Model Overview

A pre-trained model for automated detection of metastases in whole-slide histopathology images.

The model is trained based on ResNet18 [1] with the last fully connected layer replaced by a 1x1 convolution layer.\n $\"Diagram$

Data

All the data used to train, validate, and test this model is from Camelyon-16 Challenge. You can download all the images for \"CAMELYON16\" data set from various sources listed here.

Location information for training/validation patches (the location on the whole slide image where patches are extracted) are adopted from NCRF/coords.

Annotation information are adopted from NCRF/jsons.

Target: Tumor
Task: Detection
Modality: Histopathology
Size: 270 WSIs for training/validation, 48 WSIs for testing

Preprocessing

This bundle expects the training/validation data (whole slide images) reside in a {dataset_dir}/training/images. By default dataset_dir is pointing to /workspace/data/medical/pathology/ You can modify dataset_dir in the bundle config files to point to a different directory.

To reduce the computation burden during the inference, patches are extracted only where there is tissue and ignoring the background according to a tissue mask. Please also create a directory for prediction output. By default output_dir is set to eval folder under the bundle root.

Please refer to \"Annotation\" section of Camelyon challenge to prepare ground truth images, which are needed for FROC computation. By default, this data set is expected to be at /workspace/data/medical/pathology/ground_truths. But it can be modified in evaluate_froc.sh.

Training configuration

The training was performed with the following:

Config file: train.config
GPU: at least 16 GB of GPU memory.
Actual Model Input: 224 x 224 x 3
AMP: True
Optimizer: Novograd
Learning Rate: 1e-3
Loss: BCEWithLogitsLoss
Whole slide image reader: cuCIM (if running on Windows or Mac, please install OpenSlide on your system and change wsi_reader to \"OpenSlide\")

Pretrained Weights

By setting the \"pretrained\" parameter of TorchVisionFCModel in the config file to true, ImageNet pre-trained weights will be used for training. Please note that these weights are for non-commercial use. Each user is responsible for checking the content of the models/datasets and the applicable licenses and determining if suitable for the intended use. In order to use other pretrained weights, you can use CheckpointLoader in train handlers section as the first handler:

{\n    \"_target_\": \"CheckpointLoader\",\n    \"load_path\": \"$@bundle_root + '/pretrained_resnet18.pth'\",\n    \"strict\": false,\n    \"load_dict\": {\n        \"model_new\": \"@network\"\n    }\n}\n

Input

The training pipeline is a json file (dataset.json) which includes path to each WSI, the location and the label information for each training patch.

Output

A probability number of the input patch being tumor or normal.

Memory Consumption Warning

If you face memory issues in traning, you can lower the batch_size in the configurations to reduce the System RAM requirements.

Inference on a WSI

Inference is performed on WSI in a sliding window manner with specified stride. A foreground mask is needed to specify the region where the inference will be performed on, given that background region which contains no tissue at all can occupy a significant portion of a WSI. Output of the inference pipeline is a probability map of size 1/stride of original WSI size.

Note on determinism

By default this bundle use a deterministic approach to make the results reproducible. However, it comes at a cost of performance loss. Thus if you do not care about reproducibility, you can have a performance gain by replacing \"$monai.utils.set_determinism\" line with \"$setattr(torch.backends.cudnn, 'benchmark', True)\" in initialize section of training configuration (configs/train.json and configs/multi_gpu_train.json for single GPU and multi-GPU training respectively).

Performance

FROC score is used for evaluating the performance of the model. After inference is done, evaluate_froc.sh needs to be run to evaluate FROC score based on predicted probability map (output of inference) and the ground truth tumor masks.\nUsing an internal pretrained weights for ResNet18, this model deterministically achieves the 0.90 accuracy on validation patches, and FROC of 0.72 on the 48 Camelyon testing data that have ground truth annotations available.

$\"A$

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

Please notice that the benchmark results are tested on one WSI image since the images are too large to benchmark. And the inference time in the end-to-end line stands for one patch of the whole image.

\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	1.93	2.52	1.61	1.33	0.77	1.20	1.45	1.89
end2end	224.97	223.50	222.65	224.03	1.01	1.01	1.00	1.00

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: 8.5.3+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=2 -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.

Note: When using a container based on PyTorch 24.0x, you may encounter random NCCL timeout errors. To address this issue, consider the following adjustments:

Reduce the num_workers: Decreasing the number of data loader workers can help minimize these errors.
Set pin_memory to False: Disabling pinned memory may reduce the likelihood of timeouts.
Switch to the gloo backend: As a workaround, you can set the distributed training backend to gloo to avoid NCCL-related timeouts.

You can implement these settings by adding flags like --train#dataloader#num_workers 0 or --train#dataloader#pin_memory false.

