[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10,"_11":12},"actionData","errors","root",{"_67":222,"_223":224,"_225":226,"_227":228,"_229":230,"_231":26,"_232":233,"_234":235,"_236":26,"_237":26,"_238":26,"_239":26,"_240":241,"_242":-5,"_243":244,"_245":246,"_247":248,"_249":94,"_250":96,"_251":252,"_88":-5,"_253":26,"_254":-5,"_255":256,"_117":257,"_258":259,"_260":261,"_262":263,"_264":265,"_266":26,"_21":22,"_23":267,"_25":26,"_27":268,"_29":269,"_31":270},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/route",{"_103":104,"_17":105,"_106":107,"_108":109,"_110":26,"_111":112,"_113":26,"_114":26,"_115":116,"_117":14,"_118":-5,"_119":120,"_121":-5,"_122":123,"_124":125,"_126":26,"_127":128,"_21":22,"_23":129,"_25":26,"_27":130,"_29":131,"_31":132},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/_access/route",{"_64":65,"_66":-5,"_67":68,"_69":70,"_21":22,"_23":71,"_25":26,"_27":72,"_29":73,"_31":74},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/release-notes/route",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32},"versionId","21.03.0","releaseNotes","### Changelog\n\n1. April 2020\n * Initial release\n\n### Known issues\n\nThere are no known issues with this model.","html","

Changelog

April 2020

Initial release

Known issues

There are no known issues with this model.

","resourceType","resources","traceId","4f66130a2502624ee591c31fe600044d","traceStartTime",1784067404294,"isStaleResponse",false,"notifications",[],"toasts",[],"apiCalls",[33,34,35,36,37,38],{"_39":53,"_41":62,"_43":55,"_45":56,"_47":63,"_49":50},{"_39":53,"_41":60,"_43":55,"_45":56,"_47":61,"_49":50},{"_39":53,"_41":58,"_43":55,"_45":56,"_47":59,"_49":50},{"_39":53,"_41":54,"_43":55,"_45":56,"_47":57,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":48,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"apiTag","subscription-api","path","/v2/users/me/subscriptions","status","error","httpStatus",401,"durationMs",3,"retryCount",0,"oms-api","/v2/users/me","models-api","/v2/resources/org/nvidia/team/dle/efficientnet_pyt/collections?page-number=0&page-size=1","success",200,50,"/v2/resources/nvidia/dle/efficientnet_pyt/versions/21.03.0",78,"/v2/resources/nvidia/dle/efficientnet_pyt/versions?page-size=100&page-number=0&sort-order=CREATED_DATE_DESC",69,"/v2/resources/nvidia/dle/efficientnet_pyt?resolve-labels=true&remove-unresolved-labels=true",70,"canDownload",true,"accessDeniedReason","appConfig",{"_86":87,"_88":89},"debugAccessInfo",{"_82":65,"_83":84,"_85":26},1784067404286,[],[],[75,76,77,78,79,80],{"_39":53,"_41":62,"_43":55,"_45":56,"_47":63,"_49":50},{"_39":53,"_41":60,"_43":55,"_45":56,"_47":61,"_49":50},{"_39":53,"_41":58,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":55,"_45":56,"_47":57,"_49":50},{"_39":51,"_41":52,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"pending","canGuestAccess","requiredProducts",[],"hasLicenseRequirement","urls",{"_91":92,"_93":94,"_95":96,"_97":98,"_99":100,"_101":102},"user",{"_90":26},"loggedIn","build","https://build.nvidia.com","ngcApi","https://api.ngc.nvidia.com","ngcHelm","https://helm.ngc.nvidia.com","ngcRegistry","https://registry.ngc.nvidia.com","orgUi","https://org.ngc.nvidia.com","ngcUi","https://ngc.nvidia.com","resource",{"_175":182,"_183":184,"_19":20,"_185":186,"_139":140,"_141":142,"_187":188,"_144":189,"_190":191,"_149":192,"_193":194,"_195":178,"_196":-7,"_197":-7,"_198":199,"_200":201,"_202":65,"_203":151,"_204":-5,"_205":-7,"_206":-7,"_207":-7,"_208":-7,"_209":-7,"_210":-7,"_211":212,"_213":214,"_215":216,"_217":218,"_219":220,"_221":26},"

This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from NGC

EfficientNet is an image classification model family. It was first described in EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. The scripts provided enable you to train the EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0 and, EfficientNet-WideSE-B4 models.

EfficientNet-WideSE models use Squeeze-and-Excitation layers wider than original EfficientNet models, the width of SE module is proportional to the width of Depthwise Separable Convolutions instead of block width.

WideSE models are slightly more accurate than original models.

This model is trained with mixed precision using Tensor Cores on Volta and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

We use NHWC data layout when training using Mixed Precision.

Default configuration

The following sections highlight the default configurations for the EfficientNet models.

Optimizer

This model uses RMSprop with the following hyperparameters:

Momentum (0.9)
Learning rate (LR):\n
- 0.08 for 4096 batch size for B0 models
- 0.16 for 4096 batch size for B4 models\nscale the learning rate.
\n
Learning rate schedule - we use cosine LR schedule
We use linear warmup of the learning rate during the first 16 epochs
Weight decay (WD):\n
- 1e-5 for B0 models
- 5e-6 for B4 models
\n
We do not apply WD on Batch Norm trainable parameters (gamma/bias)
Label smoothing = 0.1
MixUp = 0.2
We train for 400 epochs

Optimizer for QAT

This model uses SGD optimizer for B0 models and RMSPROP optimizer alpha=0.853 epsilon=0.00422 for B4 models. Other hyperparameters we used are:

Momentum:\n
- 0.89 for B0 models
- 0.9 for B4 models
\n
Learning rate (LR):\n
- 0.0125 for 128 batch size for B0 models
- 4.09e-06 for 32 batch size for B4 models\nscale the learning rate.
\n
Learning rate schedule:\n
- cosine LR schedule for B0 models
- linear LR schedule for B4 models
\n
Weight decay (WD):\n
- 4.50e-05 for B0 models
- 9.714e-04 for B4 models
\n
We do not apply WD on Batch Norm trainable parameters (gamma/bias)
We train for:\n*10 epochs for B0 models\n*2 epochs for B4 models

Data augmentation

This model uses the following data augmentation:

\n
For training:
\n
- Auto-augmentation
- Basic augmentation:\n
  - Normalization
  - Random resized crop to target images size (depending on model version)\n
    - Scale from 8% to 100%
    - Aspect ratio from 3/4 to 4/3
    \n
  - Random horizontal flip
  \n
\n
\n
For inference:
\n
- Normalization
- Scale to target image size + 32
- Center crop to target image size
\n

Feature support matrix

The following features are supported by this model:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Feature	EfficientNet
DALI	Yes (without autoaugmentation)
APEX AMP	Yes
QAT	Yes

Features

NVIDIA DALI

DALI is a library accelerating data preparation pipeline. To accelerate your input pipeline, you only need to define your data loader\nwith the DALI library. For more information about DALI, refer to the DALI product documentation.

We use NVIDIA DALI,\nwhich speeds up data loading when CPU becomes a bottleneck.\nDALI can use CPU or GPU, and outperforms the PyTorch native dataloader.

Run training with --data-backends dali-gpu or --data-backends dali-cpu to enable DALI.\nFor DGXA100 and DGX1 we recommend --data-backends dali-cpu.

DALI currently does not support Autoaugmentation, so for best accuracy it has to be disabled.

APEX

A PyTorch extension that contains utility libraries, such as Automatic Mixed Precision (AMP), which require minimal network code changes to leverage Tensor Cores performance. Refer to the Enabling mixed precision section for more details.

QAT

Quantization aware training (QAT) is a method for changing precision to INT8 which speeds up the inference process at the price of a slight decrease of network accuracy. Refer to the Quantization section for more details.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:

Porting the model to use the FP16 data type where appropriate.
Adding loss scaling to preserve small gradient values.

The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.

For information about:

How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
APEX tools for mixed precision training, see the NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch.

Enabling mixed precision

Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP), a library from APEX that casts variables to half-precision upon retrieval,\nwhile storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients.\nIn PyTorch, loss scaling can be easily applied by using scale_loss() method provided by AMP. The scaling value to be used can be dynamic or fixed.

For an in-depth walk through on AMP, check out sample usage here. APEX is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage Tensor Cores performance.

To enable mixed precision, you can:

\n
Import AMP from APEX:
\n
```
from apex import amp\n
```
\n

Wrap model and optimizer in amp.initialize:

model, optimizer = amp.initialize(model, optimizer, opt_level=\"O1\", loss_scale=\"dynamic\")\n

Scale loss before backpropagation:

with amp.scale_loss(loss, optimizer) as scaled_loss:\n  scaled_loss.backward()\n

Enabling TF32

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 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 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.

For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.

TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.

Quantization

Quantization is the process of transforming deep learning models to use parameters and computations at a lower precision. Traditionally, DNN training and inference have relied on the IEEE single-precision floating-point format, using 32 bits to represent the floating-point model weights and activation tensors.

This compute budget may be acceptable at training as most DNNs are trained in data centers or in the cloud with NVIDIA V100 or A100 GPUs that have significantly large compute capability and much larger power budgets. However, during deployment, these models are most often required to run on devices with much smaller computing resources and lower power budgets at the edge. Running a DNN inference using the full 32-bit representation is not practical for real-time analysis given the compute, memory, and power constraints of the edge.

To help reduce the compute budget, while not compromising on the structure and number of parameters in the model, you can run inference at a lower precision. Initially, quantized inferences were run at half-point precision with tensors and weights represented as 16-bit floating-point numbers. While this resulted in compute savings of about 1.2–1.5x, there was still some compute budget and memory bandwidth that could be leveraged. In lieu of this, models are now quantized to an even lower precision, with an 8-bit integer representation for weights and tensors. This results in a model that is 4x smaller in memory and about 2–4x faster in throughput.

While 8-bit quantization is appealing to save compute and memory budgets, it is a lossy process. During quantization, a small range of floating-point numbers are squeezed to a fixed number of information buckets. This results in loss of information.

The minute differences which could originally be resolved using 32-bit representations are now lost because they are quantized to the same bucket in 8-bit representations. This is similar to rounding errors that one encounters when representing fractional numbers as integers. To maintain accuracy during inferences at a lower precision, it is important to try and mitigate errors arising due to this loss of information.

Quantization-aware training

In QAT, the quantization error is considered when training the model. The training graph is modified to simulate the lower precision behavior in the forward pass of the training process. This introduces the quantization errors as part of the training loss, which the optimizer tries to minimize during the training. Thus, QAT helps in modeling the quantization errors during training and mitigates its effects on the accuracy of the model at deployment.

However, the process of modifying the training graph to simulate lower precision behavior is intricate. To run QAT, it is necessary to insert FakeQuantization nodes for the weights of the DNN Layers and Quantize-Dequantize (QDQ) nodes to the intermediate activation tensors to compute their dynamic ranges.

For more information, see this Quantization paper and Quantization-Aware Training documentation.\nTutorial for pytoch-quantization library can be found here pytorch-quantization tutorial.

It is important to mention that EfficientNet is NN, which is hard to quantize because the activation function all across the network is the SiLU (called also the Swish), whose negative values lie in very short range, which introduce a large quantization error. More details can be found in Appendix D of the Quantization paper.

