[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10,"_11":12},"actionData","errors","root",{"_88":245,"_246":247,"_248":249,"_250":251,"_252":253,"_254":30,"_255":256,"_257":258,"_259":30,"_260":30,"_261":30,"_262":30,"_263":264,"_265":-5,"_266":267,"_268":269,"_270":271,"_272":115,"_273":117,"_274":275,"_109":-5,"_276":30,"_277":-5,"_278":279,"_138":280,"_281":282,"_283":284,"_285":286,"_287":288,"_289":30,"_25":26,"_27":290,"_29":30,"_31":291,"_33":292,"_35":293},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/route",{"_124":125,"_126":127,"_128":129,"_130":53,"_131":30,"_132":133,"_134":30,"_135":30,"_136":137,"_138":21,"_139":-5,"_140":141,"_142":-5,"_143":144,"_145":146,"_147":30,"_148":149,"_25":26,"_27":150,"_29":30,"_31":151,"_33":152,"_35":153},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/_access/route",{"_85":86,"_87":-5,"_88":89,"_90":91,"_25":26,"_27":92,"_29":30,"_31":93,"_33":94,"_35":95},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/file-browser/route",{"_13":14,"_15":16,"_17":16,"_18":19,"_20":21,"_22":21,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32,"_33":34,"_35":36},"fileTree",[72],"totalFiles",1,"page","pageSize",50,"unresolvedVersion","21.12.1","resolvedVersion","expandedFolders",[],"traceId","af5ab903f05ae18d72e9ec5b3080a164","traceStartTime",1784175746437,"isStaleResponse",false,"notifications",[],"toasts",[],"apiCalls",[37,38,39,40,41,42,43],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":64,"_48":61,"_50":62,"_52":65,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":58,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"apiTag","subscription-api","path","/v2/users/me/subscriptions","status","error","httpStatus",401,"durationMs",2,"retryCount",0,"oms-api","/v2/users/me",4,"models-api","/v2/resources/org/nvidia/team/dle/gpunet_pyt/collections?page-number=0&page-size=1","success",200,79,"/v2/resources/nvidia/dle/gpunet_pyt/versions/21.12.1/files?page-size=50&page-number=0",66,"/v2/resources/nvidia/dle/gpunet_pyt/versions/21.12.1",53,"/v2/resources/nvidia/dle/gpunet_pyt/versions?page-size=100&page-number=0&sort-order=CREATED_DATE_DESC",56,"/v2/resources/nvidia/dle/gpunet_pyt?resolve-labels=true&remove-unresolved-labels=true",77,{"_73":74,"_74":75},"type","file",{"_76":77,"_78":77,"_46":77,"_79":80,"_81":82,"_83":84},"id","gpunet_pyt.md","name","size",30748,"formattedSize","30.03 KB","updatedAt","2023-01-11T00:17:03.457Z","canDownload",true,"accessDeniedReason","appConfig",{"_107":108,"_109":110},"debugAccessInfo",{"_103":86,"_104":105,"_106":30},1784175746363,[],[],[96,97,98,99,100,101],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"pending","canGuestAccess","requiredProducts",[],"hasLicenseRequirement","urls",{"_112":113,"_114":115,"_116":117,"_118":119,"_120":121,"_122":123},"user",{"_111":30},"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",{"_76":203,"_204":205,"_206":207,"_208":209,"_161":162,"_163":164,"_210":211,"_167":212,"_213":214,"_172":215,"_216":217,"_218":21,"_219":-7,"_220":-7,"_221":222,"_223":224,"_225":86,"_226":80,"_227":-5,"_228":-7,"_229":-7,"_230":-7,"_231":-7,"_232":-7,"_233":-7,"_234":235,"_236":237,"_238":239,"_240":241,"_242":243,"_244":30},"html","

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

Developed by NVIDIA, GPUNet differs from the current ConvNets in three aspects:

\n
Designed by AI: we built an AI agent to establish SOTA GPUNet out of our years of research in Neural Architecture Search. Powered by Selene supercomputer, our AI agent can automatically orchestrate hundreds of GPUs to meticulously trade-off sophisticated design decisions w.r.t multiple design goals without intervening by the domain experts.
\n
\n
Co-designed with NVIDIA TensorRT and GPU: GPUNet only considers the most relevant factors to the model accuracy and the TensorRT inference latency, promoting GPU friendly operators (for example, larger filters) over memory-bound operators (for example, fancy activations), therefore delivering the SOTA GPU latency and the accuracy on ImageNet.
\n
\n
TensorRT deployment-ready: All the GPUNet reported latencies are after the optimization from TensorRT, including kernel fusion, quantization, etc., so GPUNet is directly deployable to users.
\n

\n \n

Because of better design trade-off and hardware and software co-design, GPUNet has established new SOTA latency and accuracy Pareto frontier on ImageNet. Specifically, GPUNet is up to 2x faster than EfficentNet, EfficientNet-X and FBNetV3. Our CVPR-2022 paper provides extensive evaluation results aginsts other networks.

Model architecture

\n \n

The above table describes the general structure of GPUNet, which consists of 8 stages, and we search for the configurations of each stage. The layers within a stage share the same configurations. The first two stages are to search for the head configurations using convolutions. Inspired by EfficientNet-V2, the 2 and 3 stages use Fused Inverted Residual Blocks(IRB); however, we observed the increasing latency after replacing the rest IRB with Fused-IRB. Therefore, from stages 4 to 7, we use IRB as the primary layer. The column #Layers shows the range of #Layers in the stage, for example, [3, 10] at stage 4 means that the stage can have three to 10 IRBs, and the column filters shows the range of filters for the layers in the stage. We also tuned the expansion ratio, activation types, kernel sizes, and the Squeeze Excitation(SE) layer inside the IRB/Fused-IRB. Finally, the dimensions of the input image increased from 224 to 512 at step 32.

GPUNet has provided seven specific model architectures at different latencies. You can easily query the architecture details from the JSON formatted model (for example, those in eval.py). The following figure describes GPUNet-0, GPUNet-1, and GPUNet-2 in the paper. Note that only the first IRB's stride is two and the stride of the rest IRBs is 1 in stages 2, 3, 4, and 6.

\n \n

Default configuration

Training features:\n
- Customize the training pipeline in Timm to support the distillation.\n
  - Here provides an example of training hyper-parameters with distillation.
  \n
- All the features in Timm, including\n
  - Random data augmentation, mean = 9, std = 0.5.
  - Exponential Moving Average (EMA).
  - Rmsproptf optimizer.
  - Multi-GPU training.
  - Automatic mixed precision (AMP).
  - GPUNet can be further improved with other training hacks such as Mixup or drop path regularization. More hacks are available at Timm.
  \n
- The exact training hyper-parameters to reproduce the GPUNet results can be found here.
\n
Inference features:\n
- Test the accuracy of pre-trained GPUNet.
- Save GPUNet to the ONNX files for the latency benchmarking.
\n

Feature support matrix

This model supports the following features:

\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	GPUNet
Multi-GPU training	✓
Automatic mixed precision (AMP)	✓
Distillation	✓

Features

Multi-GPU training: we re-use the same training pipeline from Timm to train GPUNet. Timm has adopted NCCL to optimize the multi-GPU training efficiency.

Automatic Mixed Precision (AMP): mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speed-up 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.

Timm supports AMP by default and only requires the '--amp' flag to enable the AMP training.

Distillation: originally introduced in Hinton's seminal paper, knowledge distillation uses a larger and better teacher network to supervise the training of a student network in addition to the ground truth. Generally the final accuracy of a student network is better than the training without a teacher; for example, ~+2% on ImageNet.

