Training and inference scripts with TensorFlow optimizations that use the Intel® oneAPI Deep Neural Network Library (Intel® oneDNN) and Intel® Extension for PyTorch.
The model documentation in the tables below have information on the prerequisites to run each model. The model scripts run on Linux. Select 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.
For information on running more advanced use cases using the workload containers see the: advanced options documentation.
Model | Framework | Mode | Model Documentation | Benchmark/Test Dataset |
---|---|---|---|---|
3D U-Net | TensorFlow | Inference | FP32 | BRATS 2018 |
3D U-Net MLPerf* | TensorFlow | Inference | FP32 BFloat16** Int8 | BRATS 2019 |
MaskRCNN | TensorFlow | Inference | FP32 | MS COCO 2014 |
UNet | TensorFlow | Inference | FP32 |
Model | Framework | Mode | Model Documentation | Benchmark/Test Dataset |
---|---|---|---|---|
BERT | TensorFlow | Inference | FP32 BFloat16** | SQuAD |
BERT | TensorFlow | Training | FP32 BFloat16** | SQuAD and MRPC |
BERT base | PyTorch | Inference | FP32 BFloat16** | BERT Base SQuAD1.1 |
BERT large | PyTorch | Inference | FP32 Int8 BFloat16** | BERT Large SQuAD1.1 |
BERT large | PyTorch | Training | FP32 BFloat16** | preprocessed text dataset |
DistilBERT base | PyTorch | Inference | FP32 BFloat16** | DistilBERT Base SQuAD1.1 |
RNN-T | PyTorch | Inference | FP32 BFloat16** | RNN-T dataset |
RNN-T | PyTorch | Training | FP32 BFloat16** | RNN-T dataset |
RoBERTa base | PyTorch | Inference | FP32 BFloat16** | RoBERTa Base SQuAD 2.0 |
Model | Framework | Mode | Model Documentation | Benchmark/Test Dataset |
---|---|---|---|---|
BERT | TensorFlow | Inference | FP32 | MRPC |
GNMT* | TensorFlow | Inference | FP32 | MLPerf GNMT model benchmarking dataset |
Transformer_LT_mlperf* | TensorFlow | Training | FP32 BFloat16** | WMT English-German dataset |
Transformer_LT_mlperf* | TensorFlow | Inference | FP32 BFloat16** Int8 | WMT English-German data |
Transformer_LT_Official | TensorFlow | Inference | FP32 | WMT English-German dataset |
Transformer_LT_Official | TensorFlow Serving | Inference | FP32 |
Model | Framework | Mode | Model Documentation | Benchmark/Test Dataset |
---|---|---|---|---|
Faster R-CNN | TensorFlow | Inference | Int8 FP32 | COCO 2017 validation dataset |
R-FCN | TensorFlow | Inference | Int8 FP32 | COCO 2017 validation dataset |
SSD-MobileNet* | TensorFlow | Inference | Int8 FP32 BFloat16** | COCO 2017 validation dataset |
SSD-ResNet34* | TensorFlow | Inference | Int8 FP32 BFloat16** | COCO 2017 validation dataset |
SSD-ResNet34 | TensorFlow | Training | FP32 BFloat16** | COCO 2017 training dataset |
SSD-MobileNet | TensorFlow Serving | Inference | FP32 | |
Faster R-CNN ResNet50 FPN | PyTorch | Inference | FP32 BFloat16** | COCO 2017 |
Mask R-CNN | PyTorch | Inference | FP32 BFloat16** | COCO 2017 |
Mask R-CNN | PyTorch | Training | FP32 BFloat16** | COCO 2017 |
Mask R-CNN ResNet50 FPN | PyTorch | Inference | FP32 BFloat16** | COCO 2017 |
RetinaNet ResNet-50 FPN | PyTorch | Inference | FP32 BFloat16** | COCO 2017 |
SSD-ResNet34 | PyTorch | Inference | FP32 Int8 BFloat16** | COCO 2017 |
SSD-ResNet34 | PyTorch | Training | FP32 BFloat16** | COCO 2017 |
Model | Framework | Mode | Model Documentation | Benchmark/Test Dataset |
---|---|---|---|---|
DIEN | TensorFlow | Inference | FP32 BFloat16** | DIEN dataset |
DIEN | TensorFlow | Training | FP32 | DIEN dataset |
NCF | TensorFlow | Inference | FP32 | MovieLens 1M |
Wide & Deep | TensorFlow | Inference | FP32 | Census Income dataset |
Wide & Deep Large Dataset | TensorFlow | Inference | Int8 FP32 | Large Kaggle Display Advertising Challenge dataset |
Wide & Deep Large Dataset | TensorFlow | Training | FP32 | Large Kaggle Display Advertising Challenge dataset |
DLRM | PyTorch | Inference | FP32 Int8 BFloat16** | Criteo Terabyte |
DLRM | PyTorch | Training | FP32 BFloat16** | Criteo Terabyte |
Model | Framework | Mode | Model Documentation | Benchmark/Test Dataset |
---|---|---|---|---|
MiniGo | TensorFlow | Training | FP32 |
Model | Framework | Mode | Model Documentation | Benchmark/Test Dataset |
---|---|---|---|---|
WaveNet | TensorFlow | Inference | FP32 |
Model | Framework | Mode | Model Documentation | Benchmark/Test Dataset |
---|---|---|---|---|
TransNetV2 | PyTorch | Inference | FP32 BFloat16** | Synthetic Data |
*Means the model belongs to MLPerf models and will be supported long-term.