Model Overview

A neural architecture search algorithm for volumetric (3D) segmentation of the pancreas and pancreatic tumor from CT image. This model is trained using the neural network model from the neural architecture search algorithm, DiNTS [1].

$\"image\"$

Data

The training dataset is the Pancreas Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/.

Target: Pancreas and pancreatic tumor
Modality: Portal venous phase CT
Size: 420 3D volumes (282 Training +139 Testing)
Source: Memorial Sloan Kettering Cancer Center
Challenge: Label unbalance with large (background), medium (pancreas) and small (tumour) structures.

Preprocessing

The data list/split can be created with the script scripts/prepare_datalist.py.

python scripts/prepare_datalist.py --path /path-to-Task07_Pancreas/ --output configs/dataset_0.json\n

Training configuration

The training was performed with at least 16GB-memory GPUs.

Actual Model Input: 96 x 96 x 96

Neural Architecture Search Configuration

The neural architecture search was performed with the following:

AMP: True
Optimizer: SGD
Initial Learning Rate: 0.025
Loss: DiceCELoss

Optimial Architecture Training Configuration

The training was performed with the following:

AMP: True
Optimizer: SGD
(Initial) Learning Rate: 0.025
Loss: DiceCELoss

The segmentation of pancreas region is formulated as the voxel-wise 3-class classification. Each voxel is predicted as either foreground (pancreas body, tumour) or background. And the model is optimized with gradient descent method minimizing soft dice loss and cross-entropy loss between the predicted mask and ground truth segmentation.

Input

One channel

CT image

Output

Three channels

Label 2: pancreatic tumor
Label 1: pancreas
Label 0: everything else

Memory Consumption

Dataset Manager: CacheDataset
Data Size: 420 3D Volumes
Cache Rate: 1.0
Multi GPU (8 GPUs) - System RAM Usage: 400G

Memory Consumption Warning

If you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate cache_rate in the configurations within range [0, 1] to minimize the System RAM requirements.

Performance

Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.62.

Please note that this bundle is non-deterministic because of the trilinear interpolation used in the network. Therefore, reproducing the training process may not get exactly the same performance.\nPlease refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.

Training Loss

The loss over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)

$\"Training$

Validation Dice

The mean dice score over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)

$\"Validation$

TensorRT speedup

This bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU.

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

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

method	torch_fp32(ms)	torch_amp(ms)	trt_fp32(ms)	trt_fp16(ms)	speedup amp	speedup fp32	speedup fp16	amp vs fp16
model computation	133.93	43.41	35.65	26.63	3.09	3.76	5.03	1.63
end2end	54611.72	19240.66	16104.8	11443.57	2.84	3.39	4.77	1.68

Where:

model computation means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
end2end means run the bundle end-to-end with the TensorRT based model.
torch_fp32 and torch_amp are for the PyTorch models with or without amp mode.
trt_fp32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
speedup amp, speedup fp32 and speedup fp16 are the speedup ratios of corresponding models versus the PyTorch float32 model
amp vs fp16 is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.

This result is benchmarked under:

TensorRT: 8.6.1+cuda12.0
Torch-TensorRT Version: 1.4.0
CPU Architecture: x86-64
OS: ubuntu 20.04
Python version:3.8.10
CUDA version: 12.1
GPU models and configuration: A100 80G

Searched Architecture Visualization

Users can install Graphviz for visualization of searched architectures (needed in decode_plot.py). The edges between nodes indicate global structure, and numbers next to edges represent different operations in the cell searching space. An example of searched architecture is shown as follows:

$\"Example$

MONAI Bundle Commands

In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.

For more details usage instructions, visit the MONAI Bundle Configuration Page.

Execute model searching:

python -m scripts.search run --config_file configs/search.yaml\n

Execute multi-GPU model searching (recommended):

torchrun --nnodes=1 --nproc_per_node=8 -m scripts.search run --config_file configs/search.yaml\n

Execute training:

python -m monai.bundle run --config_file configs/train.yaml\n

Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using --dataset_dir:

\n\">

python -m monai.bundle run --config_file configs/train.yaml --dataset_dir <actual dataset path>\n

Override the `train` config to execute multi-GPU training:

torchrun --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file \"['configs/train.yaml','configs/multi_gpu_train.yaml']\"\n

Override the `train` config to execute evaluation with the trained model:

python -m monai.bundle run --config_file \"['configs/train.yaml','configs/evaluate.yaml']\"\n

Execute inference:

python -m monai.bundle run --config_file configs/inference.yaml\n

Export checkpoint for TorchScript:

python -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.yaml\n

Export checkpoint to TensorRT based models with fp32 or fp16 precision:

--use_trace "True" --dynamic_batchsize "[1, 4, 8]" --converter_kwargs "{'truncate_long_and_double':True, 'torch_executed_ops': ['aten::upsample_trilinear3d']}"\n\">

python -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.yaml  --precision <fp32/fp16> --use_trace \"True\" --dynamic_batchsize \"[1, 4, 8]\" --converter_kwargs \"{'truncate_long_and_double':True, 'torch_executed_ops': ['aten::upsample_trilinear3d']}\"\n

Execute inference with the TensorRT model:

python -m monai.bundle run --config_file \"['configs/inference.yaml', 'configs/inference_trt.yaml']\"\n

References

[1] He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).

