[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10,"_11":12},"actionData","errors","root",{"_67":218,"_219":220,"_221":222,"_223":224,"_225":226,"_227":26,"_228":229,"_230":231,"_232":26,"_233":26,"_234":26,"_235":26,"_236":237,"_238":-5,"_239":240,"_241":242,"_243":244,"_245":246,"_247":94,"_248":96,"_249":250,"_88":-5,"_251":26,"_252":-5,"_253":254,"_255":256,"_117":257,"_258":259,"_260":261,"_262":263,"_264":265,"_266":26,"_21":22,"_23":267,"_25":26,"_27":268,"_29":269,"_31":270},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/route",{"_103":104,"_17":105,"_106":107,"_108":109,"_110":26,"_111":112,"_113":26,"_114":26,"_115":116,"_117":14,"_118":-5,"_119":120,"_121":-5,"_122":123,"_124":125,"_126":26,"_127":128,"_21":22,"_23":129,"_25":26,"_27":130,"_29":131,"_31":132},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/_access/route",{"_64":65,"_66":-5,"_67":68,"_69":70,"_21":22,"_23":71,"_25":26,"_27":72,"_29":73,"_31":74},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/release-notes/route",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32},"versionId","22.03.0","releaseNotes","### Changelog\n \nMay 2022\n- Initial Release\n \n### Known Issues\nThe dataloader is a bottleneck during training thus to gain any more performance boosts the dataloader needs to be improved.","html","

Changelog

May 2022

Initial Release

Known Issues

The dataloader is a bottleneck during training thus to gain any more performance boosts the dataloader needs to be improved.

","resourceType","resources","traceId","b5f7a23c6373badb53be710aacab3538","traceStartTime",1786033279922,"isStaleResponse",false,"notifications",[],"toasts",[],"apiCalls",[33,34,35,36,37,38],{"_39":53,"_41":62,"_43":55,"_45":56,"_47":63,"_49":50},{"_39":53,"_41":60,"_43":55,"_45":56,"_47":61,"_49":50},{"_39":53,"_41":58,"_43":55,"_45":56,"_47":59,"_49":50},{"_39":53,"_41":54,"_43":55,"_45":56,"_47":57,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":48,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"apiTag","subscription-api","path","/v2/users/me/subscriptions","status","error","httpStatus",401,"durationMs",3,"retryCount",0,"oms-api","/v2/users/me","models-api","/v2/resources/org/nvidia/team/dle/efficientdet_tf2/collections?page-number=0&page-size=1","success",200,61,"/v2/resources/nvidia/dle/efficientdet_tf2/versions/22.03.0",60,"/v2/resources/nvidia/dle/efficientdet_tf2/versions?page-size=100&page-number=0&sort-order=CREATED_DATE_DESC",53,"/v2/resources/nvidia/dle/efficientdet_tf2?resolve-labels=true&remove-unresolved-labels=true",55,"canDownload",true,"accessDeniedReason","appConfig",{"_86":87,"_88":89},"debugAccessInfo",{"_82":65,"_83":84,"_85":26},1786033279915,[],[],[75,76,77,78,79,80],{"_39":53,"_41":62,"_43":55,"_45":56,"_47":63,"_49":50},{"_39":53,"_41":60,"_43":55,"_45":56,"_47":61,"_49":50},{"_39":53,"_41":58,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"pending","canGuestAccess","requiredProducts",[],"hasLicenseRequirement","urls",{"_91":92,"_93":94,"_95":96,"_97":98,"_99":100,"_101":102},"user",{"_90":26},"loggedIn","build","https://build.nvidia.com","ngcApi","https://api.ngc.nvidia.com","ngcHelm","https://helm.ngc.nvidia.com","ngcRegistry","https://registry.ngc.nvidia.com","orgUi","https://org.ngc.nvidia.com","ngcUi","https://ngc.nvidia.com","resource",{"_173":178,"_179":180,"_19":20,"_181":182,"_139":140,"_141":142,"_183":184,"_144":185,"_186":187,"_149":188,"_189":190,"_191":14,"_192":-7,"_193":-7,"_194":195,"_196":197,"_198":65,"_199":151,"_200":-5,"_201":-7,"_202":-7,"_203":-7,"_204":-7,"_205":-7,"_206":-7,"_207":208,"_209":210,"_211":212,"_213":214,"_215":216,"_217":26},"

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

EfficientDet is a family of convolution-based neural networks for object detection. Specifically, this repository covers model D0. This model is based on EfficientDet: Scalable and Efficient Object Detection. NVIDIA's implementation of EfficientDet-D0 is an optimized version of TensorFlow Automl implementation, leveraging mixed precision arithmetic on NVIDIA Volta, NVIDIA Turing, and the NVIDIA Ampere GPU architectures for faster training times while maintaining target accuracy.

The EfficientDet model covered in this repository is tested against each NGC monthly released container to ensure consistent accuracy and performance over time.

The major differences between the official implementation of the paper and our version of EfficientDet are as follows:

Automatic mixed precision (AMP) training support
Multi-node training support using Horovod
XLA enabled for better performance
Lightweight logging using dllogger
EfficientNet backbone implemented by NVIDIA
Use BatchNormalization instead of SyncBatchNormalization for better performance

These techniques/optimizations improve model performance and reduce training time, allowing you to perform more efficient object detection with no additional effort.

Other publicly available implementations of EfficientDet include:

Google's automl
PyTorch version

Model architecture

EfficientDet is a one-stage detector with the following architecture components:

ImageNet-pretrained EfficientNet backbone
Weighted bi-directional feature pyramid network (BiFPN)
Bounding and classification box head
A compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time

Feature support matrix

The model supports the following features.

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

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

Feature	EfficientDet
Horovod Multi-GPU training (NCCL)	Yes
Multi-GPU training	Yes
Multi-node training	Yes
XLA	Yes
AMP (Automatic Mixed Precision)	Yes

Features

Horovod is used to implement efficient multi-GPU training with NCCL. It is also used for multi-node training. For details, refer to example sources in this repository or refer to the TensorFlow tutorial.

AMP or Automatic Mixed Precision modifies computation graphs during runtime to support mixed precision training. A detailed explanation of mixed precision can be found below.

Automatic 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 NVIDIA Volta, and following with both the NVIDIA Turing and NVIDIA Ampere Architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training previously required two steps:

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

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

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

For information about:

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

Enabling AMP

Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients.

In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.

To enable mixed precision, you can simply add --amp=True to the training command. This will enable the following code:

policy = tf.keras.mixed_precision.experimental.Policy('mixed_float16', loss_scale='dynamic')\ntf.keras.mixed_precision.experimental.set_policy(policy)\n

TensorFloat-32 (TF32) Compute Mode

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 a high dynamic range for weights or activations.

