Recommendation systems drive engagement on many of the most popular online platforms. As the volume of data available to power these systems grows exponentially, data scientists are increasingly turning from more traditional machine learning methods to highly expressive deep learning models to improve the quality of their recommendations. Google's Wide & Deep Learning for Recommender Systems has emerged as a popular model for these problems both for its robustness to signal sparsity as well as its user-friendly implementation in TensorFlow.

The differences between this Wide & Deep Recommender Model and the model from the paper is the size of the Deep part of the model. Originally, in Google's paper, the fully connected part was three layers of 1024, 512, and 256 neurons. Our model consists of 5 layers each of 1024 neurons.

The model enables you to train a recommender model that combines the memorization of the Wide part and generalization of the Deep part of the network.

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.49 times faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

Wide & Deep refers to a class of networks that use the output of two parts working in parallel - wide model and deep model - to make predictions of recommenders. The wide model is a generalized linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons each beginning with a dense embedding of features. The architecture is presented in Figure 1.

\n \n
\nFigure 1. The architecture of the Wide & Deep model.\n

Applications and dataset

As a reference dataset, we used a subset of the features engineered by the 19th place finisher in the Kaggle Outbrain Click Prediction Challenge. This competition challenged competitors to predict the likelihood with which a particular ad on a website's display would be clicked on. Competitors were given information about the user, display, document, and ad in order to train their models. More information can be found here.

Default configuration

For reference, and to give context to the acceleration numbers described below, some important properties of our features and model are as follows:

\n
Features
\n
- Request Level\n
  - 16 scalar numeric features (shape=(1,))
  - 12 one-hot categorical features (all int dtype)\n
    - 5 indicator embeddings with sizes 2, 2, 3, 3, 6
    - 7 trainable embeddings\n
      - all except two have an embedding size of 64 (remaining two have 128), though it's important to note for all categorical features that we do not leverage that information to short-circuit the lookups by treating them as a single multi-hot lookup. Our API is fully general to any combination of embedding sizes.
      - all use hash bucketing with num_buckets= 300k, 100k, 4k, 2.5k, 2k, 1k, and 300 respectively
      \n
    \n
  - 3 multi-hot categorical features (all int dtype)\n
    - all trainable embeddings
    - all with embedding size 64
    - all use hash bucketing with num_buckets= 10k, 350, and 100 respectively
    \n
  \n
- Item Level\n
  - 16 scalar numeric features
  - 4 one hot categorical features (all int dtype)\n
    - embedding sizes of 128, 64, 64, 64 respectively
    - hash bucketing with num_buckets= 250k, 4k, 2.5k, and 1k respectively
    \n
  - 3 multi-hot categorical features (all int dtype)\n
    - all with embedding size 64
    - hash bucketing with num_buckets= 10k, 350, and 100 respectively
    \n
  \n
- All features are used in both wide and deep branches of the network
\n
\n
Model
\n
- Total embedding dimension is 1328
- 5 hidden layers each with size 1024
- Output dimension is 1 (probability of click)
\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

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

Feature	Wide & Deep
Horovod Multi-GPU	Yes
Automatic mixed precision (AMP)	Yes

Features

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.

Mixed precision

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 the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:

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

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

For information about:

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

Enabling mixed precision

To enable Wide & Deep training to use mixed precision you don't need to perform input quantization, only an additional flag --amp to the training script is needed (see Quick Start Guide).

Impact of mixed precision on training accuracy

The accuracy of training, measured with MAP@12 metric was not impacted by enabling mixed precision. The obtained results were statistically similar (i.e. similar run-to-run variance was observed, with standard deviation of the level of 0.002).

Impact of mixed precision on inference accuracy

For our reference model, the average absolute error on the probability of interaction induced by reduced precision inference is 0.0002, producing a near-perfect fit between predictions produced by full and mixed precision models. Moreover, this error is uncorrelated with the magnitude of the predicted value, which means for most predictions of interest (i.e. greater than 0.01 or 0.1 likelihood of interaction), the relative magnitude of the error is approaching the noise floor of the problem.

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.

Glossary

Request level features: Features that describe the person or object to which we wish to make recommendations.

Item level features: Features that describe those objects which we are considering recommending.