","resourceVersions",[172,173,174],"resourceVersionsTotal",2,"hasMoreVersions","versionMetadata",{"_13":14,"_144":-5,"_43":145,"_146":-5,"_147":148,"_149":-5,"_150":151,"_152":153,"_154":26,"_155":-5,"_156":26,"_15":16,"_157":65,"_158":159,"_160":65,"_161":162,"_163":65,"_164":165,"_166":65,"_167":168,"_169":65,"_170":171},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/resources/efficientnet_pyt/-","version","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/dle/efficientnet_pyt:21.03.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/resources/efficientnet_pyt/21.03.0/version-history","accessCheckResult",{"_143":65},"hasRelatedCollections","context",{"_139":140,"_141":142},1784067404293,[],[],[133,134,135,136,137,138],{"_39":53,"_41":62,"_43":55,"_45":56,"_47":63,"_49":50},{"_39":53,"_41":60,"_43":55,"_45":56,"_47":61,"_49":50},{"_39":53,"_41":58,"_43":55,"_45":56,"_47":59,"_49":50},{"_39":53,"_41":54,"_43":55,"_45":56,"_47":57,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":48,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"orgName","nvidia","teamName","dle","allowed","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1663857170323],"updatedDate","totalSizeInBytes",61855,"totalFileCount",1,"isSigned","malwareScanStatus","isScanned","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to start training the EfficientNet model.\n\n### Requirements\n\nThis repository contains Dockerfile which extends the PyTorch NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n\n* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n* [PyTorch 21.03-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) or newer\n* Supported GPUs:\n * [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n * [NVIDIA Turing architecture](https://www.nvidia.com/en-us/geforce/turing/)\n * [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the\nfollowing sections from the NVIDIA GPU Cloud Documentation and the Deep Learning\nDGX Documentation:\n* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)\n* [Running PyTorch](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/running.html#running)\n\nTo set up the required environment or create your own container, as an alternative to the use of the PyTorch NGC container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed or TF32 precision with Tensor Cores or using FP32,\nperform the following steps using the default parameters of the efficientnet model on the ImageNet dataset.\nFor the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/PyTorch/Classification/\n```\n\n2. Download and pre-process the dataset.\n\nThe EfficientNet script operates on ImageNet 1k, a widely popular image classification dataset from the ILSVRC challenge.\n\nPyTorch can work directly on JPEGs, therefore, pre-processing/augmentation is not needed.\n\n3. [Download the images](http://image-net.org/download-images).\n\n4. Extract the training data:\n ```bash\n mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train\n tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar\n find . -name \"*.tar\" | while read NAME ; do mkdir -p \"${NAME%.tar}\"; tar -xvf \"${NAME}\" -C \"${NAME%.tar}\"; rm -f \"${NAME}\"; done\n cd ..\n ```\n\n5. Extract the validation data and move the images to subfolders:\n ```bash\n mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar\n wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash\n ```\n\nThe directory in which the `train/` and `val/` directories are placed, is referred to as `` in this document.\n\n6. Build the EfficientNet PyTorch NGC container.\n\n```\ndocker build . -t nvidia_efficientnet\n```\n\n7. Start an interactive session in the NGC container to run training/inference.\n```\nnvidia-docker run --rm -it -v :/imagenet --ipc=host nvidia_efficientnet\n```\n\n8. Start training\n\nTo run training for a standard configuration (DGX A100/DGX-1V, AMP/TF32/FP32, 400 Epochs),\nrun one of the scripts in the `./efficientnet/training` directory\ncalled `./efficientnet/training/{AMP, TF32, FP32}/{ DGX A100, DGX-1V }_efficientnet-_{AMP, TF32, FP32}_{ 400 }E.sh`.\n\nEnsure ImageNet is mounted in the `/imagenet` directory.\n\nFor example:\n `bash ./efficientnet/training/AMP/DGXA100_efficientnet-b0_AMP.sh `\n\n9. Start inference\n\nYou can download pre-trained weights from NGC:\n\n```bash\nwget --content-disposition -O \n\nunzip \n```\n\nTo run inference on ImageNet, run:\n\n`python ./main.py --arch efficientnet- --evaluate --epochs 1 --pretrained -b `\n\nTo run inference on JPEG image using pre-trained weights, run:\n\n`python classify.py --arch efficientnet- --pretrained --precision AMP|FP32 --image `","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Scripts and sample code\n\nFor a non-standard configuration, run:\n\n* For 1 GPU\n * FP32\n `python ./main.py --arch efficientnet- --label-smoothing 0.1 `\n `python ./main.py --arch efficientnet- --label-smoothing 0.1 --amp --static-loss-scale 256 `\n\n* For multiple GPUs\n * FP32\n `python ./multiproc.py --nproc_per_node 8 ./main.py --arch efficientnet- --label-smoothing 0.1 `\n * AMP\n `python ./multiproc.py --nproc_per_node 8 ./main.py --arch efficientnet- --label-smoothing 0.1 --amp --static-loss-scale 256 `\n\nUse `python ./main.py -h` to obtain the list of available options in the `main.py` script.\n\n### Command-line options\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:\n\n`python main.py -h`\n\n### Dataset guidelines\n\nTo use your own dataset, divide it into directories. For example:\n\n - Training images - `train//`\n - Validation images - `val//`\n\nIf your dataset has a number of classes different than 1000, you need to pass the `--num_classes N` flag to the training script.\n\n### Training process\n\nAll the results of the training will be stored in the directory specified with `--workspace` argument.\n\nThe script will store:\n - the most recent checkpoint - `checkpoint.pth.tar` (unless `--no-checkpoints` flag is used).\n - the checkpoint with the best validation accuracy - `model_best.pth.tar` (unless `--no-checkpoints` flag is used).\n - the JSON log - in the file specified with the `--raport-file` flag.\n\nMetrics gathered through training:\n - `train.loss` - training loss\n - `train.total_ips` - training speed measured in images/second\n - `train.compute_ips` - training speed measured in images/second, not counting data loading\n - `train.data_time` - time spent on waiting on data\n - `train.compute_time` - time spent in forward/backward pass\n\nTo restart training from the checkpoint use the `--resume` option.\n\nTo start training from pretrained weights (for example, downloaded from NGC) use the `--pretrained-from-file` option.\n\nThe difference between `--resume` and `--pretrained-from-file` flags is that the pretrained weights contain only model weights,\nand checkpoints, apart from model weights, contain optimizer state, LR scheduler state.\n\nCheckpoints are suitable for dividing the training into parts, for example, in order\nto divide the training job into shorter stages, or restart training after an infrastructure failure.\n\nPretrained weights can be used as a base for fine tuning the model to a different dataset,\nor as a backbone to detection models.\n\n### Inference process\n\nValidation is done every epoch, and can be also run separately on a checkpointed model.\n\n`python ./main.py --arch efficientnet- --evaluate --epochs 1 --resume -b `\n\nMetrics gathered through training:\n\n - `val.loss` - validation loss\n - `val.top1` - validation top1 accuracy\n - `val.top5` - validation top5 accuracy\n - `val.total_ips` - inference speed measured in images/second\n - `val.compute_ips` - inference speed measured in images/second, not counting data loading\n - `val.data_time` - time spent on waiting on data\n - `val.compute_time` - time spent on inference\n\nTo run inference on JPEG image, you have to first extract the model weights from checkpoint:\n\n`python checkpoint2model.py --checkpoint-path --weight-path `\n\nThen, run the classification script:\n\n`python classify.py --arch efficientnet- --pretrained-from-file --precision AMP|FP32 --image `\n\nYou can also run the ImageNet validation on pretrained weights:\n\n`python ./main.py --arch efficientnet- --evaluate --epochs 1 --pretrained-from-file -b `\n\n#### NGC pretrained weights\n\nPretrained weights can be downloaded from NGC:\n\n```bash\nwget \n```\n\nURL for each model can be found in the following table:\n\n| **Model** | **NGC weights URL** |\n|:---------:|:-------------------:|\n| efficientnet-b0 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_b0_pyt_amp/versions/20.12.0/files/nvidia_efficientnet-b0_210412.pth | \n| efficientnet-b4 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_b4_pyt_amp/versions/20.12.0/files/nvidia_efficientnet-b4_210412.pth | \n| efficientnet-widese-b0 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_widese_b0_pyt_amp/versions/20.12.0/files/nvidia_efficientnet-widese-b0_210412.pth | \n| efficientnet-widese-b4 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_widese_b4_pyt_amp/versions/20.12.0/files/nvidia_efficientnet-widese-b4_210412.pth | \n| efficientnet-quant-b0 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_b0_pyt_qat_ckpt_fp32/versions/21.03.0/files/nvidia-efficientnet-quant-b0-130421.pth | \n| efficientnet-quant-b4 | https://api.ngc.nvidia.com/v2/models/nvidia/efficientnet_b4_pyt_qat_ckpt_fp32/versions/21.03.0/files/nvidia-efficientnet-quant-b4-130421.pth | \n\nTo run inference on ImageNet, run:\n\n`python ./main.py --arch efficientnet- --evaluate --epochs 1 --pretrained -b `\n\nTo run inference on JPEG images using pretrained weights, run:\n\n`python classify.py --arch efficientnet- --pretrained --precision AMP|FP32 --image `\n\n### Quantization process\n\nEfficientNet-b0 and EfficientNet-b4 models can be quantized using the QAT process from running the `quant_main.py` script.\n\n`python ./quant_main.py --arch efficientnet-quant- --epochs <# of QAT epochs> --pretrained-from-file `\n\nDuring the QAT process, evaluation is done in the same way as during standard training. `quant_main.py` works in the same way as the original `main.py` script, but with quantized models. It means that `quant_main.py` can be used to resume the QAT process with the flag `--resume`:\n\n`python ./quant_main.py --arch efficientnet-quant- --resume ...