Model Overview

A pre-trained model for volumetric (3D) Brats MRI 3D Latent Diffusion Generative Model.

This model is trained on BraTS 2016 and 2017 data from Medical Decathlon, using the Latent diffusion model [1].

$\"model$

This model is a generator for creating images like the Flair MRIs based on BraTS 2016 and 2017 data. It was trained as a 3d latent diffusion model and accepts Gaussian random noise as inputs to produce an image output. The train_autoencoder.json file describes the training process of the variational autoencoder with GAN loss. The train_diffusion.json file describes the training process of the 3D latent diffusion model.

In this bundle, the autoencoder uses perceptual loss, which is based on ResNet50 with pre-trained weights (the network is frozen and will not be trained in the bundle). In default, the pretrained parameter is specified as False in train_autoencoder.json. To ensure correct training, changing the default settings is necessary. There are two ways to utilize pretrained weights:

if set pretrained to True, ImageNet pretrained weights from torchvision will be used. However, the weights are for non-commercial use only.
if set pretrained to True and specifies the perceptual_loss_model_weights_path parameter, users are able to load weights from a local path. This is the way this bundle used to train, and the pre-trained weights are from some internal data.

Please note that each user is responsible for checking the data source of the pre-trained models, the applicable licenses, and determining if suitable for the intended use.

Example synthetic image

An example result from inference is shown below:\n $\"Example$

This is a demonstration network meant to just show the training process for this sort of network with MONAI. To achieve better performance, users need to use larger dataset like Brats 2021 and have GPU with memory larger than 32G to enable larger networks and attention layers.

Data

The training data is BraTS 2016 and 2017 from the Medical Segmentation Decathalon. Users can find more details on the dataset (Task01_BrainTumour) at http://medicaldecathlon.com/.

Target: Image Generation
Task: Synthesis
Modality: MRI
Size: 388 3D volumes (1 channel used)

Training Configuration

If you have a GPU with less than 32G of memory, you may need to decrease the batch size when training. To do so, modify the train_batch_size parameter in the configs/train_autoencoder.json and configs/train_diffusion.json configuration files.

Training Configuration of Autoencoder

The autoencoder was trained using the following configuration:

GPU: at least 32GB GPU memory
Actual Model Input: 112 x 128 x 80
AMP: False
Optimizer: Adam
Learning Rate: 1e-5
Loss: L1 loss, perceptual loss, KL divergence loss, adversarial loss, GAN BCE loss

Input

1 channel 3D MRI Flair patches

Output

1 channel 3D MRI reconstructed patches
8 channel mean of latent features
8 channel standard deviation of latent features

Training Configuration of Diffusion Model

The latent diffusion model was trained using the following configuration:

GPU: at least 32GB GPU memory
Actual Model Input: 36 x 44 x 28
AMP: False
Optimizer: Adam
Learning Rate: 1e-5
Loss: MSE loss

Training Input

8 channel noisy latent features
a long int that indicates the time step

Training Output

8 channel predicted added noise

Inference Input

8 channel noise

Inference Output

8 channel denoised latent features

Memory Consumption Warning

If you face memory issues with data loading, you can lower the caching rate cache_rate in the configurations within range [0, 1] to minimize the System RAM requirements.

Performance

Training Loss

$\"A$

TensorRT speedup

This bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.

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

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

method	torch_tf32(ms)	torch_amp(ms)	trt_tf32(ms)	trt_fp16(ms)	speedup amp	speedup tf32	speedup fp16	amp vs fp16
model computation (diffusion)	44.57	44.59	40.89	18.79	1.00	1.09	2.37	2.37
model computation (autoencoder)	96.29	97.01	78.51	44.03	0.99	1.23	2.19	2.20
end2end	2826	2538	2759	1472	1.11	1.02	1.92	1.72

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_tf32 and torch_amp are for the PyTorch models with or without amp mode.
trt_tf32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
speedup amp, speedup tf32 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: 10.3.0+cuda12.6
Torch-TensorRT Version: 2.4.0
CPU Architecture: x86-64
OS: ubuntu 20.04
Python version:3.10.12
CUDA version: 12.6
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 Autoencoder Training

Execute Autoencoder Training on single GPU

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

Please note that if the default dataset path is not modified with the actual path (it should be the path that contains Task01_BrainTumour) in the bundle config files, you can also override it by using --dataset_dir:

\n\">

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

Override the `train` config to execute multi-GPU training for Autoencoder

To train with multiple GPUs, use the following command, which requires scaling up the learning rate according to the number of GPUs.

torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file \"['configs/train_autoencoder.json','configs/multi_gpu_train_autoencoder.json']\" --lr 8e-5\n

Check the Autoencoder Training result

The following code generates a reconstructed image from a random input image.\nWe can visualize it to see if the autoencoder is trained correctly.