We customized Timm to support the distillation. The teacher model can be any model supported by Timm. We demonstrate the usage of distillation in Training with distillation.

","resourceVersions",[195,196,197],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_166":21,"_167":-5,"_48":168,"_169":-5,"_170":171,"_172":-5,"_173":80,"_174":16,"_175":30,"_176":-5,"_177":30,"_178":179,"_180":86,"_181":182,"_183":86,"_184":185,"_186":86,"_187":188,"_189":30,"_190":191,"_192":86,"_193":194},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/resources/gpunet_pyt/-","version","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/dle/gpunet_pyt:21.12.1","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/resources/gpunet_pyt/21.12.1/version-history","accessCheckResult",{"_165":86},"hasRelatedCollections","context",{"_161":162,"_163":164},1784175746382,[],[],[154,155,156,157,158,159,160],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":64,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":58,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"orgName","nvidia","teamName","dle","allowed","versionId","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1673396222028],"updatedDate","totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","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 https://developer.nvidia.com/deep-learning-performance-training-inference.\n\n### Changelog\n\nMay 2022\n- Initial release\n\n### Known issues\nThere are no known issues with this model.","hasReleaseNotes","setup","The following section lists the requirements you need to meet to start training the GPUNet.\n\n### Requirements\n\nThis repository contains a Dockerfile that extends the PyTorch NGC container and encapsulates all dependencies. You also need the following components to get started:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [ImageNet 1K dataset](https://image-net.org/download.php)\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/design-visualization/technologies/turing-architecture/)\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, refer tothe following 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)","hasSetup","quickStartGuide","This repo allows a user to easily train GPUNet, reproduce our results, test the accuracy of pre-trained checkpoints, and benchmark GPUNet latency. For customizing GPUNet, refer to [Model customization](#model-customization).\n\n1. To get started, clone the repo:\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/PyTorch/Classification/GPUNet\n```\n\n2. Download ImageNet from the [offical website](https://image-net.org/download-images). Recursively unzip the dataset, and locate the train and val folders. Refer to [Prepare the dataset](#prepare-the-dataset) for more details.\n\n3. Build and run the GPUNet PyTorch container, assuming you have installed the docker.\n```\ndocker build -t gpunet .\ndocker run --gpus all -it --rm --network=host --shm-size 600G --ipc=host -v /path/to/imagenet:/root/data/imagenet/ gpunet\n```\n\n### Prepare the Dataset\n\n1. [Download the ImageNet](http://image-net.org/download-images).\n\n2. 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\n3. 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 where the `train/` and `val/` directories are placed is referred to as `/path/to/imagenet/` in this document.\n\n### Training\n\nWe have provided the [training launch scripts](./train_params) for you to reproduce the GPUNet accuracy by training from scratch. For example, a user can copy the launch script in GPUNet-0.train.params or the training hyper-parameters below to reproduce the accuracy.\n\nGPUNet training hyperparameters:\n\n1. GPUNet-0 \n```\n./train.sh 8 /root/data/imagenet/ --model gpunet_0 --sched step --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf -b 192 --epochs 450 --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr .06 --num-classes 1000 --enable-distill False --crop-pct 1.0 --img-size 320 --amp\n```\n\n2. GPUNet-1\n```\n./train.sh 8 /root/data/imagenet/ --model gpunet_1 --sched step --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf -b 192 --epochs 450 --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr .06 --num-classes 1000 --enable-distill False --crop-pct 1.0 --img-size 288 --amp\n```\n\n3. GPUNet-2\n```\n./train.sh 8 /root/data/imagenet/ --model gpunet_2 --sched step --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf -b 192 --epochs 450 --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr .06 --num-classes 1000 --enable-distill False --crop-pct 1.0 --img-size 384 --amp\n```\n\n4. GPUNet-D1 with distillation\n```\n./train.sh 8 /root/data/imagenet/ --model gpunet_d1 --sched step --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf -b 192 --epochs 450 --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr .06 --num-classes 1000 --enable-distill True --crop-pct 1.0 --img-size 456 --amp --test-teacher False --teacher tf_efficientnet_b5_ns --teacher-img-size 456\n```\n\n5. GPUNet-D2 with distillation\n```\n./train.sh 8 /root/data/imagenet/ --model gpunet_d2 --sched step --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf -b 128 --epochs 450 --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr .06 --num-classes 1000 --enable-distill True --crop-pct 1.0 --img-size 528 --amp --test-teacher False --teacher tf_efficientnet_b6_ns --teacher-img-size 528\n```\n\n6. GPUNet-P0 with distillation\n```\n./train.sh 8 /root/data/imagenet/ --model gpunet_p0 --sched step --decay-epochs 2.4 --decay-rate 0.97 --opt rmsproptf -b 256 --epochs 450 --opt-eps 0.001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr 0.08 --num-classes 1000 --enable-distill True --crop-pct 0.875 --img-size 224 --amp --test-teacher False --teacher tf_efficientnet_b2 --teacher-img-size 260\n```\n\n7. GPUNet-P1 with distillation\n```\n./train.sh 8 /root/data/imagenet/ --model gpunet_p1 --sched step --decay-epochs 2.4 --decay-rate 0.97 --opt rmsproptf -b 256 --epochs 450 --opt-eps 0.001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-connect 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --lr 0.08 --num-classes 1000 --enable-distill True --crop-pct 0.875 --img-size 224 --amp --test-teacher False --teacher tf_efficientnet_b2 --teacher-img-size 260\n```\n\nYou need to call train.sh to start the training, and here is an example of arguments to train.sh.\n```\n./train.sh 8 >>launch with 8 GPUs.\n/root/data/imagenet/ >>path to the imagenet.\n--model gpunet_d1 >>name of GPUNet.\n--sched step >>stepwise learning rate scheduler.\n--decay-epochs 2.4 >>epoch interval to decay LR.\n--decay-rate .97 >>LR decay rate (default: 0.1).\n--opt rmsproptf >>optimizer.\n-b 192 >>batch size.\n--epochs 450 >>total training epochs.\n--opt-eps .001 >>optimizer epsilon.\n-j 8 >>the number of threads for data loader.\n--lr .06 >>learning rate.\n--warmup-lr 1e-6 >>warmup learning rate.\n--weight-decay 1e-5 >>weight-decay rate.\n--drop 0.3 >>dropout rate.\n--drop-connect 0.2 >>drop connect rate.\n--model-ema >>enable tracking moving average of model weights.\n--model-ema-decay 0.9999 >>decay factor for model weights moving average (default: 0.9998).\n--aa rand-m9-mstd0.5 >>using the random augmentation.\n--remode pixel >>random erase mode.\n--reprob 0.2 >>random erase prob.\n--num-classes 1000 >>the number of output classes.\n--amp >>enable the amp training.\n--crop-pct 1.0 >>input image center crop percent.\n--output ./output/ >>path to output folder.\n--img-size 456 >>image size for the student model, i.e., gpunet_d1.\n--enable-distill True >>to turn on/off the distillation.\n--test-teacher False >>to test the accuracy of teacher model\n--teacher tf_efficientnet_b5 >>the name of teacher model\n--teacher-img-size 456 >>the image size to the teacher model. Note the student and teacher may have different image resolutions.\n```\n\n#### Training with distillation\nWe recommend running the distillation on a GPU with large DRAM; for example, 80G A100, since it needs to fit another teacher network.\n\n- The following describes the usage of distillation.\n * --enable-distill [Boolean]\n * Enable or disable distillation.\n * --teacher [String]\n * Specify the name of the teacher model from Timm. You can find the full list of teacher models [here](https://github.com/rwightman/pytorch-image-models/blob/master/results/results-imagenet.csv), and you can pick any models from the model column. We expect the teacher model to be larger and better than the selected GPUNet.