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

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

","resourceVersions",[171,172],"resourceVersionsTotal",1,"hasMoreVersions","versionMetadata",{"_13":14,"_144":-5,"_43":145,"_146":-5,"_147":148,"_149":-5,"_150":151,"_152":109,"_153":26,"_154":-5,"_155":26,"_15":16,"_156":65,"_157":158,"_159":65,"_160":161,"_162":65,"_163":164,"_165":65,"_166":167,"_168":65,"_169":170},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/resources/efficientdet_tf2/-","version","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/dle/efficientdet_tf2:22.03.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/resources/efficientdet_tf2/22.03.0/version-history","accessCheckResult",{"_143":65},"hasRelatedCollections","context",{"_139":140,"_141":142},1786033279921,[],[],[133,134,135,136,137,138],{"_39":53,"_41":62,"_43":55,"_45":56,"_47":63,"_49":50},{"_39":53,"_41":60,"_43":55,"_45":56,"_47":61,"_49":50},{"_39":53,"_41":58,"_43":55,"_45":56,"_47":59,"_49":50},{"_39":53,"_41":54,"_43":55,"_45":56,"_47":57,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":48,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"orgName","nvidia","teamName","dle","allowed","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1663857150369],"updatedDate","totalSizeInBytes",32253,"totalFileCount","isSigned","malwareScanStatus","isScanned","hasReleaseNotes","setup","The following sections list the requirements in order to start training the EfficientDet model.\n \n### Requirements\n \nThis repository contains a `Dockerfile` that extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n - [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n - [TensorFlow 22.03-tf2-py3 NGC container or later](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow)\n - Supported GPUs:\n - [NVIDIA Volta architecture: 32GB](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n - [NVIDIA Turing architecture](https://www.nvidia.com/en-us/geforce/turing/)\n - [NVIDIA Ampere architecture: 80GB](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n \n For more information about how to get started with NGC containers, refer to the\n following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning\n Documentation:\n - [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n - [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)\n - [Running TensorFlow](https://docs.nvidia.com/deeplearning/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, refer to the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).\n \nFor multi-node, the sample provided in this repository requires [Enroot](https://github.com/NVIDIA/enroot) and [Pyxis](https://github.com/NVIDIA/pyxis) to be set up on a [SLURM](https://slurm.schedmd.com) cluster.","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 EfficientDet on the COCO 2017 dataset. For the specifics concerning training and inference, refer to the [Advanced](#advanced) section.\n \n### 1. Clone the repository\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples.git\ncd DeepLearningExamples/Tensorflow2/Detection/EfficientDet\n```\n\n### 2. Download and preprocess the dataset\n\nTo download COCO 2017 images and annotations and convert them to tfrecords, run the script as follows:\n```bash\nbash dataset/get_coco.sh\n```\n\nBy default, the data is organized into the following structure:\n```\n\n train-*****-of-00256.tfrecord\n val-*****-of-00032.tfrecord\n```\n\n### 3. Build the EfficientDet PyTorch NGC container\n```\nbash scripts/docker/build.sh\n```\n\n### 4. Start an interactive session in the NGC container to run training/inference\nAfter you build the container image, you can start an interactive CLI session with \n```\nDATA= BACKBONE_CKPT= bash scripts/docker/interactive.sh\n```\nNote: The `interactive.sh` script requires the location of the dataset and the pretrained checkpoint to be passed.\n\n### 5. Start training\n```\nbash ./scripts/D0/convergence-{AMP, FP32, TF32}-{8, 32}x{V100-32G, A100-80G}.sh\n```\nThe training scripts train EfficientDet-D0 and perform an evaluation on the COCO 2017 dataset. By default, the training script runs training on standard configuration (DGX A100/DGX-1 V100, AMP/FP32/TF32, 300 epochs). Run one of the scripts in `scripts/D0` directory using `bash scripts/D0/convergence-{AMP, FP32, TF32}-{8, 32}x{V100-32G, A100-80G}.sh`. Ensure COCO-2017 tfrecords are mounted to `/workspace/coco` and EfficientNet-B0 backbone weights are mounted to `/workspace/checkpoints`. The backbone checkpoint can be downloaded from [this](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/models/efficientnet_tf2_savedmodel_nohead_b0_amp_cosine) location.\n\nAfter training, the logs are present in the model directory where the data is organized in the following structure:\n```\n\n ema_weights *contains the ema checkpoints of the model, checkpointed after every 10 epochs of training*\n checkpoint\n emackpt-10.data-00000-of-00001\n emackpt-10.index\n emackpt-20.data-00000-of-00001\n emackpt-20.index\n ...\n emackpt-300.data-00000-of-00001\n emackpt-300.index\n emackpt-final *final savedmodel with ema weights which can be used for inference*\n assets\n variables\n variables.data-00000-of-00001\n variables.index\n keras_metadata.pb\n saved_model.pb\n train *tensorboard logs*\n events.out.tfevents.*\n checkpoint\n ckpt.data-00000-of-00001\n ckpt.index\n ckpt-final.data-00000-of-00001\n ckpt-final.index\n time_log.txt *dllogger logs*\n train-.log *training log file*\n```\n\n### 6. Start validation/evaluation\n\nTo run validation/evaluation for a standard configuration (DGX A100/DGX-1 V100, AMP/TF32/FP32, EfficientDet-D0),\nrun one of the evaluation scripts in `scripts/D0` directory using `bash scripts/D0/evaluate-{AMP, FP32, TF32}-{8, 32}x{A100-80G, V100-32G}.sh`.\nThe script requires:\n - `CKPT` is the path to the checkpoint that needs to be evaluated. For example, `CKPT=/tmp/convergence-AMP-8xA100-80G/ema_weights/emackpt-300`\n\nEvaluation command:\n```bash\nCKPT= bash ./scripts/D0/evaluate-{AMP, FP32, TF32}-{8, 32}x{A100-80G, V100-32G}.sh\n```\n\nEnsure COCO-2017 is mounted in `/workspace/coco`.\n\n### 7. Inference benchmark\n\nInference loop can be benchmarked by running the `scripts/D0/inference-benchmark.sh` script. The script requires:\n - batch size to use for inference `BS`. For example, `BS=128`\n - Boolean to enable/disable `AMP` (Automatic Mixed Precision)\n\nInference benchmark command:\n```bash\nBS= AMP= bash scripts/D0/inference-benchmark.sh\n```\n\n### 8. Inference/Prediction\n\nModel predictions can be obtained by running the `scripts/D0/inference.sh` script. This script reads a test image and annotates the image after object detection by drawing boxes on the objects in the image. The script requires:\n - `MODEL_DIR` in which the file `checkpoint` contains path to the latest checkpoint that needs to be used for inference. For example, `MODEL_DIR=/tmp/convergence-AMP-8xA100-80G/ema_weights`\n\nInference command:\n```bash\nMODEL_DIR= bash scripts/D0/inference.sh\n```\n\nNote that the above script assumes that the test image is present in `testdata` and is named `img1.jpg`.","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n \n### Scripts and sample code\n \nDescriptions of the key scripts and folders are provided below.\n \n- `model` and `object_detection` - Contains code to build individual components of the model such as backbone, FPN, RPN, classification and bbox heads, and so on.