`\n\nor to evaluate a created checkpoint with the flag `--evaluate`:\n\n`python ./quant_main.py --arch efficientnet-quant- --evaluate --epochs 1 --resume -b ` \n\nIt also can run on multi-GPU in an identical way as the standard `main.py` script:\n\n`python ./multiproc.py --nproc_per_node 8 ./quant_main.py --arch efficientnet-quant- ... `\n\nThere is also a possibility to transform trained models (quantized or not) into ONNX format, which is needed to convert it later into TensorRT, where quantized networks are much faster during inference. Conversion to TensorRT will be supported in the next release. The conversion to ONNX consists of two steps:\n\n* translate checkpoint to pure weights:\n\n`python checkpoint2model.py --checkpoint-path --weight-path `\n\n* translate pure weights to ONNX:\n\n`python model2onnx.py --arch efficientnet-quant- --pretrained-from-file -b --trt True`\n\nQuantized models could also be used to classify new images using the `classify.py` flag. For example:\n`python classify.py --arch efficientnet-quant- --pretrained-from-file --image `","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA's latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nTo benchmark training, run:\n\n* For 1 GPU\n * FP32 (V100 GPUs only)\n `python ./launch.py --model efficientnet- --precision FP32 --mode benchmark_training --platform DGX1V --raport-file benchmark.json --epochs 1 --prof 100`\n * TF32 (A100 GPUs only)\n `python ./launch.py --model efficientnet- --precision TF32 --mode benchmark_training --platform DGXA100 --raport-file benchmark.json --epochs 1 --prof 100`\n * AMP\n `python ./launch.py --model efficientnet- --precision AMP --mode benchmark_training --platform --raport-file benchmark.json --epochs 1 --prof 100`\n\n* For multiple GPUs\n * FP32 (V100 GPUs only)\n `python ./launch.py --model efficientnet- --precision FP32 --mode benchmark_training --platform DGX1V --raport-file benchmark.json --epochs 1 --prof 100`\n * TF32 (A100 GPUs only)\n `python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet- --precision TF32 --mode benchmark_training --platform DGXA100 --raport-file benchmark.json --epochs 1 --prof 100`\n * AMP\n `python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet- --precision AMP --mode benchmark_training --platform --raport-file benchmark.json --epochs 1 --prof 100`\n\nEach of these scripts will run 100 iterations and save results in the `benchmark.json` file.\n\n#### Inference performance benchmark\n\nTo benchmark inference, run:\n\n* FP32 (V100 GPUs only)\n\n`python ./launch.py --model efficientnet- --precision FP32 --mode benchmark_inference --platform DGX1V --raport-file benchmark.json --epochs 1 --prof 100`\n\n* TF32 (A100 GPUs only)\n\n`python ./launch.py --model efficientnet- --precision TF32 --mode benchmark_inference --platform DGXA100 --raport-file benchmark.json --epochs 1 --prof 100`\n\n* AMP\n\n`python ./launch.py --model efficientnet- --precision AMP --mode benchmark_inference --platform --raport-file benchmark.json --epochs 1 --prof 100`\n\nEach of these scripts will run 100 iterations and save results in the `benchmark.json` file.\n\n### Results\n\nOur results were obtained by running the applicable training script in the pytorch-21.03 NGC container.\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the applicable `efficientnet/training//*.sh` training script in the PyTorch 20.12 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs.\n\n| **Model** | **Epochs** | **GPUs** | **Top1 accuracy - TF32** | **Top1 accuracy - mixed precision** | **Time to train - TF32** | **Time to train - mixed precision** | **Time to train speedup (TF32 to mixed precision)** |\n|:----------------------:|:----------:|:--------:|:------------------------:|:-----------------------------------:|:------------------------:|:-----------------------------------:|:---------------------------------------------------:|\n| efficientnet-b0 | 400 | 8 | 77.16 +/- 0.07 | 77.42 +/- 0.11 | 19 | 11 | 1.727 |\n| efficientnet-b4 | 400 | 8 | 82.82 +/- 0.04 | 82.85 +/- 0.09 | 126 | 66 | 1.909 |\n| efficientnet-widese-b0 | 400 | 8 | 77.84 +/- 0.08 | 77.84 +/- 0.02 | 19 | 10 | 1.900 |\n| efficientnet-widese-b4 | 400 | 8 | 83.13 +/- 0.11 | 83.1 +/- 0.09 | 126 | 66 | 1.909 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the applicable `efficientnet/training//*.sh` training script in the PyTorch 20.12 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.\n\n| **Model** | **Epochs** | **GPUs** | **Top1 accuracy - FP32** | **Top1 accuracy - mixed precision** | **Time to train - FP32** | **Time to train - mixed precision** | **Time to train speedup (FP32 to mixed precision)** |\n|:----------------------:|:----------:|:--------:|:------------------------:|:-----------------------------------:|:------------------------:|:-----------------------------------:|:---------------------------------------------------:|\n| efficientnet-b0 | 400 | 8 | 77.02 +/- 0.04 | 77.17 +/- 0.08 | 34 | 24 | 1.417 |\n| efficientnet-widese-b0 | 400 | 8 | 77.59 +/- 0.16 | 77.69 +/- 0.12 | 35 | 24 | 1.458 |\n\n##### Example plots\n\nThe following images show an A100 run.\n\n![ValidationLoss](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Classification/ConvNets/efficientnet//img/loss_plot.png)\n\n\n![ValidationTop1](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Classification/ConvNets/efficientnet//img/top1_plot.png)\n\n\n![ValidationTop5](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Classification/ConvNets/efficientnet//img/top5_plot.png)\n\n\n#### Training performance results\n\n##### Training performance: NVIDIA A100 (8x A100 80GB)\n\nOur results were obtained by running the applicable `efficientnet/training//*.sh` training script in the PyTorch 21.03 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs.\n\n| **Model** | **GPUs** | **TF32** | **Throughput - mixed precision** | **Throughput speedup (TF32 to mixed precision)** | **TF32 Strong Scaling** | **Mixed Precision Strong Scaling** |\n|:----------------------:|:--------:|:-----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|\n| efficientnet-b0 | 1 | 1078 img/s | 2489 img/s | 2.3 x | 1.0 x | 1.0 x |\n| efficientnet-b0 | 8 | 8193 img/s | 16652 img/s | 2.03 x | 7.59 x | 6.68 x |\n| efficientnet-b0 | 16 | 16137 img/s | 29332 img/s | 1.81 x | 14.96 x | 11.78 x |\n| efficientnet-b4 | 1 | 157 img/s | 331 img/s | 2.1 x | 1.0 x | 1.0 x |\n| efficientnet-b4 | 8 | 1223 img/s | 2570 img/s | 2.1 x | 7.76 x | 7.75 x |\n| efficientnet-b4 | 16 | 2417 img/s | 4813 img/s | 1.99 x | 15.34 x | 14.51 x |\n| efficientnet-b4 | 32 | 4813 img/s | 9425 img/s | 1.95 x | 30.55 x | 28.42 x |\n| efficientnet-b4 | 64 | 9146 img/s | 18900 img/s | 2.06 x | 58.05 x | 57.0 x |\n| efficientnet-widese-b0 | 1 | 1078 img/s | 2512 img/s | 2.32 x | 1.0 x | 1.0 x |\n| efficientnet-widese-b0 | 8 | 8244 img/s | 16368 img/s | 1.98 x | 7.64 x | 6.51 x |\n| efficientnet-widese-b0 | 16 | 16062 img/s | 29798 img/s | 1.85 x | 14.89 x | 11.86 x |\n| efficientnet-widese-b4 | 1 | 157 img/s | 331 img/s | 2.1 x | 1.0 x | 1.0 x |\n| efficientnet-widese-b4 | 8 | 1223 img/s | 2585 img/s | 2.11 x | 7.77 x | 7.8 x |\n| efficientnet-widese-b4 | 16 | 2399 img/s | 5041 img/s | 2.1 x | 15.24 x | 15.21 x |\n| efficientnet-widese-b4 | 32 | 4616 img/s | 9379 img/s | 2.03 x | 29.32 x | 28.3 x |\n| efficientnet-widese-b4 | 64 | 9140 img/s | 18516 img/s | 2.02 x | 58.07 x | 55.88 x |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the applicable `efficientnet/training//*.sh` training script in the PyTorch 21.03 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.\n\n| **Model** | **GPUs** | **FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **FP32 Strong Scaling** | **Mixed Precision Strong Scaling** |\n|:----------------------:|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|\n| efficientnet-b0 | 1 | 655 img/s | 1301 img/s | 1.98 x | 1.0 x | 1.0 x |\n| efficientnet-b0 | 8 | 4672 img/s | 7789 img/s | 1.66 x | 7.12 x | 5.98 x |\n| efficientnet-b4 | 1 | 83 img/s | 204 img/s | 2.46 x | 1.0 x | 1.0 x |\n| efficientnet-b4 | 8 | 616 img/s | 1366 img/s | 2.21 x | 7.41 x | 6.67 x |\n| efficientnet-widese-b0 | 1 | 655 img/s | 1299 img/s | 1.98 x | 1.0 x | 1.0 x |\n| efficientnet-widese-b0 | 8 | 4592 img/s | 7875 img/s | 1.71 x | 7.0 x | 6.05 x |\n| efficientnet-widese-b4 | 1 | 83 img/s | 204 img/s | 2.45 x | 1.0 x | 1.0 x |\n| efficientnet-widese-b4 | 8 | 612 img/s | 1356 img/s | 2.21 x | 7.34 x | 6.63 x |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 32GB)\n\nOur results were obtained by running the applicable `efficientnet/training//*.sh` training script in the PyTorch 21.03 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.\n\n| **Model** | **GPUs** | **FP32** | **Throughput - mixed precision** | **Throughput speedup (FP32 to mixed precision)** | **FP32 Strong Scaling** | **Mixed Precision Strong Scaling** |\n|:----------------------:|:--------:|:----------:|:--------------------------------:|:------------------------------------------------:|:-----------------------:|:----------------------------------:|\n| efficientnet-b0 | 1 | 646 img/s | 1401 img/s | 2.16 x | 1.0 x | 1.0 x |\n| efficientnet-b0 | 8 | 4937 img/s | 8615 img/s | 1.74 x | 7.63 x | 6.14 x |\n| efficientnet-b4 | 1 | 36 img/s | 89 img/s | 2.44 x | 1.0 x | 1.0 x |\n| efficientnet-b4 | 8 | 641 img/s | 1565 img/s | 2.44 x | 17.6 x | 17.57 x |\n| efficientnet-widese-b0 | 1 | 281 img/s | 603 img/s | 2.14 x | 1.0 x | 1.0 x |\n| efficientnet-widese-b0 | 8 | 4924 img/s | 8870 img/s | 1.8 x | 17.49 x | 14.7 x |\n| efficientnet-widese-b4 | 1 | 36 img/s | 89 img/s | 2.45 x | 1.0 x | 1.0 x |\n| efficientnet-widese-b4 | 8 | 639 img/s | 1556 img/s | 2.43 x | 17.61 x | 17.44 x |\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA A100 (1x A100 80GB)\n\nOur results were obtained by running the applicable `efficientnet/inference//*.sh` inference script in the PyTorch 21.03 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.\n\n###### TF32 Inference Latency\n\n| **Model** | **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:----------------------:|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| efficientnet-b0 | 1 | 130 img/s | 9.33 ms | 7.95 ms | 9.0 ms |\n| efficientnet-b0 | 2 | 262 img/s | 9.39 ms | 8.51 ms | 9.5 ms |\n| efficientnet-b0 | 4 | 503 img/s | 9.68 ms | 9.53 ms | 10.78 ms |\n| efficientnet-b0 | 8 | 1004 img/s | 9.85 ms | 9.89 ms | 11.49 ms |\n| efficientnet-b0 | 16 | 1880 img/s | 10.27 ms | 10.34 ms | 11.19 ms |\n| efficientnet-b0 | 32 | 3401 img/s | 11.46 ms | 12.51 ms | 14.39 ms |\n| efficientnet-b0 | 64 | 4656 img/s | 19.58 ms | 14.52 ms | 16.63 ms |\n| efficientnet-b0 | 128 | 5001 img/s | 31.03 ms | 25.72 ms | 28.34 ms |\n| efficientnet-b0 | 256 | 5154 img/s | 60.71 ms | 49.44 ms | 54.99 ms |\n| efficientnet-b4 | 1 | 69 img/s | 16.22 ms | 14.87 ms | 15.34 ms |\n| efficientnet-b4 | 2 | 133 img/s | 16.84 ms | 16.49 ms | 17.72 ms |\n| efficientnet-b4 | 4 | 259 img/s | 17.33 ms | 16.39 ms | 19.67 ms |\n| efficientnet-b4 | 8 | 491 img/s | 18.22 ms | 18.09 ms | 19.51 ms |\n| efficientnet-b4 | 16 | 606 img/s | 28.28 ms | 26.55 ms | 26.84 ms |\n| efficientnet-b4 | 32 | 651 img/s | 51.08 ms | 49.39 ms | 49.61 ms |\n| efficientnet-b4 | 64 | 684 img/s | 96.23 ms | 93.54 ms | 93.78 ms |\n| efficientnet-b4 | 128 | 700 img/s | 195.22 ms | 182.17 ms | 182.42 ms |\n| efficientnet-b4 | 256 | 702 img/s | 380.01 ms | 361.81 ms | 371.64 ms |\n| efficientnet-widese-b0 | 1 | 130 img/s | 9.49 ms | 8.76 ms | 9.68 ms |\n| efficientnet-widese-b0 | 2 | 265 img/s | 9.25 ms | 8.51 ms | 9.75 ms |\n| efficientnet-widese-b0 | 4 | 520 img/s | 9.42 ms | 8.67 ms | 9.97 ms |\n| efficientnet-widese-b0 | 8 | 996 img/s | 12.27 ms | 9.69 ms | 11.31 ms |\n| efficientnet-widese-b0 | 16 | 1916 img/s | 10.2 ms | 10.29 ms | 11.3 ms |\n| efficientnet-widese-b0 | 32 | 3293 img/s | 11.71 ms | 13.0 ms | 14.57 ms |\n| efficientnet-widese-b0 | 64 | 4639 img/s | 16.21 ms | 14.61 ms | 16.29 ms |\n| efficientnet-widese-b0 | 128 | 4997 img/s | 30.81 ms | 25.76 ms | 26.02 ms |\n| efficientnet-widese-b0 | 256 | 5166 img/s | 73.68 ms | 49.39 ms | 55.74 ms |\n| efficientnet-widese-b4 | 1 | 68 img/s | 16.41 ms | 15.14 ms | 16.59 ms |\n| efficientnet-widese-b4 | 2 | 135 img/s | 16.65 ms | 15.52 ms | 17.93 ms |\n| efficientnet-widese-b4 | 4 | 251 img/s | 17.74 ms | 17.29 ms | 20.47 ms |\n| efficientnet-widese-b4 | 8 | 501 img/s | 17.75 ms | 17.12 ms | 18.01 ms |\n| efficientnet-widese-b4 | 16 | 590 img/s | 28.94 ms | 27.29 ms | 27.81 ms |\n| efficientnet-widese-b4 | 32 | 651 img/s | 50.96 ms | 49.34 ms | 49.55 ms |\n| efficientnet-widese-b4 | 64 | 683 img/s | 99.28 ms | 93.65 ms | 93.88 ms |\n| efficientnet-widese-b4 | 128 | 700 img/s | 189.81 ms | 182.3 ms | 182.58 ms |\n| efficientnet-widese-b4 | 256 | 702 img/s | 379.36 ms | 361.84 ms | 366.05 ms |\n\n###### Mixed Precision Inference Latency\n\n| **Model** | **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:----------------------:|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| efficientnet-b0 | 1 | 105 img/s | 11.21 ms | 9.9 ms | 12.55 ms |\n| efficientnet-b0 | 2 | 214 img/s | 11.01 ms | 10.06 ms | 11.89 ms |\n| efficientnet-b0 | 4 | 412 img/s | 11.45 ms | 11.73 ms | 13.0 ms |\n| efficientnet-b0 | 8 | 803 img/s | 11.78 ms | 11.59 ms | 14.2 ms |\n| efficientnet-b0 | 16 | 1584 img/s | 11.89 ms | 11.9 ms | 13.63 ms |\n| efficientnet-b0 | 32 | 2915 img/s | 13.03 ms | 14.79 ms | 17.35 ms |\n| efficientnet-b0 | 64 | 6315 img/s | 12.71 ms | 13.59 ms | 15.27 ms |\n| efficientnet-b0 | 128 | 9311 img/s | 18.78 ms | 15.34 ms | 17.99 ms |\n| efficientnet-b0 | 256 | 10239 img/s | 39.05 ms | 24.97 ms | 29.24 ms |\n| efficientnet-b4 | 1 | 53 img/s | 20.45 ms | 19.06 ms | 20.36 ms |\n| efficientnet-b4 | 2 | 109 img/s | 20.01 ms | 19.74 ms | 21.5 ms |\n| efficientnet-b4 | 4 | 212 img/s | 20.6 ms | 19.88 ms | 22.37 ms |\n| efficientnet-b4 | 8 | 416 img/s | 21.02 ms | 21.46 ms | 24.82 ms |\n| efficientnet-b4 | 16 | 816 img/s | 21.53 ms | 22.91 ms | 26.06 ms |\n| efficientnet-b4 | 32 | 1208 img/s | 28.4 ms | 26.77 ms | 28.3 ms |\n| efficientnet-b4 | 64 | 1332 img/s | 50.55 ms | 48.23 ms | 48.49 ms |\n| efficientnet-b4 | 128 | 1418 img/s | 95.84 ms | 90.12 ms | 95.76 ms |\n| efficientnet-b4 | 256 | 1442 img/s | 191.48 ms | 176.19 ms | 189.04 ms |\n| efficientnet-widese-b0 | 1 | 104 img/s | 11.28 ms | 10.0 ms | 12.72 ms |\n| efficientnet-widese-b0 | 2 | 206 img/s | 11.41 ms | 10.65 ms | 12.72 ms |\n| efficientnet-widese-b0 | 4 | 426 img/s | 11.15 ms | 10.23 ms | 11.03 ms |\n| efficientnet-widese-b0 | 8 | 794 img/s | 11.9 ms | 12.68 ms | 14.17 ms |\n| efficientnet-widese-b0 | 16 | 1536 img/s | 12.32 ms | 13.22 ms | 14.57 ms |\n| efficientnet-widese-b0 | 32 | 2876 img/s | 14.12 ms | 14.45 ms | 16.23 ms |\n| efficientnet-widese-b0 | 64 | 6183 img/s | 13.02 ms | 14.19 ms | 16.68 ms |\n| efficientnet-widese-b0 | 128 | 9310 img/s | 20.06 ms | 15.24 ms | 17.84 ms |\n| efficientnet-widese-b0 | 256 | 10193 img/s | 36.07 ms | 25.13 ms | 34.22 ms |\n| efficientnet-widese-b4 | 1 | 53 img/s | 20.24 ms | 19.05 ms | 19.91 ms |\n| efficientnet-widese-b4 | 2 | 109 img/s | 20.98 ms | 19.24 ms | 22.58 ms |\n| efficientnet-widese-b4 | 4 | 213 img/s | 20.48 ms | 20.48 ms | 23.64 ms |\n| efficientnet-widese-b4 | 8 | 425 img/s | 20.57 ms | 20.26 ms | 22.44 ms |\n| efficientnet-widese-b4 | 16 | 800 img/s | 21.93 ms | 23.15 ms | 26.51 ms |\n| efficientnet-widese-b4 | 32 | 1201 img/s | 28.51 ms | 26.89 ms | 28.13 ms |\n| efficientnet-widese-b4 | 64 | 1322 img/s | 50.96 ms | 48.58 ms | 48.77 ms |\n| efficientnet-widese-b4 | 128 | 1417 img/s | 96.45 ms | 90.17 ms | 90.43 ms |\n| efficientnet-widese-b4 | 256 | 1439 img/s | 190.06 ms | 176.59 ms | 188.51 ms |\n\n##### Inference performance: NVIDIA V100 (1x V100 16GB)\n\nOur results were obtained by running the applicable `efficientnet/inference//*.sh` inference script in the PyTorch 21.03 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.\n\n###### FP32 Inference Latency\n\n| **Model** | **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:----------------------:|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| efficientnet-b0 | 1 | 83 img/s | 13.15 ms | 13.23 ms | 14.11 ms |\n| efficientnet-b0 | 2 | 167 img/s | 13.17 ms | 13.46 ms | 14.39 ms |\n| efficientnet-b0 | 4 | 332 img/s | 13.25 ms | 13.29 ms | 14.85 ms |\n| efficientnet-b0 | 8 | 657 img/s | 13.42 ms | 13.86 ms | 15.77 ms |\n| efficientnet-b0 | 16 | 1289 img/s | 13.78 ms | 15.02 ms | 16.99 ms |\n| efficientnet-b0 | 32 | 2140 img/s | 16.46 ms | 18.92 ms | 22.2 ms |\n| efficientnet-b0 | 64 | 2743 img/s | 25.14 ms | 23.44 ms | 23.79 ms |\n| efficientnet-b0 | 128 | 2908 img/s | 48.03 ms | 43.98 ms | 45.36 ms |\n| efficientnet-b0 | 256 | 2968 img/s | 94.86 ms | 85.62 ms | 91.01 ms |\n| efficientnet-b4 | 1 | 45 img/s | 23.31 ms | 23.3 ms | 24.9 ms |\n| efficientnet-b4 | 2 | 87 img/s | 24.07 ms | 23.81 ms | 25.14 ms |\n| efficientnet-b4 | 4 | 160 img/s | 26.29 ms | 26.78 ms | 30.85 ms |\n| efficientnet-b4 | 8 | 316 img/s | 26.65 ms | 26.44 ms | 28.61 ms |\n| efficientnet-b4 | 16 | 341 img/s | 48.18 ms | 46.9 ms | 47.13 ms |\n| efficientnet-b4 | 32 | 365 img/s | 89.07 ms | 87.83 ms | 88.02 ms |\n| efficientnet-b4 | 64 | 374 img/s | 173.2 ms | 171.61 ms | 172.27 ms |\n| efficientnet-b4 | 128 | 376 img/s | 346.32 ms | 339.74 ms | 340.37 ms |\n| efficientnet-widese-b0 | 1 | 82 img/s | 13.37 ms | 12.95 ms | 13.89 ms |\n| efficientnet-widese-b0 | 2 | 168 img/s | 13.11 ms | 12.45 ms | 13.94 ms |\n| efficientnet-widese-b0 | 4 | 346 img/s | 12.73 ms | 12.22 ms | 12.95 ms |\n| efficientnet-widese-b0 | 8 | 674 img/s | 13.07 ms | 12.75 ms | 14.93 ms |\n| efficientnet-widese-b0 | 16 | 1235 img/s | 14.3 ms | 15.05 ms | 16.53 ms |\n| efficientnet-widese-b0 | 32 | 2194 img/s | 15.99 ms | 17.37 ms | 19.01 ms |\n| efficientnet-widese-b0 | 64 | 2747 img/s | 25.05 ms | 23.38 ms | 23.71 ms |\n| efficientnet-widese-b0 | 128 | 2906 img/s | 48.05 ms | 44.0 ms | 44.59 ms |\n| efficientnet-widese-b0 | 256 | 2962 img/s | 95.14 ms | 85.86 ms | 86.25 ms |\n| efficientnet-widese-b4 | 1 | 43 img/s | 24.28 ms | 25.24 ms | 27.36 ms |\n| efficientnet-widese-b4 | 2 | 87 img/s | 24.04 ms | 24.38 ms | 26.01 ms |\n| efficientnet-widese-b4 | 4 | 169 img/s | 24.96 ms | 25.8 ms | 27.14 ms |\n| efficientnet-widese-b4 | 8 | 307 img/s | 27.39 ms | 28.4 ms | 30.7 ms |\n| efficientnet-widese-b4 | 16 | 342 img/s | 48.05 ms | 46.74 ms | 46.9 ms |\n| efficientnet-widese-b4 | 32 | 363 img/s | 89.44 ms | 88.23 ms | 88.39 ms |\n| efficientnet-widese-b4 | 64 | 373 img/s | 173.47 ms | 172.01 ms | 172.36 ms |\n| efficientnet-widese-b4 | 128 | 376 img/s | 347.18 ms | 340.09 ms | 340.45 ms |\n\n###### Mixed Precision Inference Latency\n\n| **Model** | **Batch Size** | **Throughput Avg** | **Latency Avg** | **Latency 95%** | **Latency 99%** |\n|:----------------------:|:--------------:|:------------------:|:---------------:|:---------------:|:---------------:|\n| efficientnet-b0 | 1 | 62 img/s | 17.19 ms | 18.01 ms | 18.63 ms |\n| efficientnet-b0 | 2 | 119 img/s | 17.96 ms | 18.3 ms | 19.95 ms |\n| efficientnet-b0 | 4 | 238 img/s | 17.9 ms | 17.8 ms | 19.13 ms |\n| efficientnet-b0 | 8 | 495 img/s | 17.38 ms | 18.34 ms | 19.29 ms |\n| efficientnet-b0 | 16 | 945 img/s | 18.23 ms | 19.42 ms | 21.58 ms |\n| efficientnet-b0 | 32 | 1784 img/s | 19.29 ms | 20.71 ms | 22.51 ms |\n| efficientnet-b0 | 64 | 3480 img/s | 20.34 ms | 22.22 ms | 24.62 ms |\n| efficientnet-b0 | 128 | 5759 img/s | 26.11 ms | 22.61 ms | 24.06 ms |\n| efficientnet-b0 | 256 | 6176 img/s | 49.36 ms | 41.18 ms | 43.5 ms |\n| efficientnet-b4 | 1 | 34 img/s | 30.28 ms | 30.2 ms | 32.24 ms |\n| efficientnet-b4 | 2 | 69 img/s | 30.12 ms | 30.02 ms | 31.92 ms |\n| efficientnet-b4 | 4 | 129 img/s | 32.08 ms | 33.29 ms | 34.74 ms |\n| efficientnet-b4 | 8 | 242 img/s | 34.43 ms | 37.34 ms | 41.08 ms |\n| efficientnet-b4 | 16 | 488 img/s | 34.12 ms | 36.13 ms | 39.39 ms |\n| efficientnet-b4 | 32 | 738 img/s | 44.67 ms | 44.85 ms | 47.86 ms |\n| efficientnet-b4 | 64 | 809 img/s | 80.93 ms | 79.19 ms | 79.42 ms |\n| efficientnet-b4 | 128 | 843 img/s | 156.42 ms | 152.17 ms | 152.76 ms |\n| efficientnet-b4 | 256 | 847 img/s | 311.03 ms | 301.44 ms | 302.48 ms |\n| efficientnet-widese-b0 | 1 | 64 img/s | 16.71 ms | 17.59 ms | 19.23 ms |\n| efficientnet-widese-b0 | 2 | 129 img/s | 16.63 ms | 16.1 ms | 17.34 ms |\n| efficientnet-widese-b0 | 4 | 238 img/s | 17.92 ms | 17.52 ms | 18.82 ms |\n| efficientnet-widese-b0 | 8 | 445 img/s | 19.24 ms | 19.53 ms | 20.4 ms |\n| efficientnet-widese-b0 | 16 | 936 img/s | 18.64 ms | 19.55 ms | 21.1 ms |\n| efficientnet-widese-b0 | 32 | 1818 img/s | 18.97 ms | 20.62 ms | 23.06 ms |\n| efficientnet-widese-b0 | 64 | 3572 img/s | 19.81 ms | 21.14 ms | 23.29 ms |\n| efficientnet-widese-b0 | 128 | 5748 img/s | 26.18 ms | 23.72 ms | 26.1 ms |\n| efficientnet-widese-b0 | 256 | 6187 img/s | 49.11 ms | 41.11 ms | 41.59 ms |\n| efficientnet-widese-b4 | 1 | 32 img/s | 32.1 ms | 31.6 ms | 34.69 ms |\n| efficientnet-widese-b4 | 2 | 68 img/s | 30.4 ms | 30.9 ms | 32.67 ms |\n| efficientnet-widese-b4 | 4 | 123 img/s | 33.81 ms | 39.0 ms | 40.76 ms |\n| efficientnet-widese-b4 | 8 | 257 img/s | 32.34 ms | 33.39 ms | 34.93 ms |\n| efficientnet-widese-b4 | 16 | 497 img/s | 33.51 ms | 34.92 ms | 37.24 ms |\n| efficientnet-widese-b4 | 32 | 739 img/s | 44.63 ms | 43.62 ms | 46.39 ms |\n| efficientnet-widese-b4 | 64 | 808 img/s | 81.08 ms | 79.43 ms | 79.59 ms |\n| efficientnet-widese-b4 | 128 | 840 img/s | 157.11 ms | 152.87 ms | 153.26 ms |\n| efficientnet-widese-b4 | 256 | 846 img/s | 310.73 ms | 301.68 ms | 302.9 ms |\n\n#### Quantization results\n\n##### QAT Training performance: NVIDIA DGX-1 (8x V100 32GB)\n\n| **Model** | **GPUs** | **Calibration** | **QAT model** | **FP32** | **QAT ratio** |\n|:---------------------:|:---------|:---------------:|:---------------:|:----------:|:-------------:|\n| efficientnet-quant-b0 | 8 | 14.71 img/s | 2644.62 img/s | 3798 img/s | 0.696 x |\n| efficientnet-quant-b4 | 8 | 1.85 img/s | 310.41 img/s | 666 img/s | 0.466 x |\n\n###### Quant Inference accuracy\nThe best checkpoints generated during training were used as a base for the QAT.\n\n| **Model** | **QAT Epochs** | **QAT Top1** | **Gap between FP32 Top1 and QAT Top1** |\n|:---------------------:|:--------------:|:------------:|:--------------------------------------:|\n| efficientnet-quant-b0 | 10 | 77.12 | 0.51 |\n| efficientnet-quant-b4 | 2 | 82.54 | 0.44 |","hasPerformance","policies",[],{"_175":180,"_176":181,"_146":-5,"_154":26,"_156":26,"_170":179,"_157":65},{"_175":178,"_176":178,"_146":-5,"_154":26,"_156":26,"_170":179,"_157":65},{"_175":14,"_176":14,"_146":-5,"_154":26,"_156":26,"_170":177,"_157":65},"id","name",[],"21.03.1",[],"-","Latest (21.03.1)","nvidia/dle/efficientnet_pyt","key","nvidia/dle/resources/efficientnet_pyt","resourceId","efficientnet_pyt","shortDescription","EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster.","This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/convnets-pyt)\n\n\nEfficientNet is an image classification model family. It was first described in [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946). The scripts provided enable you to train the EfficientNet-B0, EfficientNet-B4, EfficientNet-WideSE-B0 and, EfficientNet-WideSE-B4 models.