\n * --teacher-img-size [Int]\n * Specify the image resolution to the teacher model. The teacher model may use a larger resolution, and you can find the teacher's resolution from the column img_size to the selected teacher. Internally we use one data loader for both the teacher and student models, and we downsample an image batch with the teacher's resolution to the student's resolution using [Interpolation](https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html).\n\n### Inference\n\nWe also allow a user to evaluate the accuracy of pre-trained GPUNet checkpoints and benchmark the model's TensorRT latency. For evaluating GPUNet on a custom dataset, refer to [Train on your data](#train-on-your-data).\n\n#### Evaluate the pre-trained GPUNet checkpoints\nIn the `eval.py`, we have listed seven configurations to the released GPUNet models in the table below.\n\n| batch | Distillation | GPU | Latency | \n|-----------|---------------|---------------------|----------|\n| 1 | No | GV100 | 0.65ms | \n| 1 | No | GV100 | 0.85ms | \n| 1 | No | GV100 | 1.75ms | \n| 1 | Yes | GV100 | 0.5ms-D |\n| 1 | Yes | GV100 | 0.8ms-D | \n| 1 | Yes | GV100 | 1.25ms-D | \n| 1 | Yes | GV100 | 2.25ms-D |\n\nA user can easily evaluate the accuracy of a pre-trained checkpoint using the following code:\n\n```\nfrom configs.model_hub import get_configs, get_model_list\nfrom models.gpunet_builder import GPUNet_Builder\n\nmodelJSON, cpkPath = get_configs(batch=1, latency=\"0.65ms\", gpuType=\"GV100\") >>Get the model configurations and checkpoints.\nbuilder = GPUNet_Builder() >>Build an instance of GPUNet constructor.\nmodel = builder.get_model(modelJSON) >>Build the GPUNet based on the model json.\nbuilder.export_onnx(model) >>Export Pytorch model to ONNX for benchmarking the latency.\n\nbuilder.test_model( >>Test the checkpoint accuracy.\n model,\n testBatch=200,\n checkpoint=cpkPath,\n imgRes=(3, model.imgRes, model.imgRes),\n dtype=\"fp16\",\n crop_pct=1,\n val_path=\"/root/data/imagenet/val\",\n)\n```\n\n#### Benchmark the GPUNet latency\nWe will need the ONNX file of the GPUNet model to reproduce the latency. `builder.export_onnx(model)` will export an ONNX file named `gpunet.onnx`. You can get the FP16 latency with the following command:\n```\ntrtexec --onnx=gpunet.onnx --fp16 --workspace=10240\n```\nHere `gpunet.onnx` is configured to benchmark the latency at the batch = 1 to be consistent with the GPUNet [paper](https://arxiv.org/pdf/2205.00841.pdf). You can also look at the [torch.onnx](https://pytorch.org/docs/stable/onnx.html) API to benchmark different settings, such as batch sizes. Finally, we report the median GPUNet compute time; here is an example output of a network with batch=1, latency=0.65ms, gpuType=GV100.\n```\n[04/07/2022-19:40:17] [I] GPU Compute Time: min = 0.554077 ms, max = 0.572388 ms, mean = 0.564606 ms, median = 0.564209 ms, percentile(99%) = 0.570312 ms\n```","hasQuickStartGuide","advanced","","hasAdvanced","performance","This section demonstrates the GPUNet training and inference results independently benchmarked by a third party. You can also easily replicate the same results following [Quick Start Guide](#quick-start-guide).\n\n### Results\n\n#### Training Accuracy Results\nWe benchmark the training results following the steps in [Training](#training). This section lists the training results on NVIDIA DGX V100. \n\n##### NVIDIA DGX V100 (8x V100 32GB)\n\n| **Model**|**Batch**| **Epochs** | **GPUs** | **FP32 Top1** | **AMP Top1** | **FP32 (hours)
Train Time** | **AMP (hours)
Train Time** | **Training speedup
(FP32 / AMP)** |\n|:--------:|:------:|:----------:|:--------:|:--------------:|:--------------:|:-------------------:|:-----------------------:|:--------------------------------:|\n| GPUNet-0 |192 | 450 | 8 | 78.90_+/-0.03 | 78.96_+/-0.05 |71.63|46.56| 1.54 x |\n| GPUNet-1 |192 | 450 | 8 | 80.4-_+/-0.03 | 80.5_+/-0.03 |67.5 |43.5 | 1.55 x |\n| GPUNet-2 |192 | 450 | 8 | 82.1-_+/-0.04 | 82.2_+/-0.04 |171 |84.25| 2.03 x |\n\n#### Training performance results\nPlease also follow the steps in [Training](#training) to reproduce the performance results below.\n\n##### NVIDIA DGX V100 (8x V100 32GB)\n \n| **Model** | **GPUs** | **Batch** | FP32
imgs/second | **AMP
imgs/second** | Speedup
(FP32 to AMP) | \n|:---------:|:--------:|:---------:|:-----------:|:--------------------------------:|:------------------------------------------------:|\n| GPUNet-0 | 8 | 192 | 2289 img/s | 3518 img/s | 1.53 x |\n| GPUNet-1 | 8 | 192 | 2415 img/s | 3774 img/s | 1.56 x |\n| GPUNet-2 | 8 | 192 | 948 img/s | 1957 img/s | 2.03 x |\n\n##### NVIDIA DGX A100 (8x A100 80GB)\n \n| **Model** | **GPUs** | **Batch** | FP32
imgs/second | **AMP
imgs/second** | Speedup
(TF32 to AMP) | \n|:---------:|:--------:|:---------:|:-----------:|:--------------------------------:|:------------------------------------------------:|\n| GPUNet-2 | 8 | 192 | 2002 img/s | 2690 img/s | 1.34 x |\n| GPUNet-D1 | 8 | 128 | 755 img/s | 844 img/s | 1.11 x |\n\n#### Inference results\nWe benchmark the training results following the steps in [Benchmark the GPUNet latency](#benchmark-the-gpunet-latency). This section lists the inference results on NVIDIA 32G V100 and 80G A100.\n\n##### NVIDIA V100 (32GB)\n\n| **GPUNet** | **Batch** | **GPU** | **TensorRT8 FP16 Latency** | FP16 Latency | Perf Details | ImageNet Top1 |\n|:-----------------:|:--------------:|:--------:|:--------------------------:|:--------------------------:|:------------------------:|:------------:|\n| GPUNet-0 | 1 | V100 | 0.63 ms | 1.82 ms | [here](./triton/065ms) | 78.9 |\n| GPUNet-1 | 1 | V100 | 0.82 ms | 2.75 ms | [here](./triton/085ms) | 80.5 |\n| GPUNet-2 | 1 | V100 | 1.68 ms | 5.50 ms | [here](./triton/175ms) | 82.2 |\n| GPUNet-P0 | 1 | V100 | 0.63 ms | 2.11 ms | [here](./triton/05ms-D) | 80.3 |\n| GPUNet-P1 | 1 | V100 | 0.96 ms | 2.47 ms | [here](./triton/08ms-D) | 81.1 |\n| GPUNet-D1 | 1 | V100 | 1.24 ms | 2.88 ms | [here](./triton/125ms-D) | 82.5 |\n| GPUNet-D2 | 1 | V100 | 2.17 ms | 4.22 ms | [here](./triton/225ms-D) | 83.6 |\n\n##### NVIDIA A100 (80GB)\n\n| **GPUNet** | **Batch** | **GPU** | **TensorRT8 FP16 Latency** | FP16 Latency | Perf Details | ImageNet Top1 |\n|:-----------------:|:--------------:|:--------:|:--------------------------:|:--------------------------:|:------------------------:|:------------:|\n| GPUNet-0 | 1 | A100 | 0.46 ms | 1.46 ms | [here](./triton/065ms) | 78.9 |\n| GPUNet-1 | 1 | A100 | 0.59 ms | 1.81 ms | [here](./triton/085ms) | 80.5 |\n| GPUNet-2 | 1 | A100 | 1.25 ms | 4.03 ms | [here](./triton/175ms) | 82.2 |\n| GPUNet-P0 | 1 | A100 | 0.45 ms | 1.31 ms | [here](./triton/05ms-D) | 80.3 |\n| GPUNet-P1 | 1 | A100 | 0.61 ms | 1.64 ms | [here](./triton/08ms-D) | 81.1 |\n| GPUNet-D1 | 1 | A100 | 0.94 ms | 2.44 ms | [here](./triton/125ms-D) | 82.5 |\n| GPUNet-D2 | 1 | A100 | 1.40 ms | 3.06 ms | [here](./triton/225ms-D) | 83.6 |","hasPerformance","policies",[],{"_76":201,"_78":202,"_169":-5,"_175":30,"_177":30,"_193":200,"_180":86},{"_76":21,"_78":21,"_169":-5,"_175":30,"_177":30,"_193":200,"_180":86},{"_76":198,"_78":198,"_169":-5,"_175":30,"_177":30,"_193":199,"_180":86},"21.12.0",[],[],"-","Latest (21.12.1)","nvidia/dle/gpunet_pyt","key","nvidia/dle/resources/gpunet_pyt","resourceType","resources","resourceId","gpunet_pyt","shortDescription","GPUNet is a new family of Convolutional Neural Networks crafted by NVIDIA AI.","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/gpunet-pyt)\n\nDeveloped by NVIDIA, GPUNet differs from the current ConvNets in three aspects:\n- **Designed by AI**: we built an AI agent to establish SOTA GPUNet out of our years of research in Neural Architecture Search. Powered by [Selene](https://blogs.nvidia.com/blog/2020/12/18/nvidia-selene-busy/) supercomputer, our AI agent can automatically orchestrate hundreds of GPUs to meticulously trade-off sophisticated design decisions w.r.t multiple design goals without intervening by the domain experts.\n\n- **Co-designed with NVIDIA TensorRT and GPU**: GPUNet only considers the most relevant factors to the model accuracy and the TensorRT inference latency, promoting GPU friendly operators (for example, larger filters) over memory-bound operators (for example, fancy activations), therefore delivering the SOTA GPU latency and the accuracy on ImageNet.\n\n- **TensorRT deployment-ready**: All the GPUNet reported latencies are after the optimization from TensorRT, including kernel fusion, quantization, etc., so GPUNet is directly deployable to users.\n\n