License

Licensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0\n

Unless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.

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This model is trained using the neural network model from the neural architecture search algorithm, DiNTS [1].\n\n![image](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_workflow_4-1.png)\n\n### Data\n\nThe training dataset is the Pancreas Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/.\n\n- Target: Pancreas and pancreatic tumor\n- Modality: Portal venous phase CT\n- Size: 420 3D volumes (282 Training +139 Testing)\n- Source: Memorial Sloan Kettering Cancer Center\n- Challenge: Label unbalance with large (background), medium (pancreas) and small (tumour) structures.\n\n#### Preprocessing\n\nThe data list/split can be created with the script `scripts/prepare_datalist.py`.\n\n```\npython scripts/prepare_datalist.py --path /path-to-Task07_Pancreas/ --output configs/dataset_0.json\n```\n\n### Training configuration\n\nThe training was performed with at least 16GB-memory GPUs.\n\nActual Model Input: 96 x 96 x 96\n\n#### Neural Architecture Search Configuration\n\nThe neural architecture search was performed with the following:\n\n- AMP: True\n- Optimizer: SGD\n- Initial Learning Rate: 0.025\n- Loss: DiceCELoss\n\n#### Optimial Architecture Training Configuration\n\nThe training was performed with the following:\n\n- AMP: True\n- Optimizer: SGD\n- (Initial) Learning Rate: 0.025\n- Loss: DiceCELoss\n\nThe segmentation of pancreas region is formulated as the voxel-wise 3-class classification. Each voxel is predicted as either foreground (pancreas body, tumour) or background. And the model is optimized with gradient descent method minimizing soft dice loss and cross-entropy loss between the predicted mask and ground truth segmentation.\n\n#### Input\n\nOne channel\n- CT image\n\n#### Output\n\nThree channels\n- Label 2: pancreatic tumor\n- Label 1: pancreas\n- Label 0: everything else\n\n#### Memory Consumption\n\n- Dataset Manager: CacheDataset\n- Data Size: 420 3D Volumes\n- Cache Rate: 1.0\n- Multi GPU (8 GPUs) - System RAM Usage: 400G\n\n#### Memory Consumption Warning\n\nIf you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate `cache_rate` in the configurations within range [0, 1] to minimize the System RAM requirements.\n\n### Performance\n\nDice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.62.\n\nPlease note that this bundle is non-deterministic because of the trilinear interpolation used in the network. Therefore, reproducing the training process may not get exactly the same performance.\nPlease refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.\n\n##### Training Loss\n\nThe loss over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)\n\n![Training loss over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_train_4-3.png)\n\n##### Validation Dice\n\nThe mean dice score over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)\n\n![Validation mean dice score over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_validation_4-3.png)\n\n##### TensorRT speedup\n\nThis bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU.\n\n| method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| model computation | 133.93 | 43.41 | 35.65 | 26.63 | 3.09 | 3.76 | 5.03 | 1.63 |\n| end2end | 54611.72 | 19240.66 | 16104.8 | 11443.57 | 2.84 | 3.39 | 4.77 | 1.68 |\n\nWhere:\n- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing\n- `end2end` means run the bundle end-to-end with the TensorRT based model.\n- `torch_fp32` and `torch_amp` are for the PyTorch models with or without `amp` mode.\n- `trt_fp32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.\n- `speedup amp`, `speedup fp32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model\n- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.\n\nThis result is benchmarked under:\n- TensorRT: 8.6.1+cuda12.0\n- Torch-TensorRT Version: 1.4.0\n- CPU Architecture: x86-64\n- OS: ubuntu 20.04\n- Python version:3.8.10\n- CUDA version: 12.1\n- GPU models and configuration: A100 80G\n\n#### Searched Architecture Visualization\n\nUsers can install Graphviz for visualization of searched architectures (needed in [decode_plot.py](https://github.com/Project-MONAI/tutorials/blob/main/automl/DiNTS/decode_plot.py)). The edges between nodes indicate global structure, and numbers next to edges represent different operations in the cell searching space. An example of searched architecture is shown as follows:\n\n![Example of Searched Architecture](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_searched_arch_example_1.png)\n\n### MONAI Bundle Commands\n\nIn addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.