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TensorFlow and PyTorch Reference models for Gaudi(R)

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Habana Deep Learning Examples for Training

Model List and Performance Data

Please visit this page for performance information.

This repository is a collection of models that have been ported to run on Habana Gaudi training accelerators. They are intended as examples, and will be reasonably optimized for performance while still being easy to read.

NEW: TensorFlow ResNet50 Keras, TensorFlow BERT and PyTorch ResNet50 are enabled on Gaudi2. Instructions are available in the respective README files for these models.

Computer Vision

Models Framework
ResNet50 Keras TensorFlow
ResNeXt101 TensorFlow
SSD TensorFlow
Mask R-CNN TensorFlow
DenseNet TensorFlow
UNet 2D TensorFlow
UNet 3D TensorFlow
UNet Industrial TensorFlow
CycleGAN TensorFlow
EfficientDet TensorFlow
RetinaNet TensorFlow
SegNet TensorFlow
Vision Transformer TensorFlow
MobileNet V2 TensorFlow
ResNet50, ResNet152, ResNext101 PyTorch
MobileNet V2 PyTorch
UNet 2D, Unet 3D PyTorch
SSD PyTorch
GoogLeNet PyTorch
Vision Transformer PyTorch

Natural Language Processing

Models Framework
BERT TensorFlow
DistilBERT TensorFlow
ALBERT TensorFlow
Transformer TensorFlow
T5 Base TensorFlow
Electra TensorFlow
BERT Pretraining PyTorch
BERT Finetuning PyTorch
RoBERTa PyTorch
ALBERT PyTorch
DistilBERT PyTorch
Electra PyTorch
Transformer PyTorch
BART PyTorch
GPT2 PyTorch

Recommender Systems

Models Framework
Wide & Deep TensorFlow

Reporting Bugs/Feature Requests

We welcome you to use the GitHub issue tracker to report bugs or suggest features.

When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:

  • A reproducible test case or series of steps
  • The version of our code being used
  • Any modifications you've made relevant to the bug
  • Anything unusual about your environment or deployment

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  • Python 81.1%
  • Jupyter Notebook 17.6%
  • Shell 0.6%
  • Cuda 0.3%
  • C++ 0.2%
  • Cython 0.1%
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