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

An example of reconstructed image from inference is shown below. If the autoencoder is trained correctly, the reconstructed image should look similar to original image.

$\"Example$

Execute Latent Diffusion Training

Execute Latent Diffusion Model Training on single GPU

After training the autoencoder, run the following command to train the latent diffusion model. This command will print out the scale factor of the latent feature space. If your autoencoder is well trained, this value should be close to 1.0.

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

Override the `train` config to execute multi-GPU training for Latent Diffusion Model

To train with multiple GPUs, use the following command, which requires scaling up the learning rate according to the number of GPUs.

torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file \"['configs/train_autoencoder.json','configs/train_diffusion.json','configs/multi_gpu_train_autoencoder.json','configs/multi_gpu_train_diffusion.json']\"  --lr 8e-5\n

Execute inference

The following code generates a synthetic image from a random sampled noise.

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

Execute inference with the TensorRT model:

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

References

[1] Rombach, Robin, et al. \"High-resolution image synthesis with latent diffusion models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf

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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It was trained as a 3d latent diffusion model and accepts Gaussian random noise as inputs to produce an image output. The `train_autoencoder.json` file describes the training process of the variational autoencoder with GAN loss. The `train_diffusion.json` file describes the training process of the 3D latent diffusion model.\n\nIn this bundle, the autoencoder uses perceptual loss, which is based on ResNet50 with pre-trained weights (the network is frozen and will not be trained in the bundle). In default, the `pretrained` parameter is specified as `False` in `train_autoencoder.json`. To ensure correct training, changing the default settings is necessary. There are two ways to utilize pretrained weights:\n1. if set `pretrained` to `True`, ImageNet pretrained weights from [torchvision](https://pytorch.org/vision/stable/_modules/torchvision/models/resnet.html#ResNet50_Weights) will be used. However, the weights are for non-commercial use only.\n2. if set `pretrained` to `True` and specifies the `perceptual_loss_model_weights_path` parameter, users are able to load weights from a local path. This is the way this bundle used to train, and the pre-trained weights are from some internal data.\n\nPlease note that each user is responsible for checking the data source of the pre-trained models, the applicable licenses, and determining if suitable for the intended use.\n\n##### Example synthetic image\n\nAn example result from inference is shown below:\n![Example synthetic image](https://developer.download.nvidia.com/assets/Clara/Images/monai_brain_image_gen_ldm3d_example_generation_v2.png)\n\n**This is a demonstration network meant to just show the training process for this sort of network with MONAI. To achieve better performance, users need to use larger dataset like [Brats 2021](https://www.synapse.org/#!Synapse:syn25829067/wiki/610865) and have GPU with memory larger than 32G to enable larger networks and attention layers.**\n\n### Data\n\nThe training data is BraTS 2016 and 2017 from the Medical Segmentation Decathalon. Users can find more details on the dataset (`Task01_BrainTumour`) at http://medicaldecathlon.com/.\n\n- Target: Image Generation\n- Task: Synthesis\n- Modality: MRI\n- Size: 388 3D volumes (1 channel used)\n\n### Training Configuration\n\nIf you have a GPU with less than 32G of memory, you may need to decrease the batch size when training. To do so, modify the `train_batch_size` parameter in the [configs/train_autoencoder.json](../configs/train_autoencoder.json) and [configs/train_diffusion.json](../configs/train_diffusion.json) configuration files.