","resourceVersions",[428,429,430,431,432,433,434,435,436],"resourceVersionsTotal",8,"hasMoreVersions","versionMetadata",{"_397":22,"_398":-5,"_49":399,"_400":-5,"_401":402,"_403":-5,"_404":405,"_406":16,"_407":31,"_408":-5,"_409":-5,"_410":31,"_411":412,"_413":318,"_414":415,"_416":318,"_417":418,"_419":318,"_420":421,"_422":318,"_423":424,"_425":318,"_426":427},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/-/resources/wideanddeep_for_tensorflow/-","version","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/wideanddeep_for_tensorflow:20.10.1","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/-/resources/wideanddeep_for_tensorflow/20.10.1/version-history","accessCheckResult",{"_396":318},"hasRelatedCollections","context",{"_393":394,"_395":-7},1792405499727,[],[],[387,388,389,390,391,392],{"_45":60,"_47":70,"_49":62,"_51":63,"_53":71,"_55":56},{"_45":60,"_47":69,"_49":62,"_51":63,"_53":66,"_55":56},{"_45":60,"_47":67,"_49":62,"_51":63,"_53":68,"_55":56},{"_45":60,"_47":61,"_49":62,"_51":63,"_53":64,"_55":56},{"_45":57,"_47":58,"_49":334,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"orgName","nvidia","teamName","allowed","versionId","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1628049979799],"updatedDate","totalSizeInBytes",283746,"totalFileCount","isSigned","digest","malwareScanStatus","isScanned","releaseNotes","### Changelog\n\nNovember 2020\n- Updated performance tables to include numbers from 20.10-tf1-py3 NGC container\n\nJune 2020\n- Updated performance tables to include A100 results\n\nApril 2020\n- Improved Spark preprocessing scripts performance\n\nMarch 2020\n- Initial release\n\n### Known issues\n\n- Limited tf.feature_column support\n- Limited scaling for multi-GPU because of inefficient handling of embedding operations (multiple memory transfers between CPU and GPU), work in progress to cover all the operations on GPU.\n- In this model the TF32 precision can in some cases be as fast as the FP16 precision on Ampere GPUs.\nThis is because TF32 also uses Tensor Cores and doesn't need any additional logic\nsuch as maintaining FP32 master weights and casts.\nHowever, please note that W&D is, by modern recommender standards, a very small model.\nLarger models should still see significant benefits of using FP16 math.","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to start training the Wide & Deep 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- [20.10-tf1-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) NGC container\n- Supported GPUs:\n\t- [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n\t- [NVIDIA Turing architecture](https://www.nvidia.com/en-us/geforce/turing/)\n\t- [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/frameworks/user-guide/index.html#accessing_registry)\n- [Running TensorFlow](https://docs.nvidia.com/deeplearning/frameworks/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 or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the Wide & Deep model on the Outbrain dataset. For the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/TensorFlow/Recommendation/WideAndDeep\n```\n\n2. Download the Outbrain dataset.\n\nThe Outbrain dataset can be downloaded from [Kaggle](https://www.kaggle.com/c/outbrain-click-prediction/data) (requires Kaggle account).\nUnzip the downloaded archive e.g. to `/raid/outbrain/orig` and set the `HOST_OUTBRAIN_PATH` variable to the parent directory:\n\n```bash\nHOST_OUTBRAIN_PATH=/raid/outbrain\n```\n\n3. Build the Wide & Deep Tensorflow NGC container.\n\n```bash\ndocker build . -t wide_deep\n```\n\n4. Start an interactive session in the NGC container to run preprocessing/training/inference.\n\n```bash\ndocker run --runtime=nvidia --privileged --rm -ti -v ${HOST_OUTBRAIN_PATH}:/outbrain wide_deep /bin/bash\n```\n5. Start preprocessing.\n\n```bash\nbash scripts/preproc.sh 4096\n```\nThe result of preprocessing scripts are prebatched TFRecords. The argument to the script is the prebatch\nsize (4096 is the default).\n\n6. Start training.\n\nSingle GPU:\n```bash\npython -m trainer.task --gpu\n```\n8 GPU:\n```bash\nmpiexec --allow-run-as-root --bind-to socket -np 8 python -m trainer.task --gpu --hvd\n```\n\nIf you want to run validation or inference, you can either use the checkpoint obtained from the training \ncommands above, or download the pretrained checkpoint from NGC. \n\nIn order to download the checkpoint from NGC, visit [ngc.nvidia.com](https://ngc.nvidia.com) website and\nbrowse the available models.\nDownload the checkpoint files and unzip them to some path, e.g. to `/raid/outbrain/checkpoints/`\n(which is the default path for storing the checkpoints during training).\n\n7. Start validation/evaluation.\n\nIn order to validate the checkpoint on the evaluation set, run the `task.py` script with `--evaluate` flag:\n\n```bash\npython -m trainer.task --gpu --evaluate --model_dir /outbrain/checkpoints\n```\n\n8. Start inference/predictions.\n\nIn order to run inference and predict the results, run the `task.py`\nscript with `--predict` flag:\n\n```bash\npython -m trainer.task --gpu --predict --model_dir /outbrain/checkpoints\n```","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training, and the training results.\n\n### Scripts and sample code\n\nThese are the important scripts in this repository:\n* `trainer/task.py` - Python script for training the Wide & Deep recommender model\n* `trainer/features.py` - Python file describing the request and item level features\n\n### Parameters\n\nThese are the important parameters in the `trainer/task.py` script:\n\n```\n--model_dir: Path to model checkpoint directory\n--deep_hidden_units: [DEEP_LAYER1 DEEP_LAYER2 ...] hidden units per layer, separated by spaces\n--prebatch_size: Number of samples in each pre-batch in tfrecords\n--global_batch_size: Training batch size (per all GPUs, must be a multiplicity of prebatch_size)\n--eval_batch_size: Evaluation batch size (must be a multiplicity of prebatch_size)\n--num_epochs: Number of epochs to train\n--linear_learning_rate: Learning rate for the wide part of the model\n--linear_l1_regularization: L1 regularization for the wide part of the model\n--linear_l2_regularization: L2 regularization for the wide part of the model\n--deep_learning_rate: Learning rate for the deep part of the model\n--deep_dropout: Dropout probability for deep model\n--deep_warmup_epochs: Number of epochs with linear learning rate warmup\n--predict: Perform only the prediction on the validation set, do not train\n--evaluate: Perform only the evaluation on the validation set, do not train\n--gpu: Run computations on GPU\n--amp: Enable Automatic Mixed Precision\n--xla: Enable XLA\n--hvd: Use Horovod for multi-GPU training\n--eval_epoch_interval: Perform evaluation every this many epochs\n```\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:\n```bash\npython -m trainer.task --help\n```\n\n### Getting the data\n\nThe Outbrain dataset can be downloaded from [Kaggle](https://www.kaggle.com/c/outbrain-click-prediction/data) (requires Kaggle account).\n\n#### Dataset guidelines\n\nThe dataset contains a sample of users' page views and clicks, as observed on multiple publisher sites. Viewed pages and clicked recommendations have additional semantic attributes of the documents.\nThe dataset contains sets of content recommendations served to a specific user in a specific context. Each context (i.e. a set of recommended ads) is given a `display_id`. In each such recommendation set, the user has clicked on exactly one of the ads.\n\nThe original data is stored in several separate files:\n- `page_views.csv` - log of users visiting documents (2B rows, ~100GB uncompressed)\n- `clicks_train.csv` - data showing which ad was clicked in each recommendation set (87M rows)\n- `clicks_test.csv` - used only for the submission in the original Kaggle contest\n- `events.csv` - metadata about the context of each recommendation set (23M rows)\n- `promoted_content.csv` - metadata about the ads\n- `document_meta.csv`, `document_topics.csv`, `document_entities.csv`, `document_categories.csv` - metadata about the documents\n \nDuring the preprocessing stage the data is transformed into 59M rows tabular data of 54 features and eventually saved in pre-batched TFRecord format.\n\n#### Spark preprocessing\n\nThe original dataset is preprocessed using Spark scripts from the `preproc` directory. The preprocessing consists of the following operations:\n- separating out the validation set for cross-validation\n- filling missing data with the most frequent value\n- generating the user profiles from the page views data\n- joining the tables for the ad clicks data\n- computing click-through rates (CTR) for ads grouped by different contexts\n- computing cosine similarity between the features of the clicked ads and the viewed ads\n- math transformations of the numeric features (taking logarithm, scaling, binning)\n- storing the resulting set of features in TFRecord format\n\nThe `preproc1-4.py` preprocessing scripts use PySpark. \nIn the Docker image, we have installed Spark 2.3.1 as a standalone cluster of Spark. \nThe `preproc1.py` script splits the data into a training set and a validation set. \nThe `preproc2.py` script generates the user profiles from the page views data. \nThe `preproc3.py` computes the click-through rates (CTR) and cosine similarities between the features. \nThe `preproc4.py` script performs the math transformations and generates the final TFRecord files. \nThe data in the output files is pre-batched (with the default batch size of 4096) to avoid the overhead \nof the TFRecord format, which otherwise is not suitable for the tabular data - \nit stores a separate dictionary with each feature name in plain text for every data entry.