---
title: "DeepLearningExamples"
type: "tool"
slug: "nvidia-deeplearningexamples"
canonical_url: "https://www.graphcanon.com/tools/nvidia-deeplearningexamples"
github_url: "https://github.com/NVIDIA/DeepLearningExamples"
homepage_url: null
stars: 14827
forks: 3406
primary_language: "Jupyter Notebook"
license: null
categories: ["computer-vision", "developer-tools"]
tags: ["mxnet", "deep-learning", "nlp", "large-language-models", "drug-discovery", "forecasting", "paddlepaddle", "computer-vision"]
updated_at: "2026-07-07T18:30:08.127189+00:00"
---

# DeepLearningExamples

> State-of-the-Art Deep Learning scripts organized by models for easy training and deployment with reproducible accuracy on NVIDIA GPUs.

NVIDIA/DeepLearningExamples is a repository of state-of-the-art deep learning models implemented in various frameworks, optimized for use with NVIDIA's CUDA-X software stack on Volta, Turing, and Ampere GPUs. The repository includes scripts and examples for training and deploying deep learning models across different domains such as computer vision, NLP, and speech processing.

## Facts

- Repository: https://github.com/NVIDIA/DeepLearningExamples
- Stars: 14,827 · Forks: 3,406 · Open issues: 323 · Watchers: 289
- Primary language: Jupyter Notebook
- Last pushed: 2024-08-12T14:01:29+00:00

## Categories

- [Computer Vision](/categories/computer-vision.md)
- [Developer Tools](/categories/developer-tools.md)

## Tags

mxnet, deep-learning, nlp, large language models, drug-discovery, forecasting, paddlepaddle, computer-vision

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## README (excerpt)

```text
# 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](https://docs.nvidia.com/deeplearning/dgx/index.html#nvidia-optimized-frameworks-release-notes) for each of the NVIDIA optimized containers


## Computer Vision
| Models                                                                                                                                 | Framework    | AMP            | Multi-GPU | Multi-Node | TensorRT | ONNX | Triton                                                                                                                       | DLC  | NB                                                                                                                                                               |
|----------------------------------------------------------------------------------------------------------------------------------------|--------------|----------------|-----------|------------|----------|------|------------------------------------------------------------------------------------------------------------------------------|------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [EfficientNet-B0](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet)             | PyTorch      | Yes            | Yes       | -          | Supported | -    | Supported                                                                                                                    | Yes  | -                                                                                                                                                                |
| [EfficientNet-B4](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet)             | PyTorch      | Yes            | Yes       | -          | Supported | -    | Supported                                                                                                                             | Yes  | -                                                                                                                                                                |
| [EfficientNet-WideSE-B0](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet)      | PyTorch      | Yes            | Yes       | -          | Supported | -    | Supported                                                                                                                             | Yes  | -                                                                                                                                                                |
| [EfficientNet-WideSE-B4](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet)      | PyTorch      | Yes            | Yes       | -          | Supported | -    | Supported
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/nvidia-deeplearningexamples`](/api/graphcanon/tools/nvidia-deeplearningexamples)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

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