\n\nEfficientNet-WideSE models use Squeeze-and-Excitation layers wider than original EfficientNet models, the width of SE module is proportional to the width of Depthwise Separable Convolutions instead of block width.\n\nWideSE models are slightly more accurate than original models.\n\nThis model is trained with mixed precision using Tensor Cores on Volta and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\nWe use [NHWC data layout](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) when training using Mixed Precision.\n\n### Default configuration\n\nThe following sections highlight the default configurations for the EfficientNet models.\n\n**Optimizer**\n\nThis model uses RMSprop with the following hyperparameters:\n* Momentum (0.9)\n* Learning rate (LR):\n * 0.08 for 4096 batch size for B0 models\n * 0.16 for 4096 batch size for B4 models\nscale the learning rate.\n* Learning rate schedule - we use cosine LR schedule\n* We use linear warmup of the learning rate during the first 16 epochs\n* Weight decay (WD):\n * 1e-5 for B0 models\n * 5e-6 for B4 models\n* We do not apply WD on Batch Norm trainable parameters (gamma/bias)\n* Label smoothing = 0.1\n* [MixUp](https://arxiv.org/pdf/1710.09412.pdf) = 0.2\n* We train for 400 epochs\n\n**Optimizer for QAT**\n\nThis model uses SGD optimizer for B0 models and RMSPROP optimizer alpha=0.853 epsilon=0.00422 for B4 models. Other hyperparameters we used are:\n* Momentum:\n * 0.89 for B0 models\n * 0.9 for B4 models\n* Learning rate (LR):\n * 0.0125 for 128 batch size for B0 models\n * 4.09e-06 for 32 batch size for B4 models\nscale the learning rate.\n* Learning rate schedule: \n * cosine LR schedule for B0 models\n * linear LR schedule for B4 models\n* Weight decay (WD):\n * 4.50e-05 for B0 models\n * 9.714e-04 for B4 models\n* We do not apply WD on Batch Norm trainable parameters (gamma/bias)\n* We train for:\n\t*10 epochs for B0 models\n\t*2 epochs for B4 models\n\n**Data augmentation**\n\nThis model uses the following data augmentation:\n* For training:\n * Auto-augmentation\n * Basic augmentation:\n * Normalization\n * Random resized crop to target images size (depending on model version)\n * Scale from 8% to 100%\n * Aspect ratio from 3/4 to 4/3\n * Random horizontal flip\n\n* For inference:\n * Normalization\n * Scale to target image size + 32\n * Center crop to target image size\n\n### Feature support matrix\n\nThe following features are supported by this model:\n\n| Feature | EfficientNet\n|-----------------------|--------------------------\n|[DALI](https://docs.nvidia.com/deeplearning/dali/release-notes/index.html) | Yes (without autoaugmentation)\n|[APEX AMP](https://nvidia.github.io/apex/amp.html) | Yes\n|[QAT](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization) | Yes\n\n#### Features\n\n**NVIDIA DALI**\n\nDALI is a library accelerating data preparation pipeline. To accelerate your input pipeline, you only need to define your data loader\nwith the DALI library. For more information about DALI, refer to the [DALI product documentation](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html).\n\nWe use [NVIDIA DALI](https://github.com/NVIDIA/DALI),\nwhich speeds up data loading when CPU becomes a bottleneck.\nDALI can use CPU or GPU, and outperforms the PyTorch native dataloader.\n\nRun training with `--data-backends dali-gpu` or `--data-backends dali-cpu` to enable DALI.\nFor DGXA100 and DGX1 we recommend `--data-backends dali-cpu`.\n\nDALI currently does not support Autoaugmentation, so for best accuracy it has to be disabled.\n\n**[APEX](https://github.com/NVIDIA/apex)**\n\nA PyTorch extension that contains utility libraries, such as [Automatic Mixed Precision (AMP)](https://nvidia.github.io/apex/amp.html), which require minimal network code changes to leverage Tensor Cores performance. Refer to the [Enabling mixed precision](#enabling-mixed-precision) section for more details.\n\n**[QAT](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization)**\n\nQuantization aware training (QAT) is a method for changing precision to INT8 which speeds up the inference process at the price of a slight decrease of network accuracy. Refer to the [Quantization](#quantization) section for more details.\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Adding loss scaling to preserve small gradient values.\n\nThe ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.\n\nFor information about:\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).\n\n#### Enabling mixed precision\n\nMixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP), a library from [APEX](https://github.com/NVIDIA/apex) that casts variables to half-precision upon retrieval,\nwhile storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients.\nIn PyTorch, loss scaling can be easily applied by using `scale_loss()` method provided by AMP. The scaling value to be used can be [dynamic](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.DynamicLossScaler) or fixed.\n\nFor an in-depth walk through on AMP, check out sample usage [here](https://github.com/NVIDIA/apex/tree/master/apex/amp#usage-and-getting-started). [APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains utility libraries, such as AMP, which require minimal network code changes to leverage Tensor Cores performance.\n\nTo enable mixed precision, you can:\n- Import AMP from APEX:\n\n ```python\n from apex import amp\n ```\n\n- Wrap model and optimizer in `amp.initialize`:\n ```python\n model, optimizer = amp.initialize(model, optimizer, opt_level=\"O1\", loss_scale=\"dynamic\")\n ```\n\n- Scale loss before backpropagation:\n ```python\n with amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n ```\n\n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) 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. \n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.\n\n### Quantization\n\nQuantization is the process of transforming deep learning models to use parameters and computations at a lower precision. Traditionally, DNN training and inference have relied on the IEEE single-precision floating-point format, using 32 bits to represent the floating-point model weights and activation tensors.\n\nThis compute budget may be acceptable at training as most DNNs are trained in data centers or in the cloud with NVIDIA V100 or A100 GPUs that have significantly large compute capability and much larger power budgets. However, during deployment, these models are most often required to run on devices with much smaller computing resources and lower power budgets at the edge. Running a DNN inference using the full 32-bit representation is not practical for real-time analysis given the compute, memory, and power constraints of the edge.\n\nTo help reduce the compute budget, while not compromising on the structure and number of parameters in the model, you can run inference at a lower precision. Initially, quantized inferences were run at half-point precision with tensors and weights represented as 16-bit floating-point numbers. While this resulted in compute savings of about 1.2–1.5x, there was still some compute budget and memory bandwidth that could be leveraged. In lieu of this, models are now quantized to an even lower precision, with an 8-bit integer representation for weights and tensors. This results in a model that is 4x smaller in memory and about 2–4x faster in throughput.\n\nWhile 8-bit quantization is appealing to save compute and memory budgets, it is a lossy process. During quantization, a small range of floating-point numbers are squeezed to a fixed number of information buckets. This results in loss of information.\n\nThe minute differences which could originally be resolved using 32-bit representations are now lost because they are quantized to the same bucket in 8-bit representations. This is similar to rounding errors that one encounters when representing fractional numbers as integers. To maintain accuracy during inferences at a lower precision, it is important to try and mitigate errors arising due to this loss of information.\n\n#### Quantization-aware training\n\nIn QAT, the quantization error is considered when training the model. The training graph is modified to simulate the lower precision behavior in the forward pass of the training process. This introduces the quantization errors as part of the training loss, which the optimizer tries to minimize during the training. Thus, QAT helps in modeling the quantization errors during training and mitigates its effects on the accuracy of the model at deployment.\n\nHowever, the process of modifying the training graph to simulate lower precision behavior is intricate. To run QAT, it is necessary to insert FakeQuantization nodes for the weights of the DNN Layers and Quantize-Dequantize (QDQ) nodes to the intermediate activation tensors to compute their dynamic ranges.\n\nFor more information, see this [Quantization paper](https://arxiv.org/abs/2004.09602) and [Quantization-Aware Training](https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#quantization-training) documentation.\nTutorial for `pytoch-quantization` library can be found here [`pytorch-quantization` tutorial](https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/tutorials/quant_resnet50.html).\n\nIt is important to mention that EfficientNet is NN, which is hard to quantize because the activation function all across the network is the SiLU (called also the Swish), whose negative values lie in very short range, which introduce a large quantization error. More details can be found in Appendix D of the [Quantization paper](https://arxiv.org/abs/2004.09602).","displayName","EfficientNet for PyTorch",["D",1667524751502],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/efficientnet_pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_86":1399,"_88":1400},"ngcApps",[1352],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1333":1334,"_1335":1336,"_1337":1338,"_1339":1340,"_1341":233,"_117":257,"_1342":1343,"_1344":50,"_1345":65,"_1346":65,"_1347":1348,"_1349":65,"_1350":1351},"amplitude",{"_1330":1331,"_931":-7,"_1332":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1324":1325,"_1326":1327,"_1328":1329},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1206":338,"_1207":1208,"_1209":1210,"_1211":1212},"announcements",[],"consentState",{"_295":1196,"_1197":65,"_1198":26,"_1199":26,"_1200":1201},"consentConfig",{"_277":278,"_279":280,"_281":282,"_283":284,"_285":286,"_287":288,"_289":290,"_291":292,"_293":294,"_295":296},"showContactAdmin",1784067404212,[],[],[271,272,273,274,275,276],{"_39":53,"_41":62,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":60,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":58,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":81,"_45":-5,"_47":-5,"_49":50},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