\n\nThe preprocessing includes some very resource-exhausting operations, like joining 2B+ rows tables. \nSuch operations may not fit into the RAM memory, therefore we decided to use Spark which is a suitable tool \nfor handling tabular operations on large data. \nNote that the Spark job requires about 1 TB disk space and 500 GB RAM to perform the preprocessing.\nFor more information about Spark, please refer to the\n[Spark documentation](https://spark.apache.org/docs/2.3.1/).\n\n### Training process\n\nThe training can be started by running the `trainer/task.py` script. By default the script is in train mode. Other training related \nconfigs are also present in the `trainer/task.py` and can be seen using the command `python -m trainer.task --help`. Training happens for `--num_epochs` epochs with a DNNLinearCombinedClassifier estimator for the model. The model has a wide linear part and a deep feed forward network, and the networks are built according to the default configuration.\n\nTwo separate optimizers are used to optimize the wide and the deep part of the network:\n \n- FTLR (Follow the Regularized Leader) optimizer is used to optimize the wide part of the network.\n- Adagrad optimizer is used to optimize the deep part of the network.\n\nThe training log will contain information about:\n\n- Loss value after every 100 steps.\n- Training throughput if `--benchmark` option is selected.\n- Evaluation metrics after every `--eval_epoch_interval` epochs.\n\nCheckpoints are stored with every evaluation at the `--model_dir` location.","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA's latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model performance in training mode.\n\n#### Training performance benchmark\n\nWe provide 8 scripts to benchmark the performance of training:\n```bash\nbash scripts/DGXA100_benchmark_training_tf32_1gpu.sh\nbash scripts/DGXA100_benchmark_training_amp_1gpu.sh\nbash scripts/DGXA100_benchmark_training_tf32_8gpu.sh\nbash scripts/DGXA100_benchmark_training_amp_8gpu.sh\nbash scripts/DGX1_benchmark_training_fp32_1gpu.sh\nbash scripts/DGX1_benchmark_training_amp_1gpu.sh\nbash scripts/DGX1_benchmark_training_fp32_8gpu.sh\nbash scripts/DGX1_benchmark_training_amp_8gpu.sh\n```\n\n### Results\n\nThe following sections provide details on how we achieved our performance and\naccuracy in training.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the `trainer/task.py` training script in the TensorFlow NGC container on NVIDIA DGX A100 with (8x A100 40GB) GPUs.\n\n|**GPUs**|**Batch size / GPU**|**Accuracy - TF32 (MAP@12)**|**Accuracy - mixed precision (MAP@12)**|**Time to train - TF32 (minutes)**|**Time to train - mixed precision (minutes)**|**Time to train speedup (TF32 to mixed precision)**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 131,072 | 0.67683 | 0.67632 | 341 | 359 | [-](#known-issues) |\n| 8 | 16,384 | 0.67709 | 0.67721 | 93 | 107 | [-](#known-issues) |\n\nTo achieve the same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `trainer/task.py` training script in the TensorFlow NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.\n\n|**GPUs**|**Batch size / GPU**|**Accuracy - FP32 (MAP@12)**|**Accuracy - mixed precision (MAP@12)**|**Time to train - FP32 (minutes)**|**Time to train - mixed precision (minutes)**|**Time to train speedup (FP32 to mixed precision)**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 131,072 | 0.67648 | 0.67744 | 654 | 440 | 1.49 |\n| 8 | 16,384 | 0.67692 | 0.67725 | 190 | 185 | 1.03 |\n\nTo achieve the same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training accuracy plots\n\nModels trained with FP32, TF32 and Automatic Mixed Precision (AMP) achieve similar precision.\n\n![MAP12](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow/Recommendation/WideAndDeep/img/lc20.06.png)\n\n\n##### Training stability test\n\nThe Wide and Deep model was trained for 54,713 training steps, starting\nfrom 6 different initial random seeds for each setup. The training was performed in the 20.10-tf1-py3 NGC container on\nNVIDIA DGX A100 40GB and DGX-1 16GB machines with and without mixed precision enabled.\nAfter training, the models were evaluated on the validation set. The following\ntable summarizes the final MAP@12 score on the validation set.\n\n||**Average MAP@12**|**Standard deviation**|**Minimum**|**Maximum**|\n|:-------|-----------------:|---------------------:|----------:|----------:|\n| DGX A100 TF32 | 0.67709 | 0.00094 | 0.67463 | 0.67813 | \n| DGX A100 mixed precision | 0.67721 | 0.00048 | 0.67643 | 0.67783 | \n| DGX-1 FP32 | 0.67692 | 0.00060 | 0.67587 | 0.67791 | \n| DGX-1 mixed precision | 0.67725 | 0.00064 | 0.67561 | 0.67803 | \n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the benchmark scripts from the `scripts` directory in the TensorFlow NGC container on NVIDIA DGX A100 with (8x A100 40GB) GPUs. Improving model scaling for multi-GPU is [under development](#known-issues).\n\n|**GPUs**|**Batch size / GPU**|**Throughput - TF32 (samples/s)**|**Throughput - mixed precision (samples/s)**|**Strong scaling - TF32**|**Strong scaling - mixed precision**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 131,072 | 349,879 | 332,529 | 1.00 | 1.00 |\n| 8 | 16,384 | 1,283,457 | 1,111,976 | 3.67 | 3.34 |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the benchmark scripts from the `scripts` directory in the TensorFlow NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs. Improving model scaling for multi-GPU is [under development](#known-issues).\n\n|**GPUs**|**Batch size / GPU**|**Throughput - FP32 (samples/s)**|**Throughput - mixed precision (samples/s)**|**Throughput speedup (FP32 to mixed precision)**|**Strong scaling - FP32**|**Strong scaling - mixed precision**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 131,072 | 182,510 | 271,366 | 1.49 | 1.00 | 1.00 |\n| 8 | 16,384 | 626,301 | 643,334 | 1.03 | 3.43 | 2.37 |","hasPerformance","policies",[],{"_86":452,"_88":453,"_400":-5,"_407":31,"_410":31,"_426":451,"_413":318},{"_86":450,"_88":450,"_400":-5,"_407":31,"_410":31,"_426":451,"_413":318},{"_86":22,"_88":22,"_400":-5,"_407":31,"_410":31,"_426":449,"_413":318},{"_86":447,"_88":447,"_400":-5,"_407":31,"_410":31,"_426":448,"_413":318},{"_86":445,"_88":445,"_400":-5,"_407":31,"_410":31,"_426":446,"_413":318},{"_86":443,"_88":443,"_400":-5,"_407":31,"_410":31,"_426":444,"_413":318},{"_86":441,"_88":441,"_400":-5,"_407":31,"_410":31,"_426":442,"_413":318},{"_86":439,"_88":439,"_400":-5,"_407":31,"_410":31,"_426":440,"_413":318},{"_86":437,"_88":437,"_400":-5,"_407":31,"_410":31,"_426":438,"_413":318},"1",[],"2",[],"20.06.0",[],"20.06.1",[],"20.06.2",[],"20.10.0",[],[],"20.10.2",[],"-","Latest (20.10.2)","nvidia/wideanddeep_for_tensorflow","key","nvidia/resources/wideanddeep_for_tensorflow","resourceType","resources","resourceId","wideanddeep_for_tensorflow","shortDescription","Wide & Deep Recommender model.","Recommendation systems drive engagement on many of the most popular online platforms. As the volume of data available to power these systems grows exponentially, data scientists are increasingly turning from more traditional machine learning methods to highly expressive deep learning models to improve the quality of their recommendations. Google's [Wide & Deep Learning for Recommender Systems](https://arxiv.org/abs/1606.07792) has emerged as a popular model for these problems both for its robustness to signal sparsity as well as its user-friendly implementation in [TensorFlow](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNLinearCombinedClassifier).\n\nThe differences between this Wide & Deep Recommender Model and the model from the paper is the size of the Deep part of the model. Originally, in Google's paper, the fully connected part was three layers of 1024, 512, and 256 neurons. Our model consists of 5 layers each of 1024 neurons. \n\nThe model enables you to train a recommender model that combines the memorization of the Wide part and generalization of the Deep part of the network.\n\nThis model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.49 times faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n### Model architecture\n\nWide & Deep refers to a class of networks that use the output of two parts working in parallel - wide model and deep model - to make predictions of recommenders. The wide model is a generalized linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons each beginning with a dense embedding of features. The architecture is presented in Figure 1.\n\n