\n \n

\n\nBecause of better design trade-off and hardware and software co-design, GPUNet has established new SOTA latency and accuracy Pareto frontier on ImageNet. Specifically, GPUNet is up to 2x faster than EfficentNet, EfficientNet-X and FBNetV3. Our CVPR-2022 [paper](https://arxiv.org/pdf/2205.00841.pdf) provides extensive evaluation results aginsts other networks.\n\n### Model architecture\n

\n \n

\n\nThe above table describes the general structure of GPUNet, which consists of 8 stages, and we search for the configurations of each stage. The layers within a stage share the same configurations. The first two stages are to search for the head configurations using convolutions. Inspired by [EfficientNet-V2](https://arxiv.org/abs/2104.00298), the 2 and 3 stages use Fused Inverted Residual Blocks(IRB); however, we observed the increasing latency after replacing the rest IRB with Fused-IRB. Therefore, from stages 4 to 7, we use IRB as the primary layer. The column \\#Layers shows the range of \\#Layers in the stage, for example, [3, 10] at stage 4 means that the stage can have three to 10 IRBs, and the column filters shows the range of filters for the layers in the stage. We also tuned the expansion ratio, activation types, kernel sizes, and the Squeeze Excitation(SE) layer inside the IRB/Fused-IRB. Finally, the dimensions of the input image increased from 224 to 512 at step 32.\n\nGPUNet has provided seven specific model architectures at different latencies. You can easily query the architecture details from the JSON formatted model (for example, those in eval.py). The following figure describes GPUNet-0, GPUNet-1, and GPUNet-2 in the paper. Note that only the first IRB's stride is two and the stride of the rest IRBs is 1 in stages 2, 3, 4, and 6.\n