\n\nFor more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).\n\n##### Execute model searching:\n\n```\npython -m scripts.search run --config_file configs/search.yaml\n```\n\n##### Execute multi-GPU model searching (recommended):\n\n```\ntorchrun --nnodes=1 --nproc_per_node=8 -m scripts.search run --config_file configs/search.yaml\n```\n\n##### Execute training:\n\n```\npython -m monai.bundle run --config_file configs/train.yaml\n```\n\nPlease note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:\n\n```\npython -m monai.bundle run --config_file configs/train.yaml --dataset_dir \n```\n\n##### Override the `train` config to execute multi-GPU training:\n\n```\ntorchrun --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file \"['configs/train.yaml','configs/multi_gpu_train.yaml']\"\n```\n\n##### Override the `train` config to execute evaluation with the trained model:\n\n```\npython -m monai.bundle run --config_file \"['configs/train.yaml','configs/evaluate.yaml']\"\n```\n\n##### Execute inference:\n\n```\npython -m monai.bundle run --config_file configs/inference.yaml\n```\n\n##### Export checkpoint for TorchScript:\n\n```\npython -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.yaml\n```\n\n##### Export checkpoint to TensorRT based models with fp32 or fp16 precision:\n\n```\npython -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.yaml --precision --use_trace \"True\" --dynamic_batchsize \"[1, 4, 8]\" --converter_kwargs \"{'truncate_long_and_double':True, 'torch_executed_ops': ['aten::upsample_trilinear3d']}\"\n```\n\n##### Execute inference with the TensorRT model:\n\n```\npython -m monai.bundle run --config_file \"['configs/inference.yaml', 'configs/inference_trt.yaml']\"\n```\n\n## References\n\n[1] He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).\n\n## License\n\nCopyright (c) MONAI Consortium\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.","displayName",["D",1731434187809],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/monaitoolkit/models/monai_pancreas_ct_dints_segmentation","rawImageUrl","https://developer.download.nvidia.com/assets/Clara/Images/monai_toolkit_logo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[373,374,375,376,377,378,379,380,381,382,383,384],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_342":414,"_386":414,"_387":415,"_389":390},{"_342":412,"_386":412,"_387":413,"_389":390},{"_342":410,"_386":410,"_387":411,"_389":390},{"_342":406,"_386":407,"_387":408,"_389":390},{"_342":404,"_386":404,"_387":405,"_389":390},{"_342":402,"_386":402,"_387":403,"_389":390},{"_342":400,"_386":400,"_387":401,"_389":390},{"_342":398,"_386":398,"_387":399,"_389":390},{"_342":396,"_386":396,"_387":397,"_389":390},{"_342":394,"_386":394,"_387":395,"_389":390},{"_342":392,"_386":392,"_387":393,"_389":390},{"_342":385,"_386":385,"_387":388,"_389":390},"Segmentation","value","filter",{"_391":390,"_386":385},"category","label","field","Pytorch",{"_391":390,"_386":392},"Pancreas",{"_391":390,"_386":394},"NSPECT-KI94-P9RO",{"_391":390,"_386":396},"MONAI",{"_391":390,"_386":398},"Medical Imaging",{"_391":390,"_386":400},"Medical",{"_391":390,"_386":402},"Imaging",{"_391":390,"_386":404},"Healthcare","indus_healthcare",{"_391":409,"_386":407},"industry","Deep Learning",{"_391":390,"_386":410},"CT",{"_391":390,"_386":412},"Bundle",{"_391":390,"_386":414},{"_232":1397,"_234":1398},"ngcApps",[1350],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1331":1332,"_1333":1334,"_1335":1336,"_1337":1338,"_1339":427,"_264":453,"_1340":1341,"_1342":56,"_1343":211,"_1344":211,"_1345":1346,"_1347":211,"_1348":1349},"amplitude",{"_1328":1329,"_965":-7,"_1330":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1322":1323,"_1324":1325,"_1326":1327},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789251310842,"1.15.0","defaultSearchSuggestions",{"_1205":294,"_1206":1207,"_1208":1209,"_1210":1211},"announcements",[],"consentState",{"_491":1195,"_1196":31,"_1197":31,"_1198":211,"_1199":1200},"consentConfig",{"_473":474,"_475":476,"_477":478,"_479":480,"_481":482,"_483":484,"_485":486,"_487":488,"_489":490,"_491":492},"showContactAdmin",1789251310843,[],[],[467,468,469,470,471,472],{"_45":59,"_47":69,"_49":227,