\n \n
\nFigure 1. The architecture of the Wide & Deep model.\n

\n\n### Applications and dataset\n\nAs a reference dataset, we used a subset of [the features engineered](https://github.com/gabrielspmoreira/kaggle_outbrain_click_prediction_google_cloud_ml_engine) by the 19th place finisher in the [Kaggle Outbrain Click Prediction Challenge](https://www.kaggle.com/c/outbrain-click-prediction/). This competition challenged competitors to predict the likelihood with which a particular ad on a website's display would be clicked on. Competitors were given information about the user, display, document, and ad in order to train their models. More information can be found [here](https://www.kaggle.com/c/outbrain-click-prediction/data).\n\n### Default configuration\n\nFor reference, and to give context to the acceleration numbers described below, some important properties of our features and model are as follows:\n\n- Features\n - Request Level\n - 16 scalar numeric features `(shape=(1,)`)\n - 12 one-hot categorical features (all `int` dtype)\n - 5 indicator embeddings with sizes 2, 2, 3, 3, 6\n - 7 trainable embeddings\n - all except two have an embedding size of 64 (remaining two have 128), though it's important to note for *all* categorical features that we *do not* leverage that information to short-circuit the lookups by treating them as a single multi-hot lookup. Our API is fully general to any combination of embedding sizes.\n - all use hash bucketing with `num_buckets=` 300k, 100k, 4k, 2.5k, 2k, 1k, and 300 respectively\n - 3 multi-hot categorical features (all `int` dtype)\n - all trainable embeddings\n - all with embedding size 64\n - all use hash bucketing with `num_buckets=` 10k, 350, and 100 respectively\n - Item Level\n - 16 scalar numeric features\n - 4 one hot categorical features (all `int` dtype)\n - embedding sizes of 128, 64, 64, 64 respectively\n - hash bucketing with `num_buckets=` 250k, 4k, 2.5k, and 1k respectively\n - 3 multi-hot categorical features (all `int` dtype)\n - all with embedding size 64\n - hash bucketing with `num_buckets=` 10k, 350, and 100 respectively\n - All features are used in both wide *and* deep branches of the network\n\n- Model\n - Total embedding dimension is 1328\n - 5 hidden layers each with size 1024\n - Output dimension is 1 (probability of click)\n\n### Feature support matrix\n\nThe following features are supported by this model: \n\n| Feature | Wide & Deep \n|-----------------------|--------------------------\n|Horovod Multi-GPU | Yes \n|Automatic mixed precision (AMP) | Yes \n \n#### Features\n\nHorovod\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\nMulti-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### Mixed precision\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 the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThe ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in [CUDA 8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep Learning SDK.\n\nFor information about:\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/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\nTo enable Wide & Deep training to use mixed precision you don't need to perform input quantization, only an additional flag `--amp` to the training script is needed (see [Quick Start Guide](#quick-start-guide)).\n\n##### Impact of mixed precision on training accuracy\nThe accuracy of training, measured with MAP@12 metric was not impacted by enabling mixed precision. The obtained results were statistically similar (i.e. similar run-to-run variance was observed, with standard deviation of the level of `0.002`).\n\n##### Impact of mixed precision on inference accuracy\nFor our reference model, the average absolute error on the probability of interaction induced by reduced precision inference is `0.0002`, producing a near-perfect fit between predictions produced by full and mixed precision models. Moreover, this error is uncorrelated with the magnitude of the predicted value, which means for most predictions of interest (i.e. greater than `0.01` or `0.1` likelihood of interaction), the relative magnitude of the error is approaching the noise floor of the problem.\n\n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. \n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.\n\n### Glossary\n\nRequest level features: Features that describe the person or object _to which_ we wish to make recommendations.\n\nItem level features: Features that describe those objects which we are considering recommending.","displayName","Wide & Deep for TensorFlow",["D",1680631924036],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/wideanddeep_for_tensorflow","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",283832,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[498,499],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_464":509,"_501":510,"_503":511,"_505":506},{"_464":500,"_501":502,"_503":504,"_505":506},"Recommendation","value","uscs_recommedation","filter",{"_507":508,"_501":502},"category","label","field","application","Deep Learning Examples","pltfm_deep_learning_examples",{"_507":512,"_501":510},"platform",{"_339":1649,"_341":1650},"ngcApps",[1602],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1583":1584,"_1585":1586,"_1587":1588,"_1589":1590,"_1591":524,"_371":550,"_1592":1593,"_1594":56,"_1595":318,"_1596":318,"_1597":1598,"_1599":318,"_1600":1601},"amplitude",{"_1580":1581,"_1187":-7,"_1582":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1574":1575,"_1576":1577,"_1578":1579},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1792405499672,"1.22.1","defaultSearchSuggestions",{"_1457":633,"_1458":1459,"_1460":1461,"_1462":1463},"announcements",[],"consentState",{"_589":1447,"_1448":31,"_1449":31,"_1450":318,"_1451":1452},"consentConfig",{"_571":572,"_573":574,"_575":576,"_577":578,"_579":580,"_581":582,"_583":584,"_585":586,"_587":588,"_589":590},"showContactAdmin",1792405499673,[],[],[564,565,566,567,568,569],{"_45":60,"_47":70,"_49":334,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":334,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":334,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":334,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":334,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":570,"_55":56},4,"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",{"_1439":1440,"_1441":1442,"_1443":1444,"_1445":1446},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[591,592,593,594,595],{"_596":1069,"_88":1070,"_398":1071,"_600":318,"_601":18,"_603":1072,"_605":1073},{"_596":929,"_88":930,"_398":931,"_600":31,"_601":54,"_603":932,"_605":933},{"_596":823,"_88":824,"_398":825,"_600":31,"_601":570,"_603":826,"_605":827},{"_596":607,"_88":608,"_398":609,"_600":31,"_601":610,"_603":611,"_605":612},{"_596":597,"_88":598,"_398":599,"_600":31,"_601":602,"_603":604,"_605":606},"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,[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],[613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630],{"_631":732,"_464":732,"_398":633,"_603":733},{"_631":728,"_464":728,"_398":633,"_603":729},{"_631":723,"_464":723,"_398":633,"_603":724},{"_631":719,"_464":719,"_398":633,"_603":720},{"_631":714,"_464":714,"_398":633,"_603":715},{"_631":709,"_464":709,"_398":633,"_603":710},{"_631":702,"_464":702,"_398":633,"_603":703},{"_631":694,"_464":694,"_398":633,"_603":695},{"_631":689,"_464":689,"_398":633,"_603":690},{"_631":684,"_464":684,"_398":633,"_603":685},{"_631":676,"_464":676,"_398":633,"_603":677},{"_631":671,"_464":671,"_398":633,"_603":672},{"_631":666,"_464":666,"_398":633,"_603":667},{"_631":661,"_464":661,"_398":633,"_603":662},{"_631":654,"_464":654,"_398":633,"_603":655},{"_631":649,"_464":649,"_398":633,"_603":650},{"_631":642,"_464":642,"_398":633,"_603":643},{"_631":632,"_464":632,"_398":633,"_603":634},"hostName","amazon-adsystem.com","",[635,636],{"_88":641,"_638":632,"_639":31,"_398":640},{"_88":637,"_638":632,"_639":31,"_398":640},"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",[644,645],{"_88":648,"_638":642,"_639":31,"_398":647},{"_88":646,"_638":642,"_639":31,"_398":647},"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",[651],{"_88":652,"_638":649,"_639":31,"_398":653},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[656,657],{"_88":660,"_638":654,"_639":31,"_398":659},{"_88":658,"_638":654,"_639":31,"_398":659},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[663],{"_88":664,"_638":661,"_639":31,"_398":665},"_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",[668],{"_88":669,"_638":666,"_639":31,"_398":670},"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",[673],{"_88":674,"_638":671,"_639":31,"_398":675},"sp","Testing","google.com",[678,679],{"_88":682,"_638":676,"_639":31,"_398":683},{"_88":680,"_638":676,"_639":31,"_398":681},"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.","__Secure-*","Auto-detected