\n\n#### Training Configuration of Autoencoder\n\nThe autoencoder was trained using the following configuration:\n\n- GPU: at least 32GB GPU memory\n- Actual Model Input: 112 x 128 x 80\n- AMP: False\n- Optimizer: Adam\n- Learning Rate: 1e-5\n- Loss: L1 loss, perceptual loss, KL divergence loss, adversarial loss, GAN BCE loss\n\n##### Input\n\n1 channel 3D MRI Flair patches\n\n##### Output\n\n- 1 channel 3D MRI reconstructed patches\n- 8 channel mean of latent features\n- 8 channel standard deviation of latent features\n\n#### Training Configuration of Diffusion Model\n\nThe latent diffusion model was trained using the following configuration:\n\n- GPU: at least 32GB GPU memory\n- Actual Model Input: 36 x 44 x 28\n- AMP: False\n- Optimizer: Adam\n- Learning Rate: 1e-5\n- Loss: MSE loss\n\n##### Training Input\n\n- 8 channel noisy latent features\n- a long int that indicates the time step\n\n##### Training Output\n\n8 channel predicted added noise\n\n##### Inference Input\n\n8 channel noise\n\n##### Inference Output\n\n8 channel denoised latent features\n\n#### Memory Consumption Warning\n\nIf you face memory issues with data loading, you can lower the caching rate `cache_rate` in the configurations within range [0, 1] to minimize the System RAM requirements.\n\n### Performance\n\n##### Training Loss\n\n![A graph showing the autoencoder training curve](https://developer.download.nvidia.com/assets/Clara/Images/monai_brain_image_gen_ldm3d_train_autoencoder_loss_v2.png)\n\n![A graph showing the latent diffusion training curve](https://developer.download.nvidia.com/assets/Clara/Images/monai_brain_image_gen_ldm3d_train_diffusion_loss_v2.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. Please note that 32-bit precision models are benchmarked with tf32 weight format.\n\n| method | torch_tf32(ms) | torch_amp(ms) | trt_tf32(ms) | trt_fp16(ms) | speedup amp | speedup tf32 | speedup fp16 | amp vs fp16|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| model computation (diffusion) | 44.57 | 44.59 | 40.89 | 18.79 | 1.00 | 1.09 | 2.37 | 2.37 |\n| model computation (autoencoder) | 96.29 | 97.01 | 78.51 | 44.03 | 0.99 | 1.23 | 2.19 | 2.20 |\n| end2end | 2826 | 2538 | 2759 | 1472 | 1.11 | 1.02 | 1.92 | 1.72 |\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_tf32` and `torch_amp` are for the PyTorch models with or without `amp` mode.\n- `trt_tf32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.\n- `speedup amp`, `speedup tf32` 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: 10.3.0+cuda12.6\n- Torch-TensorRT Version: 2.4.0\n- CPU Architecture: x86-64\n- OS: ubuntu 20.04\n- Python version:3.10.12\n- CUDA version: 12.6\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 Autoencoder Training\n\n##### Execute Autoencoder Training on single GPU\n\n```\npython -m monai.bundle run --config_file configs/train_autoencoder.json\n```\n\nPlease note that if the default dataset path is not modified with the actual path (it should be the path that contains `Task01_BrainTumour`) 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_autoencoder.json --dataset_dir \n```\n\n##### Override the `train` config to execute multi-GPU training for Autoencoder\n\nTo train with multiple GPUs, use the following command, which requires scaling up the learning rate according to the number of GPUs.\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file \"['configs/train_autoencoder.json','configs/multi_gpu_train_autoencoder.json']\" --lr 8e-5\n```\n\n##### Check the Autoencoder Training result\n\nThe following code generates a reconstructed image from a random input image.\nWe can visualize it to see if the autoencoder is trained correctly.\n\n```\npython -m monai.bundle run --config_file configs/inference_autoencoder.json\n```\n\nAn example of reconstructed image from inference is shown below. If the autoencoder is trained correctly, the reconstructed image should look similar to original image.\n\n![Example reconstructed image](https://developer.download.nvidia.com/assets/Clara/Images/monai_brain_image_gen_ldm3d_recon_example.jpg)\n\n#### Execute Latent Diffusion Training\n\n##### Execute Latent Diffusion Model Training on single GPU\n\nAfter training the autoencoder, run the following command to train the latent diffusion model. This command will print out the scale factor of the latent feature space. If your autoencoder is well trained, this value should be close to 1.0.\n\n```\npython -m monai.bundle run --config_file \"['configs/train_autoencoder.json','configs/train_diffusion.json']\"\n```\n\n##### Override the `train` config to execute multi-GPU training for Latent Diffusion Model\n\nTo train with multiple GPUs, use the following command, which requires scaling up the learning rate according to the number of GPUs.