\n- data - Contains code to convert raw data to tfrecords\n- dataset - Contains code to build the data pipeline such as dataloader, transforms, dataset builder.\n- scripts/ - Contains shell scripts to launch training and evaluation of the model and perform inference.\n\n - D0/convergence-{AMP, TF32, FP32}-{8, 32}x{V100-32G, A100-80G}.sh - Launches model training\n \n \t- D0/evaluate-{AMP, FP32, TF32}-{8, 32}x{A100-80G, V100-32G}.sh - Performs inference and computes mAP of predictions.\n\n - D0/inference.sh - Performs inference on an image\n\n - D0/training-benchmark-{AMP, TF32, FP32}-{V100-32G, A100-80G}.sh - Launches model training for 500 iterations to benchmark training\n\n - D0/inference-benchmark.sh - Benchmarks inference\n \n \t- docker/ - Scripts to build the docker image and to start an interactive session\n \n- utils/\n - Contains utility components like default hyper parameters, checkpoint utils, training utils, and so on.\n\n- train.py - End to end to script to load data, build and train the model.\n- eval.py - End to end script to load data and checkpoint to perform inference and compute mAP scores.\n \n### Parameters\n\n#### train.py script parameters\n\nImportant parameters for training are listed below with defaults.\n\n### Command-line options\n \nTo display the full list of available options and their descriptions, use the --helpshort command-line option:\n\n```\n --amp: Enable mixed precision training\n --backbone_init: Initialize backbone weights from \n checkpoint in this directory\n --batch_size: training local batch size\n --benchmark: Train for a fixed number of steps for performance\n --benchmark_steps: Train for these many steps to benchmark training\n performance\n --checkpoint_period: Save ema model weights after every X epochs for eval\n --debug: Enable debug mode\n --enable_map_parallelization: \n Parallelize stateless map transformations in dataloader\n --eval_samples: The number of samples for evaluation.\n --hparams: Comma separated k=v pairs of hyperparameters or a module \n containing attributes to use as hyperparameters\n --log_filename: Filename for dllogger logs\n --log_steps: Interval of steps between logging of batch level stats\n --lr: Learning rate\n --lr_tb: Log learning rate at each step to TB\n --training_mode: Training mode (train/traineval/train300)\n --model_dir: Location of model_dir\n --model_name: Model name\n --num_epochs: Number of epochs for training\n --num_examples_per_epoch: \n Number of examples in one epoch (coco is 117266).\n Default is 120000.\n --pretrained_ckpt: Start training from this EfficientDet checkpoint.\n --seed: Random seed\n --set_num_threads: Set inter-op and intra-op parallelism threads\n --testdev_dir: COCO testdev dir. If not None, ignore val_json_file.\n --time_history: Get time history\n --training_file_pattern: \n Glob for training data files, e.g., coco/train-*.tfrecord.\n --use_fake_data: Use fake input\n --use_xla: Use XLA\n --val_file_pattern: Glob for eval tfrecords, e.g. coco/val-*.tfrecord.\n --val_json_file: COCO validation JSON containing golden bounding boxes. If\n None, use the ground truth from the dataloader. Ignored if \n testdev_dir is not None.\n --validate: Get validation loss after each epoch\n --warmup_epochs: Number of warmup epochs\n --warmup_value: Initial warmup value\n```\n\nThe default `training_mode` (`traineval`) is training along with evaluation. Note that evaluation starts only after 200 epochs of training, and the frequency of evaluation can be set by setting `checkpoint_period=` which is currently set to 10. Also, in the `traineval` `training_mode`, the model stops training at 300 epochs to avoid overfitting. To run only training without evaluation, set the `training_mode` to train. In this `training_mode`, the ema checkpoints are stored in path `model_dir/ema_weights/` every `checkpoint_period` (in epochs) . The training stops after training for 75% of the total number of epochs, and the last ema-weight checkpoint is evaluated.\nFor benchmarking only the training time for 300 epochs, the `training_mode` can be set to `train300` where the model trains for exactly 300 epochs without any evaluation.\n \n### Getting the data\nBy default, the EfficientDet model is trained on the [COCO 2017](http://cocodataset.org/#download) dataset. This dataset comes with a training and validation set. Follow steps in the [Quick Start Guide](#quick-start-guide) to download and pre-process the dataset into tfrecord format.\n \n### Training Process\nTraining is performed using the `train.py` script. The default parameters can be overridden by command-line arguments.\n \nThe training process can start from scratch or resume from a checkpoint.\n\nBy default, bash script `scripts/D0/convergence-{AMP, FP32, TF32}-8x{A100-80G, V100-32G}.sh` will start the training process from scratch with the following settings.\n - Use 8 GPUs\n - Saves checkpoints after every 10 epochs to `model_dir` which is `/tmp/convergence-{AMP, FP32, TF32}-8x{A100-80G, V100-32G}` folder\n\nTo resume from a checkpoint, just make sure that the `model_dir` stays the same and that the checkpoints saved are already present in it.\n\n#### Multi-node\n\nMulti-node runs can be launched on a Pyxis/enroot Slurm cluster (refer to [Requirements](#requirements)) with the `./scripts/D0/convergence-{AMP, FP32}-32xV100-32G.sub` script with the following command for a 4-node NVIDIA DGX V100 example:\n\n```\ncheckpointdir= datadir= sbatch N 4 --ntasks-per-node=8 --ntasks=32 ./scripts/D0/convergence-{AMP, FP32}-32xV100-32G.sub\n```\n \nNote that the `./scripts/D0/convergence-{AMP, FP32}-32xV100-32G.sub` script is a starting point that has to be adapted depending on the environment. In particular, variables such as `--container-image` handles the container image to train using, `datadir` handles the location of the COCO-2017 data, and `checkpointdir` has the path to the pre-trained backbone (EfficientNet) weights.\n \nRefer to the file's content to view the full list of variables to adjust for your system.","hasAdvanced","performance","### Benchmarking\nBenchmarking can be performed for both training and inference. Both the scripts run the EfficientDet model. You can specify whether benchmarking is performed in AMP, TF32, or FP32 by specifying it as an argument to the benchmarking scripts.\n \n#### Training performance benchmark\nTraining benchmarking can be performed by running the script:\n```\nNGPU= bash scripts/D0/training-benchmark-{AMP, TF32, FP32}-{V100-32G, A100-80G}.sh\n```\nTo train on 1 DGXA100-80G run script:\n```\nbash scripts/D0/training-benchmark-{AMP, TF32}-1xA100-80G.sh\n```\n\n#### Inference performance benchmark\nInference benchmarking can be performed by running the script:\n```\nAMP= BS= bash scripts/D0/inference-benchmark.sh\n```\n\n### Results\nThe following sections provide details on how we achieved our performance and accuracy in training and inference.\n#### Training Accuracy Results\n \n##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)\n \nOur results were obtained by running the `scripts/D0/convergence-{AMP, TF32}-8xA100-80G.sh` training script in the 22.03-tf2 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs while evaluating every 10 epochs after 200 epochs of training until the 300th epoch is completed.