Under certain privacy laws, you have the right to direct us not to \"sell\" or \"share\" your personal information for targeted advertising. To opt-out of the \"sale\" and \"sharing\" of personal information through cookies, you must opt-out of optional cookies using the toggles below. To opt out of the \"sale\" and \"sharing\" of data collected by other means (e.g., online forms) you must also update your data sharing preferences through the NVIDIA Preference Center.

Click on the different category headings below to find out more and change the settings according to your preference. You cannot opt out of Required Cookies as they are deployed to ensure the proper functioning of our website (such as prompting the cookie banner and remembering your settings, etc.). By clicking \"Save and Accept\" or \"Decline All\" at the bottom, you consent to the use of cookies and other tools as described in our Cookie Policy in accordance with your settings and accept our Terms of Service (which contains important waivers). For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1188":1189,"_1190":1191,"_1192":1193,"_1194":1195},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[297,298,299,300,301],{"_302":840,"_176":841,"_144":842,"_306":65,"_307":153,"_309":843,"_311":844},{"_302":630,"_176":631,"_144":632,"_306":26,"_307":48,"_309":633,"_311":634},{"_302":529,"_176":530,"_144":531,"_306":26,"_307":532,"_309":533,"_311":534},{"_302":313,"_176":314,"_144":315,"_306":26,"_307":316,"_309":317,"_311":318},{"_302":303,"_176":304,"_144":305,"_306":26,"_307":308,"_309":310,"_311":312},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481],[319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335],{"_336":443,"_190":443,"_144":338,"_309":444},{"_336":438,"_190":438,"_144":338,"_309":439},{"_336":434,"_190":434,"_144":338,"_309":435},{"_336":429,"_190":429,"_144":338,"_309":430},{"_336":424,"_190":424,"_144":338,"_309":425},{"_336":412,"_190":412,"_144":338,"_309":413},{"_336":400,"_190":400,"_144":338,"_309":401},{"_336":395,"_190":395,"_144":338,"_309":396},{"_336":384,"_190":384,"_144":338,"_309":385},{"_336":379,"_190":379,"_144":338,"_309":380},{"_336":374,"_190":374,"_144":338,"_309":375},{"_336":369,"_190":369,"_144":338,"_309":370},{"_336":364,"_190":364,"_144":338,"_309":365},{"_336":359,"_190":359,"_144":338,"_309":360},{"_336":354,"_190":354,"_144":338,"_309":355},{"_336":347,"_190":347,"_144":338,"_309":348},{"_336":337,"_190":337,"_144":338,"_309":339},"hostName","amazon-adsystem.com","",[340,341],{"_176":346,"_343":337,"_344":26,"_144":345},{"_176":342,"_343":337,"_344":26,"_144":345},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[349,350],{"_176":353,"_343":347,"_344":26,"_144":352},{"_176":351,"_343":347,"_344":26,"_144":352},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[356],{"_176":357,"_343":354,"_344":26,"_144":358},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[361],{"_176":362,"_343":359,"_344":26,"_144":363},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[366],{"_176":367,"_343":364,"_344":26,"_144":368},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[371],{"_176":372,"_343":369,"_344":26,"_144":373},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[376],{"_176":377,"_343":374,"_344":26,"_144":378},"sp","Testing","google.com",[381],{"_176":382,"_343":379,"_344":26,"_144":383},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[386,387,388,389],{"_176":394,"_343":384,"_344":26,"_144":391},{"_176":393,"_343":384,"_344":26,"_144":391},{"_176":392,"_343":384,"_344":26,"_144":391},{"_176":390,"_343":384,"_344":26,"_144":391},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[397],{"_176":398,"_343":395,"_344":26,"_144":399},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[402,403,404,405],{"_176":411,"_343":400,"_344":26,"_144":407},{"_176":409,"_343":400,"_344":26,"_144":410},{"_176":408,"_343":400,"_344":26,"_144":407},{"_176":406,"_343":400,"_344":26,"_144":407},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[414,415,416,417],{"_176":423,"_343":412,"_344":26,"_144":421},{"_176":422,"_343":412,"_344":26,"_144":421},{"_176":420,"_343":412,"_344":26,"_144":421},{"_176":418,"_343":412,"_344":26,"_144":419},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[426],{"_176":427,"_343":424,"_344":26,"_144":428},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[431],{"_176":432,"_343":429,"_344":26,"_144":433},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[436],{"_176":437,"_343":434,"_344":26,"_144":378},"R","6sc.co",[440],{"_176":441,"_343":438,"_344":26,"_144":442},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. It is primarily used for tracking and targeting in marketing automation and advertising.","hushly.com",[445],{"_176":446,"_343":443,"_344":26,"_144":378},"_hly_gvid",{"_176":528,"_343":483,"_344":26,"_144":502},{"_176":526,"_343":483,"_344":26,"_144":527},{"_176":390,"_343":487,"_344":26,"_144":516},{"_176":524,"_343":483,"_344":26,"_144":525},{"_176":393,"_343":487,"_344":26,"_144":490},{"_176":522,"_343":483,"_344":26,"_144":523},{"_176":367,"_343":483,"_344":26,"_144":521},{"_176":499,"_343":520,"_344":26,"_144":500},{"_176":392,"_343":487,"_344":26,"_144":519},{"_176":392,"_343":489,"_344":26,"_144":519},{"_176":394,"_343":487,"_344":26,"_144":503},{"_176":518,"_343":483,"_344":26,"_144":502},{"_176":497,"_343":517,"_344":26,"_144":338},{"_176":390,"_343":489,"_344":26,"_144":516},{"_176":515,"_343":483,"_344":26,"_144":338},{"_176":497,"_343":487,"_344":26,"_144":338},{"_176":513,"_343":483,"_344":26,"_144":514},{"_176":511,"_343":483,"_344":26,"_144":512},{"_176":510,"_343":483,"_344":26,"_144":338},{"_176":508,"_343":483,"_344":26,"_144":509},{"_176":506,"_343":483,"_344":26,"_144":507},{"_176":505,"_343":483,"_344":26,"_144":338},{"_176":499,"_343":487,"_344":26,"_144":500},{"_176":499,"_343":504,"_344":26,"_144":500},{"_176":394,"_343":489,"_344":26,"_144":503},{"_176":501,"_343":483,"_344":26,"_144":502},{"_176":499,"_343":483,"_344":26,"_144":500},{"_176":497,"_343":489,"_344":26,"_144":498},{"_176":495,"_343":483,"_344":26,"_144":496},{"_176":493,"_343":483,"_344":26,"_144":494},{"_176":491,"_343":483,"_344":26,"_144":492},{"_176":393,"_343":489,"_344":26,"_144":490},{"_176":486,"_343":487,"_344":26,"_144":488},{"_176":485,"_343":483,"_344":26,"_144":338},{"_176":482,"_343":483,"_344":65,"_144":484},"AMCVS_xxxxxAdobeOrg","nvidia.com","This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_twpid","This cookie is likely used to track user identity by assigning a unique user ID when the user engages with the website. It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590],[535,536,537,538,539],{"_336":429,"_190":429,"_144":338,"_309":561},{"_336":395,"_190":395,"_144":338,"_309":556},{"_336":549,"_190":549,"_144":338,"_309":550},{"_336":545,"_190":545,"_144":338,"_309":546},{"_336":540,"_190":540,"_144":338,"_309":541},"nvcrm.my.site.com",[542],{"_176":543,"_343":540,"_344":26,"_144":544},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[547],{"_176":548,"_343":545,"_344":26,"_144":378},"edge","github.com",[551,552],{"_176":555,"_343":549,"_344":26,"_144":554},{"_176":553,"_343":549,"_344":26,"_144":554},"logged_in","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform","_octo",[557,558],{"_176":560,"_343":395,"_344":26,"_144":399},{"_176":559,"_343":395,"_344":26,"_144":399},"lidc","li_gc",[562,563,564,565],{"_176":569,"_343":429,"_344":26,"_144":433},{"_176":568,"_343":429,"_344":26,"_144":433},{"_176":567,"_343":429,"_344":26,"_144":433},{"_176":566,"_343":429,"_344":26,"_144":433},"imid_secure","imid_created_secure","imid","imid_created",{"_176":629,"_343":483,"_344":26,"_144":626},{"_176":627,"_343":483,"_344":26,"_144":628},{"_176":625,"_343":483,"_344":26,"_144":626},{"_176":623,"_343":483,"_344":65,"_144":624},{"_176":621,"_343":592,"_344":26,"_144":622},{"_176":620,"_343":601,"_344":65,"_144":338},{"_176":618,"_343":592,"_344":26,"_144":619},{"_176":616,"_343":483,"_344":26,"_144":617},{"_176":613,"_343":614,"_344":26,"_144":615},{"_176":612,"_343":487,"_344":26,"_144":338},{"_176":610,"_343":483,"_344":26,"_144":611},{"_176":607,"_343":608,"_344":26,"_144":609},{"_176":605,"_343":606,"_344":26,"_144":338},{"_176":604,"_343":592,"_344":26,"_144":338},{"_176":603,"_343":483,"_344":26,"_144":338},{"_176":602,"_343":487,"_344":26,"_144":338},{"_176":600,"_343":601,"_344":65,"_144":338},{"_176":598,"_343":592,"_344":26,"_144":599},{"_176":596,"_343":483,"_344":65,"_144":597},{"_176":594,"_343":483,"_344":26,"_144":595},{"_176":591,"_343":592,"_344":65,"_144":593},"nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_biz_kvpA","This cookie is likely related to business analytics and key-value pairs for storing user preferences, which may be used to enhance functionality and user experience on the website.","_gdxxxxxxxxxxxxx","This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758],[635,636,637,638,639,640],{"_336":661,"_190":661,"_144":338,"_309":662},{"_336":656,"_190":656,"_144":338,"_309":657},{"_336":652,"_190":652,"_144":338,"_309":653},{"_336":412,"_190":412,"_144":338,"_309":649},{"_336":549,"_190":549,"_144":338,"_309":646},{"_336":359,"_190":359,"_144":338,"_309":641},[642,643],{"_176":645,"_343":359,"_344":26,"_144":363},{"_176":644,"_343":359,"_344":26,"_144":363},"ncid","VisitorID",[647],{"_176":648,"_343":549,"_344":65,"_144":554},"_gh_sess",[650],{"_176":651,"_343":412,"_344":65,"_144":421},"YSC","t.contentsquare.net",[654],{"_176":655,"_343":652,"_344":65,"_144":507},"_cs_same_site","bizible.com",[658],{"_176":659,"_343":656,"_344":26,"_144":660},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[663],{"_176":664,"_343":661,"_344":26,"_144":665},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_176":838,"_343":483,"_344":26,"_144":839},{"_176":777,"_343":487,"_344":26,"_144":597},{"_176":811,"_343":592,"_344":65,"_144":812},{"_176":837,"_343":483,"_344":26,"_144":814},{"_176":836,"_343":483,"_344":26,"_144":814},{"_176":809,"_343":614,"_344":65,"_144":810},{"_176":762,"_343":487,"_344":26,"_144":338},{"_176":792,"_343":487,"_344":26,"_144":597},{"_176":777,"_343":592,"_344":26,"_144":597},{"_176":777,"_343":504,"_344":26,"_144":789},{"_176":779,"_343":487,"_344":26,"_144":611},{"_176":777,"_343":832,"_344":26,"_144":597},{"_176":765,"_343":504,"_344":26,"_144":597},{"_176":779,"_343":782,"_344":26,"_144":611},{"_176":834,"_343":483,"_344":26,"_144":835},{"_176":833,"_343":483,"_344":26,"_144":507},{"_176":792,"_343":832,"_344":26,"_144":597},{"_176":777,"_343":774,"_344":26,"_144":789},{"_176":765,"_343":832,"_344":26,"_144":597},{"_176":809,"_343":504,"_344":65,"_144":810},{"_176":817,"_343":592,"_344":26,"_144":338},{"_176":831,"_343":483,"_344":26,"_144":502},{"_176":765,"_343":592,"_344":26,"_144":597},{"_176":792,"_343":601,"_344":26,"_144":823},{"_176":829,"_343":483,"_344":26,"_144":830},{"_176":792,"_343":774,"_344":26,"_144":823},{"_176":792,"_343":801,"_344":26,"_144":597},{"_176":827,"_343":483,"_344":65,"_144":828},{"_176":792,"_343":504,"_344":26,"_144":597},{"_176":777,"_343":801,"_344":26,"_144":597},{"_176":809,"_343":487,"_344":65,"_144":810},{"_176":777,"_343":601,"_344":26,"_144":789},{"_176":773,"_343":592,"_344":26,"_144":338},{"_176":825,"_343":601,"_344":26,"_144":826},{"_176":809,"_343":801,"_344":65,"_144":810},{"_176":770,"_343":824,"_344":26,"_144":771},{"_176":792,"_343":489,"_344":26,"_144":823},{"_176":770,"_343":487,"_344":26,"_144":771},{"_176":821,"_343":483,"_344":65,"_144":822},{"_176":817,"_343":614,"_344":26,"_144":338},{"_176":820,"_343":592,"_344":65,"_144":791},{"_176":818,"_343":483,"_344":65,"_144":819},{"_176":792,"_343":782,"_344":26,"_144":597},{"_176":817,"_343":487,"_344":26,"_144":338},{"_176":815,"_343":483,"_344":26,"_144":816},{"_176":765,"_343":782,"_344":26,"_144":597},{"_176":813,"_343":483,"_344":26,"_144":814},{"_176":776,"_343":487,"_344":26,"_144":338},{"_176":811,"_343":483,"_344":65,"_144":812},{"_176":809,"_343":592,"_344":65,"_144":810},{"_176":807,"_343":483,"_344":26,"_144":808},{"_176":761,"_343":487,"_344":26,"_144":338},{"_176":787,"_343":483,"_344":26,"_144":788},{"_176":805,"_343":483,"_344":65,"_144":806},{"_176":802,"_343":803,"_344":26,"_144":804},{"_176":770,"_343":592,"_344":26,"_144":771},{"_176":765,"_343":801,"_344":26,"_144":597},{"_176":765,"_343":489,"_344":26,"_144":775},{"_176":792,"_343":778,"_344":26,"_144":597},{"_176":800,"_343":483,"_344":26,"_144":507},{"_176":765,"_343":778,"_344":26,"_144":597},{"_176":772,"_343":487,"_344":26,"_144":338},{"_176":798,"_343":483,"_344":26,"_144":799},{"_176":795,"_343":796,"_344":26,"_144":797},{"_176":793,"_343":483,"_344":65,"_144":794},{"_176":762,"_343":592,"_344":26,"_144":338},{"_176":792,"_343":592,"_344":26,"_144":597},{"_176":790,"_343":592,"_344":65,"_144":791},{"_176":765,"_343":601,"_344":26,"_144":775},{"_176":777,"_343":489,"_344":26,"_144":789},{"_176":787,"_343":614,"_344":65,"_144":788},{"_176":785,"_343":483,"_344":65,"_144":786},{"_176":783,"_343":483,"_344":26,"_144":784},{"_176":777,"_343":782,"_344":26,"_144":597},{"_176":780,"_343":483,"_344":26,"_144":781},{"_176":772,"_343":592,"_344":26,"_144":338},{"_176":779,"_343":483,"_344":26,"_144":611},{"_176":776,"_343":592,"_344":26,"_144":338},{"_176":777,"_343":778,"_344":26,"_144":597},{"_176":773,"_343":487,"_344":26,"_144":338},{"_176":776,"_343":614,"_344":26,"_144":338},{"_176":761,"_343":614,"_344":26,"_144":338},{"_176":765,"_343":774,"_344":26,"_144":775},{"_176":773,"_343":614,"_344":26,"_144":338},{"_176":772,"_343":614,"_344":26,"_144":338},{"_176":770,"_343":517,"_344":26,"_144":771},{"_176":768,"_343":483,"_344":26,"_144":769},{"_176":766,"_343":483,"_344":26,"_144":767},{"_176":765,"_343":487,"_344":26,"_144":597},{"_176":763,"_343":483,"_344":65,"_144":764},{"_176":762,"_343":614,"_344":26,"_144":338},{"_176":761,"_343":592,"_344":26,"_144":338},{"_176":759,"_343":483,"_344":26,"_144":760},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","__sec__token","__sec__cid","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","_gd_visitor","_biz_dfsA","This cookie is associated with CloudFlare, used to speed up page load times. It is not used to track users but rather to limit the number of requests that have to be made to doubleclick.net.