Model Overview

A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data.

The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the \"enhancing tumor\" (ET), the \"tumor core\" (TC), the \"whole tumor\" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR).

The ET is described by areas that show hyper intensity in T1c when compared to T1, but also when compared to \"healthy\" white matter in T1c.
The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor.
The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.

$\"Model$

Data

The training data is from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018.

Target: 3 tumor subregions
Task: Segmentation
Modality: MRI
Size: 285 3D volumes (4 channels each)

The provided labelled data was partitioned, based on our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets.

Preprocessing

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

python scripts/prepare_datalist.py --path your-brats18-dataset-path\n

Training configuration

This model utilized a similar approach described in 3D MRI brain tumor segmentation using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following:

GPU: At least 16GB of GPU memory.
Actual Model Input: 224 x 224 x 144
AMP: True
Optimizer: Adam
Learning Rate: 1e-4
Loss: DiceLoss

Input

4 channel aligned MRIs at 1 x 1 x 1 mm

T1c
T1
T2
FLAIR

Output

3 channels

Label 0: TC tumor subregion
Label 1: WT tumor subregion
Label 2: ET tumor subregion

Performance

Dice score was used for evaluating the performance of the model. This model achieved Dice scores on the validation data of:

Tumor core (TC): 0.8559
Whole tumor (WT): 0.9026
Enhancing tumor (ET): 0.7905
Average: 0.8518

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 and Dice

$\"A$

Validation Dice

$\"A$

TensorRT speedup

The brats_mri_segmentation bundle supports acceleration with TensorRT through the ONNX-TensorRT method. 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	5.49	4.36	2.35	2.09	1.26	2.34	2.63	2.09
end2end	592.01	434.59	395.73	394.93	1.36	1.50	1.50	1.10

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.

Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.

This result is benchmarked under:

TensorRT: 8.5.3+cuda11.8
Torch-TensorRT Version: 1.4.0
CPU Architecture: x86-64
OS: ubuntu 20.04
Python version:3.8.10
CUDA version: 12.0
GPU models and configuration: A100 80G

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 training:

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

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

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

Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove --standalone, modify --nnodes, or do some other necessary changes according to the machine used. For more details, please refer to pytorch's official tutorial.

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

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

Execute inference:

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

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

--input_shape "[1, 4, 240, 240, 160]" --use_onnx "True" \\\n--use_trace "True"\n\">

python -m monai.bundle trt_export --net_id network_def \\\n--filepath models/model_trt.ts --ckpt_file models/model.pt \\\n--meta_file configs/metadata.json --config_file configs/inference.json \\\n--precision <fp32/fp16> --input_shape \"[1, 4, 240, 240, 160]\" --use_onnx \"True\" \\\n--use_trace \"True\"\n

Execute inference with the TensorRT model:

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

References

[1] Myronenko, Andriy. \"3D MRI brain tumor segmentation using autoencoder regularization.\" International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.

