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

This UNet model is adapted from the original version of the UNet model which is\na convolutional auto-encoder for 2D image segmentation. UNet was first introduced by\nOlaf Ronneberger, Philip Fischer, and Thomas Brox in the paper:\nUNet: Convolutional Networks for Biomedical Image Segmentation.

This work proposes a modified version of UNet, called TinyUNet which performs efficiently and with very high accuracy\non the industrial anomaly dataset DAGM2007.\nTinyUNet, like the original UNet is composed of two parts:

an encoding sub-network (left-side)
a decoding sub-network (right-side).

It repeatedly applies 3 downsampling blocks composed of two 2D convolutions followed by a 2D max pooling\nlayer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D\nlayer followed by a 2D convolution, a concatenation operation with the residual connection and two 2D convolutions.

TinyUNet has been introduced to reduce the model capacity which was leading to a high degree of over-fitting on a\nsmall dataset like DAGM2007. The complete architecture is presented in the figure below:

$\"UnetModel\"$

Figure 1. Architecture of the UNet Industrial

Default Configuration

This model trains in 2500 epochs, under the following setup:

\n
Global Batch Size: 16
\n
\n
Optimizer RMSProp:
\n
- decay: 0.9
- momentum: 0.8
- centered: True
\n
\n
Learning Rate Schedule: Exponential Step Decay
\n
- decay: 0.8
- steps: 500
- initial learning rate: 1e-4
\n
\n
Weight Initialization: He Uniform Distribution (introduced by Kaiming He et al. in 2015 to address issues related ReLU activations in deep neural networks)
\n
\n
Loss Function: Adaptive
\n
- When DICE Loss < 0.3, Loss = Binary Cross Entropy
- Else, Loss = DICE Loss
\n
\n
Data Augmentation
\n
- Random Horizontal Flip (50% chance)
- Random Rotation 90°
\n
\n
Activation Functions:
\n
- ReLU is used for all layers
- Sigmoid is used at the output to ensure that the ouputs are between [0, 1]
\n
\n
Weight decay: 1e-5
\n

Feature support matrix

The following features are supported by this model.

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

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

Feature	UNet Medical
Automatic mixed precision (AMP)	Yes
Horovod Multi-GPU (NCCL)	Yes
Accelerated Linear Algebra (XLA)	Yes

Features

Automatic Mixed Precision (AMP)

This implementation of UNet uses AMP to implement mixed precision training. It allows us to use FP16 training with FP32 master weights by modifying just a few lines of code.

Horovod

Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the Horovod: Official repository.

Multi-GPU training with Horovod

Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the TensorFlow tutorial.

XLA support (experimental)

XLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage: most internal benchmarks run ~1.1-1.5x faster after XLA is enabled.

Mixed precision training

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

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

This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full mixed precision methodology in your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow framework code makes all necessary model changes internally.

In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.

How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.

Enabling mixed precision

This implementation exploits the TensorFlow Automatic Mixed Precision feature. In order to enable mixed precision training, the following environment variables must be defined with the correct value before the training starts:

TF_ENABLE_AUTO_MIXED_PRECISION=1\n

Exporting these variables ensures that loss scaling is performed correctly and automatically.\nBy supplying the --amp flag to the main.py script while training in FP32, the following variables are set to their correct value for mixed precision training:

if params.use_amp:\n  os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n

Enabling TF32

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.

