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NVIDIA Deep Learning Examples for Tensor Cores

Introduction

This repository provides the latest deep learning example networks for training. These examples focus on achieving the best performance and convergence from NVIDIA Volta Tensor Cores.

NVIDIA GPU Cloud (NGC) Container Registry

These examples, along with our NVIDIA deep learning software stack, are provided in a monthly updated Docker container on the NGC container registry (https://ngc.nvidia.com). These containers include:

  • The latest NVIDIA examples from this repository
  • The latest NVIDIA contributions shared upstream to the respective framework
  • The latest NVIDIA Deep Learning software libraries, such as cuDNN, NCCL, cuBLAS, etc. which have all been through a rigorous monthly quality assurance process to ensure that they provide the best possible performance
  • Monthly release notes for each of the NVIDIA optimized containers

Directory structure

The examples are organized first by framework, such as TensorFlow, PyTorch, etc. and second by use case, such as computer vision, natural language processing, etc. We hope this structure enables you to quickly locate the example networks that best suit your needs. Here are the currently supported models:

Computer Vision

Natural Language Processing

Recommender Systems

Text to Speech

  • Tacotron2 & WaveGlow [PyTorch]
  • FastPitch (modified FastSpeech) [PyTorch]

Speech Recognition

CUDA Accelerated Applications

Jupyter Notebooks

Models TensorFlow PyTorch TensorRT Triton
SSD Inference Inference - -
MaskRCNN - Training & Inference - -
Jasper - - PyTorch Inference TensorRT Colab, PyTorch Inference TensorRT PyTorch Inference TRTIS
Tacotron2 & WaveGlow - Training & Inference - PyTorch Inference TRTIS
BERT Inference Movie Review Sentiment, Fine-Tuning SQuaD, Inference Colab, Inference - - -
BioBERT Inference - - -
UNet Industrial Export and Inference Colab, Inference - - -
Automatic Mixed Precision AMP Training - - -

Feature Matrix

Models Framework DALI AMP Multi-GPU Multi-Node TensorRT ONNX Triton TF-TRT
ResNet50 v1.5 PyTorch Yes Yes Yes - - - - -
ResNeXt101-32x4d PyTorch Yes Yes Yes - - - - -
SE-ResNeXt101-32x4d PyTorch Yes Yes Yes - - - - -
SSD300 v1.1 PyTorch Yes Yes Yes - - - - -
BERT PyTorch N/A Yes Yes Yes - - Yes -
Transformer-XL PyTorch N/A Yes Yes Yes - - - -
Neural Collaborative Filtering PyTorch N/A Yes Yes - - - - -
DLRM PyTorch N/A Yes Yes - - - - -
Mask R-CNN PyTorch N/A Yes Yes - - - - -
Jasper PyTorch N/A Yes Yes - Yes Yes Yes -
Tacotron 2 And WaveGlow v1.10 PyTorch N/A Yes Yes - Yes Yes Yes -
FastPitch PyTorch N/A Yes Yes - - - - -
GNMT v2 PyTorch N/A Yes Yes - - - - -
Transformer PyTorch N/A Yes Yes - - - - -
ResNet-50 v1.5 TensorFlow Yes Yes Yes - - - - -
SSD320 v1.2 TensorFlow N/A Yes Yes - - - - -
BERT TensorFlow N/A Yes Yes Yes Yes - Yes Yes
BioBert TensorFlow N/A Yes Yes - - - - -
Transformer-XL TensorFlow N/A Yes Yes - - - - -
Neural Collaborative Filtering TensorFlow N/A Yes Yes - - - - -
Variational Autoencoder Collaborative Filtering TensorFlow N/A Yes Yes - - - - -
WideAndDeep TensorFlow N/A Yes Yes - - - - -
U-Net Industrial TensorFlow N/A Yes Yes - Yes - - Yes
U-Net Medical TensorFlow N/A Yes Yes - Yes - - Yes
V-Net Medical TensorFlow N/A Yes Yes - Yes Yes - Yes
Mask R-CNN TensorFlow N/A Yes Yes - - - - -
GNMT v2 TensorFlow N/A Yes Yes - - - - -
Faster Transformer Tensorflow N/A - - - Yes - - -
Transformer-XL TensorFlow N/A Yes Yes - - - - -
U-Net Medical TensorFlow-2 N/A Yes Yes - Yes - - Yes
Mask R-CNN TensorFlow-2 N/A Yes Yes - - - - -
ResNet50 v1.5 MXNet Yes Yes Yes - - - - -
HMM Kaldi N/A - Yes - - - Yes -

NVIDIA support

In each of the network READMEs, we indicate the level of support that will be provided. The range is from ongoing updates and improvements to a point-in-time release for thought leadership.

Feedback / Contributions

We're posting these examples on GitHub to better support the community, facilitate feedback, as well as collect and implement contributions using GitHub Issues and pull requests. We welcome all contributions!

Known issues

In each of the network READMEs, we indicate any known issues and encourage the community to provide feedback.

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  • Python 45.2%
  • Jupyter Notebook 44.1%
  • C++ 5.8%
  • Shell 2.8%
  • Cuda 1.5%
  • CMake 0.3%
  • Other 0.3%