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NVIDIA深度学习张量核示例

NVIDIA 深度学习张量核示例。「NVIDIA Deep Learning Examples for Tensor Cores」

Github 资源库
主要指标

Github星跟踪图

NVIDIA深度学习张量核示例

介绍

该资源库提供了最先进的深度学习示例，这些示例易于训练和部署，通过在 NVIDIA Volta、Turing 和 Ampere GPU 上运行的 NVIDIA CUDA-X 软件栈，实现了最佳的可重复精度和性能。

英伟达 GPU 云（NGC）容器注册中心

这些例子以及我们的 NVIDIA 深度学习软件堆栈，都是以每月更新的 Docker 容器的形式提供给 NGC 容器注册处（https://ngc.nvidia.com）。这些容器包括：

  • 该资源库中的最新 NVIDIA 示例
  • 最新的 NVIDIA 贡献分享到各个框架的上游。
  • 最新的 NVIDIA 深度学习软件库，如 cuDNN、NCCL、cuBLAS 等，这些软件库都经过了每月严格的质量保证流程，以确保提供最佳的性能。
  • 每个 NVIDIA 优化容器的 每月发布说明

计算机视觉

Models Framework A100 AMP Multi-GPU Multi-Node TRT ONNX Triton TF-TRT NB
ResNet-50 PyTorch Yes Yes Yes - Yes - Yes - -
ResNeXt-101 PyTorch Yes Yes Yes - Yes - Yes - -
SE-ResNeXt-101 PyTorch Yes Yes Yes - Yes - Yes - -
Mask R-CNN PyTorch Yes Yes Yes - - - - - Yes
SSD PyTorch Yes Yes Yes - - - - - Yes
ResNet-50 TensorFlow Yes Yes Yes - - - - - -
ResNeXt101 TensorFlow Yes Yes Yes - - - - - -
SE-ResNeXt-101 TensorFlow Yes Yes Yes - - - - - -
Mask R-CNN TensorFlow Yes Yes Yes - - - - - -
SSD TensorFlow Yes Yes Yes - - - - - Yes
U-Net Ind TensorFlow Yes Yes Yes - - - - Yes Yes
U-Net Med TensorFlow Yes Yes Yes - - - - - -
U-Net 3D TensorFlow Yes Yes Yes - - - - - -
V-Net Med TensorFlow Yes Yes Yes - - - - - -
U-Net Med TensorFlow2 Yes Yes Yes - - - - - -
Mask R-CNN TensorFlow2 Yes Yes Yes - - - - - -
ResNet-50 MXNet - Yes Yes - - - - - -

自然语言处理

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

推荐系统

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

语音转文字

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

文字转语音

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

NVIDIA 支持

在每个网络自述中，我们都指出了将提供的支持水平。范围包括从持续的更新和改进，到及时发布的思想领导力。

反馈/贡献

我们将这些例子发布在 GitHub 上，以便更好地支持社区，促进反馈，以及使用 GitHub Issues 和 pull request 收集和实施贡献。我们欢迎所有的贡献

已知问题

在每个网络自述中，我们都会指出任何已知的问题，并鼓励社区提供反馈。

主要指标

概览
名称与所有者NVIDIA/DeepLearningExamples
主编程语言Jupyter Notebook
编程语言Shell (语言数: 11)
平台Docker, Linux, Online
许可证
所有者活动
创建于2018-05-02 17:04:05
推送于2024-08-12 14:01:29
最后一次提交2024-04-04 06:37:56
发布数0
用户参与
星数14.3k
关注者数295
派生数3.3k
提交数1.4k
已启用问题?
问题数862
打开的问题数288
拉请求数359
打开的拉请求数70
关闭的拉请求数116
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已启用Wiki?
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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

Speech Recognition

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.