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

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

","resourceVersions",[196,197],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_167":23,"_168":-5,"_49":169,"_170":-5,"_171":172,"_173":-5,"_174":81,"_175":16,"_176":31,"_177":-5,"_178":31,"_179":180,"_181":87,"_182":183,"_184":87,"_185":186,"_187":87,"_188":189,"_190":87,"_191":192,"_193":87,"_194":195},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/resources/unet_2d-industrial_tf1/-","version","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/dle/unet_2d-industrial_tf1:20.06.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/resources/unet_2d-industrial_tf1/-/version-history","accessCheckResult",{"_166":87},"hasRelatedCollections","context",{"_162":163,"_164":165},1785920710979,[],[],[155,156,157,158,159,160,161],{"_45":60,"_47":71,"_49":62,"_51":63,"_53":72,"_55":56},{"_45":60,"_47":69,"_49":62,"_51":63,"_53":70,"_55":56},{"_45":60,"_47":67,"_49":62,"_51":63,"_53":68,"_55":56},{"_45":60,"_47":65,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":62,"_51":63,"_53":64,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":59,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"orgName","nvidia","teamName","dle","allowed","versionId","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1663857890072],"updatedDate","totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","### Changelog\n\nJune 2020\n\n* Updated training and inference accuracy with A100 results\n* Updated training and inference performance with A100 results\n\nOctober 2019\n * Jupyter notebooks added\n\nMarch,2019\n * Initial release\n\n### Known issues\nThere are no known issues with this model.","hasReleaseNotes","setup","The following section lists the requirements in order to start training the UNet Medical model.\n \n### Requirements\n \nThis repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- TensorFlow 20.06-tf1-py3 [NGC container](https://ngc.nvidia.com/registry/nvidia-tensorflow)\n- GPU-based architecture:\n - [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n - [NVIDIA Turing](https://www.nvidia.com/en-us/geforce/turing/)\n - [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning 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 TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)\n \nFor those unable to use the TensorFlow NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the UNet model on the [EM segmentation challenge dataset](http://brainiac2.mit.edu/isbi_challenge/home). These steps enable you to build the UNet TensorFlow NGC container, train and evaluate your model, and generate predictions on the test data. Furthermore, you can then choose to:\n* compare your evaluation accuracy with our [Training accuracy results](#training-accuracy-results),\n* compare your training performance with our [Training performance benchmark](#training-performance-benchmark),\n* compare your inference performance with our [Inference performance benchmark](#inference-performance-benchmark).\n \nFor the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n\n ```bash\n git clone https://github.com/NVIDIA/DeepLearningExamples\n cd DeepLearningExamples/TensorFlow/Segmentation/UNet_Industrial\n ```\n\n2. Build the UNet TensorFlow NGC container.\n\n ```bash\n # Build the docker container\n docker build . --rm -t unet_industrial:latest\n ```\n3. Start an interactive session in the NGC container to run preprocessing/training/inference.\n\n ```bash\n # make a directory for the dataset, for example ./dataset\n mkdir \n # make a directory for results, for example ./results\n mkdir \n # start the container with nvidia-docker\n nvidia-docker run -it --rm --gpus all \\\n --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 \\\n -v :/data/ \\\n -v :/results \\\n unet_industrial:latest\n ```\n\n4. Download and preprocess the dataset: DAGM2007\n\n In order to download the dataset. You can execute the following:\n \n ```bash\n ./download_and_preprocess_dagm2007.sh /data\n ```\n\n **Important Information:** Some files of the dataset require an account to be downloaded, the script will invite you to download them manually and put them in the correct directory.\n\n5. Start training.\n\nTo run training for a default configuration (as described in Default configuration, for example 1/4/8 GPUs,\nFP32/TF-AMP), launch one of the scripts in the `./scripts` directory called\n`./scripts/UNet{_AMP}_{1, 4, 8}GPU.sh`\n\nEach of the scripts requires three parameters:\n* path to the results directory of the model as the first argument\n* path to the dataset as a second argument\n* class ID from DAGM used (between 1-10)\n\nFor example, for class 1:\n\n```bash\ncd scripts/\n./UNet_1GPU.sh /results /data 1\n```\n\n6. Run evaluation\n\nModel evaluation on a checkpoint can be launched by running one of the scripts in the `./scripts` directory\ncalled `./scripts/UNet{_AMP}_EVAL.sh`.\n\nEach of the scripts requires three parameters:\n* path to the results directory of the model as the first argument\n* path to the dataset as a second argument\n* class ID from DAGM used (between 1-10)\n\nFor example, for class 1:\n\n```bash\ncd scripts/\n./UNet_EVAL.sh /results /data 1\n```","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Command line options\n\nTo see the full list of available options and their descriptions, use the -h or --help command line option, for example:\n\n```bash\npython main.py --help\n```\n\nThe following mandatory flags must be used to tune different aspects of the training:\n\ngeneral\n-------\n`--exec_mode=train_and_evaluate` Which execution mode to run the model into.\n\n`--iter_unit=batch` Will the model be run for X batches or X epochs ?\n\n`--num_iter=2500` Number of iterations to run.\n\n`--batch_size=16` Size of each minibatch per GPU.\n\n`--results_dir=/path/to/results` Directory in which to write training logs, summaries and checkpoints.\n\n`--data_dir=/path/to/dataset` Directory which contains the DAGM2007 dataset.\n\n`--dataset_name=DAGM2007` Name of the dataset used in this run (only DAGM2007 is supported atm).\n\n`--dataset_classID=1` ClassID to consider to train or evaluate the network (used for DAGM).\n\nmodel\n-----\n\n`--amp` Enable Automatic Mixed Precision to speedup FP32 computation using tensor cores.\n\n`--xla` Enable TensorFlow XLA to maximise performance.\n\n`--use_auto_loss_scaling` Use AutoLossScaling in TF-AMP\n\n#### Dataset guidelines\n\nThe UNet model was trained with the [Weakly Supervised Learning for Industrial Optical Inspection (DAGM 2007)](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html) dataset.