during scan","tags.srv.stackadapt.com",[686],{"_88":687,"_638":684,"_639":31,"_398":688},"sa-user-*","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","linkedin.com",[691],{"_88":692,"_638":689,"_639":31,"_398":693},"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",[696,697],{"_88":700,"_638":694,"_639":31,"_398":701},{"_88":698,"_638":694,"_639":31,"_398":699},"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",[704,705],{"_88":682,"_638":702,"_639":31,"_398":708},{"_88":706,"_638":702,"_639":31,"_398":707},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","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",[711],{"_88":712,"_638":709,"_639":31,"_398":713},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[716],{"_88":717,"_638":714,"_639":31,"_398":718},"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",[721],{"_88":722,"_638":719,"_639":31,"_398":675},"R","6sc.co",[725],{"_88":726,"_638":723,"_639":31,"_398":727},"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",[730],{"_88":731,"_638":728,"_639":31,"_398":675},"_hly_gvid","t.contentsquare.net",[734],{"_88":735,"_638":732,"_639":318,"_398":736},"_cs_*","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.",{"_88":798,"_638":797,"_639":31,"_398":799},{"_88":821,"_638":776,"_639":31,"_398":822},{"_88":687,"_638":806,"_639":31,"_398":633},{"_88":687,"_638":812,"_639":31,"_398":633},{"_88":819,"_638":776,"_639":31,"_398":820},{"_88":817,"_638":776,"_639":31,"_398":818},{"_88":664,"_638":776,"_639":31,"_398":816},{"_88":815,"_638":776,"_639":31,"_398":794},{"_88":781,"_638":776,"_639":31,"_398":783},{"_88":814,"_638":776,"_639":31,"_398":794},{"_88":813,"_638":776,"_639":31,"_398":633},{"_88":790,"_638":779,"_639":31,"_398":633},{"_88":790,"_638":812,"_639":31,"_398":792},{"_88":810,"_638":776,"_639":31,"_398":811},{"_88":808,"_638":776,"_639":31,"_398":809},{"_88":807,"_638":776,"_639":31,"_398":633},{"_88":790,"_638":806,"_639":31,"_398":792},{"_88":804,"_638":776,"_639":31,"_398":805},{"_88":802,"_638":782,"_639":31,"_398":803},{"_88":781,"_638":801,"_639":318,"_398":783},{"_88":798,"_638":779,"_639":31,"_398":799},{"_88":798,"_638":782,"_639":31,"_398":799},{"_88":800,"_638":776,"_639":31,"_398":794},{"_88":798,"_638":776,"_639":31,"_398":799},{"_88":778,"_638":797,"_639":31,"_398":780},{"_88":796,"_638":776,"_639":31,"_398":794},{"_88":735,"_638":776,"_639":31,"_398":736},{"_88":795,"_638":776,"_639":31,"_398":794},{"_88":793,"_638":776,"_639":31,"_398":794},{"_88":790,"_638":791,"_639":31,"_398":792},{"_88":674,"_638":788,"_639":31,"_398":789},{"_88":687,"_638":779,"_639":31,"_398":633},{"_88":786,"_638":776,"_639":31,"_398":787},{"_88":784,"_638":776,"_639":31,"_398":785},{"_88":658,"_638":776,"_639":31,"_398":633},{"_88":781,"_638":782,"_639":318,"_398":783},{"_88":778,"_638":779,"_639":31,"_398":780},{"_88":775,"_638":776,"_639":318,"_398":777},"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.","_zitok","marketplace.nvidia.com","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_gat_gtag_UA_140825383_2","Google Analytics","_gat_gtag_UA_162631457_1","_gat_gtag_UA_53775284_8","forums.developer.nvidia.com","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","_gat_gtag_UA_146100304_1","catalog.ngc.nvidia.com","aws-waf-token","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.","_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. ","ai-podcast.nvidia.com","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","research.nvidia.com","_twpid","_gat_gtag_UA_141699104_1","_gat_gtag_UA_53775284_6","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.",[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],[828,829,830,831,832,833],{"_631":714,"_464":714,"_398":633,"_603":857},{"_631":689,"_464":689,"_398":633,"_603":852},{"_631":847,"_464":847,"_398":633,"_603":848},{"_631":842,"_464":842,"_398":633,"_603":843},{"_631":838,"_464":838,"_398":633,"_603":839},{"_631":834,"_464":834,"_398":633,"_603":835},"nvcrm.my.site.com",[836],{"_88":837,"_638":834,"_639":31,"_398":633},"BrowserId","nvidia.partners",[840],{"_88":841,"_638":838,"_639":31,"_398":675},"edge","app.hushly.com",[844],{"_88":845,"_638":842,"_639":31,"_398":846},"*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",[849],{"_88":850,"_638":847,"_639":31,"_398":851},"_octo","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform",[853,854],{"_88":856,"_638":689,"_639":31,"_398":693},{"_88":855,"_638":689,"_639":31,"_398":693},"lidc","li_gc",[858,859],{"_88":861,"_638":714,"_639":31,"_398":718},{"_88":860,"_638":714,"_639":31,"_398":718},"imid_*","imid",{"_88":893,"_638":779,"_639":31,"_398":895},{"_88":927,"_638":776,"_639":31,"_398":928},{"_88":893,"_638":926,"_639":31,"_398":895},{"_88":924,"_638":776,"_639":318,"_398":925},{"_88":922,"_638":776,"_639":31,"_398":923},{"_88":921,"_638":902,"_639":318,"_398":633},{"_88":893,"_638":897,"_639":31,"_398":895},{"_88":919,"_638":776,"_639":31,"_398":920},{"_88":893,"_638":782,"_639":31,"_398":895},{"_88":904,"_638":779,"_639":31,"_398":633},{"_88":918,"_638":776,"_639":31,"_398":633},{"_88":893,"_638":917,"_639":31,"_398":895},{"_88":916,"_638":776,"_639":31,"_398":910},{"_88":913,"_638":914,"_639":31,"_398":915},{"_88":911,"_638":912,"_639":31,"_398":633},{"_88":909,"_638":776,"_639":31,"_398":910},{"_88":893,"_638":908,"_639":31,"_398":895},{"_88":893,"_638":806,"_639":31,"_398":895},{"_88":907,"_638":776,"_639":31,"_398":633},{"_88":906,"_638":779,"_639":31,"_398":633},{"_88":905,"_638":779,"_639":31,"_398":633},{"_88":904,"_638":806,"_639":31,"_398":633},{"_88":903,"_638":902,"_639":318,"_398":633},{"_88":893,"_638":812,"_639":31,"_398":895},{"_88":893,"_638":902,"_639":31,"_398":895},{"_88":893,"_638":791,"_639":31,"_398":895},{"_88":893,"_638":901,"_639":31,"_398":895},{"_88":900,"_638":776,"_639":31,"_398":633},{"_88":899,"_638":776,"_639":318,"_398":895},{"_88":896,"_638":897,"_639":318,"_398":898},{"_88":893,"_638":894,"_639":31,"_398":895},"_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","__ngc_ui_apps","nvidianews.nvidia.com","docs.api.nvidia.com","readme_library","wcmmode","_im_id.1006927","_im_ses.1006927","geo_locator","la.blogs.nvidia.com","_semaJ2rWcw","This cookie is categorized as Functionality