{"data":{"slug":"nvidia-deeplearningexamples","name":"DeepLearningExamples","tagline":"State-of-the-Art Deep Learning scripts for various applications","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":14844,"forks":3408,"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":14,"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":"Provides deep learning examples that are easy to train and deploy with reproducible accuracy on NVIDIA GPUs, supporting multiple frameworks and use cases.","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":"2026-08-17T06:01:39.451415+00:00","categories":[{"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":"2026-08-17T06:01:38.452Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":734,"last_release_at":null,"stars_delta_30d":14,"open_issues_delta_30d":-1},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:01:42.239Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-17T06:01:38.953Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-08-17T06:01:38.953Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["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.","This tool is geared towards enterprises or researchers who have access to NVIDIA GPUs (Volta, Turing, Ampere) and are seeking maximum performance from their deep learning tasks with NVIDIA-optimized,掐"],"when_not_to_use":["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"],"source":"enrich:decision_facts","observed_at":"2026-07-11T15:03:27.783Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Curated facts for DeepLearningExamples, tailored to its unique features and offerings."}]}}