\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run --config_file \"['configs/train_autoencoder.json','configs/train_diffusion.json','configs/multi_gpu_train_autoencoder.json','configs/multi_gpu_train_diffusion.json']\" --lr 8e-5\n```\n\n##### Execute inference\n\nThe following code generates a synthetic image from a random sampled noise.\n\n```\npython -m monai.bundle run --config_file configs/inference.json\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] Rombach, Robin, et al. \"High-resolution image synthesis with latent diffusion models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf\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 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1404":1405,"_1406":1407,"_1408":1409,"_1410":1411},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[515,516,517,518,519],{"_520":1056,"_81":1057,"_313":1058,"_524":232,"_525":18,"_527":1059,"_529":1060},{"_520":846,"_81":847,"_313":848,"_524":31,"_525":494,"_527":849,"_529":850},{"_520":746,"_81":747,"_313":748,"_524":31,"_525":54,"_527":749,"_529":750},{"_520":531,"_81":532,"_313":533,"_524":31,"_525":534,"_527":535,"_529":536},{"_520":521,"_81":522,"_313":523,"_524":31,"_525":526,"_527":528,"_529":530},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698],[537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553],{"_554":660,"_362":660,"_313":314,"_527":661},{"_554":655,"_362":655,"_313":314,"_527":656},{"_554":651,"_362":651,"_313":314,"_527":652},{"_554":646,"_362":646,"_313":314,"_527":647},{"_554":641,"_362":641,"_313":314,"_527":642},{"_554":629,"_362":629,"_313":314,"_527":630},{"_554":617,"_362":617,"_313":314,"_527":618},{"_554":612,"_362":612,"_313":314,"_527":613},{"_554":601,"_362":601,"_313":314,"_527":602},{"_554":596,"_362":596,"_313":314,"_527":597},{"_554":591,"_362":591,"_313":314,"_527":592},{"_554":586,"_362":586,"_313":314,"_527":587},{"_554":581,"_362":581,"_313":314,"_527":582},{"_554":576,"_362":576,"_313":314,"_527":577},{"_554":571,"_362":571,"_313":314,"_527":572},{"_554":564,"_362":564,"_313":314,"_527":565},{"_554":555,"_362":555,"_313":314,"_527":556},"hostName","amazon-adsystem.com",[557,558],{"_81":563,"_560":555,"_561":31,"_313":562},{"_81":559,"_560":555,"_561":31,"_313":562},"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",[566,567],{"_81":570,"_560":564,"_561":31,"_313":569},{"_81":568,"_560":564,"_561":31,"_313":569},"IDE","This domain is owned by Doubleclick (Google). 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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",[603,604,605,606],{"_81":611,"_560":601,"_561":31,"_313":608},{"_81":610,"_560":601,"_561":31,"_313":608},{"_81":609,"_560":601,"_561":31,"_313":608},{"_81":607,"_560":601,"_561":31,"_313":608},"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",[614],{"_81":615,"_560":612,"_561":31,"_313":616},"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",[619,620,621,622],{"_81":628,"_560":617,"_561":31,"_313":624},{"_81":626,"_560":617,"_561":31,"_313":627},{"_81":625,"_560":617,"_561":31,"_313":624},{"_81":623,"_560":617,"_561":31,"_313":624},"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",[631,632,633,634],{"_81":640,"_560":629,"_561":31,"_313":638},{"_81":639,"_560":629,"_561":31,"_313":638},{"_81":637,"_560":629,"_561":31,"_313":638},{"_81":635,"_560":629,"_561":31,"_313":636},"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. 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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.",[786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806],[751,752,753,754,755],{"_554":646,"_362":646,"_313":314,"_527":777},{"_554":612,"_362":612,"_313":314,"_527":772},{"_554":765,"_362":765,"_313":314,"_527":766},{"_554":761,"_362":761,"_313":314,"_527":762},{"_554":756,"_362":756,"_313":314,"_527":757},"nvcrm.my.site.com",[758],{"_81":759,"_560":756,"_561":31,"_313":760},"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",[763],{"_81":764,"_560":761,"_561":31,"_313":595},"edge","github.com",[767,768],{"_81":771,"_560":765,"_561":31,"_313":770},{"_81":769,"_560":765,"_561":31,"_313":770},"logged_in","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.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