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","__sec_tid","__sec__fid","crowdsource.nvidia.com","This cookie name is associated with 6sense which is used to collect visitor data related to the user’s visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","__sec__ghost","_gd_svisitor","info.nvidia.com","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","nv.referrerHref","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","__sec_crid","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","s_tp","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","This _gd_session cookie is used to collect visitor data related to the user's visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","catalog.ngc.nvidia.com","ekfls-2025-03-27","This cookie name is associated with readme.io which is used to help maintain data for the API reference support page.","s_sq","Adobe Site Catalyst cookie, stores information about the previous link clicked within the site","s_getNewRepeat","This cookie is used to determine whether a visitor is a new visitor or a repeat visitor to the site, and is used for analytical purposes.","AMP_92dea1e784","nvidianews.nvidia.com","_cs_s","_hly_sid","This cookie is likely associated with website analytics, as the prefix '_hly_' suggests a connection to Holistics, a business intelligence and analytics platform.","nv.buttonReferrer","nv.referrerId","_hjSession_xxxxxx","A cookie that holds the current session data. This ensues that subsequent requests within the session window will be attributed to the same Hotjar session.","C0001","Required Cookies","These cookies enable core functionality such as security, network management, and accessibility. These cookies are required for the site to function and cannot be turned off.",[993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081],[845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869],{"_336":988,"_190":988,"_144":338,"_309":989},{"_336":982,"_190":982,"_144":338,"_309":983},{"_336":970,"_190":970,"_144":338,"_309":971},{"_336":967,"_190":967,"_144":338,"_309":968},{"_336":964,"_190":964,"_144":338,"_309":965},{"_336":959,"_190":959,"_144":338,"_309":960},{"_336":954,"_190":954,"_144":338,"_309":955},{"_336":412,"_190":412,"_144":338,"_309":951},{"_336":947,"_190":947,"_144":338,"_309":948},{"_336":400,"_190":400,"_144":338,"_309":945},{"_336":395,"_190":395,"_144":338,"_309":943},{"_336":938,"_190":938,"_144":338,"_309":939},{"_336":933,"_190":933,"_144":338,"_309":934},{"_336":928,"_190":928,"_144":338,"_309":929},{"_336":925,"_190":925,"_144":338,"_309":926},{"_336":369,"_190":369,"_144":338,"_309":923},{"_336":915,"_190":915,"_144":338,"_309":916},{"_336":545,"_190":545,"_144":338,"_309":909},{"_336":903,"_190":903,"_144":338,"_309":904},{"_336":899,"_190":899,"_144":338,"_309":900},{"_336":894,"_190":894,"_144":338,"_309":895},{"_336":890,"_190":890,"_144":338,"_309":891},{"_336":885,"_190":885,"_144":338,"_309":886},{"_336":540,"_190":540,"_144":338,"_309":878},{"_336":870,"_190":870,"_144":338,"_309":871},"pexels.com",[872,873],{"_176":876,"_343":870,"_344":26,"_144":877},{"_176":874,"_343":870,"_344":65,"_144":875},"_cfuvid","Pexels provides high quality and completely free stock photos licensed under the Pexels license. These photos are often used for personal and commercial purposes.","__cf_bm","This is a CloudFoundry cookie",[879,880],{"_176":883,"_343":540,"_344":26,"_144":884},{"_176":881,"_343":540,"_344":26,"_144":882},"CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","LSKey-c$CookieConsentPolicy","Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","www.recaptcha.net",[887],{"_176":888,"_343":885,"_344":26,"_144":889},"_GRECAPTCHA","This domain is used by Google reCAPTCHA, which provides spam protection services to websites by detecting if a user is a human or a bot.","api2.hcaptcha.com",[892],{"_176":893,"_343":890,"_344":26,"_144":378},"__cflb","instagram.com",[896],{"_176":897,"_343":894,"_344":26,"_144":898},"csrftoken","Instagram is a social networking service owned by Facebook, Inc. It's designed for sharing photos and videos from a smartphone. The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[901],{"_176":876,"_343":899,"_344":26,"_144":902},"The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.","nvidiacorp.us-5.evergage.com",[905,906],{"_176":908,"_343":903,"_344":26,"_144":378},{"_176":907,"_343":903,"_344":26,"_144":378},"AWSALBTG","AWSALBTGCORS",[910,911],{"_176":913,"_343":545,"_344":26,"_144":914},{"_176":912,"_343":545,"_344":26,"_144":378},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.","app.hushly.com",[917,918,919],{"_176":922,"_343":915,"_344":26,"_144":378},{"_176":921,"_343":915,"_344":65,"_144":378},{"_176":920,"_343":915,"_344":26,"_144":378},"AWSALBCORS","JSESSIONID","AWSALB",[924],{"_176":876,"_343":369,"_344":26,"_144":877},"hcaptcha.com",[927],{"_176":876,"_343":925,"_344":26,"_144":877},"tracking.intentsify.io",[930],{"_176":931,"_343":928,"_344":26,"_144":932},"userId","Used to track usage of the embedded video player.","static.reo.dev",[935],{"_176":936,"_343":933,"_344":26,"_144":937},"cookietest","Common cookie name could have a number of different origins. Where this is first party and a session cookie, its most likely to do with checking to see if the browser is set to block or allow cookies.","www.datadoghq-browser-agent.com",[940],{"_176":941,"_343":938,"_344":26,"_144":942},"dd_cookie_test_","dd_cookie_test",[944],{"_176":876,"_343":395,"_344":26,"_144":877},[946],{"_176":876,"_343":400,"_344":26,"_144":877},"mynvidia.force.com",[949,950],{"_176":883,"_343":947,"_344":26,"_144":884},{"_176":881,"_343":947,"_344":26,"_144":882},[952],{"_176":953,"_343":412,"_344":26,"_144":421},"VISITOR_PRIVACY_METADATA","prod.cloudmatchbeta.nvidiagrid.net",[956],{"_176":957,"_343":954,"_344":65,"_144":958},"SERVERID","Usually used for load balancing. Identifies the server that delivered the last page to the browser. Associated with the HAProxy Load Balancer software.","dosa-prd.azurewebsites.net",[961],{"_176":962,"_343":959,"_344":26,"_144":963},"access_token","This cookie is used to obtain the permissions for users of the portal to allow for API access in order to retrieve information required to perform portal functions.","w.hcaptcha.com",[966],{"_176":876,"_343":964,"_344":26,"_144":877},"cdn.readme.io",[969],{"_176":876,"_343":967,"_344":26,"_144":877},"queue-it.net",[972,973,974,975,976],{"_176":981,"_343":970,"_344":26,"_144":338},{"_176":980,"_343":970,"_344":26,"_144":338},{"_176":979,"_343":970,"_344":26,"_144":338},{"_176":978,"_343":970,"_344":26,"_144":338},{"_176":977,"_343":970,"_344":26,"_144":338},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","api.hcaptcha.com",[984,985],{"_176":987,"_343":982,"_344":26,"_144":378},{"_176":893,"_343":982,"_344":26,"_144":986},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[990,991],{"_176":876,"_343":988,"_344":26,"_144":877},{"_176":874,"_343":988,"_344":65,"_144":992},"This domain is owned by Vimeo. The main business activity is: Video Hosting/Sharing",{"_176":1178,"_343":1148,"_344":65,"_144":1179},{"_176":1107,"_343":483,"_344":26,"_144":1187},{"_176":1185,"_343":778,"_344":65,"_144":1186},{"_176":1174,"_343":1141,"_344":65,"_144":1175},{"_176":1184,"_343":517,"_344":65,"_144":338},{"_176":1183,"_343":483,"_344":65,"_144":502},{"_176":1181,"_343":483,"_344":26,"_144":1182},{"_176":908,"_343":1162,"_344":26,"_144":1093},{"_176":1134,"_343":824,"_344":65,"_144":1180},{"_176":1178,"_343":1141,"_344":65,"_144":1179},{"_176":1100,"_343":1173,"_344":65,"_144":1102},{"_176":907,"_343":1092,"_344":26,"_144":1093},{"_176":881,"_343":1173,"_344":26,"_144":882},{"_176":1176,"_343":483,"_344":65,"_144":1177},{"_176":1174,"_343":1148,"_344":65,"_144":1175},{"_176":883,"_343":1173,"_344":26,"_144":1099},{"_176":1171,"_343":483,"_344":26,"_144":1172},{"_176":907,"_343":1084,"_344":26,"_144":1093},{"_176":1089,"_343":1108,"_344":26,"_144":1091},{"_176":1169,"_343":483,"_344":26,"_144":1170},{"_176":920,"_343":1090,"_344":26,"_144":1105},{"_176":908,"_343":1084,"_344":26,"_144":1093},{"_176":922,"_343":1090,"_344":26,"_144":1166},{"_176":881,"_343":1098,"_344":26,"_144":882},{"_176":1167,"_343":592,"_344":26,"_144":1168},{"_176":908,"_343":1113,"_344":26,"_144":1114},{"_176":922,"_343":801,"_344":26,"_144":1166},{"_176":1164,"_343":483,"_344":26,"_144":1165},{"_176":913,"_343":801,"_344":26,"_144":1158},{"_176":1131,"_343":782,"_344":65,"_144":338},{"_176":1163,"_343":832,"_344":65,"_144":338},{"_176":907,"_343":1162,"_344":26,"_144":1093},{"_176":1160,"_343":1092,"_344":65,"_144":1161},{"_176":1119,"_343":483,"_344":26,"_144":1106},{"_176":1089,"_343":483,"_344":26,"_144":1091},{"_176":1159,"_343":1112,"_344":26,"_144":338},{"_176":913,"_343":483,"_344":26,"_144":1158},{"_176":922,"_343":1084,"_344":26,"_144":1157},{"_176":1155,"_343":803,"_344":65,"_144":1156},{"_176":1153,"_343":832,"_344":65,"_144":1154},{"_176":1151,"_343":483,"_344":65,"_144":1152},{"_176":1150,"_343":483,"_344":26,"_144":338},{"_176":1107,"_343":1090,"_344":26,"_144":1109},{"_176":1149,"_343":1112,"_344":26,"_144":338},{"_176":1140,"_343":1148,"_344":65,"_144":1142},{"_176":1146,"_343":782,"_344":26,"_144":1147},{"_176":1144,"_343":517,"_344":26,"_144":1145},{"_176":1143,"_343":504,"_344":26,"_144":338},{"_176":1140,"_343":1141,"_344":65,"_144":1142},{"_176":1138,"_343":483,"_344":26,"_144":1139},{"_176":1136,"_343":1098,"_344":65,"_144":1137},{"_176":1134,"_343":504,"_344":65,"_144":1135},{"_176":1132,"_343":483,"_344":26,"_144":1133},{"_176":1131,"_343":487,"_344":65,"_144":338},{"_176":883,"_343":1101,"_344":26,"_144":1099},{"_176":912,"_343":1120,"_344":26,"_144":1106},{"_176":1129,"_343":487,"_344":26,"_144":1130},{"_176":1128,"_343":483,"_344":26,"_144":626},{"_176":941,"_343":824,"_344":26,"_144":1122},{"_176":1126,"_343":483,"_344":26,"_144":1127},{"_176":1124,"_343":483,"_344":26,"_144":1125},{"_176":1123,"_343":483,"_344":26,"_144":338},{"_176":941,"_343":487,"_344":26,"_144":1122},{"_176":1119,"_343":801,"_344":26,"_144":1106},{"_176":876,"_343":778,"_344":26,"_144":1121},{"_176":1119,"_343":1120,"_344":26,"_144":1106},{"_176":1118,"_343":1112,"_344":26,"_144":338},{"_176":1117,"_343":483,"_344":26,"_144":1087},{"_176":1115,"_343":1098,"_344":26,"_144":1116},{"_176":907,"_343":1113,"_344":26,"_144":1114},{"_176":1111,"_343":1112,"_344":26,"_144":338},{"_176":1110,"_343":483,"_344":26,"_144":338},{"_176":1100,"_343":1098,"_344":65,"_144":1102},{"_176":912,"_343":483,"_344":26,"_144":1106},{"_176":1107,"_343":1108,"_344":26,"_144":1109},{"_176":912,"_343":801,"_344":26,"_144":1106},{"_176":920,"_343":801,"_344":26,"_144":1105},{"_176":1103,"_343":483,"_344":26,"_144":1104},{"_176":881,"_343":1101,"_344":26,"_144":882},{"_176":1100,"_343":1101,"_344":65,"_144":1102},{"_176":883,"_343":1098,"_344":26,"_144":1099},{"_176":1096,"_343":1092,"_344":65,"_144":1097},{"_176":1094,"_343":606,"_344":26,"_144":1095},{"_176":908,"_343":1092,"_344":26,"_144":1093},{"_176":1089,"_343":1090,"_344":26,"_144":1091},{"_176":876,"_343":601,"_344":26,"_144":1088},{"_176":1086,"_343":483,"_344":26,"_144":1087},{"_176":920,"_343":1084,"_344":26,"_144":1085},{"_176":1082,"_343":483,"_344":26,"_144":1083},"LoginHints","Keeps users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking \"Accept All\", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on \"Manage Settings.\" By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.

