[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9680":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":18,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":40,"readmeContent":41,"aiSummary":42,"trendingCount":16,"starSnapshotCount":16,"syncStatus":43,"lastSyncTime":44,"discoverSource":45},9680,"DeepLearningExamples","NVIDIA\u002FDeepLearningExamples","NVIDIA","State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.","",null,"Jupyter Notebook",14814,3407,289,253,0,1,4,14,72.4,false,"master",true,[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"computer-vision","deep-learning","drug-discovery","forecasting","large-language-models","mxnet","nlp","paddlepaddle","pytorch","recommender-systems","speech-recognition","speech-synthesis","tensorflow","tensorflow2","translation","2026-06-12 04:00:46","# 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:\u002F\u002Fngc.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:\u002F\u002Fdocs.nvidia.com\u002Fdeeplearning\u002Fdgx\u002Findex.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:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Fefficientnet) | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |\n| [EfficientNet-B4](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Fefficientnet) | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |\n| [EfficientNet-WideSE-B0](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Fefficientnet) | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |\n| [EfficientNet-WideSE-B4](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Fefficientnet) | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |\n| [EfficientNet v1-B0](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FClassification\u002FConvNets\u002Fefficientnet_v1) | TensorFlow2 | Yes | Yes | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT\u002Ftree\u002Fmain\u002Fsamples\u002Fpython\u002Fefficientnet) | - | Supported | Yes | - |\n| [EfficientNet v1-B4](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FClassification\u002FConvNets\u002Fefficientnet_v1) | TensorFlow2 | Yes | Yes | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT\u002Ftree\u002Fmain\u002Fsamples\u002Fpython\u002Fefficientnet) | - | Supported | Yes | - |\n| [EfficientNet v2-S](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FClassification\u002FConvNets\u002Fefficientnet_v2) | TensorFlow2 | Yes | Yes | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT\u002Ftree\u002Fmain\u002Fsamples\u002Fpython\u002Fefficientnet) | - | Supported | Yes | - |\n| [GPUNet](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FGPUNet) | PyTorch | Yes | Yes | - | Example | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FGPUNet\u002Ftriton\u002F) | Yes | - |\n| [Mask R-CNN](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSegmentation\u002FMaskRCNN) | PyTorch | Yes | Yes | - | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT\u002Ftree\u002Fmain\u002Fsamples\u002Fpython\u002Fdetectron2) | - | Supported | - | [Yes](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Fblob\u002Fmaster\u002FPyTorch\u002FSegmentation\u002FMaskRCNN\u002Fpytorch\u002Fnotebooks\u002Fpytorch_MaskRCNN_pyt_train_and_inference.ipynb) |\n| [Mask R-CNN](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FSegmentation\u002FMaskRCNN) | TensorFlow2 | Yes | Yes | - | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT\u002Ftree\u002Fmain\u002Fsamples\u002Fpython\u002Fdetectron2) | - | Supported | Yes | - |\n| [nnUNet](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSegmentation\u002FnnUNet) | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |\n| [ResNet-50](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FMxNet\u002FClassification\u002FRN50v1.5) | MXNet | Yes | Yes | - | Supported | - | Supported | - | - |\n| [ResNet-50](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPaddlePaddle\u002FClassification\u002FRN50v1.5) | PaddlePaddle | Yes | Yes | - | Example | - | Supported | - | - |\n| [ResNet-50](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Fresnet50v1.5) | PyTorch | Yes | Yes | - | Example | - | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Ftriton\u002Fresnet50) | Yes | - |\n| [ResNet-50](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FClassification\u002FConvNets\u002Fresnet50v1.5) | TensorFlow | Yes | Yes | - | Supported | - | Supported | Yes | - |\n| [ResNeXt-101](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Fresnext101-32x4d) | PyTorch | Yes | Yes | - | Example | - | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Ftriton\u002Fresnext101-32x4d) | Yes | - |\n| [ResNeXt-101](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FClassification\u002FConvNets\u002Fresnext101-32x4d) | TensorFlow | Yes | Yes | - | Supported | - | Supported | Yes | - |\n| [SE-ResNeXt-101](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Fse-resnext101-32x4d) | PyTorch | Yes | Yes | - | Example | - | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FClassification\u002FConvNets\u002Ftriton\u002Fse-resnext101-32x4d) | Yes | - |\n| [SE-ResNeXt-101](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FClassification\u002FConvNets\u002Fse-resnext101-32x4d) | TensorFlow | Yes | Yes | - | Supported | - | Supported | Yes | - |\n| [SSD](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FDetection\u002FSSD) | PyTorch | Yes | Yes | - | Supported | - | Supported | - | [Yes](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Fblob\u002Fmaster\u002FPyTorch\u002FDetection\u002FSSD\u002Fexamples\u002Finference.ipynb) |\n| [SSD](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FDetection\u002FSSD) | TensorFlow | Yes | Yes | - | Supported | - | Supported | Yes | [Yes](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Fblob\u002Fmaster\u002FTensorFlow\u002FDetection\u002FSSD\u002Fmodels\u002Fresearch\u002Fobject_detection\u002Fobject_detection_tutorial.ipynb) |\n| [U-Net Med](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FSegmentation\u002FUNet_Medical) | TensorFlow2 | Yes | Yes | - | Example | - | Supported | Yes | - |\n\n## Natural Language Processing\n| Models | Framework | AMP | Multi-GPU | Multi-Node | TensorRT | ONNX | Triton | DLC | NB |\n|------------------------------------------------------------------------------------------------------------------------|-------------|------|-----------|------------|----------|------|-----------------------------------------------------------------------------------------------------------|------|---------------------------------------------------------------------------------------------------------------------------------------------|\n| [BERT](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FLanguageModeling\u002FBERT) | PyTorch | Yes | Yes | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT\u002Ftree\u002Fmain\u002Fdemo\u002FBERT) | - | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FLanguageModeling\u002FBERT\u002Ftriton) | Yes | - |\n| [GNMT](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FTranslation\u002FGNMT) | PyTorch | Yes | Yes | - | Supported | - | Supported | - | - |\n| [ELECTRA](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FLanguageModeling\u002FELECTRA) | TensorFlow2 | Yes | Yes | Yes | Supported | - | Supported | Yes | - |\n| [BERT](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FLanguageModeling\u002FBERT) | TensorFlow | Yes | Yes | Yes | Example | - | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FLanguageModeling\u002FBERT\u002Ftriton) | Yes | [Yes](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FLanguageModeling\u002FBERT\u002Fnotebooks) |\n| [BERT](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FLanguageModeling\u002FBERT) | TensorFlow2 | Yes | Yes | Yes | Supported | - | Supported | Yes | - |\n| [GNMT](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FTranslation\u002FGNMT) | TensorFlow | Yes | Yes | - | Supported | - | Supported | - | - |\n| [Faster Transformer](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FFasterTransformer) | Tensorflow | - | - | - | Example | - | Supported | - | - |\n\n\n## Recommender Systems\n| Models | Framework | AMP | Multi-GPU | Multi-Node | ONNX | Triton | DLC | NB |\n|----------------------------------------------------------------------------------------------------------------|-------------|-------|-----------|--------------|--------|------------------------------------------------------------------------------------------------------|------|--------------------------------------------------------------------------------------------------------|\n| [DLRM](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FRecommendation\u002FDLRM) | PyTorch | Yes | Yes | - | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FRecommendation\u002FDLRM\u002Ftriton) | Yes | [Yes](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FRecommendation\u002FDLRM\u002Fnotebooks) |\n| [DLRM](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FRecommendation\u002FDLRM) | TensorFlow2 | Yes | Yes | Yes | - | Supported | Yes | - |\n| [NCF](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FRecommendation\u002FNCF) | PyTorch | Yes | Yes | - | - | Supported | - | - |\n| [Wide&Deep](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FRecommendation\u002FWideAndDeep) | TensorFlow | Yes | Yes | - | - | Supported | Yes | - |\n| [Wide&Deep](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FRecommendation\u002FWideAndDeep) | TensorFlow2 | Yes | Yes | - | - | Supported | Yes | - |\n| [NCF](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FRecommendation\u002FNCF) | TensorFlow | Yes | Yes | - | - | Supported | Yes | - |\n| [VAE-CF](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow\u002FRecommendation\u002FVAE-CF) | TensorFlow | Yes | Yes | - | - | Supported | - | - |\n| [SIM](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FTensorFlow2\u002FRecommendation\u002FSIM) | TensorFlow2 | Yes | Yes | - | - | Supported | Yes | - |\n\n\n## Speech to Text\n| Models | Framework | AMP | Multi-GPU | Multi-Node | TensorRT | ONNX | Triton | DLC | NB |\n|--------------------------------------------------------------------------------------------------------------|-------------|------|------------|--------------|----------|--------|----------------------------------------------------------------------------------------------------------|-------|--------------------------------------------------------------------------------------------------------------|\n| [Jasper](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechRecognition\u002FJasper) | PyTorch | Yes | Yes | - | Example | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechRecognition\u002FJasper\u002Ftrtis) | Yes | [Yes](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechRecognition\u002FJasper\u002Fnotebooks) |\n| [QuartzNet](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechRecognition\u002FQuartzNet) | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |\n\n## Text to Speech\n| Models | Framework | AMP | Multi-GPU | Multi-Node | TensorRT | ONNX | Triton | DLC | NB |\n|-------------------------------------------------------------------------------------------------------------------------|-------------|------|------------|-------------|----------|--------|---------------------------------------------------------------------------------------------------------------|-------|-----|\n| [FastPitch](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechSynthesis\u002FFastPitch) | PyTorch | Yes | Yes | - | Example | - | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechSynthesis\u002FFastPitch\u002Ftriton) | Yes | Yes |\n| [FastSpeech](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FCUDA-Optimized\u002FFastSpeech) | PyTorch | Yes | Yes | - | Example | - | Supported | - | - |\n| [Tacotron 2 and WaveGlow](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechSynthesis\u002FTacotron2) | PyTorch | Yes | Yes | - | Example | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechSynthesis\u002FTacotron2\u002Ftrtis_cpp) | Yes | - |\n| [HiFi-GAN](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FSpeechSynthesis\u002FHiFiGAN) | PyTorch | Yes | Yes | - | Supported | - | Supported | Yes | - |\n\n## Graph Neural Networks\n| Models | Framework | AMP | Multi-GPU | Multi-Node | ONNX | Triton | DLC | NB |\n|-------------------------------------------------------------------------------------------------------------------------|------------|------|------------|--------------|--------|----------|------|------|\n| [SE(3)-Transformer](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FDGLPyTorch\u002FDrugDiscovery\u002FSE3Transformer) | PyTorch | Yes | Yes | - | - | Supported | - | - |\n| [MoFlow](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FDrugDiscovery\u002FMoFlow) | PyTorch | Yes | Yes | - | - | Supported | - | - |\n\n## Time-Series Forecasting\n| Models | Framework | AMP | Multi-GPU | Multi-Node | TensorRT | ONNX | Triton | DLC | NB |\n|-------------------------------------------------------------------------------------------------------------------|------------|------|-------------|--------------|----------|--------|--------------------------------------------------------------------------------------------------|-------|-----|\n| [Temporal Fusion Transformer](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FForecasting\u002FTFT) | PyTorch | Yes | Yes | - | Example | Yes | [Example](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002Fmaster\u002FPyTorch\u002FForecasting\u002FTFT\u002Ftriton) | Yes | - |\n\n## NVIDIA support\nIn each of the network READMEs, we indicate the level of support that will be provided. The range is from ongoing updates and improvements to a point-in-time release for thought leadership.\n\n## Glossary\n\n**Multinode Training**\nSupported on a pyxis\u002Fenroot Slurm cluster.\n\n**Deep Learning Compiler (DLC)**\nTensorFlow XLA and PyTorch JIT and\u002For TorchScript\n\n**Accelerated Linear Algebra (XLA)**\nXLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage.\n\n**PyTorch JIT and\u002For TorchScript**\nTorchScript is a way to create serializable and optimizable models from PyTorch code. TorchScript, an intermediate representation of a PyTorch model (subclass of nn.Module) that can then be run in a high-performance environment such as C++.\n\n**Automatic Mixed Precision (AMP)**\nAutomatic Mixed Precision (AMP) enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically.\n\n**TensorFloat-32 (TF32)**\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fa100\u002F) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.\n\n**Jupyter Notebooks (NB)**\nThe Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.\n\n\n## Feedback \u002F Contributions\nWe're posting these examples on GitHub to better support the community, facilitate feedback, as well as collect and implement contributions using GitHub Issues and pull requests. We welcome all contributions!\n\n## Known issues\nIn each of the network READMEs, we indicate any known issues and encourage the community to provide feedback.\n","NVIDIA\u002FDeepLearningExamples 项目提供了一系列先进的深度学习模型脚本，便于在企业级基础设施上进行训练和部署，确保可重现的准确性和性能。该项目涵盖了计算机视觉、自然语言处理、语音识别与合成等多个领域，支持包括PyTorch、TensorFlow在内的主流深度学习框架，并针对NVIDIA GPU进行了优化。通过NVIDIA GPU Cloud (NGC) 容器注册表每月更新的Docker容器，用户可以获得最新的NVIDIA示例代码及经过严格质量保证测试的CUDA-X软件栈组件。适用于需要高性能计算支持的研究机构、企业和开发者，在追求模型训练效率与部署便捷性的同时保证了结果的一致性。",2,"2026-06-11 03:24:11","top_topic"]