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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The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor.\n- The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.\n\n![Model workflow](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_brain_mri_segmentation_workflow.png)\n\n### Data\n\nThe training data is from the [Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018](https://www.med.upenn.edu/sbia/brats2018.html).\n\n- Target: 3 tumor subregions\n- Task: Segmentation\n- Modality: MRI\n- Size: 285 3D volumes (4 channels each)\n\nThe provided labelled data was partitioned, based on our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets.\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 your-brats18-dataset-path\n```\n\n### Training configuration\n\nThis model utilized a similar approach described in 3D MRI brain tumor segmentation using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following:\n\n- GPU: At least 16GB of GPU memory.\n- Actual Model Input: 224 x 224 x 144\n- AMP: True\n- Optimizer: Adam\n- Learning Rate: 1e-4\n- Loss: DiceLoss\n\n### Input\n\n4 channel aligned MRIs at 1 x 1 x 1 mm\n- T1c\n- T1\n- T2\n- FLAIR\n\n### Output\n\n3 channels\n- Label 0: TC tumor subregion\n- Label 1: WT tumor subregion\n- Label 2: ET tumor subregion\n\n### Performance\n\nDice score was used for evaluating the performance of the model. This model achieved Dice scores on the validation data of:\n- Tumor core (TC): 0.8559\n- Whole tumor (WT): 0.9026\n- Enhancing tumor (ET): 0.7905\n- Average: 0.8518\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 and Dice\n\n![A graph showing the training loss and the mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_train.png)\n\n##### Validation Dice\n\n![A graph showing the validation mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_val.png)\n\n##### TensorRT speedup\n\nThe `brats_mri_segmentation` bundle supports acceleration with TensorRT through the ONNX-TensorRT method. 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 | 5.49 | 4.36 | 2.35 | 2.09 | 1.26 | 2.34 | 2.63 | 2.09 |\n| end2end | 592.01 | 434.59 | 395.73 | 394.93 | 1.36 | 1.50 | 1.50 | 1.10 |\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\nCurrently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.\n\nThis result is benchmarked under:\n- TensorRT: 8.5.3+cuda11.8\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: 12.0\n- GPU models and configuration: A100 80G\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 training:\n\n```\npython -m monai.bundle run --config_file configs/train.json\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.json --dataset_dir \n```\n\n##### Override the `train` config to execute multi-GPU training:\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\"\n```\n\nPlease note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).\n\n##### Override the `train` config to execute evaluation with the trained model:\n\n```\npython -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n```\n\n##### Execute inference:\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\n##### Export checkpoint to TensorRT based models with fp32 or fp16 precision:\n\n```bash\npython -m monai.bundle trt_export --net_id network_def \\\n--filepath models/model_trt.ts --ckpt_file models/model.pt \\\n--meta_file configs/metadata.json --config_file configs/inference.json \\\n--precision --input_shape \"[1, 4, 240, 240, 160]\" --use_onnx \"True\" \\\n--use_trace \"True\"\n```\n\n##### Execute inference with the TensorRT model:\n\n```\npython -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n```\n\n## References\n\n[1] Myronenko, Andriy. \"3D MRI brain tumor segmentation using autoencoder regularization.\" International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.\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",1731434163501],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/monaitoolkit/models/monai_brats_mri_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",[344,345,346,347,348,349,350,351,352,353,354,355],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_313":385,"_357":385,"_358":386,"_360":361},{"_313":383,"_357":383,"_358":384,"_360":361},{"_313":381,"_357":381,"_358":382,"_360":361},{"_313":377,"_357":378,"_358":379,"_360":361},{"_313":375,"_357":375,"_358":376,"_360":361},{"_313":373,"_357":373,"_358":374,"_360":361},{"_313":371,"_357":371,"_358":372,"_360":361},{"_313":369,"_357":369,"_358":370,"_360":361},{"_313":367,"_357":367,"_358":368,"_360":361},{"_313":365,"_357":365,"_358":366,"_360":361},{"_313":363,"_357":363,"_358":364,"_360":361},{"_313":356,"_357":356,"_358":359,"_360":361},"Segmentation","value","filter",{"_362":361,"_357":356},"category","label","field","Pytorch",{"_362":361,"_357":363},"NSPECT-0J37-9WF8",{"_362":361,"_357":365},"MR",{"_362":361,"_357":367},"MONAI",{"_362":361,"_357":369},"Medical Imaging",{"_362":361,"_357":371},"Medical",{"_362":361,"_357":373},"Imaging",{"_362":361,"_357":375},"Healthcare","indus_healthcare",{"_362":380,"_357":378},"industry","Deep