DeepLearningExamples
State-of-the-Art Deep Learning scripts for various applications
GraphCanon updated 4d · GitHub synced 4d
Decision brief
Curated facts for DeepLearningExamples, tailored to its unique features and offerings.
Good fit when
- The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance.
- DeepLearningExamples supports frameworks like PyTorch, offering specific models such as EfficientNet-B0 and B4 for immediate research and application testing on state-of-the-art hardware.
Avoid when
- Avoid using DeepLearningExamples if you do not have access to NVIDIA GPUs, as it is heavily optimized for these specific hardware configurations to provide maximum utilization of Tensor Cores.
- If your project requires frameworks that are less common (e.g., MXNet or PaddlePaddle) without the same level of support as PyTorch and TensorFlow on this platform, consider other repositories that n原
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (734d since push)
- As of 4d
- Provenance
- Not a fork · Organization account
- As of 4d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Nvidia. Display-only - separate from trust and ranking.
- Company
- NVIDIA Corporation·GitHub org profile·1mo
- Employees
- 11,528·Wikidata (P1128 employees)·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
git clone https://github.com/NVIDIA/DeepLearningExamplesHow it fits your stack(5)
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Evidence and technical details
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Overview
Provides deep learning examples that are easy to train and deploy with reproducible accuracy on NVIDIA GPUs, supporting multiple frameworks and use cases.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 17, 2026
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README
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 |
For agents
This page has a .md twin and JSON over the API.