\n \n

\n\n### Default configuration\n\n* Training features:\n * Customize the training pipeline in [Timm](https://github.com/rwightman/pytorch-image-models) to support the distillation.\n * [Here](./train_params/GPUNet-D1.train.params) provides an example of training hyper-parameters with distillation.\n * All the features in [Timm](https://github.com/rwightman/pytorch-image-models), including\n * Random data augmentation, mean = 9, std = 0.5. \n * Exponential Moving Average (EMA).\n * Rmsproptf optimizer.\n * Multi-GPU training.\n * Automatic mixed precision (AMP).\n * GPUNet can be further improved with other training hacks such as Mixup or drop path regularization. More hacks are available at [Timm](https://github.com/rwightman/pytorch-image-models).\n * The exact training hyper-parameters to reproduce the GPUNet results can be found [here](./train_params). \n* Inference features:\n * Test the accuracy of pre-trained GPUNet.\n * Save GPUNet to the ONNX files for the latency benchmarking.\n\n### Feature support matrix\nThis model supports the following features:\n\n| Feature | GPUNet \n|-----------------------|--------------------------\n|Multi-GPU training | ✓\n|Automatic mixed precision (AMP) | ✓\n|Distillation | ✓ \n\n#### Features\n\n**Multi-GPU training**: we re-use the same training pipeline from [Timm](https://github.com/rwightman/pytorch-image-models) to train GPUNet. Timm has adopted NCCL to optimize the multi-GPU training efficiency.\n\n**Automatic Mixed Precision (AMP)**: mixed 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 speed-up 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.\n\n[Timm](https://github.com/rwightman/pytorch-image-models) supports AMP by default and only requires the '--amp' flag to enable the AMP training.\n\n**Distillation**: originally introduced in [Hinton's seminal paper](https://arxiv.org/pdf/1503.02531.pdf), knowledge distillation uses a larger and better teacher network to supervise the training of a student network in addition to the ground truth. Generally the final accuracy of a student network is better than the training without a teacher; for example, ~+2% on ImageNet.\n\nWe customized [Timm](https://github.com/rwightman/pytorch-image-models) to support the distillation. The teacher model can be any model supported by [Timm](https://github.com/rwightman/pytorch-image-models). We demonstrate the usage of distillation in [Training with distillation](#training-with-distillation).","displayName","GPUNet for PyTorch",["D",1673396225210],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/gpunet_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",{"_107":1421,"_109":1422},"ngcApps",[1374],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1355":1356,"_1357":1358,"_1359":1360,"_1361":1362,"_1363":256,"_138":280,"_1364":1365,"_1366":55,"_1367":86,"_1368":86,"_1369":1370,"_1371":86,"_1372":1373},"amplitude",{"_1352":1353,"_953":-7,"_1354":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1346":1347,"_1348":1349,"_1350":1351},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1228":188,"_1229":1230,"_1231":1232,"_1233":1234},"announcements",[],"consentState",{"_318":1218,"_1219":86,"_1220":30,"_1221":30,"_1222":1223},"consentConfig",{"_300":301,"_302":303,"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319},"showContactAdmin",1784175746289,[],[],[294,295,296,297,298,299],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":102,"_50":-5,"_52":-5,"_54":55},"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",{"_1210":1211,"_1212":1213,"_1214":1215,"_1216":1217},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[320,321,322,323,324],{"_325":862,"_78":863,"_167":864,"_329":86,"_330":16,"_332":865,"_334":866},{"_325":651,"_78":652,"_167":653,"_329":30,"_330":654,"_332":655,"_334":656},{"_325":551,"_78":552,"_167":553,"_329":30,"_330":58,"_332":554,"_334":555},{"_325":336,"_78":337,"_167":338,"_329":30,"_330":339,"_332":340,"_334":341},{"_325":326,"_78":327,"_167":328,"_329":30,"_330":331,"_332":333,"_334":335},"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,[469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503],[342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358],{"_359":465,"_213":465,"_167":188,"_332":466},{"_359":460,"_213":460,"_167":188,"_332":461},{"_359":456,"_213":456,"_167":188,"_332":457},{"_359":451,"_213":451,"_167":188,"_332":452},{"_359":446,"_213":446,"_167":188,"_332":447},{"_359":434,"_213":434,"_167":188,"_332":435},{"_359":422,"_213":422,"_167":188,"_332":423},{"_359":417,"_213":417,"_167":188,"_332":418},{"_359":406,"_213":406,"_167":188,"_332":407},{"_359":401,"_213":401,"_167":188,"_332":402},{"_359":396,"_213":396,"_167":188,"_332":397},{"_359":391,"_213":391,"_167":188,"_332":392},{"_359":386,"_213":386,"_167":188,"_332":387},{"_359":381,"_213":381,"_167":188,"_332":382},{"_359":376,"_213":376,"_167":188,"_332":377},{"_359":369,"_213":369,"_167":188,"_332":370},{"_359":360,"_213":360,"_167":188,"_332":361},"hostName","amazon-adsystem.com",[362,363],{"_78":368,"_365":360,"_366":30,"_167":367},{"_78":364,"_365":360,"_366":30,"_167":367},"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",[371,372],{"_78":375,"_365":369,"_366":30,"_167":374},{"_78":373,"_365":369,"_366":30,"_167":374},"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",[378],{"_78":379,"_365":376,"_366":30,"_167":380},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[383],{"_78":384,"_365":381,"_366":30,"_167":385},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[388],{"_78":389,"_365":386,"_366":30,"_167":390},"_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",[393],{"_78":394,"_365":391,"_366":30,"_167":395},"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",[398],{"_78":399,"_365":396,"_366":30,"_167":400},"sp","Testing","google.com",[403],{"_78":404,"_365":401,"_366":30,"_167":405},"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",[408,409,410,411],{"_78":416,"_365":406,"_366":30,"_167":413},{"_78":415,"_365":406,"_366":30,"_167":413},{"_78":414,"_365":406,"_366":30,"_167":413},{"_78":412,"_365":406,"_366":30,"_167":413},"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",[419],{"_78":420,"_365":417,"_366":30,"_167":421},"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",[424,425,426,427],{"_78":433,"_365":422,"_366":30,"_167":429},{"_78":431,"_365":422,"_366":30,"_167":432},{"_78":430,"_365":422,"_366":30,"_167":429},{"_78":428,"_365":422,"_366":30,"_167":429},"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",[436,437,438,439],{"_78":445,"_365":434,"_366":30,"_167":443},{"_78":444,"_365":434,"_366":30,"_167":443},{"_78":442,"_365":434,"_366":30,"_167":443},{"_78":440,"_365":434,"_366":30,"_167":441},"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",[448],{"_78":449,"_365":446,"_366":30,"_167":450},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[453],{"_78":454,"_365":451,"_366":30,"_167":455},"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",[458],{"_78":459,"_365":456,"_366":30,"_167":400},"R","6sc.co",[462],{"_78":463,"_365":460,"_366":30,"_167":464},"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",[467],{"_78":468,"_365":465,"_366":30,"_167":400},"_hly_gvid",{"_78":550,"_365":505,"_366":30,"_167":524},{"_78":548,"_365":505,"_366":30,"_167":549},{"_78":412,"_365":509,"_366":30,"_167":538},{"_78":546,"_365":505,"_366":30,"_167":547},{"_78":415,"_365":509,"_366":30,"_167":512},{"_78":544,"_365":505,"_366":30,"_167":545},{"_78":389,"_365":505,"_366":30,"_167":543},{"_78":521,"_365":542,"_366":30,"_167":522},{"_78":414,"_365":509,"_366":30,"_167":541},{"_78":414,"_365":511,"_366":30,"_167":541},{"_78":416,"_365":509,"_366":30,"_167":525},{"_78":540,"_365":505,"_366":30,"_167":524},{"_78":519,"_365":539,"_366":30,"_167":188},{"_78":412,"_365":511,"_366":30,"_167":538},{"_78":537,"_365":505,"_366":30,"_167":188},{"_78":519,"_365":509,"_366":30,"_167":188},{"_78":535,"_365":505,"_366":30,"_167":536},{"_78":533,"_365":505,"_366":30,"_167":534},{"_78":532,"_365":505,"_366":30,"_167":188},{"_78":530,"_365":505,"