NVIDIA Deep Learning Examples for Tensor Cores

Overview

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 accuracy 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 A100 AMP Multi-GPU Multi-Node TRT ONNX Triton DLC NB
ResNet-50 PyTorch Yes Yes Yes - Yes - Yes Yes -
ResNeXt-101 PyTorch Yes Yes Yes - Yes - Yes Yes -
SE-ResNeXt-101 PyTorch Yes Yes Yes - Yes - Yes Yes -
EfficientNet-B0 PyTorch Yes Yes Yes - - - - Yes -
EfficientNet-B4 PyTorch Yes Yes Yes - - - - Yes -
EfficientNet-WideSE-B0 PyTorch Yes Yes Yes - - - - Yes -
EfficientNet-WideSE-B4 PyTorch Yes Yes Yes - - - - Yes -
Mask R-CNN PyTorch Yes Yes Yes - - - - - Yes
nnUNet PyTorch Yes Yes Yes - - - - Yes -
SSD PyTorch Yes Yes Yes - - - - - Yes
ResNet-50 TensorFlow Yes Yes Yes - - - - Yes -
ResNeXt101 TensorFlow Yes Yes Yes - - - - Yes -
SE-ResNeXt-101 TensorFlow Yes Yes Yes - - - - Yes -
Mask R-CNN TensorFlow Yes Yes Yes - - - - Yes -
SSD TensorFlow Yes Yes Yes - - - - Yes Yes
U-Net Ind TensorFlow Yes Yes Yes - - - - Yes Yes
U-Net Med TensorFlow Yes Yes Yes - - - - Yes -
U-Net 3D TensorFlow Yes Yes Yes - - - - Yes -
V-Net Med TensorFlow Yes Yes Yes - - - - Yes -
U-Net Med TensorFlow2 Yes Yes Yes - - - - Yes -
Mask R-CNN TensorFlow2 Yes Yes Yes - - - - Yes -
EfficientNet TensorFlow2 Yes Yes Yes Yes - - - Yes -
ResNet-50 MXNet - Yes Yes - - - - - -

Natural Language Processing

Models Framework A100 AMP Multi-GPU Multi-Node TRT ONNX Triton DLC NB
BERT PyTorch Yes Yes Yes Yes - - Yes Yes -
TransformerXL PyTorch Yes Yes Yes Yes - - - Yes -
GNMT PyTorch Yes Yes Yes - - - - - -
Transformer PyTorch Yes Yes Yes - - - - - -
ELECTRA TensorFlow2 Yes Yes Yes Yes - - - Yes -
BERT TensorFlow Yes Yes Yes Yes Yes - Yes Yes Yes
BERT TensorFlow2 Yes Yes Yes Yes - - - Yes -
BioBert TensorFlow Yes Yes Yes - - - - Yes Yes
TransformerXL TensorFlow Yes Yes Yes - - - - - -
GNMT TensorFlow Yes Yes Yes - - - - - -
Faster Transformer Tensorflow - - - - Yes - - - -

Recommender Systems

Models Framework A100 AMP Multi-GPU Multi-Node TRT ONNX Triton DLC NB
DLRM PyTorch Yes Yes Yes - - Yes Yes Yes Yes
DLRM TensorFlow2 Yes Yes Yes Yes - - - Yes -
NCF PyTorch Yes Yes Yes - - - - - -
Wide&Deep TensorFlow Yes Yes Yes - - - - Yes -
Wide&Deep TensorFlow2 Yes Yes Yes - - - - Yes -
NCF TensorFlow Yes Yes Yes - - - - Yes -
VAE-CF TensorFlow Yes Yes Yes - - - - - -

Speech to Text

Models Framework A100 AMP Multi-GPU Multi-Node TRT ONNX Triton DLC NB
Jasper PyTorch Yes Yes Yes - Yes Yes Yes Yes Yes
Hidden Markov Model Kaldi - - Yes - - - Yes - -

Text to Speech

Models Framework A100 AMP Multi-GPU Multi-Node TRT ONNX Triton DLC NB
FastPitch PyTorch Yes Yes Yes - - - - Yes -
FastSpeech PyTorch - Yes Yes - Yes - - - -
Tacotron 2 and WaveGlow PyTorch Yes Yes Yes - Yes Yes Yes Yes -

Graph Neural Networks

Models Framework A100 AMP Multi-GPU Multi-Node TRT ONNX Triton DLC NB
SE(3)-Transformer PyTorch Yes 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.

Glossary

Multinode Training
Supported on a pyxis/enroot Slurm cluster.

Deep Learning Compiler (DLC)
TensorFlow XLA and PyTorch JIT and/or TorchScript

Accelerated Linear Algebra (XLA)
XLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage.