\n\n| GPUs | Image size | Precision | Local Batch size | BBOX mAP | Time to train | Time to train - speedup (TF32 to mixed precision)\n| --| --| -- | -- | -- | -- | -- \n| 8 | 512 x 512 | TF32 | 104 | 34.53 | 8.5 hrs | -\n| 8 | 512 x 512 | FP16 | 200 | 34.27 | 4.6 hrs | 1.84x\n \n##### Training accuracy: NVIDIA DGX-1 (8x V100 32GB)\n\nOur results were obtained by running the `scripts/D0/convergence-{AMP, FP32}-8xV100-32G.sh` training script in the 22.03-tf2 NGC container on NVIDIA DGX-1 with 8x V100 32GB GPUs with no intermediate evaluation.\n \n| GPUs | Image size | Precision | Local Batch size | BBOX mAP | Time to train | Time to train - speedup (FP32 to mixed precision)\n| --| --| -- | -- | -- | -- | -- \n| 8 | 512 x 512 | FP32 | 40 | 34.42 | 16.9 hrs | -\n| 8 | 512 x 512 | FP16 | 64 | 34.45 | 14.3 hrs | 1.18x\n\n##### Training accuracy: NVIDIA DGX-1 (32x V100 32GB)\n \nOur results were obtained by running the `scripts/D0/convergence-{AMP, FP32}-32xV100-32G.sub` training script in the 22.03-tf2 NGC container on NVIDIA DGX-1 with 32x V100 32GB GPUs with no intermediate evaluation.\n\n| GPUs | Image size | Precision | Local Batch size | BBOX mAP | Time to train | Time to train - speedup (FP32 to mixed precision)\n| --| --| -- | -- | -- | -- | -- \n| 32 | 512 x 512 | FP32 | 40 | 34.14 | 5.6 hrs | -\n| 32 | 512 x 512 | FP16 | 64 | 34.02 | 4.19 hrs | 1.33x\n\n##### Training loss curves\n \n![Loss Curve](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow2/Detection/Efficientdet//testdata/loss.png)\n\n \nHere, the loss is simply the weighted sum of losses on the classification head and the bounding box head.\n \n##### Training Stability Test\nThe following tables compare mAP scores across five different training runs with different seeds. The runs showcase consistent convergence on all five seeds with very little deviation.\n \n| **Config** | **Seed 1** | **Seed 2** | **Seed 3** | **Seed 4** | **Seed 5** | **Mean** | **Standard Deviation** |\n| --- | --- | ----- | ----- | --- | --- | ----- | ----- |\n| 8 GPUs, final AP BBox | 34.38 | 34.56 | 34.3 | 34.34 | 34.4 | 34.39 | 0.1 |\n \n#### Training Performance Results\n \n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n \nOur results were obtained by running the `scripts/D0/training-benchmark-{AMP, TP32}-A100-80G.sh` training script in the 22.03-tf2 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers in images per second were averaged over an entire training epoch. The number of GPUs used to benchmark can be set as `NGPU=<4/8>`. For 1-gpu benchmarking run script `scripts/D0/training-benchmark-{AMP, TP32}-1xA100-80G.sh`\n \n| GPUs | Throughput - TF32 (BS=104) | Throughput - mixed precision (BS=200) | Throughput speedup (TF32 - mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision\n| --- | ----- | ----- | --- | --- | ----- |\n| 1 | 162 | 397 | 2.45 | 1 | 1 |\n| 8 | 1266 | 2711 | 2.14 | 7.81 | 6.82 |\n \n##### Training performance: NVIDIA DGX-1 (8x V100 32GB)\n \nOur results were obtained by running the `scripts/D0/training-benchmark-{AMP, FP32}-V100-32G.sh` training script in the 22.03-tf2 NGC container on NVIDIA DGX-1 with (8x V100 32GB) GPUs. Performance numbers in images per second were averaged over an entire training epoch. The number of GPUs used to benchmark can be set as `NGPU=<1/4/8>`.\n \n| GPUs | Throughput - FP32 (BS=40) | Throughput - mixed precision (BS=64) | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision |\n| --- | ----- | ----- | --- | --- | ----- |\n| 1 | 113 | 232 | 2.05 | 1 | 1 |\n| 8 | 645 | 777 | 1.2 | 5.7 | 3.34 |\n\nTo achieve similar results, follow the steps in the [Quick Start Guide](#quick-start-guide).\nNote: The dataloader is a performance bottleneck for this model. So the training throughputs could be higher if the bottleneck is optimized further.\n \n#### Inference performance results\n \n##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)\n \nOur results were obtained by running the `scripts/inference-benchmark.sh` training script in the 22.03-tf2 NGC container on NVIDIA DGX A100 (1x A100 80GB) GPU. The image resolution is 512 x 512.\n\nFP16 Inference Latency\n\n| Batch size | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) | Latency 95% (ms) | Latency 99% (ms)\n| --- | ----- | ----- | ----- | ----- | ----- |\n| 1 | 38 | 26.31 | 26.27 | 26.29 | 26.31 |\n| 2 | 40 | 49.75 | 49.68 | 49.71 | 49.74 |\n| 4 | 80 | 50.12 | 50.06 | 50.08 | 50.11 |\n| 8 | 153 | 52.16 | 52.09 | 52.12 | 52.15 |\n| 16 | 276 | 57.83 | 57.77 | 57.79 | 57.81 |\n| 32 | 465 | 68.75 | 68.69 | 68.72 | 68.74 |\n| 64 | 706 | 90.63 | 90.56 | 90.59 | 90.62 |\n| 128 | 791 | 161.65 | 160.94 | 161.08 | 161.14 |\n| 256 | 858 | 298.33 | 296.1 | 296.62 | 297.76 |\n\nTF32 Inference Latency\n\n| Batch size | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) | Latency 95% (ms) | Latency 99% (ms)\n| --- | ----- | ----- | ----- | ----- | ----- |\n| 1 | 38 | 26.09 | 26 | 26.03 | 26.07 |\n| 2 | 40 | 49.94 | 49.84 | 49.88 | 49.91 |\n| 4 | 78 | 50.98 | 50.91 | 50.94 | 50.96 |\n| 8 | 144 | 55.21 | 55.16 | 55.19 | 55.21 |\n| 16 | 250 | 63.76 | 63.69 | 63.72 | 63.75 |\n| 32 | 394 | 81.06 | 80.97 | 81 | 81.04 |\n| 64 | 563 | 113.54 | 113.44 | 113.47 | 113.51 |\n| 128 | 623 | 205.33 | 205.06 | 205.16 | 205.28 |\n\nTo achieve similar results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 32GB)\n \nOur results were obtained by running the `scripts/inference-benchmark.sh` training script in the 22.03-tf2 NGC container on NVIDIA DGX-1 with 1x V100 32GB GPUs. The image resolution is 512 x 512.\n \nFP16 Inference Latency\n\n| Batch size | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) | Latency 95% (ms) | Latency 99% (ms)\n| --- | ----- | ----- | ----- | ----- | ----- |\n| 1 | 35 | 27.84 | 27.67 | 27.74 | 27.82 |\n| 2 | 40 | 49.81 | 49.42 | 49.62 | 49.77 |\n| 4 | 81 | 49.35 | 49.3 | 49.32 | 49.34 |\n| 8 | 146 | 54.51 | 54.44 | 54.47 | 54.5 |\n| 16 | 245 | 65.07 | 65.01 | 65.04 | 65.06 |\n| 32 | 366 | 87.24 | 87.1 | 87.16 | 87.22 |\n| 64 | 477 | 134.09 | 133.98 | 134.02 | 134.07 |\n| 128 | 497 | 257.39 | 257.09 | 257.19 | 257.34 |\n\nFP32 Inference Latency\n\n| Batch size | Throughput Avg | Latency Avg (ms) | Latency 90% (ms) | Latency 95% (ms) | Latency 99% (ms)\n| --- | ----- | ----- | ----- | ----- | ----- |\n| 1 | 36 | 27.21 | 27.02 | 27.11 | 27.19 |\n| 2 | 39 | 51.04 | 50.81 | 50.91 | 51.01 |\n| 4 | 78 | 51.23 | 51.19 | 51.21 | 51.22 |\n| 8 | 135 | 59.06 | 58.98 | 59.02 | 59.06 |\n| 16 | 214 | 74.73 | 74.64 | 74.68 | 74.71 |\n| 32 | 305 | 104.76 | 104.67 | 104.72 | 104.76 |\n| 64 | 374 | 171.08 | 170.92 | 170.98 | 171.05 |\n| 128 | 385 | 332.11 | 331.81 | 331.92 | 332.06 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).","hasPerformance","policies",[],{"_173":176,"_174":177,"_146":-5,"_153":26,"_155":26,"_169":175,"_156":65},{"_173":14,"_174":14,"_146":-5,"_153":26,"_155":26,"_169":175,"_156":65},"id","name",[],"-","Latest (22.03.0)","nvidia/dle/efficientdet_tf2","key","nvidia/dle/resources/efficientdet_tf2","resourceId","efficientdet_tf2","shortDescription","A convolution-based neural network for the task of object detection.","This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/efficientdet-tf2)\n\n \nEfficientDet is a family of convolution-based neural networks for object detection. Specifically, this repository covers model D0. This model is based on [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/abs/1911.09070). NVIDIA's implementation of EfficientDet-D0 is an optimized version of [TensorFlow Automl](https://github.com/google/automl/tree/master/efficientdet) implementation, leveraging mixed precision arithmetic on NVIDIA Volta, NVIDIA Turing, and the NVIDIA Ampere GPU architectures for faster training times while maintaining target accuracy.