\n\n> The competition is inspired by problems from industrial image processing. In order to satisfy their customers' needs, companies have to guarantee the quality of their products, which can often be achieved only by inspection of the finished product. Automatic visual defect detection has the potential to reduce the cost of quality assurance significantly.\n>\n> The competitors have to design a stand-alone algorithm which is able to detect miscellaneous defects on various background textures.\n>\n> The particular challenge of this contest is that the algorithm must learn, without human intervention, to discern defects automatically from a weakly labeled (i.e., labels are not exact to the pixel level) training set, the exact characteristics of which are unknown at development time. During the competition, the programs have to be trained on new data without any human guidance.\n\n**Source:** https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html\n\n> The provided data is artificially generated, but similar to real world problems. It consists of multiple data sets, each consisting of 1000 images showing the background texture without defects, and of 150 images with one labeled defect each on the background texture. The images in a single data set are very similar, but each data set is generated by a different texture model and defect model.\n\n> Not all deviations from the texture are necessarily defects. The algorithm will need to use the weak labels provided during the training phase to learn the properties that characterize a defect.\n\n> Below are two sample images from two data sets. In both examples, the left images are without defects; the right ones contain a scratch-shaped defect which appears as a thin dark line, and a diffuse darker area, respectively. The defects are weakly labeled by a surrounding ellipse, shown in red.\n\n![DAGM_Examples](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow/Segmentation/UNet_Industrial//images/dagm2007.png)\n\n\nThe DAGM2007 challenge comes in propose two different challenges:\n- A development set: public and available for download from\n[here](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html).\nThe number of classes and sub-challenges for the development set is 6.\n- A competition set: which requires an account to be downloaded from [here](https://hci.iwr.uni-heidelberg.de/node/3616).\nThe number of classes and sub-challenges for the competition set is 10.\n\nThe challenge consists in designing a single model with a set of predefined hyper-parameters which will not change\nacross the 10 different classes or sub-challenges of the competition set.\n\nThe performance shall be measured on the competition set which is normalized and more complex that the public dataset\nwhile offering the most unbiased evaluation method.\n\n### Training Process\n\n*Laplace Smoothing*\n\nWe use this technique in the DICE loss to improve the training efficiency. This technique consists in replacing the\nepsilon parameter (used to avoid dividing by zero and very small: +/- 1e-7) by 1. You can find more information on:\n[https://en.wikipedia.org/wiki/Additive_smoothing](https://en.wikipedia.org/wiki/Additive_smoothing)\n\n*Adaptive Loss*\n\nThe DICE Loss is not able to provide a meaningful gradient at initialisation. This leads to a model instability which\noften push the model to diverge. Nonetheless, once the model starts to converge, DICE loss is able to very efficiently\nfully train the model. Therefore, we implemented an *adaptive loss* which is composed of two sub-losses:\n\n- Binary Cross-Entropy (BCE)\n- DICE Loss\n\nThe model is trained with the BCE loss until the DICE Loss reach a experimentally defined threshold (0.3).\nThereafter, DICE loss is used to finish training.\n\n*Weak Labelling*\n\nThis dataset is referred as weakly labelled. That means that the segmentation labels are not given at the pixel level\nbut rather in an approximate fashion.","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA's latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\n\nThe following sections shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nTo benchmark the inference performance, you can run one of the scripts in the `./scripts/benchmarking/` directory\ncalled `./scripts/benchmarking/UNet_trainbench{_AMP}_{1, 4, 8}GPU.sh`.\n\nEach of the scripts requires three parameters:\n* path to the dataset as the first argument\n* class ID from DAGM used (between 1-10)\n\nFor example:\n\n```bash\ncd scripts/benchmarking/\n./UNet_trainbench_1GPU.sh /data 1\n```\n\n#### Inference performance benchmark\n\nTo benchmark the training performance, you can run one of the scripts in the `./scripts/benchmarking/` directory\ncalled `./scripts/benchmarking/UNet_evalbench{_AMP}.sh`.\n\nEach of the scripts requires three parameters:\n* path to the dataset as the first argument\n* class ID from DAGM used (between 1-10)\n\nFor example:\n\n```bash\ncd scripts/benchmarking/\n./UNet_evalbench_AMP.sh /data 1\n```\n\n### Results\n\nThe following sections provide details on the achieved results in training accuracy, performance and inference performance.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)\n \nOur results were obtained by running the `./scripts/UNet{_AMP}_{1, 8}GPU.sh` training\nscript in the `tensorflow:20.06-tf1-py3` NGC container on NVIDIA DGX A100 (8x A100 40GB) GPUs.\n\n| GPUs | Batch size / GPU | Accuracy - TF32 | Accuracy - mixed precision | Time to train - TF32 [min] | Time to train - mixed precision [min] | Time to train speedup (TF32 to mixed precision) |\n|:----:|:----------------:|:---------------:|:--------------------------:|:--------------------:|:-------------------------------:|:-----------------------------------------------:|\n| 1 | 16 | 0.9717 | 0.9726 | 3.6 | 2.3 | 1.57 |\n| 8 | 2 | 0.9733 | 0.9683 | 4.3 | 3.5 | 1.23 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `./scripts/UNet{_AMP}_{1, 8}GPU.sh` training\nscript in the `tensorflow:20.06-tf1-py3` NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.\n\n| GPUs | Batch size / GPU | Accuracy - FP32 | Accuracy - mixed precision | Time to train - FP32 [min] | Time to train - mixed precision [min] | Time to train speedup (FP32 to mixed precision) |\n|:----:|:----------------:|:---------------:|:--------------------------:|:--------------------:|:-------------------------------:|:-----------------------------------------------:|\n| 1 | 16 | 0.9643 | 0.9653 | 10 | 8 | 1.25 |\n| 8 | 2 | 0.9637 | 0.9655 | 2.5 | 2.5 | 1.00 |\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the scripts\n`./scripts/benchmarking/UNet_trainbench{_AMP}_{1, 4, 8}GPU.sh` training script in the\nTensorFlow `20.06-tf1-py3` NGC container on NVIDIA DGX A100 (8x A100 40GB) GPUs.