PyTorch JIT and/or TorchScript
TorchScript is a way to create serializable and optimizable models from PyTorch code. TorchScript, an intermediate representation of a PyTorch model (subclass of nn.Module) that can then be run in a high-performance environment such as C++.

Automatic Mixed Precision (AMP)
Automatic Mixed Precision (AMP) enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically.

TensorFloat-32 (TF32)
TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.

Jupyter Notebooks (NB)
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.

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.

Owner
NVIDIA Corporation
NVIDIA Corporation
This repository contains an implementation of the Permutohedral Attention Module in Pytorch

Permutohedral_attention_module This repository contains an implementation of the Permutohedral Attention Module

Samuel JOUTARD 26 Nov 27, 2022
Contrastive Feature Loss for Image Prediction

Contrastive Feature Loss for Image Prediction We provide a PyTorch implementation of our contrastive feature loss presented in: Contrastive Feature Lo

Alex Andonian 44 Oct 05, 2022
PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

halo 368 Dec 06, 2022
Semantic Image Synthesis with SPADE

Semantic Image Synthesis with SPADE New implementation available at imaginaire repository We have a reimplementation of the SPADE method that is more

NVIDIA Research Projects 7.3k Jan 07, 2023
We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.

Auto-exposure fusion for single-image shadow removal We propose a new method for effective shadow removal by regarding it as an exposure fusion proble

Qing Guo 146 Dec 31, 2022
PyTorch code of my WACV 2022 paper Improving Model Generalization by Agreement of Learned Representations from Data Augmentation

Improving Model Generalization by Agreement of Learned Representations from Data Augmentation (WACV 2022) Paper ArXiv Why it matters? When data augmen

Rowel Atienza 5 Mar 04, 2022
PyTorch implementation of the ExORL: Exploratory Data for Offline Reinforcement Learning

ExORL: Exploratory Data for Offline Reinforcement Learning This is an original PyTorch implementation of the ExORL framework from Don't Change the Alg

Denis Yarats 52 Jan 01, 2023
Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.

Semi-supervised Transfer Learning for Image Rain Removal This package contains the Python implementation of "Semi-supervised Transfer Learning for Ima

Wei Wei 59 Dec 26, 2022
Official code for ICCV2021 paper "M3D-VTON: A Monocular-to-3D Virtual Try-on Network"

M3D-VTON: A Monocular-to-3D Virtual Try-On Network Official code for ICCV2021 paper "M3D-VTON: A Monocular-to-3D Virtual Try-on Network" Paper | Suppl

109 Dec 29, 2022
shufflev2-yolov5:lighter, faster and easier to deploy

shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size

pogg 1.5k Jan 05, 2023
(to be released) [NeurIPS'21] Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs

Higher-Order Transformers Kim J, Oh S, Hong S, Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs, NeurIPS 2021. [arxiv] W

Jinwoo Kim 44 Dec 28, 2022
Code for "Optimizing risk-based breast cancer screening policies with reinforcement learning"

Tempo: Optimizing risk-based breast cancer screening policies with reinforcement learning Introduction This repository was used to develop Tempo, as d

Adam Yala 12 Oct 11, 2022
[ICLR 2021] Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments.

[ICLR 2021] RAPID: A Simple Approach for Exploration in Reinforcement Learning This is the Tensorflow implementation of ICLR 2021 paper Rank the Episo

Daochen Zha 48 Nov 21, 2022
Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

One Thing One Click One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation (CVPR2021) Code for the paper One Thi

44 Dec 12, 2022
TensorFlow Tutorials with YouTube Videos

TensorFlow Tutorials Original repository on GitHub Original author is Magnus Erik Hvass Pedersen Introduction These tutorials are intended for beginne

9.1k Jan 02, 2023
Pydantic models for pywttr and aiopywttr.

Pydantic models for pywttr and aiopywttr.

Almaz 2 Dec 08, 2022
Benchmark library for high-dimensional HPO of black-box models based on Weighted Lasso regression

LassoBench LassoBench is a library for high-dimensional hyperparameter optimization benchmarks based on Weighted Lasso regression. Note: LassoBench is

Kenan Šehić 5 Mar 15, 2022
DROPO: Sim-to-Real Transfer with Offline Domain Randomization

DROPO: Sim-to-Real Transfer with Offline Domain Randomization Gabriele Tiboni, Karol Arndt, Ville Kyrki. This repository contains the code for the pap

Gabriele Tiboni 8 Dec 19, 2022
A study project using the AA-RMVSNet to reconstruct buildings from multiple images

3d-building-reconstruction This is part of a study project using the AA-RMVSNet to reconstruct buildings from multiple images. Introduction It is exci

17 Oct 17, 2022
Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, L

3 Dec 02, 2022