\n \nThe EfficientDet model covered in this repository is tested against each NGC monthly released container to ensure consistent accuracy and performance over time.\n \nThe major differences between the official implementation of the paper and our version of EfficientDet are as follows:\n - Automatic mixed precision (AMP) training support\n - Multi-node training support using [Horovod](https://github.com/horovod/horovod)\n - XLA enabled for better performance\n - Lightweight logging using [dllogger](https://github.com/NVIDIA/dllogger)\n - [EfficientNet backbone](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Classification/ConvNets/efficientnet) implemented by NVIDIA\n - Use [BatchNormalization](https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization) instead of [SyncBatchNormalization](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/SyncBatchNormalization) for better performance\n \nThese techniques/optimizations improve model performance and reduce training time, allowing you to perform more efficient object detection with no additional effort.\n\nOther publicly available implementations of EfficientDet include:\n\n - [Google's automl](https://github.com/google/automl/tree/master/efficientdet)\n - [PyTorch version](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/Efficientdet)\n \n### Model architecture\n \nEfficientDet is a one-stage detector with the following architecture components:\n- ImageNet-pretrained EfficientNet backbone\n- Weighted bi-directional feature pyramid network (BiFPN)\n- Bounding and classification box head\n- A compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time\n \n### Feature support matrix\n \nThe model supports the following features. \n \n| **Feature** | **EfficientDet** |\n|:---------:|:----------:|\n|Horovod Multi-GPU training (NCCL)|Yes|\n|Multi-GPU training|Yes|\n|Multi-node training|Yes|\n|XLA|Yes|\n|AMP (Automatic Mixed Precision)|Yes|\n\n#### Features\n\nHorovod is used to implement efficient multi-GPU training with NCCL. It is also used for multi-node training. For details, refer to example sources in this repository or refer to the [TensorFlow tutorial](https://github.com/horovod/horovod/#usage).\n\nAMP or Automatic Mixed Precision modifies computation graphs during runtime to support mixed precision training. A detailed explanation of mixed precision can be found below.\n \n### Automatic 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 NVIDIA Volta, and following with both the NVIDIA Turing and NVIDIA Ampere Architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on NVIDIA Volta, NVIDIA Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16. The loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\n- How to train using mixed precision, refer to the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, refer to 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, refer to [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n\n#### Enabling AMP\n\nMixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients. \n\nIn TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.\n\nTo enable mixed precision, you can simply add `--amp=True` to the training command. This will enable the following code:\n\n```\npolicy = tf.keras.mixed_precision.experimental.Policy('mixed_float16', loss_scale='dynamic')\ntf.keras.mixed_precision.experimental.set_policy(policy)\n```\n\n### TensorFloat-32 (TF32) Compute Mode\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 a high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.","displayName","EfficientDet For TensorFlow2",["D",1667524739117],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/efficientdet_tf2","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_86":1204,"_88":1205},"ngcApps",[1157],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1138":1139,"_1140":1141,"_1142":1143,"_1144":1145,"_1146":229,"_117":257,"_1147":1148,"_1149":50,"_1150":65,"_1151":65,"_1152":1153,"_1154":65,"_1155":1156},"amplitude",{"_1135":1136,"_771":-7,"_1137":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1129":1130,"_1131":1132,"_1133":1134},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786033279860,"1.11.0","defaultSearchSuggestions",{"_1011":338,"_1012":1013,"_1014":1015,"_1016":1017},"announcements",[],"consentState",{"_296":1001,"_1002":65,"_1003":26,"_1004":26,"_1005":1006},"consentConfig",{"_278":279,"_280":281,"_282":283,"_284":285,"_286":287,"_288":289,"_290":291,"_292":293,"_294":295,"_296":297},"showContactAdmin",1786033279861,[],[],[271,272,273,274,275,276],{"_39":53,"_41":62,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":60,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":58,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":277,"_49":50},5,"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",{"_993":994,"_995":996,"_997":998,"_999":1000},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[298,299,300,301,302],{"_303":683,"_174":684,"_144":685,"_307":65,"_308":109,"_310":686,"_312":687},{"_303":586,"_174":587,"_144":588,"_307":26,"_308":48,"_310":589,"_312":590},{"_303":493,"_174":494,"_144":495,"_307":26,"_308":496,"_310":497,"_312":498},{"_303":314,"_174":315,"_144":316,"_307":26,"_308":277,"_310":317,"_312":318},{"_303":304,"_174":305,"_144":306,"_307":26,"_308":309,"_310":311,"_312":313},"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.",[435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456],[319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335],{"_336":430,"_186":430,"_144":338,"_310":431},{"_336":426,"_186":426,"_144":338,"_310":427},{"_336":421,"_186":421,"_144":338,"_310":422},{"_336":417,"_186":417,"_144":338,"_310":418},{"_336":412,"_186":412,"_144":338,"_310":413},{"_336":407,"_186":407,"_144":338,"_310":408},{"_336":399,"_186":399,"_144":338,"_310":400},{"_336":391,"_186":391,"_144":338,"_310":392},{"_336":386,"_186":386,"_144":338,"_310":387},{"_336":381,"_186":381,"_144":338,"_310":382},{"_336":376,"_186":376,"_144":338,"_310":377},{"_336":371,"_186":371,"_144":338,"_310":372},{"_336":366,"_186":366,"_144":338,"_310":367},{"_336":359,"_186":359,"_144":338,"_310":360},{"_336":354,"_186":354,"_144":338,"_310":355},{"_336":347,"_186":347,"_144":338,"_310":348},{"_336":337,"_186":337,"_144":338,"_310":339},"hostName","amazon-adsystem.com","",[340,341],{"_174":346,"_343":337,"_344":26,"_144":345},{"_174":342,"_343":337,"_344":26,"_144":345},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[349,350],{"_174":353,"_343":347,"_344":26,"_144":352},{"_174":351,"_343":347,"_344":26,"_144":352},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[356],{"_174":357,"_343":354,"_344":26,"_144":358},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[361,362],{"_174":365,"_343":359,"_344":26,"_144":364},{"_174":363,"_343":359,"_344":26,"_144":364},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[368],{"_174":369,"_343":366,"_344":26,"_144":370},"_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",[373],{"_174":374,"_343":371,"_344":26,"_144":375},"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",[378],{"_174":379,"_343":376,"_344":26,"_144":380},"sp","Testing","google.com",[383],{"_174":384,"_343