\n\n| GPUs | Batch size / GPU | Throughput - TF32 [img/s] | Throughput - mixed precision [img/s] | Throughput speedup (TF32 - mixed precision) | Strong scaling - TF32 | Strong scaling - mixed precision |\n|:----:|:----------------:|:-------------------------:|:------------------------------------:|:-------------------------------------------:|:---------------------:|:--------------------------------:|\n| 1 | 16 | 135.95 | 255.26 | 1.88 | - | - |\n| 4 | 4 | 420.2 | 691.19 | 1.64 | 3.09 | 2.71 |\n| 8 | 2 | 655.05 | 665.66 | 1.02 | 4.82 | 2.61 |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the scripts\n`./scripts/benchmarking/UNet_trainbench{_AMP}_{1, 4, 8}GPU.sh` training script in the\nTensorFlow `20.06-tf1-py3` NGC container on an NVIDIA DGX-1 (8 V100 16GB) GPUs.\n\n| GPUs | Batch size / GPU | Throughput - FP32 [img/s] | Throughput - mixed precision [img/s] | Throughput speedup (FP32 - mixed precision) | Strong scaling - FP32 | Strong scaling - mixed precision |\n|:----:|:----------------:|:-------------------------:|:------------------------------------:|:-------------------------------------------:|:---------------------:|:--------------------------------:|\n| 1 | 16 | 86.95 | 168.54 | 1.94 | - | - |\n| 4 | 4 | 287.01 | 459.07 | 1.60 | 3.30 | 2.72 |\n| 8 | 2 | 474.77 | 444.13 | 0.94 | 5.46 | 2.64 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.\n\n#### Inference performance results\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)\n\nOur results were obtained by running the scripts `./scripts/benchmarking/UNet_evalbench{_AMP}.sh`\nevaluation script in the `20.06-tf1-py3` NGC container on NVIDIA DGX A100 (1x A100 40GB) GPUs.\n\nFP16\n\n| Batch size | Resolution | Throughput Avg [img/s] |\n|:----------:|:----------:|:----------------------:|\n| 1 | 512x512x1 | 247.83 |\n| 8 | 512x512x1 | 761.41 |\n| 16 | 512x512x1 | 823.46 |\n\nTF32\n\n| Batch size | Resolution | Throughput Avg [img/s] |\n|:----------:|:----------:|:----------------------:|\n| 1 | 512x512x1 | 227.97 |\n| 8 | 512x512x1 | 419.70 |\n| 16 | 512x512x1 | 424.57 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nOur results were obtained by running the scripts `./scripts/benchmarking/UNet_evalbench{_AMP}.sh`\nevaluation script in the `20.06-tf1-py3` NGC container on NVIDIA DGX-1 (1x V100 16GB) GPUs.\n\nFP16\n\n| Batch size | Resolution | Throughput Avg [img/s] |\n|:----------:|:----------:|:----------------------:|\n| 1 | 512x512x1 | 157.91 |\n| 8 | 512x512x1 | 438.00 |\n| 16 | 512x512x1 | 469.27 |\n\nFP32\n\n| Batch size | Resolution | Throughput Avg [img/s] |\n|:----------:|:----------:|:----------------------:|\n| 1 | 512x512x1 | 159.65 |\n| 8 | 512x512x1 | 243.99 |\n| 16 | 512x512x1 | 250.23 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.","hasPerformance","policies",[],{"_77":21,"_79":199,"_170":-5,"_176":31,"_178":31,"_194":198,"_181":87},{"_77":23,"_79":23,"_170":-5,"_176":31,"_178":31,"_194":198,"_181":87},[],"Latest (20.06.0)","nvidia/dle/unet_2d-industrial_tf1","key","nvidia/dle/resources/unet_2d-industrial_tf1","resourceType","resources","resourceId","unet_2d-industrial_tf1","shortDescription","This model is a convolutional neural network for 2D image segmentation tuned to avoid overfitting.","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/unet2dind-tf1)\n\n\nThis UNet model is adapted from the original version of the [UNet model](https://arxiv.org/abs/1505.04597) which is\na convolutional auto-encoder for 2D image segmentation. UNet was first introduced by\nOlaf Ronneberger, Philip Fischer, and Thomas Brox in the paper:\n[UNet: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597).\n\nThis work proposes a modified version of UNet, called `TinyUNet` which performs efficiently and with very high accuracy\non the industrial anomaly dataset [DAGM2007](https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html).\n*TinyUNet*, like the original *UNet* is composed of two parts:\n- an encoding sub-network (left-side)\n- a decoding sub-network (right-side).\n\nIt repeatedly applies 3 downsampling blocks composed of two 2D convolutions followed by a 2D max pooling\nlayer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D\nlayer followed by a 2D convolution, a concatenation operation with the residual connection and two 2D convolutions.\n\n`TinyUNet` has been introduced to reduce the model capacity which was leading to a high degree of over-fitting on a\nsmall dataset like DAGM2007. The complete architecture is presented in the figure below:\n\n![UnetModel](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow/Segmentation/UNet_Industrial//images/unet.png)\n\n\nFigure 1. Architecture of the UNet Industrial\n\n### Default Configuration\n\nThis model trains in 2500 epochs, under the following setup:\n\n* Global Batch Size: 16\n\n* Optimizer RMSProp:\n * decay: 0.9\n * momentum: 0.8\n * centered: True\n\n* Learning Rate Schedule: Exponential Step Decay\n * decay: 0.8\n * steps: 500\n * initial learning rate: 1e-4\n\n* Weight Initialization: He Uniform Distribution (introduced by [Kaiming He et al. in 2015](https://arxiv.org/abs/1502.01852) to address issues related ReLU activations in deep neural networks)\n\n* Loss Function: Adaptive\n * When DICE Loss < 0.3, Loss = Binary Cross Entropy\n * Else, Loss = DICE Loss\n\n* Data Augmentation\n * Random Horizontal Flip (50% chance)\n * Random Rotation 90°\n\n* Activation Functions:\n * ReLU is used for all layers\n * Sigmoid is used at the output to ensure that the ouputs are between [0, 1]\n\n* Weight decay: 1e-5\n\n### Feature support matrix\n \nThe following features are supported by this model.\n \n| **Feature** | **UNet Medical** |\n|---------------------------------|-----|\n| Automatic mixed precision (AMP) | Yes |\n| Horovod Multi-GPU (NCCL) | Yes |\n| Accelerated Linear Algebra (XLA)| Yes |\n \n#### Features\n \n**Automatic Mixed Precision (AMP)**\n \nThis implementation of UNet uses AMP to implement mixed precision training. It allows us to use FP16 training with FP32 master weights by modifying just a few lines of code.\n \n**Horovod**\n \nHorovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the [Horovod: Official repository](https://github.com/horovod/horovod).\n \n**Multi-GPU training with Horovod**\n \nOur model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the [TensorFlow tutorial](https://github.com/horovod/horovod/#usage).\n \n**XLA support (experimental)**\n \nXLA is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. The results are improvements in speed and memory usage: most internal benchmarks run ~1.1-1.5x faster after XLA is enabled.