DeepLearningExamples
NVIDIA/DeepLearningExamples
State-of-the-Art Deep Learning scripts organized by models for easy training and deployment with reproducible accuracy on NVIDIA GPUs.
Overview
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.
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Install
git clone https://github.com/NVIDIA/DeepLearningExamplesREADME
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 | AMP | Multi-GPU | Multi-Node | TensorRT | ONNX | Triton | DLC | NB |
|---|---|---|---|---|---|---|---|---|---|
| EfficientNet-B0 | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |
| EfficientNet-B4 | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |
| EfficientNet-WideSE-B0 | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |
| EfficientNet-WideSE-B4 | PyTorch | Yes | Yes | - | Supported | - | Supported |