_366":30,"_167":531},{"_78":528,"_365":505,"_366":30,"_167":529},{"_78":527,"_365":505,"_366":30,"_167":188},{"_78":521,"_365":509,"_366":30,"_167":522},{"_78":521,"_365":526,"_366":30,"_167":522},{"_78":416,"_365":511,"_366":30,"_167":525},{"_78":523,"_365":505,"_366":30,"_167":524},{"_78":521,"_365":505,"_366":30,"_167":522},{"_78":519,"_365":511,"_366":30,"_167":520},{"_78":517,"_365":505,"_366":30,"_167":518},{"_78":515,"_365":505,"_366":30,"_167":516},{"_78":513,"_365":505,"_366":30,"_167":514},{"_78":415,"_365":511,"_366":30,"_167":512},{"_78":508,"_365":509,"_366":30,"_167":510},{"_78":507,"_365":505,"_366":30,"_167":188},{"_78":504,"_365":505,"_366":86,"_167":506},"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.",[591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611],[556,557,558,559,560],{"_359":451,"_213":451,"_167":188,"_332":582},{"_359":417,"_213":417,"_167":188,"_332":577},{"_359":570,"_213":570,"_167":188,"_332":571},{"_359":566,"_213":566,"_167":188,"_332":567},{"_359":561,"_213":561,"_167":188,"_332":562},"nvcrm.my.site.com",[563],{"_78":564,"_365":561,"_366":30,"_167":565},"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",[568],{"_78":569,"_365":566,"_366":30,"_167":400},"edge","github.com",[572,573],{"_78":576,"_365":570,"_366":30,"_167":575},{"_78":574,"_365":570,"_366":30,"_167":575},"logged_in","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform","_octo",[578,579],{"_78":581,"_365":417,"_366":30,"_167":421},{"_78":580,"_365":417,"_366":30,"_167":421},"lidc","li_gc",[583,584,585,586],{"_78":590,"_365":451,"_366":30,"_167":455},{"_78":589,"_365":451,"_366":30,"_167":455},{"_78":588,"_365":451,"_366":30,"_167":455},{"_78":587,"_365":451,"_366":30,"_167":455},"imid_secure","imid_created_secure","imid","imid_created",{"_78":650,"_365":505,"_366":30,"_167":647},{"_78":648,"_365":505,"_366":30,"_167":649},{"_78":646,"_365":505,"_366":30,"_167":647},{"_78":644,"_365":505,"_366":86,"_167":645},{"_78":642,"_365":613,"_366":30,"_167":643},{"_78":641,"_365":622,"_366":86,"_167":188},{"_78":639,"_365":613,"_366":30,"_167":640},{"_78":637,"_365":505,"_366":30,"_167":638},{"_78":634,"_365":635,"_366":30,"_167":636},{"_78":633,"_365":509,"_366":30,"_167":188},{"_78":631,"_365":505,"_366":30,"_167":632},{"_78":628,"_365":629,"_366":30,"_167":630},{"_78":626,"_365":627,"_366":30,"_167":188},{"_78":625,"_365":613,"_366":30,"_167":188},{"_78":624,"_365":505,"_366":30,"_167":188},{"_78":623,"_365":509,"_366":30,"_167":188},{"_78":621,"_365":622,"_366":86,"_167":188},{"_78":619,"_365":613,"_366":30,"_167":620},{"_78":617,"_365":505,"_366":86,"_167":618},{"_78":615,"_365":505,"_366":30,"_167":616},{"_78":612,"_365":613,"_366":86,"_167":614},"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.",3,[688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780],[657,658,659,660,661,662],{"_359":683,"_213":683,"_167":188,"_332":684},{"_359":678,"_213":678,"_167":188,"_332":679},{"_359":674,"_213":674,"_167":188,"_332":675},{"_359":434,"_213":434,"_167":188,"_332":671},{"_359":570,"_213":570,"_167":188,"_332":668},{"_359":381,"_213":381,"_167":188,"_332":663},[664,665],{"_78":667,"_365":381,"_366":30,"_167":385},{"_78":666,"_365":381,"_366":30,"_167":385},"ncid","VisitorID",[669],{"_78":670,"_365":570,"_366":86,"_167":575},"_gh_sess",[672],{"_78":673,"_365":434,"_366":86,"_167":443},"YSC","t.contentsquare.net",[676],{"_78":677,"_365":674,"_366":86,"_167":529},"_cs_same_site","bizible.com",[680],{"_78":681,"_365":678,"_366":30,"_167":682},"_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",[685],{"_78":686,"_365":683,"_366":30,"_167":687},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":860,"_365":505,"_366":30,"_167":861},{"_78":799,"_365":509,"_366":30,"_167":618},{"_78":833,"_365":613,"_366":86,"_167":834},{"_78":859,"_365":505,"_366":30,"_167":836},{"_78":858,"_365":505,"_366":30,"_167":836},{"_78":831,"_365":635,"_366":86,"_167":832},{"_78":784,"_365":509,"_366":30,"_167":188},{"_78":814,"_365":509,"_366":30,"_167":618},{"_78":799,"_365":613,"_366":30,"_167":618},{"_78":799,"_365":526,"_366":30,"_167":811},{"_78":801,"_365":509,"_366":30,"_167":632},{"_78":799,"_365":854,"_366":30,"_167":618},{"_78":787,"_365":526,"_366":30,"_167":618},{"_78":801,"_365":804,"_366":30,"_167":632},{"_78":856,"_365":505,"_366":30,"_167":857},{"_78":855,"_365":505,"_366":30,"_167":529},{"_78":814,"_365":854,"_366":30,"_167":618},{"_78":799,"_365":796,"_366":30,"_167":811},{"_78":787,"_365":854,"_366":30,"_167":618},{"_78":831,"_365":526,"_366":86,"_167":832},{"_78":839,"_365":613,"_366":30,"_167":188},{"_78":853,"_365":505,"_366":30,"_167":524},{"_78":787,"_365":613,"_366":30,"_167":618},{"_78":814,"_365":622,"_366":30,"_167":845},{"_78":851,"_365":505,"_366":30,"_167":852},{"_78":814,"_365":796,"_366":30,"_167":845},{"_78":814,"_365":823,"_366":30,"_167":618},{"_78":849,"_365":505,"_366":86,"_167":850},{"_78":814,"_365":526,"_366":30,"_167":618},{"_78":799,"_365":823,"_366":30,"_167":618},{"_78":831,"_365":509,"_366":86,"_167":832},{"_78":799,"_365":622,"_366":30,"_167":811},{"_78":795,"_365":613,"_366":30,"_167":188},{"_78":847,"_365":622,"_366":30,"_167":848},{"_78":831,"_365":823,"_366":86,"_167":832},{"_78":792,"_365":846,"_366":30,"_167":793},{"_78":814,"_365":511,"_366":30,"_167":845},{"_78":792,"_365":509,"_366":30,"_167":793},{"_78":843,"_365":505,"_366":86,"_167":844},{"_78":839,"_365":635,"_366":30,"_167":188},{"_78":842,"_365":613,"_366":86,"_167":813},{"_78":840,"_365":505,"_366":86,"_167":841},{"_78":814,"_365":804,"_366":30,"_167":618},{"_78":839,"_365":509,"_366":30,"_167":188},{"_78":837,"_365":505,"_366":30,"_167":838},{"_78":787,"_365":804,"_366":30,"_167":618},{"_78":835,"_365":505,"_366":30,"_167":836},{"_78":798,"_365":509,"_366":30,"_167":188},{"_78":833,"_365":505,"_366":86,"_167":834},{"_78":831,"_365":613,"_366":86,"_167":832},{"_78":829,"_365":505,"_366":30,"_167":830},{"_78":783,"_365":509,"_366":30,"_167":188},{"_78":809,"_365":505,"_366":30,"_167":810},{"_78":827,"_365":505,"_366":86,"_167":828},{"_78":824,"_365":825,"_366":30,"_167":826},{"_78":792,"_365":613,"_366":30,"_167":793},{"_78":787,"_365":823,"_366":30,"_167":618},{"_78":787,"_365":511,"_366":30,"_167":797},{"_78":814,"_365":800,"_366":30,"_167":618},{"_78":822,"_365":505,"_366":30,"_167":529},{"_78":787,"_365":800,"_366":30,"_167":618},{"_78":794,"_365":509,"_366":30,"_167":188},{"_78":820,"_365":505,"_366":30,"_167":821},{"_78":817,"_365":818,"_366":30,"_167":819},{"_78":815,"_365":505,"_366":86,"_167":816},{"_78":784,"_365":613,"_366":30,"_167":188},{"_78":814,"_365":613,"_366":30,"_167":618},{"_78":812,"_365":613,"_366":86,"_167":813},{"_78":787,"_365":622,"_366":30,"_167":797},{"_78":799,"_365":511,"_366":30,"_167":811},{"_78":809,"_365":635,"_366":86,"_167":810},{"_78":807,"_365":505,"_366":86,"_167":808},{"_78":805,"_365":505,"_366":30,"_167":806},{"_78":799,"_365":804,"_366":30,"_167":618},{"_78":802,"_365":505,"_366":30,"_167":803},{"_78":794,"_365":613,"_366":30,"_167":188},{"_78":801,"_365":505,"_366":30,"_167":632},{"_78":798,"_365":613,"_366":30,"_167":188},{"_78":799,"_365":800,"_366":30,"_167":618},{"_78":795,"_365":509,"_366":30,"_167":188},{"_78":798,"_365":635,"_366":30,"_167":188},{"_78":783,"_365":635,"_366":30,"_167":188},{"_78":787,"_365":796,"_366":30,"_167":797},{"_78":795,"_365":635,"_366":30,"_167":188},{"_78":794,"_365":635,"_366":30,"_167":188},{"_78":792,"_365":539,"_366":30,"_167":793},{"_78":790,"_365":505,"_366":30,"_167":791},{"_78":788,"_365":505,"_366":30,"_167":789},{"_78":787,"_365":509,"_366":30,"_167":618},{"_78":785,"_365":505,"_366":86,"_167":786},{"_78":784,"_365":635,"_366":30,"_167":188},{"_78":783,"_365":613,"_366":30,"_167":188},{"_78":781,"_365":505,"_366":30,"_167":782},"_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.",[1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103],[867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891],{"_359":1010,"_213":1010,"_167":188,"_332":1011},{"_359":1004,"_213":1004,"_167":188,"_332":1005},{"_359":992,"_213":992,"_167":188,"_332":993},{"_359":989,"_213":989