Learning",{"_362":361,"_357":381},"Bundle",{"_362":361,"_357":383},"Brain",{"_362":361,"_357":385},{"_204":1561,"_206":1562},"ngcApps",[1514],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1495":1496,"_1497":1498,"_1499":1500,"_1501":1502,"_1503":398,"_236":422,"_1504":1505,"_1506":56,"_1507":183,"_1508":183,"_1509":1510,"_1511":183,"_1512":1513},"amplitude",{"_1492":1493,"_1094":-7,"_1494":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1486":1487,"_1488":1489,"_1490":1491},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1369":265,"_1370":1371,"_1372":1373,"_1374":1375},"announcements",[],"consentState",{"_460":1359,"_1360":183,"_1361":31,"_1362":31,"_1363":1364},"consentConfig",{"_442":443,"_444":445,"_446":447,"_448":449,"_450":451,"_452":453,"_454":455,"_456":457,"_458":459,"_460":461},"showContactAdmin",1784024972372,[],[],[436,437,438,439,440,441],{"_45":59,"_47":70,"_49":199,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":199,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":199,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":199,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":199,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_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,[611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645],[484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500],{"_501":607,"_313":607,"_264":265,"_474":608},{"_501":602,"_313":602,"_264":265,"_474":603},{"_501":598,"_313":598,"_264":265,"_474":599},{"_501":593,"_313":593,"_264":265,"_474":594},{"_501":588,"_313":588,"_264":265,"_474":589},{"_501":576,"_313":576,"_264":265,"_474":577},{"_501":564,"_313":564,"_264":265,"_474":565},{"_501":559,"_313":559,"_264":265,"_474":560},{"_501":548,"_313":548,"_264":265,"_474":549},{"_501":543,"_313":543,"_264":265,"_474":544},{"_501":538,"_313":538,"_264":265,"_474":539},{"_501":533,"_313":533,"_264":265,"_474":534},{"_501":528,"_313":528,"_264":265,"_474":529},{"_501":523,"_313":523,"_264":265,"_474":524},{"_501":518,"_313":518,"_264":265,"_474":519},{"_501":511,"_313":511,"_264":265,"_474":512},{"_501":502,"_313":502,"_264":265,"_474":503},"hostName","amazon-adsystem.com",[504,505],{"_82":510,"_507":502,"_508":31,"_264":509},{"_82":506,"_507":502,"_508":31,"_264":509},"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",[513,514],{"_82":517,"_507":511,"_508":31,"_264":516},{"_82":515,"_507":511,"_508":31,"_264":516},"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",[520],{"_82":521,"_507":518,"_508":31,"_264":522},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[525],{"_82":526,"_507":523,"_508":31,"_264":527},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[530],{"_82":531,"_507":528,"_508":31,"_264":532},"_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",[535],{"_82":536,"_507":533,"_508":31,"_264":537},"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",[540],{"_82":541,"_507":538,"_508":31,"_264":542},"sp","Testing","google.com",[545],{"_82":546,"_507":543,"_508":31,"_264":547},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[550,551,552,553],{"_82":558,"_507":548,"_508":31,"_264":555},{"_82":557,"_507":548,"_508":31,"_264":555},{"_82":556,"_507":548,"_508":31,"_264":555},{"_82":554,"_507":548,"_508":31,"_264":555},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[561],{"_82":562,"_507":559,"_508":31,"_264":563},"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",[566,567,568,569],{"_82":575,"_507":564,"_508":31,"_264":571},{"_82":573,"_507":564,"_508":31,"_264":574},{"_82":572,"_507":564,"_508":31,"_264":571},{"_82":570,"_507":564,"_508":31,"_264":571},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[578,579,580,581],{"_82":587,"_507":576,"_508":31,"_264":585},{"_82":586,"_507":576,"_508":31,"_264":585},{"_82":584,"_507":576,"_508":31,"_264":585},{"_82":582,"_507":576,"_508":31,"_264":583},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[590],{"_82":591,"_507":588,"_508":31,"_264":592},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[595],{"_82":596,"_507":593,"_508":31,"_264":597},"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",[600],{"_82":601,"_507":598,"_508":31,"_264":542},"R","6sc.co",[604],{"_82":605,"_507":602,"_508":31,"_264":606},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753],[698,699,700,701,702],{"_501":593,"_313":593,"_264":265,"_474":724},{"_501":559,"_313":559,"_264":265,"_474":719},{"_501":712,"_313":712,"_264":265,"_474":713},{"_501":708,"_313":708,"_264":265,"_474":709},{"_501":703,"_313":703,"_264":265,"_474":704},"nvcrm.my.site.com",[705],{"_82":706,"_507":703,"_508":31,"_264":707},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[710],{"_82":711,"_507":708,"_508":31,"_264":542},"edge","github.com",[714,715],{"_82":718,"_507":712,"_508":31,"_264":717},{"_82":716,"_507":712,"_508":31,"_264":717},"logged_in","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform","_octo",[720,721],{"_82":723,"_507":559,"_508":31,"_264":563},{"_82":722,"_507":559,"_508":31,"_264":563},"lidc","li_gc",[725,726,727,728],{"_82":732,"_507":593,"_508":31,"_264":597},{"_82":731,"_507":593,"_508":31,"_264":597},{"_82":730,"_507":593,"_508":31,"_264":597},{"_82":729,"_507":593,"_508":31,"_264":597},"imid_secure","imid_created_secure","imid","imid_created",{"_82":792,"_507":647,"_508":31,"_264":789},{"_82":790,"_507":647,"_508":31,"_264":791},{"_82":788,"_507":647,"_508":31,"_264":789},{"_82":786,"_507":647,"_508":183,"_264":787},{"_82":784,"_507":755,"_508":31,"_264":785},{"_82":783,"_507":764,"_508":183,"_264":265},{"_82":781,"_507":755,"_508":31,"_264":782},{"_82":779,"_507":647,"_508":31,"_264":780},{"_82":776,"_507":777,"_508":31,"_264":778},{"_82":775,"_507":651,"_508":31,"_264":265},{"_82":773,"_507":647,"_508":31,"_264":774},{"_82":770,"_507":771,"_508":31,"_264":772},{"_82":768,"_507":769,"_508":31,"_264":265},{"_82":767,"_507":755,"_508":31,"_264":265},{"_82":766,"_507":647,"_508":31,"_264":265},{"_82":765,"_507":651,"_508":31,"_264":265},{"_82":763,"_507":764,"_508":183,"_264":265},{"_82":761,"_507":755,"_508":31,"_264":762},{"_82":759,"_507":647,"_508":183,"_264":760},{"_82":757,"_507":647,"_508":31,"_264":758},{"_82":754,"_507":755,"_508":183,"_264":756},"nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_biz_kvpA","This cookie is likely related to business analytics and key-value pairs for storing user preferences, which may be used to enhance functionality and user experience on the website.","_gdxxxxxxxxxxxxx","This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download