\n \n### Mixed precision training\n \nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically. The TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n\n#### Enabling mixed precision\n \nThis implementation exploits the TensorFlow Automatic Mixed Precision feature. In order to enable mixed precision training, the following environment variables must be defined with the correct value before the training starts:\n```\nTF_ENABLE_AUTO_MIXED_PRECISION=1\n```\nExporting these variables ensures that loss scaling is performed correctly and automatically.\nBy supplying the `--amp` flag to the `main.py` script while training in FP32, the following variables are set to their correct value for mixed precision training:\n```\nif params.use_amp:\n os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n```\n \n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. \n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.","displayName","UNet Industrial for TensorFlow1",["D",1667525203236],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/unet_2d-industrial_tf1","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",{"_108":1227,"_110":1228},"ngcApps",[1180],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1161":1162,"_1163":1164,"_1165":1166,"_1167":1168,"_1169":253,"_139":281,"_1170":1171,"_1172":56,"_1173":87,"_1174":87,"_1175":1176,"_1177":87,"_1178":1179},"amplitude",{"_1158":1159,"_794":-7,"_1160":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1152":1153,"_1154":1155,"_1156":1157},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1785920710870,"1.11.0","defaultSearchSuggestions",{"_1034":361,"_1035":1036,"_1037":1038,"_1039":1040},"announcements",[],"consentState",{"_318":1024,"_1025":87,"_1026":31,"_1027":31,"_1028":1029},"consentConfig",{"_300":301,"_302":303,"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319},"showContactAdmin",1785920710871,[],[],[295,296,297,298,299],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":103,"_51":-5,"_53":-5,"_55":56},"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",{"_1016":1017,"_1018":1019,"_1020":1021,"_1022":1023},"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":706,"_79":707,"_168":708,"_329":87,"_330":16,"_332":709,"_334":710},{"_325":609,"_79":610,"_168":611,"_329":31,"_330":59,"_332":612,"_334":613},{"_325":516,"_79":517,"_168":518,"_329":31,"_330":519,"_332":520,"_334":521},{"_325":336,"_79":337,"_168":338,"_329":31,"_330":339,"_332":340,"_334":341},{"_325":326,"_79":327,"_168":328,"_329":31,"_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,[458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479],[342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358],{"_359":453,"_210":453,"_168":361,"_332":454},{"_359":449,"_210":449,"_168":361,"_332":450},{"_359":444,"_210":444,"_168":361,"_332":445},{"_359":440,"_210":440,"_168":361,"_332":441},{"_359":435,"_210":435,"_168":361,"_332":436},{"_359":430,"_210":430,"_168":361,"_332":431},{"_359":422,"_210":422,"_168":361,"_332":423},{"_359":414,"_210":414,"_168":361,"_332":415},{"_359":409,"_210":409,"_168":361,"_332":410},{"_359":404,"_210":404,"_168":361,"_332":405},{"_359":399,"_210":399,"_168":361,"_332":400},{"_359":394,"_210":394,"_168":361,"_332":395},{"_359":389,"_210":389,"_168":361,"_332":390},{"_359":382,"_210":382,"_168":361,"_332":383},{"_359":377,"_210":377,"_168":361,"_332":378},{"_359":370,"_210":370,"_168":361,"_332":371},{"_359":360,"_210":360,"_168":361,"_332":362},"hostName","amazon-adsystem.com","",[363,364],{"_79":369,"_366":360,"_367":31,"_168":368},{"_79":365,"_366":360,"_367":31,"_168":368},"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",[372,373],{"_79":376,"_366":370,"_367":31,"_168":375},{"_79":374,"_366":370,"_367":31,"_168":375},"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",[379],{"_79":380,"_366":377,"_367":31,"_168":381},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[384,385],{"_79":388,"_366":382,"_367":31,"_168":387},{"_79":386,"_366":382,"_367":31,"_168":387},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[391],{"_79":392,"_366":389,"_367":31,"_168":393},"_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",[396],{"_79":397,"_366":394,"_367":31,"_168":398},"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",[401],{"_79":402,"_366":399,"_367":31,"_168":403},"sp","Testing","google.com",[406],{"_79":407,"_366":404,"_367":31,"_168":408},"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.","linkedin.com",[411],{"_79":412,"_366":409,"_367":31,"_168":413},"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",[416,417],{"_79":420,"_366":414,"_367":31,"_168":421},{"_79":418,"_366":414,"_367":31,"_168":419},"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_*","This domain is owned by X (formerly Twitter). The main business activity is: Social Networking Services. Where X (formerly 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.","youtube.com",[424,425],{"_79":428,"_366":422,"_367":31,"_168":429},{"_79":426,"_366":422,"_367":31,"_168":427},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-*","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.","demdex.net",[432],{"_79":433,"_366":430,"_367":31,"_168":434},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[437],{"_79":438,"_366":435,"_367":31,"_168":439},"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",[442],{"_79":443,"_366":440,"_367":31,"_168":403},"R","6sc.co",[446],{"_79":447,"_366":444,"_367":31,"_168":448},"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",[451],{"_79":452,"_366":449,"_367":31,"_168":403},"_hly_gvid","t.contentsquare.net",[455],{"_79":456,"_366":453,"_367":87,"_168":457},"_cs_*","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.",{"_79":514,"_366":481,"_367":31,"_168":515},{"_79":512,"_366":481,"_367":31,"_168":513},{"_79":510,"_366":481,"_367":31,"_168":511},{"_79":392,"_366":481,"_367":31,"_168":509},{"_79":486,"_366":481,"_367":87,"_168":488},{"_79":508,"_366":481,"_367":31,"_168":361},{"_79":507,"_366":484,"_367":31,"_168":361},{"_79":505,"_366":481,"_367":31,"_168":506},{"_79":503,"_366":481,"_367":31,"_168":504},{"_79":502,"_366":481,"_367":31,"_168":361},{"_79":500,"_366":481,"_367":31,"_168":501},{"_79":486,"_366":499,"_367":87,"_168":488},{"_79":497,"_366":484,"_367":31,"_168":498},{"_79":497,"_366":487,"_367":31,"_168":498},{"_79":456,"_366":481,"_367":31,"_168":457},{"_79":495,"_366":481,"_367":31,"_168":496},{"_79":402,"_366":493,"_367":31,"_168":494},{"_79":491,"_366":481,"_367":31,"_168":492},{"_79":489,"_366":481,"_367":31,"_168":490},{"_79":486,"_366":487,"_367":87,"_168":488},{"_79":483,"_366":484,"_367":31,"_168":485},{"_79":480,"_366":481,"_367":87,"_168":482},"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.","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","AMP_*","build.nvidia.com","Supports Amplitude product analytics.","_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.