","gpc","

We have detected the Global Privacy Control (GPC) signal and recorded your rejection of all optional cookies on this site for this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. To opt out of non-cookie personal information \"sales\" / \"sharing\" for targeted advertising purposes, please visit the NVIDIA Preference Center. Please see our Privacy Policy for more information on our privacy practices.

","gpcLoggedIn","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies. You can manage these settings in the NVIDIA NVIDIA Preference Center. Please see our Privacy Policy for more information.

","gpcLoggedInOverridden","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies which overrides at least one of your previous settings. You can manage them in the NVIDIA Preference Center. Please see our Privacy Policy for more information.

",{"_840":153,"_630":50,"_529":50,"_313":50,"_303":50},"hasConsented","gpcApplied","showBanner","geolocation",{"_1202":1203,"_1204":1205},"country","US","state","OR","query","categories",[1225,1226,1227,1228,1229],"optionUrls",[1213,1214,1215,1216,1217,1218,1219,1220,1221,1222,1223,1224],"total",12,"/orgs/nim/deepseek-ai/containers/deepseek-r1/-","/orgs/nim/nvidia/containers/llama-3.1-nemotron-70b-instruct/-","/orgs/nvidia/-/containers/pytorch/-","/orgs/nvidia/omniverse/collections/kit/-","/orgs/nvidia/-/collections/deepstream_sdk/-","/orgs/nvidia/omniverse/collections/kit-appstreaming-collection/-","/orgs/nvidia/research/models/stylegan3/-","/orgs/nvidia/tao/models/peoplenet/-","/orgs/nvidia/tao/models/trafficcamnet/-","/orgs/nvidia/riva/resources/riva_quickstart/-","/orgs/nvidia/-/helm-charts/gpu-operator/-","/orgs/nvidia/-/helm-charts/k8s-nim-operator/-",{"_19":1304,"_1231":1305,"_1233":50},{"_19":1269,"_1231":1270,"_1233":48},{"_19":1252,"_1231":1253,"_1233":1254},{"_19":20,"_1231":1245,"_1233":1246},{"_19":1230,"_1231":1232,"_1233":1234},"helm-charts","items",[1235,1236],"startIndex",10,{"_19":1230,"_190":1242,"_139":140,"_141":-7,"_185":1243,"_193":1239,"_219":1244,"_1241":1223},{"_19":1230,"_190":1237,"_139":140,"_141":-7,"_185":1238,"_193":1239,"_219":1240,"_1241":1224},"NVIDIA NIM Operator","k8s-nim-operator","NVIDIA",[],"url","GPU Operator","gpu-operator",[],[1247],9,{"_19":20,"_190":1248,"_139":140,"_141":1249,"_185":1250,"_193":1239,"_219":1251,"_1241":1222},"Riva Skills Quick Start","riva","riva_quickstart",[],"models",[1255,1256,1257],6,{"_19":1252,"_190":1265,"_139":140,"_141":1266,"_185":1267,"_193":1239,"_219":1268,"_1241":1219},{"_19":1252,"_190":1262,"_139":140,"_141":1259,"_185":1263,"_193":1239,"_219":1264,"_1241":1220},{"_19":1252,"_190":1258,"_139":140,"_141":1259,"_185":1260,"_193":1239,"_219":1261,"_1241":1221},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1271,1272,1273],{"_19":1269,"_190":1301,"_139":140,"_141":1275,"_185":1302,"_193":1239,"_219":1303,"_1241":1216},{"_19":1269,"_190":1298,"_139":140,"_141":-7,"_185":1299,"_193":1239,"_219":1300,"_1241":1217},{"_19":1269,"_190":1274,"_139":140,"_141":1275,"_185":1276,"_193":1239,"_219":1277,"_1241":1218},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1278,1279,1280],{"_176":1292,"_190":1293,"_187":1294,"_198":1295,"_1285":1296},{"_176":1275,"_190":1288,"_187":1289,"_198":1284,"_1285":1290},{"_176":1281,"_190":1282,"_187":1283,"_198":1284,"_1285":1286},"omniverse-dev","NVIDIA Developer Program","Join the Developer Program for access to free tools, support, and tech resources.","https://store-images.s-microsoft.com/image/apps.33936.6f1bcf42-55d0-45d1-b4b6-f08c68a50bbe.e91ca707-db64-49d6-9761-4781590a9685.7ba64fc5-7c44-4864-a5e6-7bde05126935","supportedFeatures",[1287],"UNIFIED_CATALOG","NVIDIA Omniverse","NVIDIA Omniverse is a platform of APIs, services, and software development kits (SDKs) that enable developers to build generative AI-enabled tools and applications.",[1287,1291],"SHAREABLE_TO_USER_GROUPS","nv-ai-enterprise","NVIDIA AI Enterprise","Accelerate your AI agent development","https://assets.ngc.nvidia.com/products/catalog/images/product-NVAIE.jpg",[1287,1291,1297],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1278,1279,1280],"containers",[1306,1307,1308],{"_19":1304,"_190":1320,"_139":1313,"_141":1321,"_185":1322,"_193":1239,"_219":1323,"_1241":1213},{"_19":1304,"_190":1312,"_139":1313,"_141":140,"_185":1314,"_193":1239,"_219":1315,"_1241":1214},{"_19":1304,"_190":1309,"_139":140,"_141":-7,"_185":1310,"_193":1239,"_219":1311,"_1241":1215},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[1316,1278],{"_176":1317,"_190":1282,"_187":1283,"_198":1318,"_1285":1319},"nim-dev","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[1287],"DeepSeek-R1","deepseek-ai","deepseek-r1",[1316,1278],"ownerOrgs",[],"memberOrgs",[],"externalOrgs",[],"apiKey","2b44f86fea50e90ffd2db29ee2028419","consentGranted","applicationId","4c857e4f-5ffa-45fa-b75e-af773c22f0ad","clientToken","pubb6927db33dae60ba5031a186cde0242a","site","datadoghq.com","service","catalog-ui","env","sessionSampleRate",100,"sessionReplaySampleRate","compressIntakeRequests","storeContextsAcrossPages","defaultPrivacyLevel","mask-user-input","trackViewsManually","trackingConsent","not-granted",{"_1353":1354,"_1355":252,"_1356":26,"_1357":1358,"_1231":1359,"_1360":1361,"_1362":1354,"_1363":26,"_1364":26,"_1365":26,"_1366":1367},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1368,1369,1370,1371,1372,1373,1374],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1366":1396,"_41":1397,"_1360":1398},{"_1389":1390,"_1366":1391,"_41":1392,"_1362":1292},{"_1366":1387,"_41":1388,"_1360":1361},{"_1366":1384,"_41":1385,"_1360":1386},{"_1366":1381,"_41":1382,"_1360":1383},{"_1366":1378,"_41":1379,"_1360":1380},{"_1366":1375,"_41":1376,"_1360":1377},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1393],"Enterprise Catalog","/enterprise",[1394,1395],"NVAIE","READ","Explore Catalog","/","common-home",{"_91":92,"_93":94,"_95":96,"_97":98,"_99":100,"_101":102},{"_90":26}]