Releases: intel/ai-reference-models
Intel® AI Reference Models v3.2
Supported Frameworks
- Intel® Optimizations for TensorFlow
v2.14.0
- Intel® Extension for TensorFlow
v2.15.0.0
- Intel® Extension for PyTorch
v2.3.100+cpu
- Intel® Extension for PyTorch
v2.1.30+xpu
New features
-
Added new workloads scripts for the best known configurations on CPU platforms (Intel® Core™ Processors, Intel® Xeon® processors, Intel® Xeon® Scalable Processors) and GPU platforms (Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series).
-
New supported workloads for Intel® Xeon® Scalable Processors:
- PyTorch: Vision Transformer, GPT-J 6B, Llama2 (7B and 13B), ChatGLMv3 6B, LCM, YOLOv7
- TensorFlow: Bert Large Hugging Face, GraphSAGE
-
New supported workloads for Intel® Data Center GPU platforms:
- PyTorch: FBNet, IFRNet, RIFE
-
Added support for SRF: Updated PyTorch workload scripts for the best known configurations on CPU platforms (Intel® Xeon® Scalable Processors ).
-
Added support for TensorFlow+ XLA.
-
Fixed all CVEs and security issues.
Supported Configurations
Intel® AI Reference Models v3.2 is validated on the following environment:
- Ubuntu 22.04 LTS
- Ubuntu 20.04 LTS
- Windows 11
- Windows Subsystem for Linux 2 (WSL2)
- Python 3.9, 3.10, 3.11
Intel® AI Reference Models v3.1.1
Supported Frameworks
- Intel® Optimizations for TensorFlow
v2.14.0
- Intel® Extension for TensorFlow
v2.14.0.1
- Intel® Extension for PyTorch
v2.1.100+cpu
- Intel® Extension for PyTorch
v2.1.10+xpu
New features
-
Added new workloads scripts for the best known configurations on GPU platforms (Intel® Data Center GPU Flex Series and Intel® Data Center GPU Max Series).
-
New supported workloads and precisions for Intel® Data Center GPU platforms:
- Swin-Transformer, FastPitch, UNet++ and RNNT for Inference for Intel® Extension for PyTorch.
- Updated YOLOv5, DLRM v2, and 3D-Unet for Inference for Intel® Extension for PyTorch.
-
Updated workload scripts for the best known configurations on CPU platforms (Intel® Xeon® Scalable Processors ).
Supported Configurations
Intel® AI Reference Models v3.1.1 is validated on the following environment:
- Ubuntu 22.04 LTS
- Ubuntu 20.04 LTS
- Windows 11
- Windows Subsystem for Linux 2 (WSL2)
- Python 3.9, 3.10
Intel® AI Reference Models v3.1.0
Supported Frameworks
- Intel® Optimizations for TensorFlow
v2.14.0
- Intel® Extension for TensorFlow
v2.14.0.1
- Intel® Extension for PyTorch
v2.1.100+cpu
- Intel® Extension for PyTorch
v2.1.10+xpu
New features
-
Updated workloads scripts for the best known configurations on GPU platforms (Intel® Data Center GPU Flex Series, Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics).
-
Experimental support for Intel® Arc™ A-Series (Intel® Arc™ A770 Graphic card) GPUs on Windows Subsystem for Linux 2 with Ubuntu Linux installed and native Ubuntu Linux.
-
New supported workloads and precisions for Intel® Data Center GPU platforms:
- Wide and Deep Large Model Inference and EfficientNet B4 for Intel® Extension for TensorFlow
- DistilBert and DLRM v1 Inference for Intel® Extension for PyTorch
- 3D-Unet for Intel® Extension for TensorFlow and Intel® Extension for PyTorch
-
Updated workload scripts for the best known configurations on CPU platforms (Intel® Xeon® Scalable Processors ).
-
Updated Transfer Learning Jupyter notebooks.
-
This release contains many bug and CVE fixes to the previous versions.
