NVIDIA Deep Learning Examples for Tensor Cores

Introduction

This repository provides State-of-the-Art Deep Learning examples that are easy to train and deploy, achieving the best reproducible convergence and performance with NVIDIA CUDA-X software stack running on NVIDIA Volta, Turing and Ampere GPUs.

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

Computer Vision

Models	Framework	DALI	AMP	Multi-GPU	Multi-Node	TensorRT	ONNX	Triton	TF-TRT	Notebook
Computer Vision
ResNet-50 v1.5	PyTorch	Yes	Yes	Yes	-	-	-	-	-	-
ResNeXt101-32x4d	PyTorch	Yes	Yes	Yes	-	-	-	-	-	-
SE-ResNeXt101-32x4d	PyTorch	Yes	Yes	Yes	-	-	-	-	-	-
Mask R-CNN	PyTorch	N/A	Yes	Yes	-	-	-	-	-	Yes
SSD300 v1.1	PyTorch	Yes	Yes	Yes	-	-	-	-	-	Yes
ResNet-50 v1.5	TensorFlow	Yes	Yes	Yes	-	-	-	-	-	-
ResNeXt101-32x4d	TensorFlow	Yes	Yes	Yes	-	-	-	-	-	-
SE-ResNeXt101-32x4d	TensorFlow	Yes	Yes	Yes	-	-	-	-	-	-
Mask R-CNN	TensorFlow	N/A	Yes	Yes	-	-	-	-	-	-
SSD320 v1.2	TensorFlow	N/A	Yes	Yes	-	-	-	-	-	Yes
U-Net Industrial	TensorFlow	N/A	Yes	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	-
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	-	-	-	-	-	-

Natural Language Processing

Models	Framework	DALI	AMP	Multi-GPU	Multi-Node	TensorRT	ONNX	Triton	TF-TRT	Notebook
BERT	PyTorch	N/A	Yes	Yes	Yes	-	-	Yes	-	-
Transformer-XL	PyTorch	N/A	Yes	Yes	Yes	-	-	-	-	-
GNMT v2	PyTorch	N/A	Yes	Yes	-	-	-	-	-	-
Transformer	PyTorch	N/A	Yes	Yes	-	-	-	-	-	-
BERT	TensorFlow	N/A	Yes	Yes	Yes	Yes	-	Yes	Yes	Yes
BioBert	TensorFlow	N/A	Yes	Yes	-	-	-	-	-	Yes
Transformer-XL	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	-	-	-	-	-	-

Recommender Systems

Models	Framework	DALI	AMP	Multi-GPU	Multi-Node	TensorRT	ONNX	Triton	TF-TRT	Notebook
DLRM	PyTorch	N/A	Yes	Yes	-	-	Yes	Yes	-	Yes
Neural Collaborative Filtering	PyTorch	N/A	Yes	Yes	-	-	-	-	-	-
Wide and Deep	TensorFlow	N/A	Yes	Yes	-	-	-	-	-	-
Neural Collaborative Filtering	TensorFlow	N/A	Yes	Yes	-	-	-	-	-	-
Variational Autoencoder Collaborative Filtering	TensorFlow	N/A	Yes	Yes	-	-	-	-	-	-

Speech to Text

Models	Framework	DALI	AMP	Multi-GPU	Multi-Node	TensorRT	ONNX	Triton	TF-TRT	Notebook
Jasper	PyTorch	N/A	Yes	Yes	-	Yes	Yes	Yes	-	Yes
HMM	Kaldi	N/A	-	Yes	-	-	-	Yes	-	-

Text to Speech

Models	Framework	DALI	AMP	Multi-GPU	Multi-Node	TensorRT	ONNX	Triton	TF-TRT	Notebook
Tacotron 2 and WaveGlow	PyTorch	N/A	Yes	Yes	-	Yes	Yes	Yes	-	-
FastPitch	PyTorch	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.