":381,"_344":26,"_144":385},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[388],{"_174":389,"_343":386,"_344":26,"_144":390},"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",[393,394],{"_174":397,"_343":391,"_344":26,"_144":398},{"_174":395,"_343":391,"_344":26,"_144":396},"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",[401,402],{"_174":405,"_343":399,"_344":26,"_144":406},{"_174":403,"_343":399,"_344":26,"_144":404},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-*","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","demdex.net",[409],{"_174":410,"_343":407,"_344":26,"_144":411},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[414],{"_174":415,"_343":412,"_344":26,"_144":416},"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",[419],{"_174":420,"_343":417,"_344":26,"_144":380},"R","6sc.co",[423],{"_174":424,"_343":421,"_344":26,"_144":425},"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",[428],{"_174":429,"_343":426,"_344":26,"_144":380},"_hly_gvid","t.contentsquare.net",[432],{"_174":433,"_343":430,"_344":65,"_144":434},"_cs_*","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.",{"_174":491,"_343":458,"_344":26,"_144":492},{"_174":489,"_343":458,"_344":26,"_144":490},{"_174":487,"_343":458,"_344":26,"_144":488},{"_174":369,"_343":458,"_344":26,"_144":486},{"_174":463,"_343":458,"_344":65,"_144":465},{"_174":485,"_343":458,"_344":26,"_144":338},{"_174":484,"_343":461,"_344":26,"_144":338},{"_174":482,"_343":458,"_344":26,"_144":483},{"_174":480,"_343":458,"_344":26,"_144":481},{"_174":479,"_343":458,"_344":26,"_144":338},{"_174":477,"_343":458,"_344":26,"_144":478},{"_174":463,"_343":476,"_344":65,"_144":465},{"_174":474,"_343":461,"_344":26,"_144":475},{"_174":474,"_343":464,"_344":26,"_144":475},{"_174":433,"_343":458,"_344":26,"_144":434},{"_174":472,"_343":458,"_344":26,"_144":473},{"_174":379,"_343":470,"_344":26,"_144":471},{"_174":468,"_343":458,"_344":26,"_144":469},{"_174":466,"_343":458,"_344":26,"_144":467},{"_174":463,"_343":464,"_344":65,"_144":465},{"_174":460,"_343":461,"_344":26,"_144":462},{"_174":457,"_343":458,"_344":65,"_144":459},"AMCVS_xxxxxAdobeOrg","nvidia.com","This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","AMP_*","build.nvidia.com","Supports Amplitude product analytics.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552],[499,500,501,502,503],{"_336":412,"_186":412,"_144":338,"_310":523},{"_336":386,"_186":386,"_144":338,"_310":518},{"_336":513,"_186":513,"_144":338,"_310":514},{"_336":508,"_186":508,"_144":338,"_310":509},{"_336":504,"_186":504,"_144":338,"_310":505},"nvidia.partners",[506],{"_174":507,"_343":504,"_344":26,"_144":380},"edge","app.hushly.com",[510],{"_174":511,"_343":508,"_344":26,"_144":512},"*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",[515],{"_174":516,"_343":513,"_344":26,"_144":517},"_octo","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform",[519,520],{"_174":522,"_343":386,"_344":26,"_144":390},{"_174":521,"_343":386,"_344":26,"_144":390},"lidc","li_gc",[524,525],{"_174":527,"_343":412,"_344":26,"_144":416},{"_174":526,"_343":412,"_344":26,"_144":416},"imid_*","imid",{"_174":553,"_343":461,"_344":26,"_144":555},{"_174":584,"_343":458,"_344":26,"_144":585},{"_174":553,"_343":583,"_344":26,"_144":555},{"_174":581,"_343":458,"_344":65,"_144":582},{"_174":579,"_343":458,"_344":26,"_144":580},{"_174":578,"_343":561,"_344":65,"_144":338},{"_174":553,"_343":557,"_344":26,"_144":555},{"_174":576,"_343":458,"_344":26,"_144":577},{"_174":553,"_343":464,"_344":26,"_144":555},{"_174":564,"_343":461,"_344":26,"_144":338},{"_174":553,"_343":575,"_344":26,"_144":555},{"_174":573,"_343":458,"_344":26,"_144":574},{"_174":570,"_343":571,"_344":26,"_144":572},{"_174":568,"_343":569,"_344":26,"_144":338},{"_174":553,"_343":565,"_344":26,"_144":555},{"_174":567,"_343":458,"_344":26,"_144":338},{"_174":553,"_343":566,"_344":26,"_144":555},{"_174":564,"_343":565,"_344":26,"_144":338},{"_174":563,"_343":561,"_344":65,"_144":338},{"_174":553,"_343":562,"_344":26,"_144":555},{"_174":553,"_343":561,"_344":26,"_144":555},{"_174":553,"_343":560,"_344":26,"_144":555},{"_174":559,"_343":458,"_344":65,"_144":555},{"_174":556,"_343":557,"_344":65,"_144":558},{"_174":553,"_343":554,"_344":26,"_144":555},"_gd_*","info.nvidia.com","This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639],[591,592,593,594],{"_336":606,"_186":606,"_144":338,"_310":607},{"_336":601,"_186":601,"_144":338,"_310":602},{"_336":399,"_186":399,"_144":338,"_310":598},{"_336":513,"_186":513,"_144":338,"_310":595},[596],{"_174":597,"_343":513,"_344":65,"_144":517},"_gh_sess",[599],{"_174":600,"_343":399,"_344":65,"_144":406},"YSC","bizible.com",[603],{"_174":604,"_343":601,"_344":26,"_144":605},"_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",[608],{"_174":609,"_343":606,"_344":26,"_144":610},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_174":681,"_343":458,"_344":26,"_144":682},{"_174":666,"_343":680,"_344":65,"_144":667},{"_174":649,"_343":461,"_344":26,"_144":574},{"_174":649,"_343":562,"_344":26,"_144":574},{"_174":678,"_343":458,"_344":26,"_144":679},{"_174":666,"_343":464,"_344":65,"_144":667},{"_174":676,"_343":458,"_344":26,"_144":677},{"_174":666,"_343":461,"_344":65,"_144":667},{"_174":674,"_343":561,"_344":26,"_144":675},{"_174":666,"_343":583,"_344":65,"_144":667},{"_174":646,"_343":461,"_344":26,"_144":648},{"_174":672,"_343":458,"_344":65,"_144":673},{"_174":670,"_343":458,"_344":26,"_144":671},{"_174":668,"_343":458,"_344":65,"_144":669},{"_174":666,"_343":557,"_344":65,"_144":667},{"_174":664,"_343":458,"_344":26,"_144":665},{"_174":662,"_343":458,"_344":26,"_144":663},{"_174":660,"_343":458,"_344":65,"_144":661},{"_174":658,"_343":566,"_344":26,"_144":659},{"_174":646,"_343":557,"_344":26,"_144":648},{"_174":656,"_343":458,"_344":26,"_144":657},{"_174":654,"_343":458,"_344":65,"_144":655},{"_174":652,"_343":458,"_344":26,"_144":653},{"_174":650,"_343":458,"_344":26,"_144":651},{"_174":649,"_343":458,"_344":26,"_144":574},{"_174":646,"_343":647,"_344":26,"_144":648},{"_174":644,"_343":458,"_344":26,"_144":645},{"_174":642,"_343":458,"_344":65,"_144":643},{"_174":640,"_343":458,"_344":26,"_144":641},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","ekfls-2025-03-27","This cookie name is associated with readme.io which is used to help maintain data for the API reference support page.","s_getNewRepeat","This cookie is used to determine whether a visitor is a new visitor or a repeat visitor to the site, and is used for analytical purposes.","_hly_sid","This cookie is likely associated with website analytics, as the prefix '_hly_' suggests a connection to Holistics, a business intelligence and analytics platform.","docs.nvidia.com","_hjSession_xxxxxx","A cookie that holds the current session data. This ensues that subsequent requests within the session window will be attributed to the same Hotjar session.","C0001","Required Cookies","These cookies enable core functionality such as security, network management, and accessibility. These cookies are required for the site to function and cannot be turned