Cookie.","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","crowdsource.nvidia.com","__ngc_ui","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.",[962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005],[934,935,936,937,938],{"_631":957,"_464":957,"_398":633,"_603":958},{"_631":952,"_464":952,"_398":633,"_603":953},{"_631":702,"_464":702,"_398":633,"_603":949},{"_631":942,"_464":942,"_398":633,"_603":943},{"_631":847,"_464":847,"_398":633,"_603":939},[940],{"_88":941,"_638":847,"_639":318,"_398":851},"_gh_sess","statcounter.com",[944,945],{"_88":948,"_638":942,"_639":31,"_398":947},{"_88":946,"_638":942,"_639":31,"_398":947},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[950],{"_88":951,"_638":702,"_639":318,"_398":708},"YSC","bizible.com",[954],{"_88":955,"_638":952,"_639":31,"_398":956},"_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",[959],{"_88":960,"_638":957,"_639":31,"_398":961},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_88":1067,"_638":776,"_639":31,"_398":1068},{"_88":1065,"_638":776,"_639":318,"_398":1066},{"_88":1062,"_638":1063,"_639":31,"_398":1064},{"_88":1040,"_638":1053,"_639":318,"_398":1041},{"_88":1060,"_638":776,"_639":31,"_398":1061},{"_88":1014,"_638":779,"_639":31,"_398":910},{"_88":1014,"_638":812,"_639":31,"_398":910},{"_88":1058,"_638":776,"_639":31,"_398":1059},{"_88":1042,"_638":897,"_639":318,"_398":1043},{"_88":1040,"_638":782,"_639":318,"_398":1041},{"_88":1056,"_638":776,"_639":31,"_398":1057},{"_88":1054,"_638":776,"_639":31,"_398":1055},{"_88":1048,"_638":776,"_639":31,"_398":633},{"_88":1039,"_638":1053,"_639":31,"_398":683},{"_88":1040,"_638":779,"_639":318,"_398":1041},{"_88":1051,"_638":812,"_639":31,"_398":1052},{"_88":1049,"_638":902,"_639":31,"_398":1050},{"_88":1040,"_638":926,"_639":318,"_398":1041},{"_88":1048,"_638":897,"_639":31,"_398":633},{"_88":1012,"_638":779,"_639":31,"_398":1013},{"_88":1039,"_638":779,"_639":31,"_398":683},{"_88":1046,"_638":776,"_639":318,"_398":1047},{"_88":1044,"_638":776,"_639":31,"_398":1045},{"_88":1042,"_638":776,"_639":318,"_398":1043},{"_88":1040,"_638":897,"_639":318,"_398":1041},{"_88":1039,"_638":897,"_639":31,"_398":683},{"_88":1037,"_638":776,"_639":31,"_398":1038},{"_88":1035,"_638":776,"_639":31,"_398":1036},{"_88":1033,"_638":776,"_639":318,"_398":1034},{"_88":1030,"_638":1031,"_639":31,"_398":1032},{"_88":1012,"_638":897,"_639":31,"_398":1013},{"_88":1028,"_638":776,"_639":318,"_398":1029},{"_88":1026,"_638":776,"_639":31,"_398":1027},{"_88":1025,"_638":897,"_639":318,"_398":1020},{"_88":1023,"_638":776,"_639":318,"_398":1024},{"_88":1021,"_638":791,"_639":31,"_398":1022},{"_88":1019,"_638":897,"_639":318,"_398":1020},{"_88":1017,"_638":776,"_639":31,"_398":1018},{"_88":1015,"_638":776,"_639":31,"_398":1016},{"_88":1014,"_638":776,"_639":31,"_398":910},{"_88":1012,"_638":791,"_639":31,"_398":1013},{"_88":1010,"_638":776,"_639":31,"_398":1011},{"_88":1008,"_638":776,"_639":318,"_398":1009},{"_88":1006,"_638":776,"_639":31,"_398":1007},"_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","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.","s_tp","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.","_an_uid","This cookie is likely used for analytics purposes to track user behavior and site usage, which may include unique user identification for tracking.","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.","s_ips","_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.","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.","_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.","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.","__sec_*","_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.","nv.*","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.","sc_is_visitor_unique","This cookie is used by StatCounter to determine whether a visitor is a unique visitor or a returning visitor, helping to estimate the number of unique visitors to the site.","docs.nvidia.com","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.","_gat","This cookie name is associated with Google Universal Analytics, according to documentation it is used to throttle the request rate - limiting the collection of data on high traffic sites. It expires after 10 minutes.","_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.","_biz_*","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.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","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.","_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.",[1252,1253,1254,1255,1256,1257,1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274,1275,1276,1277,1278,1279,1280,1281,1282,1283,1284,1285,1286,1287,1288,1289,1290,1291,1292,1293,1294,1295,1296,1297,1298,1299,1300,1301,1302,1303,1304,1305,1306,1307,1308,1309,1310,1311,1312,1313,1314,1315,1316,1317,1318,1319,1320,1321,1322,1323,1324,1325,1326,1327,1328,1329,1330,1331,1332,1333,1334,1335,1336,1337,1338,1339,1340,1341,1342,1343],[1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104],{"_631":1249,"_464":1249,"_398":633,"_603":1250},{"_631":1243,"_464":1243,"_398":633,"_603":1244},{"_631":1240,"_464":1240,"_398":633,"_603":1241},{"_631":1237,"_464":1237,"_398":633,"_603":1238},{"_631":1225,"_464":1225,"_398":633,"_603":1226},{"_631":1222,"_464":1222,"_398":633,"_603":1223},{"_631":1219,"_464":1219,"_398":633,"_603":1220},{"_631":1214,"_464":1214,"_398":633,"_603":1215},{"_631":702,"_464":702,"_398":633,"_603":1211},{"_631":1208,"_464":1208,"_398":633,"_603":1209},{"_631":1203,"_464":1203,"_398":633,"_603":1204},{"_631":694,"_464":694,"_398":633,"_603":1201},{"_631":689,"_464":689,"_398":633,"_603":1199},{"_631":1194,"_464":1194,"_398":633,"_603":1195},{"_631":1189,"_464":1189,"_398":633,"_603":1190},{"_631":1184,"_464":1184,"_398":633,"_603":1185},{"_631":1180,"_464":1180,"_398":633,"_603":1181},{"_631":1177,"_464":1177,"_398":633,"_603":1178},{"_631":847,"_464":847,"_398":633,"_603":1174},{"_631":666,"_464":666,"_398":633,"_603":1171},{"_631":842,"_464":842,"_398":633,"_603":1165},{"_631":838,"_464":838,"_398":633,"_603":1159},{"_631":1152,"_464":1152,"_398":633,"_603":1153},{"_631":1147,"_464":1147,"_398":633,"_603":1148},{"_631":1142,"_464":1142,"_398":633,"_603":1143},{"_631":1138,"_464":1138,"_398":633,"_603":1139},{"_631":1133,"_464":1133,"_398":633,"_603":1134},{"_631":1128,"_464":1128,"_398":633,"_603":1129},{"_631":834,"_464":834,"_398":633,"_603":1121},{"_631":1113,"_464":1113,"_398":633,"_603":1114},{"_631":1105,"_464":1105,"_398":633,"_603":1106},"www.pny.com",[1107,1108],{"_88":1111,"_638":1105,"_639":31,"_398":1112},{"_88":1109,"_638":1105,"_639":318,"_398":1110},"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.","AWSALB","AWS ELB application load balancer","polyfill.io",[1115,1116],{"_88":1119,"_638":1113,"_639":31,"_398":1120},{"_88":1117,"_638":1113,"_639":31,"_398":1118},"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. ",[1122,1123],{"_88":1126,"_638":834,"_639":31,"_398":1127},{"_88":1124,"_638":834,"_639":31,"_398":1125},"CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","LSKey-c$CookieConsentPolicy","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","web.prd.q4inc.com",[1130],{"_88":1131,"_638":1128,"_639":318,"_398":1132},"_cfuvid","The _cfuvid cookie is used by Cloudflare to distinguish individual users who share the same IP address for the purpose of enforcing rate limiting rules.","www.recaptcha.net",[1135],{"_88":1136,"_638":1133,"_639":31,"_398":1137},"_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",[1140],{"_88":1141,"_638":1138,"_639":31,"_398":675},"__cflb","instagram.com",[1144],{"_88":1145,"_638":1142,"_639":31,"_398":1146},"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",[1149],{"_88":1150,"_638":1147,"_639":31,"_398":1151},"__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",[1154,1155],{"_88":1158,"_638":1152,"_639":31,"_398":675},{"_88":1156,"_638":1152,"_639":31,"_398":1157},"AWSALBTG","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","AWSALBTGCORS",[1160,1161],{"_88":1163,"_638":838,"_639":31,"_398":1164},{"_88":1162,"_638":838,"_639":31,"_398":675},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.",[1166,1167,1168],{"_88":1111,"_638":842,"_639":31,"_398":675},{"_88":1170,"_638":842,"_639":318,"_398":675},{"_88":1169,"_638":842,"_639":31,"_398":675},"AWSALBCORS","JSESSIONID",[1172],{"_88":1150,"_638":666,"_639":31,"_398":1173},"This is a CloudFoundry cookie",[1175],{"_88":1176,"_638":847,"_639":31,"_398":851},"logged_in","hcaptcha.com",[1179],{"_88":1150,"_638":1177,"_639":31,"_398":1173},"ferndocs.com",[1182],{"_88":1150,"_638":1180,"_639":31,"_398":1183},"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.