,"_167":188,"_332":990},{"_359":986,"_213":986,"_167":188,"_332":987},{"_359":981,"_213":981,"_167":188,"_332":982},{"_359":976,"_213":976,"_167":188,"_332":977},{"_359":434,"_213":434,"_167":188,"_332":973},{"_359":969,"_213":969,"_167":188,"_332":970},{"_359":422,"_213":422,"_167":188,"_332":967},{"_359":417,"_213":417,"_167":188,"_332":965},{"_359":960,"_213":960,"_167":188,"_332":961},{"_359":955,"_213":955,"_167":188,"_332":956},{"_359":950,"_213":950,"_167":188,"_332":951},{"_359":947,"_213":947,"_167":188,"_332":948},{"_359":391,"_213":391,"_167":188,"_332":945},{"_359":937,"_213":937,"_167":188,"_332":938},{"_359":566,"_213":566,"_167":188,"_332":931},{"_359":925,"_213":925,"_167":188,"_332":926},{"_359":921,"_213":921,"_167":188,"_332":922},{"_359":916,"_213":916,"_167":188,"_332":917},{"_359":912,"_213":912,"_167":188,"_332":913},{"_359":907,"_213":907,"_167":188,"_332":908},{"_359":561,"_213":561,"_167":188,"_332":900},{"_359":892,"_213":892,"_167":188,"_332":893},"pexels.com",[894,895],{"_78":898,"_365":892,"_366":30,"_167":899},{"_78":896,"_365":892,"_366":86,"_167":897},"_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",[901,902],{"_78":905,"_365":561,"_366":30,"_167":906},{"_78":903,"_365":561,"_366":30,"_167":904},"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",[909],{"_78":910,"_365":907,"_366":30,"_167":911},"_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",[914],{"_78":915,"_365":912,"_366":30,"_167":400},"__cflb","instagram.com",[918],{"_78":919,"_365":916,"_366":30,"_167":920},"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",[923],{"_78":898,"_365":921,"_366":30,"_167":924},"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",[927,928],{"_78":930,"_365":925,"_366":30,"_167":400},{"_78":929,"_365":925,"_366":30,"_167":400},"AWSALBTG","AWSALBTGCORS",[932,933],{"_78":935,"_365":566,"_366":30,"_167":936},{"_78":934,"_365":566,"_366":30,"_167":400},"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",[939,940,941],{"_78":944,"_365":937,"_366":30,"_167":400},{"_78":943,"_365":937,"_366":86,"_167":400},{"_78":942,"_365":937,"_366":30,"_167":400},"AWSALBCORS","JSESSIONID","AWSALB",[946],{"_78":898,"_365":391,"_366":30,"_167":899},"hcaptcha.com",[949],{"_78":898,"_365":947,"_366":30,"_167":899},"tracking.intentsify.io",[952],{"_78":953,"_365":950,"_366":30,"_167":954},"userId","Used to track usage of the embedded video player.","static.reo.dev",[957],{"_78":958,"_365":955,"_366":30,"_167":959},"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",[962],{"_78":963,"_365":960,"_366":30,"_167":964},"dd_cookie_test_","dd_cookie_test",[966],{"_78":898,"_365":417,"_366":30,"_167":899},[968],{"_78":898,"_365":422,"_366":30,"_167":899},"mynvidia.force.com",[971,972],{"_78":905,"_365":969,"_366":30,"_167":906},{"_78":903,"_365":969,"_366":30,"_167":904},[974],{"_78":975,"_365":434,"_366":30,"_167":443},"VISITOR_PRIVACY_METADATA","prod.cloudmatchbeta.nvidiagrid.net",[978],{"_78":979,"_365":976,"_366":86,"_167":980},"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",[983],{"_78":984,"_365":981,"_366":30,"_167":985},"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",[988],{"_78":898,"_365":986,"_366":30,"_167":899},"cdn.readme.io",[991],{"_78":898,"_365":989,"_366":30,"_167":899},"queue-it.net",[994,995,996,997,998],{"_78":1003,"_365":992,"_366":30,"_167":188},{"_78":1002,"_365":992,"_366":30,"_167":188},{"_78":1001,"_365":992,"_366":30,"_167":188},{"_78":1000,"_365":992,"_366":30,"_167":188},{"_78":999,"_365":992,"_366":30,"_167":188},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","api.hcaptcha.com",[1006,1007],{"_78":1009,"_365":1004,"_366":30,"_167":400},{"_78":915,"_365":1004,"_366":30,"_167":1008},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[1012,1013],{"_78":898,"_365":1010,"_366":30,"_167":899},{"_78":896,"_365":1010,"_366":86,"_167":1014},"This domain is owned by Vimeo. The main business activity is: Video Hosting/Sharing",{"_78":1200,"_365":1170,"_366":86,"_167":1201},{"_78":1129,"_365":505,"_366":30,"_167":1209},{"_78":1207,"_365":800,"_366":86,"_167":1208},{"_78":1196,"_365":1163,"_366":86,"_167":1197},{"_78":1206,"_365":539,"_366":86,"_167":188},{"_78":1205,"_365":505,"_366":86,"_167":524},{"_78":1203,"_365":505,"_366":30,"_167":1204},{"_78":930,"_365":1184,"_366":30,"_167":1115},{"_78":1156,"_365":846,"_366":86,"_167":1202},{"_78":1200,"_365":1163,"_366":86,"_167":1201},{"_78":1122,"_365":1195,"_366":86,"_167":1124},{"_78":929,"_365":1114,"_366":30,"_167":1115},{"_78":903,"_365":1195,"_366":30,"_167":904},{"_78":1198,"_365":505,"_366":86,"_167":1199},{"_78":1196,"_365":1170,"_366":86,"_167":1197},{"_78":905,"_365":1195,"_366":30,"_167":1121},{"_78":1193,"_365":505,"_366":30,"_167":1194},{"_78":929,"_365":1106,"_366":30,"_167":1115},{"_78":1111,"_365":1130,"_366":30,"_167":1113},{"_78":1191,"_365":505,"_366":30,"_167":1192},{"_78":942,"_365":1112,"_366":30,"_167":1127},{"_78":930,"_365":1106,"_366":30,"_167":1115},{"_78":944,"_365":1112,"_366":30,"_167":1188},{"_78":903,"_365":1120,"_366":30,"_167":904},{"_78":1189,"_365":613,"_366":30,"_167":1190},{"_78":930,"_365":1135,"_366":30,"_167":1136},{"_78":944,"_365":823,"_366":30,"_167":1188},{"_78":1186,"_365":505,"_366":30,"_167":1187},{"_78":935,"_365":823,"_366":30,"_167":1180},{"_78":1153,"_365":804,"_366":86,"_167":188},{"_78":1185,"_365":854,"_366":86,"_167":188},{"_78":929,"_365":1184,"_366":30,"_167":1115},{"_78":1182,"_365":1114,"_366":86,"_167":1183},{"_78":1141,"_365":505,"_366":30,"_167":1128},{"_78":1111,"_365":505,"_366":30,"_167":1113},{"_78":1181,"_365":1134,"_366":30,"_167":188},{"_78":935,"_365":505,"_366":30,"_167":1180},{"_78":944,"_365":1106,"_366":30,"_167":1179},{"_78":1177,"_365":825,"_366":86,"_167":1178},{"_78":1175,"_365":854,"_366":86,"_167":1176},{"_78":1173,"_365":505,"_366":86,"_167":1174},{"_78":1172,"_365":505,"_366":30,"_167":188},{"_78":1129,"_365":1112,"_366":30,"_167":1131},{"_78":1171,"_365":1134,"_366":30,"_167":188},{"_78":1162,"_365":1170,"_366":86,"_167":1164},{"_78":1168,"_365":804,"_366":30,"_167":1169},{"_78":1166,"_365":539,"_366":30,"_167":1167},{"_78":1165,"_365":526,"_366":30,"_167":188},{"_78":1162,"_365":1163,"_366":86,"_167":1164},{"_78":1160,"_365":505,"_366":30,"_167":1161},{"_78":1158,"_365":1120,"_366":86,"_167":1159},{"_78":1156,"_365":526,"_366":86,"_167":1157},{"_78":1154,"_365":505,"_366":30,"_167":1155},{"_78":1153,"_365":509,"_366":86,"_167":188},{"_78":905,"_365":1123,"_366":30,"_167":1121},{"_78":934,"_365":1142,"_366":30,"_167":1128},{"_78":1151,"_365":509,"_366":30,"_167":1152},{"_78":1150,"_365":505,"_366":30,"_167":647},{"_78":963,"_365":846,"_366":30,"_167":1144},{"_78":1148,"_365":505,"_366":30,"_167":1149},{"_78":1146,"_365":505,"_366":30,"_167":1147},{"_78":1145,"_365":505,"_366":30,"_167":188},{"_78":963,"_365":509,"_366":30,"_167":1144},{"_78":1141,"_365":823,"_366":30,"_167":1128},{"_78":898,"_365":800,"_366":30,"_167":1143},{"_78":1141,"_365":1142,"_366":30,"_167":1128},{"_78":1140,"_365":1134,"_366":30,"_167":188},{"_78":1139,"_365":505,"_366":30,"_167":1109},{"_78":1137,"_365":1120,"_366":30,"_167":1138},{"_78":929,"_365":1135,"_366":30,"_167":1136},{"_78":1133,"_365":1134,"_366":30,"_167":188},{"_78":1132,"_365":505,"_366":30,"_167":188},{"_78":1122,"_365":1120,"_366":86,"_167":1124},{"_78":934,"_365":505,"_366":30,"_167":1128},{"_78":1129,"_365":1130,"_366":30,"_167":1131},{"_78":934,"_365":823,"_366":30,"_167":1128},{"_78":942,"_365":823,"_366":30,"_167":1127},{"_78":1125,"_365":505,"_366":30,"_167":1126},{"_78":903,"_365":1123,"_366":30,"_167":904},{"_78":1122,"_365":1123,"_366":86,"_167":1124},{"_78":905,"_365":1120,"_366":30,"_167":1121},{"_78":1118,"_365":1114,"_366":86,"_167":1119},{"_78":1116,"_365":627,"_366":30,"_167":1117},{"_78":930,"_365":1114,"_366":30,"_167":1115},{"_78":1111,"_365":1112,"_366":30,"_167":1113},{"_78":898,"_365":622,"_366":30,"_167":1110},{"_78":1108,"_365":505,"_366":30,"_167":1109},{"_78":942,"_365":1106,"_366":30,"_167":1107},{"_78":1104,"_365":505,"_366":30,"_167":1105},"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.