Supported Configurations
Intel® AI Reference Models v3.1.0 is validated on the following environment:
- Ubuntu 22.04 LTS
- Ubuntu 20.04 LTS
- Windows 11
- Windows Subsystem for Linux 2 (WSL2)
- Python 3.9, 3.10
Intel® AI Reference Models v3.0.0
New features
-
Model Zoo for Intel® Architecture is rebranded as Intel® AI Reference Models to reflect it's purpose of showcasing to external audiences the internally achieved best performance configurations for critical workloads on Intel® Architecture.
-
Updates Intel® Data Center GPU Max Series 1550 x4 OAM workloads scripts for the best known configurations.
-
Introduces the initial version of Jupyter notebook interface for Intel® AI Reference Models.
-
Distributed training is supported for the following PyTorch models with different precisions: ResNet50, SSD-ResNet34, DLRM, MaskRCNN, RNNT and BERT Large.
-
Updated Transfer Learning Jupyter notebooks.
-
New supported workloads:
- MemRec DLRM FP32 inference: introducing Memory Efficient Recommendation System using Alternative Representation (MemRec). MemRec is a technology for alternative representation of embedding tables. It uses bloom filters and hashing techniques to encode embedding tables in a much smaller memory footprint optimized to make use of hierarchical cache architecture of Intel Xeon platforms. MemRec encodes DLRM embedding tables into two cache-friendly embedding tables to maximize predictive performance and increase recommendation accuracy.
- DLRM v2 training and inference with different precisions for GPU and CPU platforms.
-
This release contains many bug and CVE fixes to the previous versions.
Supported Configurations
Intel® AI Reference Models v3.0.0 is validated on the following environment:
- Ubuntu 22.04 LTS
- Ubuntu 20.04 LTS
- Windows 11
- Windows Subsystem for Linux 2 (WSL2)
- Python 3.9, 3.10
Model Zoo for Intel® Architecture v2.12.1
New features
- Updated Intel® Data Center GPU Flex 140 and 170 workloads scripts for the best known configurations.
- New supported workloads for Intel® Data Center GPU Flex:
- EfficientNet B0 and B3 for Intel® Extension for TensorFlow
- MaskRCNN for Intel® Extension for TensorFlow
- Stable Diffusion for Intel® Extension for TensorFlow and Intel® Extension for PyTorch
- YOLO v5 for Intel® Extension for PyTorch
Deprecation Notice:
The next release of Model Zoo for Intel® Architecture will be rebranded as Intel® AI Reference Models to reflect it's purpose of showcasing to external audiences the internally achieved best performance configurations for critical workloads on Intel® Architecture.
Bug fixes:
- This release contains many bug fixes to the previous versions. Please see the commit history here: https://github.com/IntelAI/models/commits/v2.12.1
Supported Configurations
Intel Model Zoo v2.12.1 is validated on the following environment:
- Ubuntu 22.04 LTS
- Ubuntu 20.04 LTS
- Windows 11
- Windows Subsystem for Linux 2 (WSL2)
- Python 3.9, 3.10
Model Zoo for Intel® Architecture v2.11.1
New features
- Updated Intel® Data Center GPU Flex and Max Series workloads scripts for the best known configurations.
- Single dockerfile support for both Intel® Data Center GPU Flex 170 and Intel® Data Center GPU Flex 140.
- Updated the name and license for the Cloud Data Connector.
- Added comparative samples between cloud providers SDK and Cloud Data Connector.
- Added the source code for the initial releases of the dataset-librarian python library and dataset-librarian Anaconda package.
- Bump transformers from 4.25.1 to 4.30.0 to fix CVE.
Bug fixes:
- This release contains many bug fixes to the previous versions. Please see the commit history here: https://github.com/IntelAI/models/commits/v2.11.1
Supported Configurations
Intel Model Zoo v2.11.1 is validated on the following environment:
- Ubuntu 22.04 LTS
- Ubuntu 20.04 LTS
- Windows 11
- Windows Subsystem for Linux 2 (WSL2)
- Python 3.9, 3.10
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
andBFloat16
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 TensorFlowv1.2.0
. - 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