off.",[828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900],[688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712],{"_336":825,"_186":825,"_144":338,"_310":826},{"_336":819,"_186":819,"_144":338,"_310":820},{"_336":816,"_186":816,"_144":338,"_310":817},{"_336":804,"_186":804,"_144":338,"_310":805},{"_336":801,"_186":801,"_144":338,"_310":802},{"_336":798,"_186":798,"_144":338,"_310":799},{"_336":793,"_186":793,"_144":338,"_310":794},{"_336":399,"_186":399,"_144":338,"_310":790},{"_336":787,"_186":787,"_144":338,"_310":788},{"_336":391,"_186":391,"_144":338,"_310":785},{"_336":386,"_186":386,"_144":338,"_310":783},{"_336":778,"_186":778,"_144":338,"_310":779},{"_336":773,"_186":773,"_144":338,"_310":774},{"_336":768,"_186":768,"_144":338,"_310":769},{"_336":765,"_186":765,"_144":338,"_310":766},{"_336":513,"_186":513,"_144":338,"_310":762},{"_336":371,"_186":371,"_144":338,"_310":759},{"_336":508,"_186":508,"_144":338,"_310":752},{"_336":504,"_186":504,"_144":338,"_310":746},{"_336":740,"_186":740,"_144":338,"_310":741},{"_336":735,"_186":735,"_144":338,"_310":736},{"_336":730,"_186":730,"_144":338,"_310":731},{"_336":726,"_186":726,"_144":338,"_310":727},{"_336":721,"_186":721,"_144":338,"_310":722},{"_336":713,"_186":713,"_144":338,"_310":714},"polyfill.io",[715,716],{"_174":719,"_343":713,"_344":26,"_144":720},{"_174":717,"_343":713,"_344":26,"_144":718},"cf_ob_info","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information.","cf_use_ob","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information. ","www.recaptcha.net",[723],{"_174":724,"_343":721,"_344":26,"_144":725},"_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",[728],{"_174":729,"_343":726,"_344":26,"_144":380},"__cflb","instagram.com",[732],{"_174":733,"_343":730,"_344":26,"_144":734},"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",[737],{"_174":738,"_343":735,"_344":26,"_144":739},"__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",[742,743],{"_174":745,"_343":740,"_344":26,"_144":380},{"_174":744,"_343":740,"_344":26,"_144":380},"AWSALBTG","AWSALBTGCORS",[747,748],{"_174":750,"_343":504,"_344":26,"_144":751},{"_174":749,"_343":504,"_344":26,"_144":380},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.",[753,754,755],{"_174":758,"_343":508,"_344":26,"_144":380},{"_174":757,"_343":508,"_344":65,"_144":380},{"_174":756,"_343":508,"_344":26,"_144":380},"AWSALBCORS","JSESSIONID","AWSALB",[760],{"_174":738,"_343":371,"_344":26,"_144":761},"This is a CloudFoundry cookie",[763],{"_174":764,"_343":513,"_344":26,"_144":517},"logged_in","hcaptcha.com",[767],{"_174":738,"_343":765,"_344":26,"_144":761},"tracking.intentsify.io",[770],{"_174":771,"_343":768,"_344":26,"_144":772},"userId","Used to track usage of the embedded video player.","static.reo.dev",[775],{"_174":776,"_343":773,"_344":26,"_144":777},"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",[780],{"_174":781,"_343":778,"_344":26,"_144":782},"dd_cookie_test_","dd_cookie_test",[784],{"_174":738,"_343":386,"_344":26,"_144":761},[786],{"_174":738,"_343":391,"_344":26,"_144":761},"supabase.co",[789],{"_174":738,"_343":787,"_344":26,"_144":761},[791],{"_174":792,"_343":399,"_344":26,"_144":406},"VISITOR_PRIVACY_METADATA","dosa-prd.azurewebsites.net",[795],{"_174":796,"_343":793,"_344":26,"_144":797},"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",[800],{"_174":738,"_343":798,"_344":26,"_144":761},"cdn.readme.io",[803],{"_174":738,"_343":801,"_344":26,"_144":761},"queue-it.net",[806,807,808,809,810],{"_174":815,"_343":804,"_344":26,"_144":338},{"_174":814,"_343":804,"_344":26,"_144":338},{"_174":813,"_343":804,"_344":26,"_144":338},{"_174":812,"_343":804,"_344":26,"_144":338},{"_174":811,"_343":804,"_344":26,"_144":338},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","x.com",[818],{"_174":738,"_343":816,"_344":26,"_144":739},"api.hcaptcha.com",[821,822],{"_174":824,"_343":819,"_344":26,"_144":380},{"_174":729,"_343":819,"_344":26,"_144":823},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[827],{"_174":738,"_343":825,"_344":26,"_144":761},{"_174":961,"_343":458,"_344":26,"_144":992},{"_174":990,"_343":554,"_344":65,"_144":991},{"_174":983,"_343":952,"_344":65,"_144":984},{"_174":989,"_343":647,"_344":65,"_144":338},{"_174":987,"_343":458,"_344":26,"_144":988},{"_174":745,"_343":973,"_344":26,"_144":912},{"_174":920,"_343":982,"_344":65,"_144":922},{"_174":744,"_343":911,"_344":26,"_144":912},{"_174":923,"_343":982,"_344":26,"_144":924},{"_174":985,"_343":458,"_344":65,"_144":986},{"_174":983,"_343":959,"_344":65,"_144":984},{"_174":917,"_343":982,"_344":26,"_144":919},{"_174":980,"_343":458,"_344":26,"_144":981},{"_174":744,"_343":903,"_344":26,"_144":912},{"_174":908,"_343":979,"_344":26,"_144":910},{"_174":756,"_343":909,"_344":26,"_144":925},{"_174":758,"_343":909,"_344":26,"_144":976},{"_174":923,"_343":918,"_344":26,"_144":924},{"_174":977,"_343":557,"_344":26,"_144":978},{"_174":758,"_343":583,"_344":26,"_144":976},{"_174":974,"_343":458,"_344":26,"_144":975},{"_174":750,"_343":583,"_344":26,"_144":969},{"_174":946,"_343":562,"_344":65,"_144":338},{"_174":744,"_343":973,"_344":26,"_144":912},{"_174":971,"_343":911,"_344":65,"_144":972},{"_174":935,"_343":458,"_344":26,"_144":926},{"_174":908,"_343":458,"_344":26,"_144":910},{"_174":970,"_343":470,"_344":26,"_144":338},{"_174":750,"_343":458,"_344":26,"_144":969},{"_174":758,"_343":903,"_344":26,"_144":968},{"_174":966,"_343":566,"_344":65,"_144":967},{"_174":964,"_343":458,"_344":65,"_144":965},{"_174":963,"_343":458,"_344":26,"_144":338},{"_174":961,"_343":909,"_344":26,"_144":962},{"_174":960,"_343":470,"_344":26,"_144":338},{"_174":951,"_343":959,"_344":65,"_144":953},{"_174":957,"_343":562,"_344":26,"_144":958},{"_174":955,"_343":647,"_344":26,"_144":956},{"_174":954,"_343":464,"_344":26,"_144":338},{"_174":951,"_343":952,"_344":65,"_144":953},{"_174":949,"_343":918,"_344":65,"_144":950},{"_174":947,"_343":458,"_344":26,"_144":948},{"_174":946,"_343":461,"_344":65,"_144":338},{"_174":917,"_343":921,"_344":26,"_144":919},{"_174":749,"_343":936,"_344":26,"_144":926},{"_174":944,"_343":461,"_344":26,"_144":945},{"_174":942,"_343":458,"_344":26,"_144":943},{"_174":940,"_343":458,"_344":26,"_144":941},{"_174":939,"_343":458,"_344":26,"_144":338},{"_174":781,"_343":461,"_344":26,"_144":938},{"_174":935,"_343":583,"_344":26,"_144":926},{"_174":738,"_343":554,"_344":26,"_144":937},{"_174":935,"_343":936,"_344":26,"_144":926},{"_174":934,"_343":470,"_344":26,"_144":338},{"_174":933,"_343":458,"_344":26,"_144":906},{"_174":931,"_343":918,"_344":26,"_144":932},{"_174":744,"_343":929,"_344":26,"_144":930},{"_174":928,"_343":470,"_344":26,"_144":338},{"_174":927,"_343":458,"_344":26,"_144":338},{"_174":920,"_343":918,"_344":65,"_144":922},{"_174":749,"_343":583,"_344":26,"_144":926},{"_174":756,"_343":583,"_344":26,"_144":925},{"_174":923,"_343":921,"_344":26,"_144":924},{"_174":920,"_343":921,"_344":65,"_144":922},{"_174":917,"_343":918,"_344":26,"_144":919},{"_174":915,"_343":911,"_344":65,"_144":916},{"_174":913,"_343":569,"_344":26,"_144":914},{"_174":745,"_343":911,"_344":26,"_144":912},{"_174":908,"_343":909,"_344":26,"_144":910},{"_174":738,"_343":561,"_344":26,"_144":907},{"_174":905,"_343":458,"_344":26,"_144":906},{"_174":756,"_343":903,"_344":26,"_144":904},{"_174":901,"_343":458,"_344":26,"_144":902},"LoginHints","Keeps users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","LSKey-c$CookieConsentPolicy","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","OptanonAlertBoxClosed","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","docs.ngc.nvidia.com","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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