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.","tracking.intentsify.io",[1186],{"_88":1187,"_638":1184,"_639":31,"_398":1188},"userId","Used to track usage of the embedded video player.","static.reo.dev",[1191],{"_88":1192,"_638":1189,"_639":31,"_398":1193},"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",[1196],{"_88":1197,"_638":1194,"_639":31,"_398":1198},"dd_cookie_test_","dd_cookie_test",[1200],{"_88":1150,"_638":689,"_639":31,"_398":1173},[1202],{"_88":1150,"_638":694,"_639":31,"_398":1173},"upload.wikimedia.org",[1205],{"_88":1206,"_638":1203,"_639":31,"_398":1207},"WMF-Uniq","This domain is used by Wikimedia Foundation to host media files (images, videos, audio, etc.) for Wikipedia and other Wikimedia websites.","supabase.co",[1210],{"_88":1150,"_638":1208,"_639":31,"_398":1173},[1212],{"_88":1213,"_638":702,"_639":31,"_398":708},"VISITOR_PRIVACY_METADATA","dosa-prd.azurewebsites.net",[1216],{"_88":1217,"_638":1214,"_639":31,"_398":1218},"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",[1221],{"_88":1150,"_638":1219,"_639":31,"_398":1173},"cdn.readme.io",[1224],{"_88":1150,"_638":1222,"_639":31,"_398":1173},"queue-it.net",[1227,1228,1229,1230,1231],{"_88":1236,"_638":1225,"_639":31,"_398":633},{"_88":1235,"_638":1225,"_639":31,"_398":633},{"_88":1234,"_638":1225,"_639":31,"_398":633},{"_88":1233,"_638":1225,"_639":31,"_398":633},{"_88":1232,"_638":1225,"_639":31,"_398":633},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","x.com",[1239],{"_88":1150,"_638":1237,"_639":31,"_398":1151},"app-sj14.marketo.com",[1242],{"_88":1150,"_638":1240,"_639":31,"_398":1173},"api.hcaptcha.com",[1245,1246],{"_88":1248,"_638":1243,"_639":31,"_398":675},{"_88":1141,"_638":1243,"_639":31,"_398":1247},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[1251],{"_88":1150,"_638":1249,"_639":31,"_398":1173},{"_88":1350,"_638":776,"_639":31,"_398":1438},{"_88":1436,"_638":894,"_639":318,"_398":1437},{"_88":1109,"_638":1400,"_639":318,"_398":1110},{"_88":1426,"_638":791,"_639":318,"_398":633},{"_88":1434,"_638":776,"_639":31,"_398":1435},{"_88":1158,"_638":1421,"_639":31,"_398":1362},{"_88":1432,"_638":901,"_639":318,"_398":1433},{"_88":1368,"_638":1429,"_639":318,"_398":1370},{"_88":1156,"_638":1361,"_639":31,"_398":1362},{"_88":1124,"_638":1429,"_639":31,"_398":1125},{"_88":1430,"_638":776,"_639":318,"_398":1431},{"_88":1109,"_638":1409,"_639":318,"_398":1110},{"_88":1126,"_638":1429,"_639":31,"_398":1127},{"_88":1427,"_638":776,"_639":31,"_398":1428},{"_88":1156,"_638":1346,"_639":31,"_398":1362},{"_88":1356,"_638":1351,"_639":31,"_398":1358},{"_88":1426,"_638":776,"_639":318,"_398":1373},{"_88":1169,"_638":1357,"_639":31,"_398":1371},{"_88":1111,"_638":1357,"_639":31,"_398":1376},{"_88":1124,"_638":1367,"_639":31,"_398":1125},{"_88":1424,"_638":897,"_639":31,"_398":1425},{"_88":1356,"_638":1383,"_639":31,"_398":1358},{"_88":1111,"_638":926,"_639":31,"_398":1376},{"_88":1422,"_638":776,"_639":31,"_398":1423},{"_88":1163,"_638":926,"_639":31,"_398":1381},{"_88":1394,"_638":812,"_639":318,"_398":633},{"_88":1156,"_638":1421,"_639":31,"_398":1362},{"_88":1419,"_638":1361,"_639":318,"_398":1420},{"_88":1126,"_638":1383,"_639":31,"_398":1127},{"_88":1384,"_638":776,"_639":31,"_398":1353},{"_88":1417,"_638":776,"_639":31,"_398":1418},{"_88":1356,"_638":776,"_639":31,"_398":1358},{"_88":1416,"_638":788,"_639":31,"_398":633},{"_88":1163,"_638":776,"_639":31,"_398":1381},{"_88":1111,"_638":1346,"_639":31,"_398":1112},{"_88":1384,"_638":1354,"_639":31,"_398":1353},{"_88":1414,"_638":1031,"_639":318,"_398":1415},{"_88":1412,"_638":776,"_639":318,"_398":1413},{"_88":1356,"_638":812,"_639":31,"_398":1358},{"_88":1411,"_638":776,"_639":31,"_398":633},{"_88":1350,"_638":1357,"_639":31,"_398":1352},{"_88":1410,"_638":788,"_639":31,"_398":633},{"_88":1399,"_638":1409,"_639":318,"_398":1401},{"_88":1407,"_638":812,"_639":31,"_398":1408},{"_88":1405,"_638":791,"_639":31,"_398":1406},{"_88":1403,"_638":776,"_639":31,"_398":1404},{"_88":1402,"_638":782,"_639":31,"_398":633},{"_88":1399,"_638":1400,"_639":318,"_398":1401},{"_88":1397,"_638":1367,"_639":318,"_398":1398},{"_88":1395,"_638":776,"_639":31,"_398":1396},{"_88":1394,"_638":779,"_639":318,"_398":633},{"_88":1126,"_638":1369,"_639":31,"_398":1127},{"_88":1162,"_638":1385,"_639":31,"_398":1353},{"_88":1359,"_638":779,"_639":31,"_398":1360},{"_88":1392,"_638":776,"_639":31,"_398":1393},{"_88":1390,"_638":776,"_639":31,"_398":1391},{"_88":1389,"_638":776,"_639":31,"_398":633},{"_88":1111,"_638":1388,"_639":31,"_398":1376},{"_88":1124,"_638":1383,"_639":31,"_398":1125},{"_88":1197,"_638":779,"_639":31,"_398":1387},{"_88":1384,"_638":926,"_639":31,"_398":1353},{"_88":1150,"_638":894,"_639":31,"_398":1386},{"_88":1384,"_638":1385,"_639":31,"_398":1353},{"_88":1350,"_638":1383,"_639":31,"_398":1352},{"_88":1382,"_638":788,"_639":31,"_398":633},{"_88":1163,"_638":1354,"_639":31,"_398":1381},{"_88":1380,"_638":776,"_639":31,"_398":1349},{"_88":1378,"_638":1367,"_639":31,"_398":1379},{"_88":1156,"_638":1377,"_639":31,"_398":1157},{"_88":1111,"_638":908,"_639":31,"_398":1376},{"_88":1375,"_638":788,"_639":31,"_398":633},{"_88":1374,"_638":776,"_639":31,"_398":633},{"_88":1368,"_638":1367,"_639":318,"_398":1370},{"_88":1162,"_638":926,"_639":31,"_398":1353},{"_88":1372,"_638":776,"_639":31,"_398":1373},{"_88":1169,"_638":926,"_639":31,"_398":1371},{"_88":1124,"_638":1369,"_639":31,"_398":1125},{"_88":1368,"_638":1369,"_639":318,"_398":1370},{"_88":1126,"_638":1367,"_639":31,"_398":1127},{"_88":1365,"_638":1361,"_639":318,"_398":1366},{"_88":1363,"_638":912,"_639":31,"_398":1364},{"_88":1158,"_638":1361,"_639":31,"_398":1362},{"_88":1359,"_638":776,"_639":31,"_398":1360},{"_88":1356,"_638":1357,"_639":31,"_398":1358},{"_88":1350,"_638":812,"_639":31,"_398":1352},{"_88":1150,"_638":902,"_639":31,"_398":1355},{"_88":1162,"_638":1354,"_639":31,"_398":1353},{"_88":1162,"_638":776,"_639":31,"_398":1353},{"_88":1350,"_638":1351,"_639":31,"_398":1352},{"_88":1348,"_638":776,"_639":31,"_398":1349},{"_88":1169,"_638":1346,"_639":31,"_398":1347},{"_88":1344,"_638":776,"_639":31,"_398":1345},"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.","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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.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.\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.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","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","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.","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.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","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.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","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_ctl36_uccaptcha","programs.nvidia.com","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","buildapi.ngc.nvidia.com","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.","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","_pf_consent","This cookie name is associated with Command Alkon Incorporated which is used to store user consent status and expiration.","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.","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.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","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.","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.","_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.","c_code","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","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.","session","This cookie is typically used to maintain an anonymized user session by the server.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","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.

