Model Zoo for Intel® Architecture v2.11.0

Supported Frameworks

• Intel® Optimizations for TensorFlow v2.12.0
• Intel® Optimizations for TensorFlow v2.11.dev202242 for optimized performance on Sapphire Rapids
• Intel® Extension for TensorFlow v1.2.0
• Intel® Extension for PyTorch v2.0.0+cpu
• Intel® Extension for PyTorch v1.13.120+xpu

New models

• New precisions FP16 and BFloat16 for different workloads

New features

• Intel® Data Center GPU Flex and Max Series workloads validated with Intel® Extension for PyTorch v1.13.120+xpu and Intel® Extension for TensorFlow v1.2.0.
• Intel® Cloud Data Connector, a tool that helps to use cloud storage tools as AWS Buckets, Google Storage and Azure Storage. Also helps to configure Machine Learning jobs on AzureML. This tool is a helper to use cloud services in Machine Learning process, also provides a common way to interact between cloud providers.
• Dataset Downloader command line interface, a tool to download and apply the preprocessing needed for the list of supported datasets.

Bug fixes:

• This release contains many bug fixes to the previous versions. Please see the commit history here: https://github.com/IntelAI/models/commits/v2.11.0

Supported Configurations

Intel Model Zoo v2.11.0 is validated on the following environment:

• Ubuntu 22.04 LTS
• Ubuntu 20.04 LTS
• Windows 11
• Windows Subsystem for Linux 2 (WSL2)
• Python 3.8, 3.9

Model Zoo for Intel® Architecture v2.7.0

Supported Frameworks

• TensorFlow v2.8.0
• PyTorch v1.11.0 and IPEX v1.11.0

New models

• N/A

New features

• Transfer Learning notebooks for NLP and Computer Vision: https://github.com/IntelAI/models/tree/v2.7.0/docs/notebooks/transfer_learning
• Consolidate TensorFlow and PyTorch benchmark tables based on the use case: https://github.com/IntelAI/models/tree/v2.7.0#use-cases
• Added links for required dataset for each use case: https://github.com/IntelAI/models/tree/v2.7.0/benchmarks
• Initial support for running several models on Windows platform: https://github.com/IntelAI/models/blob/master/docs/general/tensorflow/Windows.md
• Experimental support for running models on CentOS 8 Stream, Red Hat 8 and SLES 15

Bug fixes:

• This release contains many bug fixes to the previous versions. Please see the commit history here: https://github.com/IntelAI/models/commits/v2.7.0

Supported Configurations

Intel Model Zoo 2.7.0 is validated on the following environment:

• Ubuntu 20.04 LTS
• Python 3.8, 3.9
• Docker Server v19+
• Docker Client v18+

Model Zoo for Intel® Architecture v2.6.1

Features and bug fixes

• Update ImageNet dataset preprocessing instructions here: datasets/imagenet

Supported Configurations

Intel Model Zoo 2.6.1 is validated on the following environment:

• Ubuntu 20.04 LTS
• Python 3.8, 3.9
• Docker Server v19+
• Docker Client v18+

Model Zoo for Intel® Architecture v2.6.0

TensorFlow Framework

• Support for TensorFlow v2.7.0

New TensorFlow models

• N/A

Other features and bug fixes for TensorFlow models

• Updates to only use docker --privileged when required and check --cpuset
• • Except for BERT Large and Wide and Deep models
• Updated the ImageNet download link
• Fix platform_util.py for systems with only one socket or subset of cores within a socket
• Replace USE_DAAL4PY_SKLEARN env var with patch_sklearn
• Add error handling for when a frozen graph isn't passed for BERT large FP32 inference*

PyTorch Framework

• Support for PyTorch v1.10.0 and IPEX v1.10.0

New PyTorch models

• GoogLeNet Inference(FP32, BFloat16**)
• Inception v3 Inference(FP32, BFloat16**)
• MNASNet 0.5 Inference(FP32, BFloat16**)
• MNASNet 1.0 Inference(FP32, BFloat16**)
• ResNet 50 Inference(Int8)
• ResNet 50 Training(FP32, BFloat16**)
• ResNet 101 Inference(FP32, BFloat16**)
• ResNet 152 Inference(FP32, BFloat16**)
• ResNext 32x4d Inference(FP32, BFloat16**)
• ResNext 32x16d Inference(FP32, Int8, BFloat16**)
• VGG-11 Inference(FP32, BFloat16**)
• VGG-11 with batch normalization Inference(FP32, BFloat16**)
• Wide ResNet-50-2 Inference(FP32, BFloat16**)
• Wide ResNet-101-2 Inference(FP32, BFloat16**)
• BERT base Inference(FP32, BFloat16**)
• BERT large Inference(FP32, Int8, BFloat16**)
• BERT large Training(FP32, BFloat16**)
• DistilBERT base Inference(FP32, BFloat16**)
• RNN-T Inference(FP32, BFloat16**)
• RNN-T Training(FP32, BFloat16**)
• RoBERTa base Inference(FP32, BFloat16**)
• Faster R-CNN ResNet50 FPN Inference(FP32
• Mask R-CNN Inference(FP32, BFloat16**)
• Mask R-CNN Training(FP32, BFloat16**)
• Mask R-CNN ResNet50 FPN Inference(FP32)
• RetinaNet ResNet-50 FPN Inference(FP32)
• SSD-ResNet34 Inference(FP32, Int8, BFloat16**)
• SSD-ResNet34 Training(FP32, BFloat16**)
• DLRM Inference(FP32, Int8, BFloat16**)
• DLRM Training(FP32)

Other features and bug fixes for PyTorch models

• DLRM and ResNet 50 documentation updates

Supported Configurations

Intel Model Zoo 2.6.0 is validated on the following environment:

• Ubuntu 20.04 LTS
• Python 3.8, 3.9
• Docker Server v19+
• Docker Client v18+

Intel Model Zoo v2.5.0

New Functionality

New Models

• ML-Perf Transformer-LT Training (FP32 and BFloat16)
• ML-Perf Transformer-LT Inference (FP32, BFloat16 and INT8)
• ML-Perf 3D-Unet Inference (FP32, BFloat16 and INT8)
• DIEN Training (FP32)
• DIEN Inference (FP32 and BFloat16)

Other features and bug fixes

• Added IPython Notebook with BERT classifier fine tuning using IMDb
• Documentation for creating an LPOT Container with Intel® Optimizations for TensorFlow
• Advanced documentation for wide deep large ds fp32 training
• Increase Unit testing coverage

DL Frameworks (TensorFlow)

• Support for TensorFlow v2.6.0 and TensorFlow Serving v2.6.0

DL Frameworks (PyTorch)

• Support for PyTorch v1.9.0 and IPEX v1.9.0

Supported Configurations

Intel Model Zoo 2.5.0 is validated on the following environment:

• Ubuntu 20.04 LTS
• Python 3.8
• Docker Server v19+
• Docker Client v18+

v2.4.0

New Functionality

• Added links to Intel oneContainer Portal
• Added documentation for running most workflows inside Intel® oneAPI AI Analytics Toolkit
• Experimental support for running workflows onCentOS 8

DL Frameworks (TensorFlow)

• Support for TensorFlow v2.5.0 and TensorFlow Serving v2.5.1

Supported Configurations

Intel Model Zoo 2.4 is validated on the following environment:

• Ubuntu 20.04 LTS
• Python 3.8
• Docker Server v19+
• Docker Client v18+