",{"_862":16,"_651":55,"_551":55,"_336":55,"_326":55},"hasConsented","gpcApplied","showBanner","geolocation",{"_1224":1225,"_1226":1227},"country","US","state","OR","query","categories",[1247,1248,1249,1250,1251],"optionUrls",[1235,1236,1237,1238,1239,1240,1241,1242,1243,1244,1245,1246],"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/-",{"_206":1326,"_1253":1327,"_1255":55},{"_206":1291,"_1253":1292,"_1255":654},{"_206":1274,"_1253":1275,"_1255":1276},{"_206":207,"_1253":1267,"_1255":1268},{"_206":1252,"_1253":1254,"_1255":1256},"helm-charts","items",[1257,1258],"startIndex",10,{"_206":1252,"_213":1264,"_161":162,"_163":-7,"_208":1265,"_216":1261,"_242":1266,"_1263":1245},{"_206":1252,"_213":1259,"_161":162,"_163":-7,"_208":1260,"_216":1261,"_242":1262,"_1263":1246},"NVIDIA NIM Operator","k8s-nim-operator","NVIDIA",[],"url","GPU Operator","gpu-operator",[],[1269],9,{"_206":207,"_213":1270,"_161":162,"_163":1271,"_208":1272,"_216":1261,"_242":1273,"_1263":1244},"Riva Skills Quick Start","riva","riva_quickstart",[],"models",[1277,1278,1279],6,{"_206":1274,"_213":1287,"_161":162,"_163":1288,"_208":1289,"_216":1261,"_242":1290,"_1263":1241},{"_206":1274,"_213":1284,"_161":162,"_163":1281,"_208":1285,"_216":1261,"_242":1286,"_1263":1242},{"_206":1274,"_213":1280,"_161":162,"_163":1281,"_208":1282,"_216":1261,"_242":1283,"_1263":1243},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1293,1294,1295],{"_206":1291,"_213":1323,"_161":162,"_163":1297,"_208":1324,"_216":1261,"_242":1325,"_1263":1238},{"_206":1291,"_213":1320,"_161":162,"_163":-7,"_208":1321,"_216":1261,"_242":1322,"_1263":1239},{"_206":1291,"_213":1296,"_161":162,"_163":1297,"_208":1298,"_216":1261,"_242":1299,"_1263":1240},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1300,1301,1302],{"_78":1314,"_213":1315,"_210":1316,"_221":1317,"_1307":1318},{"_78":1297,"_213":1310,"_210":1311,"_221":1306,"_1307":1312},{"_78":1303,"_213":1304,"_210":1305,"_221":1306,"_1307":1308},"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",[1309],"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.",[1309,1313],"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",[1309,1313,1319],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1300,1301,1302],"containers",[1328,1329,1330],{"_206":1326,"_213":1342,"_161":1335,"_163":1343,"_208":1344,"_216":1261,"_242":1345,"_1263":1235},{"_206":1326,"_213":1334,"_161":1335,"_163":162,"_208":1336,"_216":1261,"_242":1337,"_1263":1236},{"_206":1326,"_213":1331,"_161":162,"_163":-7,"_208":1332,"_216":1261,"_242":1333,"_1263":1237},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[1338,1300],{"_78":1339,"_213":1304,"_210":1305,"_221":1340,"_1307":1341},"nim-dev","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[1309],"DeepSeek-R1","deepseek-ai","deepseek-r1",[1338,1300],"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",{"_1375":1376,"_1377":275,"_1378":30,"_1379":1380,"_1253":1381,"_1382":1383,"_1384":1376,"_1385":30,"_1386":30,"_1387":30,"_1388":1389},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1390,1391,1392,1393,1394,1395,1396],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1388":1418,"_46":1419,"_1382":1420},{"_1411":1412,"_1388":1413,"_46":1414,"_1384":1314},{"_1388":1409,"_46":1410,"_1382":1383},{"_1388":1406,"_46":1407,"_1382":1408},{"_1388":1403,"_46":1404,"_1382":1405},{"_1388":1400,"_46":1401,"_1382":1402},{"_1388":1397,"_46":1398,"_1382":1399},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1415],"Enterprise Catalog","/enterprise",[1416,1417],"NVAIE","READ","Explore Catalog","/","common-home",{"_112":113,"_114":115,"_116":117,"_118":119,"_120":121,"_122":123},{"_111":30}]