel platforms, visit https://www.intel.ai/blog.

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

License

The Intel® AI Reference Models is licensed under Apache License Version 2.0.

Datasets

To the extent that any public datasets are referenced by Intel or accessed using tools or code on this site those datasets are provided by the third party indicated as the data source. Intel does not create the data, or datasets, and does not warrant their accuracy or quality. By accessing the public dataset(s) you agree to the terms associated with those datasets and that your use complies with the applicable license.

Please check the list of datasets used in Intel® AI Reference Models in datasets directory.

Intel expressly disclaims the accuracy, adequacy, or completeness of any public datasets, and is not liable for any errors, omissions, or defects in the data, or for any reliance on the data. Intel is not liable for any liability or damages relating to your use of public datasets.

Use cases

The model documentation in the tables below have information on the prerequisites to run each model. The model scripts run on Linux. Certain models are also able to run using bare metal on Windows. For more information and a list of models that are supported on Windows, see the documentation here.

Instructions available to run on Sapphire Rapids.

For best performance on Intel® Data Center GPU Flex and Max Series, please check the list of supported workloads. It provides instructions to run inference and training using Intel(R) Extension for PyTorch or Intel(R) Extension for TensorFlow.

Image Recognition

Model Framework Mode Model Documentation Benchmark/Test Dataset
ResNet 50v1.5 Sapphire Rapids TensorFlow Inference Int8 FP32 BFloat16 BFloat32 ImageNet 2012
ResNet 50v1.5 Sapphire Rapids TensorFlow Training FP32 BFloat16 BFloat32 ImageNet 2012
ResNet 50 PyTorch Inference Int8 FP32 BFloat16 BFloat32 [ImageNet 2012]
ResNet 50 PyTorch Training FP32 BFloat16 BFloat32 [ImageNet 2012]
Vision Transformer PyTorch Inference FP32 BFloat16 BFloat32 FP16 INT8 [ImageNet 2012]

Image Segmentation

Model Framework Mode Model Documentation Benchmark/Test Dataset
3D U-Net TensorFlow Inference FP32 BFloat16 Int8 BRATS 2018

Language Modeling

Model Framework Mode Model Documentation Benchmark/Test Dataset
BERT large Sapphire Rapids Tensorflow Inference FP32 BFloat16 Int8 BFloat32 SQuAD
BERT large Sapphire Rapids Tensorflow Training FP32 BFloat16 BFloat32 SQuAD
BERT large (Hugging Face) TensorFlow Inference FP32 FP16 BFloat16 BFloat32 SQuAD
BERT large PyTorch Inference FP32 Int8 BFloat16 BFloat32 BERT Large SQuAD1.1
BERT large PyTorch Training FP32 BFloat16 BFloat32 preprocessed text dataset
DistilBERT base PyTorch Inference FP32 BF32 BF16Int8-FP32 Int8-BFloat16 BFloat32 DistilBERT Base SQuAD1.1
RNN-T PyTorch Inference FP32 BFloat16 BFloat32 RNN-T dataset
RNN-T PyTorch Training FP32 BFloat16 BFloat32 RNN-T dataset
GPTJ 6B PyTorch Inference FP32 FP16 BFloat16 BF32 INT8
GPTJ 6B MLPerf PyTorch Inference INT4 CNN-Daily Mail dataset
LLAMA2 7B PyTorch Inference FP32 FP16 BFloat16 BF32 INT8
LLAMA2 7B PyTorch Training FP32 FP16 BFloat16 BF32
LLAMA2 13B PyTorch Inference FP32 FP16 BFloat16 BF32 INT8
ChatGLMv3 6B PyTorch Inference FP32 FP16 BFloat16 BF32 INT8

Language Translation

Model Framework Mode Model Documentation Benchmark/Test Dataset
BERT TensorFlow Inference FP32 MRPC

Object Detection

Model Framework Mode Model Documentation Benchmark/Test Dataset
Mask R-CNN PyTorch Inference FP32 BFloat16 BFloat32 COCO 2017
Mask R-CNN PyTorch Training FP32 BFloat16 BFloat32 COCO 2017
SSD-ResNet34 PyTorch Inference FP32 Int8 BFloat16 BFloat32 COCO 2017
SSD-ResNet34 PyTorch Training FP32 BFloat16 BFloat32 COCO 2017
Yolo V7 PyTorch Inference Int8 FP32 FP16 BFloat16 BFloat32 [COCO 2017](/models_v2/pytorch/yolov7/inference/cpu/README.md## Prepare Dataset)

