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.
| Models | Framework | Gaudi | Gaudi2 |
|---|---|---|---|
| ResNet50 Keras | TensorFlow | ✔ | ✔ |
| ResNeXt101 | TensorFlow | ✔ | ✔ |
| SSD | TensorFlow | ✔ | ✔ |
| Mask R-CNN | TensorFlow | ✔ | ✔ |
| UNet 2D | TensorFlow | ✔ | ✔ |
| UNet 3D | TensorFlow | ✔ | ✔ |
| UNet Industrial | TensorFlow | ✔ | |
| DenseNet | TensorFlow | ✔ | |
| EfficientDet | TensorFlow | ✔ | |
| RetinaNet | TensorFlow | ✔ | |
| SegNet | TensorFlow | ✔ | |
| Vision Transformer | TensorFlow | ✔ | |
| MobileNetV2 | TensorFlow | ✔ | |
| ResNet50, ResNeXt101 | PyTorch | ✔ | ✔ |
| ResNet152 | PyTorch | ✔ | |
| MobileNetV2 | PyTorch | ✔ | |
| UNet 2D, Unet 3D | PyTorch | ✔ | ✔ |
| SSD | PyTorch | ✔ | ✔ |
| GoogLeNet | PyTorch | ✔ | |
| Vision Transformer | PyTorch | ✔ | |
| Swin Transformer | PyTorch | ✔ | |
| DINO | PyTorch | ✔ | |
| YOLOX | PyTorch | ✔ |
| Models | Framework | Gaudi | Gaudi2 |
|---|---|---|---|
| BERT | TensorFlow | ✔ | ✔ |
| DistilBERT | TensorFlow | ✔ | |
| ALBERT | TensorFlow | ✔ | |
| Transformer | TensorFlow | ✔ | ✔ |
| T5 Base | TensorFlow | ✔ | |
| Electra | TensorFlow | ✔ | |
| BERT Pretraining | PyTorch | ✔ | ✔ |
| BERT Finetuning | PyTorch | ✔ | ✔ |
| DeepSpeed BERT-1.5B, BERT-5B | PyTorch | ✔ | |
| RoBERTa | PyTorch | ✔ | |
| ALBERT | PyTorch | ✔ | |
| DistilBERT | PyTorch | ✔ | |
| Electra | PyTorch | ✔ | |
| Transformer | PyTorch | ✔ | ✔ |
| BART | PyTorch | ✔ |
| Models | Framework | Gaudi | Gaudi2 |
|---|---|---|---|
| Wide & Deep | TensorFlow | ✔ |
| Models | Framework | Gaudi | Gaudi2 |
|---|---|---|---|
| Wav2vec 2.0 | PyTorch | ✔ |
| Models | Framework | Gaudi | Gaudi2 |
|---|---|---|---|
| CycleGAN | TensorFlow | ✔ | |
| V-Diffusion | PyTorch | ✔ |
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