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_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","_zitok","_twpid","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.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575],[522,523,524,525,526],{"_359":435,"_210":435,"_168":361,"_332":546},{"_359":409,"_210":409,"_168":361,"_332":541},{"_359":536,"_210":536,"_168":361,"_332":537},{"_359":531,"_210":531,"_168":361,"_332":532},{"_359":527,"_210":527,"_168":361,"_332":528},"nvidia.partners",[529],{"_79":530,"_366":527,"_367":31,"_168":403},"edge","app.hushly.com",[533],{"_79":534,"_366":531,"_367":31,"_168":535},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[538],{"_79":539,"_366":536,"_367":31,"_168":540},"_octo","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform",[542,543],{"_79":545,"_366":409,"_367":31,"_168":413},{"_79":544,"_366":409,"_367":31,"_168":413},"lidc","li_gc",[547,548],{"_79":550,"_366":435,"_367":31,"_168":439},{"_79":549,"_366":435,"_367":31,"_168":439},"imid_*","imid",{"_79":576,"_366":484,"_367":31,"_168":578},{"_79":607,"_366":481,"_367":31,"_168":608},{"_79":576,"_366":606,"_367":31,"_168":578},{"_79":604,"_366":481,"_367":87,"_168":605},{"_79":602,"_366":481,"_367":31,"_168":603},{"_79":601,"_366":584,"_367":87,"_168":361},{"_79":576,"_366":580,"_367":31,"_168":578},{"_79":599,"_366":481,"_367":31,"_168":600},{"_79":576,"_366":487,"_367":31,"_168":578},{"_79":587,"_366":484,"_367":31,"_168":361},{"_79":576,"_366":598,"_367":31,"_168":578},{"_79":596,"_366":481,"_367":31,"_168":597},{"_79":593,"_366":594,"_367":31,"_168":595},{"_79":591,"_366":592,"_367":31,"_168":361},{"_79":576,"_366":588,"_367":31,"_168":578},{"_79":590,"_366":481,"_367":31,"_168":361},{"_79":576,"_366":589,"_367":31,"_168":578},{"_79":587,"_366":588,"_367":31,"_168":361},{"_79":586,"_366":584,"_367":87,"_168":361},{"_79":576,"_366":585,"_367":31,"_168":578},{"_79":576,"_366":584,"_367":31,"_168":578},{"_79":576,"_366":583,"_367":31,"_168":578},{"_79":582,"_366":481,"_367":87,"_168":578},{"_79":579,"_366":580,"_367":87,"_168":581},{"_79":576,"_366":577,"_367":31,"_168":578},"_gd_*","info.nvidia.com","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_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.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","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.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","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.","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.",[634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662],[614,615,616,617],{"_359":629,"_210":629,"_168":361,"_332":630},{"_359":624,"_210":624,"_168":361,"_332":625},{"_359":422,"_210":422,"_168":361,"_332":621},{"_359":536,"_210":536,"_168":361,"_332":618},[619],{"_79":620,"_366":536,"_367":87,"_168":540},"_gh_sess",[622],{"_79":623,"_366":422,"_367":87,"_168":429},"YSC","bizible.com",[626],{"_79":627,"_366":624,"_367":31,"_168":628},"_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",[631],{"_79":632,"_366":629,"_367":31,"_168":633},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_79":704,"_366":481,"_367":31,"_168":705},{"_79":689,"_366":703,"_367":87,"_168":690},{"_79":672,"_366":484,"_367":31,"_168":597},{"_79":672,"_366":585,"_367":31,"_168":597},{"_79":701,"_366":481,"_367":31,"_168":702},{"_79":689,"_366":487,"_367":87,"_168":690},{"_79":699,"_366":481,"_367":31,"_168":700},{"_79":689,"_366":484,"_367":87,"_168":690},{"_79":697,"_366":584,"_367":31,"_168":698},{"_79":689,"_366":606,"_367":87,"_168":690},{"_79":669,"_366":484,"_367":31,"_168":671},{"_79":695,"_366":481,"_367":87,"_168":696},{"_79":693,"_366":481,"_367":31,"_168":694},{"_79":691,"_366":481,"_367":87,"_168":692},{"_79":689,"_366":580,"_367":87,"_168":690},{"_79":687,"_366":481,"_367":31,"_168":688},{"_79":685,"_366":481,"_367":31,"_168":686},{"_79":683,"_366":481,"_367":87,"_168":684},{"_79":681,"_366":589,"_367":31,"_168":682},{"_79":669,"_366":580,"_367":31,"_168":671},{"_79":679,"_366":481,"_367":31,"_168":680},{"_79":677,"_366":481,"_367":87,"_168":678},{"_79":675,"_366":481,"_367":31,"_168":676},{"_79":673,"_366":481,"_367":31,"_168":674},{"_79":672,"_366":481,"_367":31,"_168":597},{"_79":669,"_366":670,"_367":31,"_168":671},{"_79":667,"_366":481,"_367":31,"_168":668},{"_79":665,"_366":481,"_367":87,"_168":666},{"_79":663,"_366":481,"_367":31,"_168":664},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","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","marketplace.nvidia.com","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).","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.","_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.","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.","_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.","_session_id","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.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_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.","_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.","_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.","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_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.","_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.","docs.nvidia.com","_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.",[851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923],[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],{"_359":848,"_210":848,"_168":361,"_332":849},{"_359":842,"_210":842,"_168":361,"_332":843},{"_359":839,"_210":839,"_168":361,"_332":840},{"_359":827,"_210":827,"_168":361,"_332":828},{"_359":824,"_210":824,"_168":361,"_332":825},{"_359":821,"_210":821,"_168":361,"_332":822},{"_359":816,"_210":816,"_168":361,"_332":817},{"_359":422,"_210":422,"_168":361,"_332":813},{"_359":810,"_210":810,"_168":361,"_332":811},{"_359":414,"_210":414,"_168":361,"_332":808},{"_359":409,"_210":409,"_168":361,"_332":806},{"_359":801,"_210":801,"_168":361,"_332":802},{"_359":796,"_210":796,"_168":361,"_332":797},{"_359":791,"_210":791,"_168":361,"_332":792},{"_359":788,"_210":788,"_168":361,"_332":789},{"_359":536,"_210":536,"_168":361,"_332":785},{"_359":394,"_210":394,"_168":361,"_332":782},{"_359":531,"_210":531,"_168":361,"_332":775},{"_359":527,"_210":527,"_168":361,"_332":769},{"_359":763,"_210":763,"_168":361,"_332":764},{"_359":758,"_210":758,"_168":361,"_332":759},{"_359":753,"_210":753,"_168":361,"_332":754},{"_359":749,"_210":749,"_168":361,"_332":750},{"_359":744,"_210":744,"_168":361,"_332":745},{"_359":736,"_210":736,"_168":361,"_332":737},"polyfill.io",[738,739],{"_79":742,"_366":736,"_367":31,"_168":743},{"_79":740,"_366":736,"_367":31,"_168":741},"cf_ob_info","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information.","