Recommendation

Model Framework Mode Model Documentation Benchmark/Test Dataset
Wide & Deep TensorFlow Inference FP32 Census Income dataset
DLRM PyTorch Inference FP32 Int8 BFloat16 BFloat32 Criteo Terabyte
DLRM PyTorch Training FP32 BFloat16 BFloat32 Criteo Terabyte
DLRM v2 PyTorch Inference FP32 FP16 BFloat16 BFloat32 Int8 Criteo 1TB Click Logs dataset

Diffusion

Model Framework Mode Model Documentation Benchmark/Test Dataset
Stable Diffusion TensorFlow Inference FP32 BFloat16 FP16 COCO 2017 validation dataset
Stable Diffusion PyTorch Inference FP32 BFloat16 FP16 BFloat32 Int8-FP32 Int8-BFloat16 COCO 2017 validation dataset
Stable Diffusion PyTorch Training FP32 BFloat16 FP16 BFloat32 cat images
Latent Consistency Models(LCM) PyTorch Inference FP32 BFloat16 FP16 BFloat32 Int8-FP32 Int8-BFloat16 COCO 2017 validation dataset

Graph Networks

Model Framework Mode Model Documentation Benchmark/Test Dataset
GraphSAGE TensorFlow Inference FP32 BFloat16 FP16 Int8 BFloat32 Protein Protein Interaction

*Means the model belongs to MLPerf models and will be supported long-term.

Intel® Data Center GPU Workloads

Model Framework Mode GPU Type Model Documentation
ResNet 50v1.5 TensorFlow Inference Flex Series Float32 TF32 Float16 BFloat16 Int8
ResNet 50 v1.5 TensorFlow Training Max Series BFloat16 FP32
ResNet 50 v1.5 PyTorch Inference Flex Series, Max Series, Arc Series Int8 FP32 FP16 TF32
ResNet 50 v1.5 PyTorch Training Max Series, Arc Series BFloat16 TF32 FP32
DistilBERT PyTorch Inference Flex Series, Max Series FP32 FP16 BF16 TF32
DLRM v1 PyTorch Inference Flex Series FP16 FP32
SSD-MobileNet* PyTorch Inference Arc Series INT8 FP16 FP32
EfficientNet PyTorch Inference Flex Series FP16 BF16 FP32
EfficientNet TensorFlow Inference Flex Series FP16
FBNet PyTorch Inference Flex Series FP16 BF16 FP32
Wide Deep Large Dataset TensorFlow Inference Flex Series FP16
YOLO V5 PyTorch Inference Flex Series FP16
BERT large PyTorch Inference Max Series, Arc Series BFloat16 FP32 FP16
BERT large PyTorch Training Max Series, Arc Series BFloat16 FP32 TF32
BERT large TensorFlow Training Max Series BFloat16 TF32 FP32
DLRM v2 PyTorch Inference Max Series FP32 BF16
DLRM v2 PyTorch Training Max Series FP32 TF32 BF16
3D-Unet PyTorch Inference Max Series FP16 INT8 FP32
3D-Unet TensorFlow Training Max Series BFloat16 FP32
Stable Diffusion PyTorch Inference Flex Series, Max Series, Arc Series FP16 FP32
Stable Diffusion TensorFlow Inference Flex Series FP16 FP32
Mask R-CNN TensorFlow Inference Flex Series FP32 Float16
Mask R-CNN TensorFlow Training Max Series FP32 BFloat16
Swin Transformer PyTorch Inference Flex Series FP16
FastPitch PyTorch Inference Flex Series FP16
UNet++ PyTorch Inference Flex Series FP16
RNN-T PyTorch Inference Max Series FP16 BF16 FP32
RNN-T PyTorch Training Max Series FP32 BF16 TF32
IFRNet PyTorch Inference Flex Series FP16
RIFE PyTorch Inference Flex Series FP16

How to Contribute

If you would like to add a new benchmarking script, please use this guide.