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":67,"_49":227,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":65,"_49":227,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":227,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":256,"_55":56},{"_45":46,"_47":48,"_49":227,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651],[515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531],{"_532":625,"_342":625,"_293":294,"_505":626},{"_532":621,"_342":621,"_293":294,"_505":622},{"_532":616,"_342":616,"_293":294,"_505":617},{"_532":612,"_342":612,"_293":294,"_505":613},{"_532":607,"_342":607,"_293":294,"_505":608},{"_532":602,"_342":602,"_293":294,"_505":603},{"_532":594,"_342":594,"_293":294,"_505":595},{"_532":586,"_342":586,"_293":294,"_505":587},{"_532":581,"_342":581,"_293":294,"_505":582},{"_532":576,"_342":576,"_293":294,"_505":577},{"_532":571,"_342":571,"_293":294,"_505":572},{"_532":566,"_342":566,"_293":294,"_505":567},{"_532":561,"_342":561,"_293":294,"_505":562},{"_532":554,"_342":554,"_293":294,"_505":555},{"_532":549,"_342":549,"_293":294,"_505":550},{"_532":542,"_342":542,"_293":294,"_505":543},{"_532":533,"_342":533,"_293":294,"_505":534},"hostName","amazon-adsystem.com",[535,536],{"_81":541,"_538":533,"_539":31,"_293":540},{"_81":537,"_538":533,"_539":31,"_293":540},"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",[544,545],{"_81":548,"_538":542,"_539":31,"_293":547},{"_81":546,"_538":542,"_539":31,"_293":547},"IDE","This domain is owned by Doubleclick (Google). 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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. 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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.",[722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746],[693,694,695,696,697],{"_532":607,"_342":607,"_293":294,"_505":717},{"_532":581,"_342":581,"_293":294,"_505":712},{"_532":707,"_342":707,"_293":294,"_505":708},{"_532":702,"_342":702,"_293":294,"_505":703},{"_532":698,"_342":698,"_293":294,"_505":699},"nvidia.partners",[700],{"_81":701,"_538":698,"_539":31,"_293":575},"edge","app.hushly.com",[704],{"_81":705,"_538":702,"_539":31,"_293":706},"*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",[709],{"_81":710,"_538":707,"_539":31,"_293":711},"_octo","This domain is owned by Github. 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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.",[805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833],[785,786,787,788],{"_532":800,"_342":800,"_293":294,"_505":801},{"_532":795,"_342":795,"_293":294,"_505":796},{"_532":594,"_342":594,"_293":294,"_505":792},{"_532":707,"_342":707,"_293":294,"_505":789},[790],{"_81":791,"_538":707,"_539":211,"_293":711},"_gh_sess",[793],{"_81":794,"_538":594,"_539":211,"_293":601},"YSC","bizible.com",[797],{"_81":798,"_538":795,"_539":31,"_293":799},"_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",[802],{"_81":803,"_538":800,"_539":31,"_293":804},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_81":875,"_538":653,"_539":31,"_293":876},{"_81":860,"_538":874,"_539":211,"_293":861},{"_81":843,"_538":656,"_539":31,"_293":768},{"_81":843,"_538":756,"_539":31,"_293":768},{"_81":872,"_538":653,"_539":31,"_293":873},{"_81":860,"_538":659,"_539":211,"_293":861},{"_81":870,"_538":653,"_539":31,"_293":871},{"_81":860,"_538":656,"_539":211,"_293":861},{"_81":868,"_538":755,"_539":31,"_293":869},{"_81":860,"_538":777,"_539":211,"_293":861},{"_81":840,"_538":656,"_539":31,"_293":842},{"_81":866,"_538":653,"_539":211,"_293":867},{"_81":864,"_538":653,"_539":31,"_293":865},{"_81":862,"_538":653,"_539":211,"_293":863},{"_81":860,"_538":751,"_539":211,"_293":861},{"_81":858,"_538":653,"_539":31,"_293":859},{"_81":856,"_538":653,"_539":31,"_293":857},{"_81":854,"_538":653,"_539":211,"_293":855},{"_81":852,"_538":760,"_539":31,"_293":853},{"_81":840,"_538":751,"_539":31,"_293":842},{"_81":850,"_538":653,"_539":31,"_293":851},{"_81":848,"_538":653,"_539":211,"_293":849},{"_81":846,"_538":653,"_539":31,"_293":847},{"_81":844,"_538":653,"_539":31,"_293":845},{"_81":843,"_538":653,"_539":31,"_293":768},{"_81":840,"_538":841,"_539":31,"_293":842},{"_81":838,"_538":653,"_539":31,"_293":839},{"_81":836,"_538":653,"_539":211,"_293":837},{"_81":834,"_538":653,"_539":31,"_293":835},"_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. 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LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download