cf_use_ob","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information. ","www.recaptcha.net",[746],{"_79":747,"_366":744,"_367":31,"_168":748},"_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",[751],{"_79":752,"_366":749,"_367":31,"_168":403},"__cflb","instagram.com",[755],{"_79":756,"_366":753,"_367":31,"_168":757},"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",[760],{"_79":761,"_366":758,"_367":31,"_168":762},"__cf_bm","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",[765,766],{"_79":768,"_366":763,"_367":31,"_168":403},{"_79":767,"_366":763,"_367":31,"_168":403},"AWSALBTG","AWSALBTGCORS",[770,771],{"_79":773,"_366":527,"_367":31,"_168":774},{"_79":772,"_366":527,"_367":31,"_168":403},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.",[776,777,778],{"_79":781,"_366":531,"_367":31,"_168":403},{"_79":780,"_366":531,"_367":87,"_168":403},{"_79":779,"_366":531,"_367":31,"_168":403},"AWSALBCORS","JSESSIONID","AWSALB",[783],{"_79":761,"_366":394,"_367":31,"_168":784},"This is a CloudFoundry cookie",[786],{"_79":787,"_366":536,"_367":31,"_168":540},"logged_in","hcaptcha.com",[790],{"_79":761,"_366":788,"_367":31,"_168":784},"tracking.intentsify.io",[793],{"_79":794,"_366":791,"_367":31,"_168":795},"userId","Used to track usage of the embedded video player.","static.reo.dev",[798],{"_79":799,"_366":796,"_367":31,"_168":800},"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",[803],{"_79":804,"_366":801,"_367":31,"_168":805},"dd_cookie_test_","dd_cookie_test",[807],{"_79":761,"_366":409,"_367":31,"_168":784},[809],{"_79":761,"_366":414,"_367":31,"_168":784},"supabase.co",[812],{"_79":761,"_366":810,"_367":31,"_168":784},[814],{"_79":815,"_366":422,"_367":31,"_168":429},"VISITOR_PRIVACY_METADATA","dosa-prd.azurewebsites.net",[818],{"_79":819,"_366":816,"_367":31,"_168":820},"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",[823],{"_79":761,"_366":821,"_367":31,"_168":784},"cdn.readme.io",[826],{"_79":761,"_366":824,"_367":31,"_168":784},"queue-it.net",[829,830,831,832,833],{"_79":838,"_366":827,"_367":31,"_168":361},{"_79":837,"_366":827,"_367":31,"_168":361},{"_79":836,"_366":827,"_367":31,"_168":361},{"_79":835,"_366":827,"_367":31,"_168":361},{"_79":834,"_366":827,"_367":31,"_168":361},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","x.com",[841],{"_79":761,"_366":839,"_367":31,"_168":762},"api.hcaptcha.com",[844,845],{"_79":847,"_366":842,"_367":31,"_168":403},{"_79":752,"_366":842,"_367":31,"_168":846},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[850],{"_79":761,"_366":848,"_367":31,"_168":784},{"_79":984,"_366":481,"_367":31,"_168":1015},{"_79":1013,"_366":577,"_367":87,"_168":1014},{"_79":1006,"_366":975,"_367":87,"_168":1007},{"_79":1012,"_366":670,"_367":87,"_168":361},{"_79":1010,"_366":481,"_367":31,"_168":1011},{"_79":768,"_366":996,"_367":31,"_168":935},{"_79":943,"_366":1005,"_367":87,"_168":945},{"_79":767,"_366":934,"_367":31,"_168":935},{"_79":946,"_366":1005,"_367":31,"_168":947},{"_79":1008,"_366":481,"_367":87,"_168":1009},{"_79":1006,"_366":982,"_367":87,"_168":1007},{"_79":940,"_366":1005,"_367":31,"_168":942},{"_79":1003,"_366":481,"_367":31,"_168":1004},{"_79":767,"_366":926,"_367":31,"_168":935},{"_79":931,"_366":1002,"_367":31,"_168":933},{"_79":779,"_366":932,"_367":31,"_168":948},{"_79":781,"_366":932,"_367":31,"_168":999},{"_79":946,"_366":941,"_367":31,"_168":947},{"_79":1000,"_366":580,"_367":31,"_168":1001},{"_79":781,"_366":606,"_367":31,"_168":999},{"_79":997,"_366":481,"_367":31,"_168":998},{"_79":773,"_366":606,"_367":31,"_168":992},{"_79":969,"_366":585,"_367":87,"_168":361},{"_79":767,"_366":996,"_367":31,"_168":935},{"_79":994,"_366":934,"_367":87,"_168":995},{"_79":958,"_366":481,"_367":31,"_168":949},{"_79":931,"_366":481,"_367":31,"_168":933},{"_79":993,"_366":493,"_367":31,"_168":361},{"_79":773,"_366":481,"_367":31,"_168":992},{"_79":781,"_366":926,"_367":31,"_168":991},{"_79":989,"_366":589,"_367":87,"_168":990},{"_79":987,"_366":481,"_367":87,"_168":988},{"_79":986,"_366":481,"_367":31,"_168":361},{"_79":984,"_366":932,"_367":31,"_168":985},{"_79":983,"_366":493,"_367":31,"_168":361},{"_79":974,"_366":982,"_367":87,"_168":976},{"_79":980,"_366":585,"_367":31,"_168":981},{"_79":978,"_366":670,"_367":31,"_168":979},{"_79":977,"_366":487,"_367":31,"_168":361},{"_79":974,"_366":975,"_367":87,"_168":976},{"_79":972,"_366":941,"_367":87,"_168":973},{"_79":970,"_366":481,"_367":31,"_168":971},{"_79":969,"_366":484,"_367":87,"_168":361},{"_79":940,"_366":944,"_367":31,"_168":942},{"_79":772,"_366":959,"_367":31,"_168":949},{"_79":967,"_366":484,"_367":31,"_168":968},{"_79":965,"_366":481,"_367":31,"_168":966},{"_79":963,"_366":481,"_367":31,"_168":964},{"_79":962,"_366":481,"_367":31,"_168":361},{"_79":804,"_366":484,"_367":31,"_168":961},{"_79":958,"_366":606,"_367":31,"_168":949},{"_79":761,"_366":577,"_367":31,"_168":960},{"_79":958,"_366":959,"_367":31,"_168":949},{"_79":957,"_366":493,"_367":31,"_168":361},{"_79":956,"_366":481,"_367":31,"_168":929},{"_79":954,"_366":941,"_367":31,"_168":955},{"_79":767,"_366":952,"_367":31,"_168":953},{"_79":951,"_366":493,"_367":31,"_168":361},{"_79":950,"_366":481,"_367":31,"_168":361},{"_79":943,"_366":941,"_367":87,"_168":945},{"_79":772,"_366":606,"_367":31,"_168":949},{"_79":779,"_366":606,"_367":31,"_168":948},{"_79":946,"_366":944,"_367":31,"_168":947},{"_79":943,"_366":944,"_367":87,"_168":945},{"_79":940,"_366":941,"_367":31,"_168":942},{"_79":938,"_366":934,"_367":87,"_168":939},{"_79":936,"_366":592,"_367":31,"_168":937},{"_79":768,"_366":934,"_367":31,"_168":935},{"_79":931,"_366":932,"_367":31,"_168":933},{"_79":761,"_366":584,"_367":31,"_168":930},{"_79":928,"_366":481,"_367":31,"_168":929},{"_79":779,"_366":926,"_367":31,"_168":927},{"_79":924,"_366":481,"_367":31,"_168":925},"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.","LSKey-c$CookieConsentPolicy","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.","CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","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.","nv_co","__uxq412__ses.88c1","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.","_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.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","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","OptanonAlertBoxClosed","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.","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.","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","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.","docs.ngc.nvidia.com","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.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","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.

