{"data":{"slug":"nvidia-deeplearningexamples","name":"DeepLearningExamples","tagline":"State-of-the-Art Deep Learning scripts for easy training and deployment with reproducible accuracy and performance on enterprise-grade infrastructure","github_url":"https://github.com/NVIDIA/DeepLearningExamples","owner":"NVIDIA","repo":"DeepLearningExamples","owner_avatar_url":"https://avatars.githubusercontent.com/u/1728152?v=4","primary_language":"Jupyter Notebook","stars":14847,"forks":3406,"topics":["computer-vision","deep-learning","drug-discovery","forecasting","large-language-models","mxnet","nlp","paddlepaddle","pytorch","recommender-systems","speech-recognition","speech-synthesis","tensorflow","tensorflow2","translation"],"archived":false,"github_pushed_at":"2024-08-12T14:01:29+00:00","maintenance_label":"Dormant","stars_delta_30d":3,"url":"https://www.graphcanon.com/tools/nvidia-deeplearningexamples","markdown_url":"https://www.graphcanon.com/tools/nvidia-deeplearningexamples.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nvidia-deeplearningexamples","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nvidia-deeplearningexamples","description":"State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.","homepage_url":null,"license":null,"open_issues":321,"watchers":290,"ai_summary":"Repository contains deep learning examples for various applications including computer vision, natural language processing, speech recognition, and more, optimized for NVIDIA GPUs","readme_excerpt":"# NVIDIA Deep Learning Examples for Tensor Cores\n\n## Introduction\nThis 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.\n\n## NVIDIA GPU Cloud (NGC) Container Registry\nThese 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:\n\n- The latest NVIDIA examples from this repository\n- The latest NVIDIA contributions shared upstream to the respective framework\n- 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\n- [Monthly release notes](https://docs.nvidia.com/deeplearning/dgx/index.html#nvidia-optimized-frameworks-release-notes) for each of the NVIDIA optimized containers\n\n\n## Computer Vision\n| Models                                                                                                                                 | Framework    | AMP            | Multi-GPU | Multi-Node | TensorRT | ONNX | Triton                                                                                                                       | DLC  | NB                                                                                                                                                               |\n|----------------------------------------------------------------------------------------------------------------------------------------|--------------|----------------|-----------|------------|----------|------|------------------------------------------------------------------------------------------------------------------------------|------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [EfficientNet-B0](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet)             | PyTorch      | Yes            | Yes       | -          | Supported | -    | Supported                                                                                                                    | Yes  | -                                                                                                                                                                |\n| [EfficientNet-B4](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet)             | PyTorch      | Yes            | Yes       | -          | Supported | -    | Supported                                                                                                                             | Yes  | -                                                                                                                                                                |\n| [EfficientNet-WideSE-B0](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet)      | PyTorch      | Yes            | Yes       | -          | Supported | -    | Supported                                                                                                                             | Yes  | -                                                                                                                                                                |\n| [EfficientNet-WideSE-B4](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/efficientnet)      | PyTorch      | Yes            | Yes       | -          | Supported | -    | Supported","github_created_at":"2018-05-02T17:04:05+00:00","created_at":"2026-07-07T17:33:40.395164+00:00","updated_at":"2026-09-20T04:33:32.08018+00:00","categories":[{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"computer-vision","name":"computer-vision"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"drug-discovery","name":"drug-discovery"},{"slug":"forecasting","name":"forecasting"},{"slug":"large-language-models","name":"large-language-models"},{"slug":"mxnet","name":"mxnet"},{"slug":"nlp","name":"nlp"},{"slug":"paddlepaddle","name":"paddlepaddle"}],"trust":{"provenance":{"is_fork":false,"github_id":131881622,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:48:29.976Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":766,"last_release_at":null,"stars_delta_30d":3,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-09-18T08:48:31.874Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:48:28.877Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-09-18T08:48:28.877Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Requires NVIDIA GPUs for optimal performance.","Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS."]},"constraints":null,"when_to_use":["When you need deep learning scripts optimized for NVIDIA GPUs, including Volta, Turing, and Ampere architectures, for tasks like computer vision, NLP, and speech recognition.","If you require reproducible accuracy and performance on enterprise-grade infrastructure, leveraging the latest NVIDIA CUDA-X software stack.","When you want to use the NGC container registry for easy access to the latest NVIDIA-optimized deep learning software libraries and frameworks."],"when_not_to_use":["If your infrastructure does not include NVIDIA GPUs, as the scripts are specifically optimized for NVIDIA hardware.","If you are looking for a tool that supports a wider range of hardware or non-NVIDIA GPU environments.","When you need a solution that is not tied to specific frameworks like PyTorch, TensorFlow, or PaddlePaddle, as DeepLearningExamples focuses on these frameworks."],"source":"enrich:decision_facts","observed_at":"2026-09-18T11:10:17.904Z"},"constraint_facets":null,"decision_summary":[{"label":"Requirements","value":"Requires NVIDIA GPUs for optimal performance.; Uses NVIDIA's CUDA-X software stack, including libraries like cuDNN, NCCL, and cuBLAS."},{"label":"Adopt for","value":"DeepLearningExamples offers state-of-the-art deep learning scripts optimized for NVIDIA GPUs, with a focus on reproducibility and performance. It supports a wide range of applications from computer vision to speech and N"},{"label":"License detail","value":"The license information for DeepLearningExamples is unknown."}]}}