","gpc","

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

","gpcLoggedIn","

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

","gpcLoggedInOverridden","

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

",{"_683":109,"_586":50,"_493":50,"_314":50,"_304":50},"hasConsented","gpcApplied","showBanner","geolocation",{"_1007":1008,"_1009":1010},"country","US","state","OR","query","categories",[1030,1031,1032,1033,1034],"optionUrls",[1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029],"total",12,"/orgs/nim/deepseek-ai/containers/deepseek-r1/-","/orgs/nim/nvidia/containers/llama-3.1-nemotron-70b-instruct/-","/orgs/nvidia/-/containers/pytorch/-","/orgs/nvidia/omniverse/collections/kit/-","/orgs/nvidia/-/collections/deepstream_sdk/-","/orgs/nvidia/omniverse/collections/kit-appstreaming-collection/-","/orgs/nvidia/research/models/stylegan3/-","/orgs/nvidia/tao/models/peoplenet/-","/orgs/nvidia/tao/models/trafficcamnet/-","/orgs/nvidia/riva/resources/riva_quickstart/-","/orgs/nvidia/-/helm-charts/gpu-operator/-","/orgs/nvidia/-/helm-charts/k8s-nim-operator/-",{"_19":1109,"_1036":1110,"_1038":50},{"_19":1074,"_1036":1075,"_1038":48},{"_19":1057,"_1036":1058,"_1038":1059},{"_19":20,"_1036":1050,"_1038":1051},{"_19":1035,"_1036":1037,"_1038":1039},"helm-charts","items",[1040,1041],"startIndex",10,{"_19":1035,"_186":1047,"_139":140,"_141":-7,"_181":1048,"_189":1044,"_215":1049,"_1046":1028},{"_19":1035,"_186":1042,"_139":140,"_141":-7,"_181":1043,"_189":1044,"_215":1045,"_1046":1029},"NVIDIA NIM Operator","k8s-nim-operator","NVIDIA",[],"url","GPU Operator","gpu-operator",[],[1052],9,{"_19":20,"_186":1053,"_139":140,"_141":1054,"_181":1055,"_189":1044,"_215":1056,"_1046":1027},"Riva Skills Quick Start","riva","riva_quickstart",[],"models",[1060,1061,1062],6,{"_19":1057,"_186":1070,"_139":140,"_141":1071,"_181":1072,"_189":1044,"_215":1073,"_1046":1024},{"_19":1057,"_186":1067,"_139":140,"_141":1064,"_181":1068,"_189":1044,"_215":1069,"_1046":1025},{"_19":1057,"_186":1063,"_139":140,"_141":1064,"_181":1065,"_189":1044,"_215":1066,"_1046":1026},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1076,1077,1078],{"_19":1074,"_186":1106,"_139":140,"_141":1080,"_181":1107,"_189":1044,"_215":1108,"_1046":1021},{"_19":1074,"_186":1103,"_139":140,"_141":-7,"_181":1104,"_189":1044,"_215":1105,"_1046":1022},{"_19":1074,"_186":1079,"_139":140,"_141":1080,"_181":1081,"_189":1044,"_215":1082,"_1046":1023},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1083,1084,1085],{"_174":1097,"_186":1098,"_183":1099,"_194":1100,"_1090":1101},{"_174":1080,"_186":1093,"_183":1094,"_194":1089,"_1090":1095},{"_174":1086,"_186":1087,"_183":1088,"_194":1089,"_1090":1091},"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",[1092],"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.",[1092,1096],"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",[1092,1096,1102],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1083,1084,1085],"containers",[1111,1112,1113],{"_19":1109,"_186":1125,"_139":1118,"_141":1126,"_181":1127,"_189":1044,"_215":1128,"_1046":1018},{"_19":1109,"_186":1117,"_139":1118,"_141":140,"_181":1119,"_189":1044,"_215":1120,"_1046":1019},{"_19":1109,"_186":1114,"_139":140,"_141":-7,"_181":1115,"_189":1044,"_215":1116,"_1046":1020},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[1121,1083],{"_174":1122,"_186":1087,"_183":1088,"_194":1123,"_1090":1124},"nim-dev","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[1092],"DeepSeek-R1","deepseek-ai","deepseek-r1",[1121,1083],"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",{"_1158":1159,"_1160":250,"_1161":26,"_1162":1163,"_1036":1164,"_1165":1166,"_1167":1159,"_1168":26,"_1169":26,"_1170":26,"_1171":1172},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1173,1174,1175,1176,1177,1178,1179],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1171":1201,"_41":1202,"_1165":1203},{"_1194":1195,"_1171":1196,"_41":1197,"_1167":1097},{"_1171":1192,"_41":1193,"_1165":1166},{"_1171":1189,"_41":1190,"_1165":1191},{"_1171":1186,"_41":1187,"_1165":1188},{"_1171":1183,"_41":1184,"_1165":1185},{"_1171":1180,"_41":1181,"_1165":1182},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1198],"Enterprise Catalog","/enterprise",[1199,1200],"NVAIE","READ","Explore Catalog","/","common-home",{"_91":92,"_93":94,"_95":96,"_97":98,"_99":100,"_101":102},{"_90":26}]