",{"_706":16,"_609":56,"_516":56,"_336":56,"_326":56},"hasConsented","gpcApplied","showBanner","geolocation",{"_1030":1031,"_1032":1033},"country","US","state","OR","query","categories",[1053,1054,1055,1056,1057],"optionUrls",[1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052],"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/-",{"_203":1132,"_1059":1133,"_1061":56},{"_203":1097,"_1059":1098,"_1061":59},{"_203":1080,"_1059":1081,"_1061":1082},{"_203":204,"_1059":1073,"_1061":1074},{"_203":1058,"_1059":1060,"_1061":1062},"helm-charts","items",[1063,1064],"startIndex",10,{"_203":1058,"_210":1070,"_162":163,"_164":-7,"_205":1071,"_213":1067,"_239":1072,"_1069":1051},{"_203":1058,"_210":1065,"_162":163,"_164":-7,"_205":1066,"_213":1067,"_239":1068,"_1069":1052},"NVIDIA NIM Operator","k8s-nim-operator","NVIDIA",[],"url","GPU Operator","gpu-operator",[],[1075],9,{"_203":204,"_210":1076,"_162":163,"_164":1077,"_205":1078,"_213":1067,"_239":1079,"_1069":1050},"Riva Skills Quick Start","riva","riva_quickstart",[],"models",[1083,1084,1085],6,{"_203":1080,"_210":1093,"_162":163,"_164":1094,"_205":1095,"_213":1067,"_239":1096,"_1069":1047},{"_203":1080,"_210":1090,"_162":163,"_164":1087,"_205":1091,"_213":1067,"_239":1092,"_1069":1048},{"_203":1080,"_210":1086,"_162":163,"_164":1087,"_205":1088,"_213":1067,"_239":1089,"_1069":1049},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1099,1100,1101],{"_203":1097,"_210":1129,"_162":163,"_164":1103,"_205":1130,"_213":1067,"_239":1131,"_1069":1044},{"_203":1097,"_210":1126,"_162":163,"_164":-7,"_205":1127,"_213":1067,"_239":1128,"_1069":1045},{"_203":1097,"_210":1102,"_162":163,"_164":1103,"_205":1104,"_213":1067,"_239":1105,"_1069":1046},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1106,1107,1108],{"_79":1120,"_210":1121,"_207":1122,"_218":1123,"_1113":1124},{"_79":1103,"_210":1116,"_207":1117,"_218":1112,"_1113":1118},{"_79":1109,"_210":1110,"_207":1111,"_218":1112,"_1113":1114},"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",[1115],"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.",[1115,1119],"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",[1115,1119,1125],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1106,1107,1108],"containers",[1134,1135,1136],{"_203":1132,"_210":1148,"_162":1141,"_164":1149,"_205":1150,"_213":1067,"_239":1151,"_1069":1041},{"_203":1132,"_210":1140,"_162":1141,"_164":163,"_205":1142,"_213":1067,"_239":1143,"_1069":1042},{"_203":1132,"_210":1137,"_162":163,"_164":-7,"_205":1138,"_213":1067,"_239":1139,"_1069":1043},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[1144,1106],{"_79":1145,"_210":1110,"_207":1111,"_218":1146,"_1113":1147},"nim-dev","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[1115],"DeepSeek-R1","deepseek-ai","deepseek-r1",[1144,1106],"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",{"_1181":1182,"_1183":274,"_1184":31,"_1185":1186,"_1059":1187,"_1188":1189,"_1190":1182,"_1191":31,"_1192":31,"_1193":31,"_1194":1195},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1196,1197,1198,1199,1200,1201,1202],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1194":1224,"_47":1225,"_1188":1226},{"_1217":1218,"_1194":1219,"_47":1220,"_1190":1120},{"_1194":1215,"_47":1216,"_1188":1189},{"_1194":1212,"_47":1213,"_1188":1214},{"_1194":1209,"_47":1210,"_1188":1211},{"_1194":1206,"_47":1207,"_1188":1208},{"_1194":1203,"_47":1204,"_1188":1205},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1221],"Enterprise Catalog","/enterprise",[1222,1223],"NVAIE","READ","Explore Catalog","/","common-home",{"_113":114,"_115":116,"_117":118,"_119":120,"_121":122,"_123":124},{"_112":31}]