",{"_1069":18,"_929":56,"_823":56,"_607":56,"_597":56},"hasConsented","gpcApplied","showBanner","geolocation",{"_1453":1454,"_1455":1456},"country","US","state","OR","query","categories",[1476,1477,1478,1479,1480],"optionUrls",[1464,1465,1466,1467,1468,1469,1470,1471,1472,1473,1474,1475],"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/-",{"_457":1554,"_1482":1555,"_1484":56},{"_457":1519,"_1482":1520,"_1484":54},{"_457":1502,"_1482":1503,"_1484":1504},{"_457":458,"_1482":1495,"_1484":1496},{"_457":1481,"_1482":1483,"_1484":1485},"helm-charts","items",[1486,1487],"startIndex",10,{"_457":1481,"_464":1492,"_393":394,"_395":-7,"_459":1493,"_467":468,"_494":1494,"_1491":1474},{"_457":1481,"_464":1488,"_393":394,"_395":-7,"_459":1489,"_467":468,"_494":1490,"_1491":1475},"NVIDIA NIM Operator","k8s-nim-operator",[],"url","GPU Operator","gpu-operator",[],[1497],9,{"_457":458,"_464":1498,"_393":394,"_395":1499,"_459":1500,"_467":468,"_494":1501,"_1491":1473},"Riva Skills Quick Start","riva","riva_quickstart",[],"models",[1505,1506,1507],6,{"_457":1502,"_464":1515,"_393":394,"_395":1516,"_459":1517,"_467":468,"_494":1518,"_1491":1470},{"_457":1502,"_464":1512,"_393":394,"_395":1509,"_459":1513,"_467":468,"_494":1514,"_1491":1471},{"_457":1502,"_464":1508,"_393":394,"_395":1509,"_459":1510,"_467":468,"_494":1511,"_1491":1472},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1521,1522,1523],{"_457":1519,"_464":1551,"_393":394,"_395":1525,"_459":1552,"_467":468,"_494":1553,"_1491":1467},{"_457":1519,"_464":1548,"_393":394,"_395":-7,"_459":1549,"_467":468,"_494":1550,"_1491":1468},{"_457":1519,"_464":1524,"_393":394,"_395":1525,"_459":1526,"_467":468,"_494":1527,"_1491":1469},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1528,1529,1530],{"_88":1542,"_464":1543,"_461":1544,"_472":1545,"_1535":1546},{"_88":1525,"_464":1538,"_461":1539,"_472":1534,"_1535":1540},{"_88":1531,"_464":1532,"_461":1533,"_472":1534,"_1535":1536},"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",[1537],"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.",[1537,1541],"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",[1537,1541,1547],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1528,1529,1530],"containers",[1556,1557,1558],{"_457":1554,"_464":1570,"_393":1563,"_395":1571,"_459":1572,"_467":468,"_494":1573,"_1491":1464},{"_457":1554,"_464":1562,"_393":1563,"_395":394,"_459":1564,"_467":468,"_494":1565,"_1491":1465},{"_457":1554,"_464":1559,"_393":394,"_395":-7,"_459":1560,"_467":468,"_494":1561,"_1491":1466},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[1566,1528],{"_88":1567,"_464":1532,"_461":1533,"_472":1568,"_1535":1569},"nim-dev","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[1537],"DeepSeek-R1","deepseek-ai","deepseek-r1",[1528,1566],"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",{"_1603":1604,"_1605":543,"_1606":31,"_1607":1608,"_1482":1609,"_1610":1611,"_1612":1604,"_1613":31,"_1614":31,"_1615":31,"_1616":1617},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1618,1619,1620,1621,1622,1623,1624],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1616":1646,"_47":1647,"_1610":1648},{"_1639":1640,"_1616":1641,"_47":1642,"_1612":1542},{"_1616":1637,"_47":1638,"_1610":1611},{"_1616":1634,"_47":1635,"_1610":1636},{"_1616":1631,"_47":1632,"_1610":1633},{"_1616":1628,"_47":1629,"_1610":1630},{"_1616":1625,"_47":1626,"_1610":1627},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1643],"Enterprise Catalog","/enterprise",[1644,1645],"NVAIE","READ","Explore Catalog","/","common-home",{"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355},{"_343":31}]

Name	Size	Updated	Actions





__init__.py	609 B	August 4, 2021 UTC	curl wget Download
Dockerfile	2.11 KB	August 4, 2021 UTC	curl wget Download
LICENSE	11.08 KB	August 4, 2021 UTC	curl wget Download
NOTICE	203 B	August 4, 2021 UTC	curl wget Download
README.md	27.74 KB	August 4, 2021 UTC	curl wget Download
setup.py	1.44 KB	August 4, 2021 UTC	curl wget Download