Execute inference

CUDA_LAUNCH_BLOCKING=1 python -m monai.bundle run --config_file configs/inference.json\n

Evaluate FROC metric

cd scripts && source evaluate_froc.sh\n

Export checkpoint to TorchScript file

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.json\n

Export checkpoint to TensorRT based models with fp32 or fp16 precision

--dynamic_batchsize "[1, 400, 600]"\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.json --precision <fp32/fp16> --dynamic_batchsize \"[1, 400, 600]\"\n

Execute inference with the TensorRT model

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

References

[1] He, Kaiming, et al, \"Deep Residual Learning for Image Recognition.\" In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. https://arxiv.org/pdf/1512.03385.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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You can download all the images for \"CAMELYON16\" data set from various sources listed [here](https://camelyon17.grand-challenge.org/Data/).\n\nLocation information for training/validation patches (the location on the whole slide image where patches are extracted) are adopted from [NCRF/coords](https://github.com/baidu-research/NCRF/tree/master/coords).\n\nAnnotation information are adopted from [NCRF/jsons](https://github.com/baidu-research/NCRF/tree/master/jsons).\n\n- Target: Tumor\n- Task: Detection\n- Modality: Histopathology\n- Size: 270 WSIs for training/validation, 48 WSIs for testing\n\n#### Preprocessing\n\nThis bundle expects the training/validation data (whole slide images) reside in a `{dataset_dir}/training/images`. By default `dataset_dir` is pointing to `/workspace/data/medical/pathology/` You can modify `dataset_dir` in the bundle config files to point to a different directory.\n\nTo reduce the computation burden during the inference, patches are extracted only where there is tissue and ignoring the background according to a tissue mask. Please also create a directory for prediction output. By default `output_dir` is set to `eval` folder under the bundle root.\n\nPlease refer to \"Annotation\" section of [Camelyon challenge](https://camelyon17.grand-challenge.org/Data/) to prepare ground truth images, which are needed for FROC computation. By default, this data set is expected to be at `/workspace/data/medical/pathology/ground_truths`. But it can be modified in `evaluate_froc.sh`.\n\n### Training configuration\n\nThe training was performed with the following:\n\n- Config file: train.config\n- GPU: at least 16 GB of GPU memory.\n- Actual Model Input: 224 x 224 x 3\n- AMP: True\n- Optimizer: Novograd\n- Learning Rate: 1e-3\n- Loss: BCEWithLogitsLoss\n- Whole slide image reader: cuCIM (if running on Windows or Mac, please install `OpenSlide` on your system and change `wsi_reader` to \"OpenSlide\")\n\n#### Pretrained Weights\n\nBy setting the `\"pretrained\"` parameter of `TorchVisionFCModel` in the config file to `true`, ImageNet pre-trained weights will be used for training. Please note that these weights are for non-commercial use. Each user is responsible for checking the content of the models/datasets and the applicable licenses and determining if suitable for the intended use. In order to use other pretrained weights, you can use `CheckpointLoader` in train handlers section as the first handler:\n\n```json\n{\n \"_target_\": \"CheckpointLoader\",\n \"load_path\": \"$@bundle_root + '/pretrained_resnet18.pth'\",\n \"strict\": false,\n \"load_dict\": {\n \"model_new\": \"@network\"\n }\n}\n```\n\n#### Input\n\nThe training pipeline is a json file (dataset.json) which includes path to each WSI, the location and the label information for each training patch.\n\n#### Output\n\nA probability number of the input patch being tumor or normal.\n\n#### Memory Consumption Warning\n\nIf you face memory issues in traning, you can lower the `batch_size` in the configurations to reduce the System RAM requirements.\n\n#### Inference on a WSI\n\nInference is performed on WSI in a sliding window manner with specified stride. A foreground mask is needed to specify the region where the inference will be performed on, given that background region which contains no tissue at all can occupy a significant portion of a WSI. Output of the inference pipeline is a probability map of size 1/stride of original WSI size.\n\n#### Note on determinism\n\nBy default this bundle use a deterministic approach to make the results reproducible. However, it comes at a cost of performance loss. Thus if you do not care about reproducibility, you can have a performance gain by replacing `\"$monai.utils.set_determinism\"` line with `\"$setattr(torch.backends.cudnn, 'benchmark', True)\"` in initialize section of training configuration (`configs/train.json` and `configs/multi_gpu_train.json` for single GPU and multi-GPU training respectively).\n\n### Performance\n\nFROC score is used for evaluating the performance of the model. After inference is done, `evaluate_froc.sh` needs to be run to evaluate FROC score based on predicted probability map (output of inference) and the ground truth tumor masks.\nUsing an internal pretrained weights for ResNet18, this model deterministically achieves the 0.90 accuracy on validation patches, and FROC of 0.72 on the 48 Camelyon testing data that have ground truth annotations available.\n\n![A Graph showing Train Acc, Train Loss, and Validation Acc](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_tumor_detection_train_and_val_metrics_v5.png)\n\nThe `pathology_tumor_detection` bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU.\n\nPlease notice that the benchmark results are tested on one WSI image since the images are too large to benchmark. And the inference time in the end-to-end line stands for one patch of the whole image.\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 |1.93 | 2.52 | 1.61 | 1.33 | 0.77 | 1.20 | 1.45 | 1.89 |\n| end2end |224.97 | 223.50 | 222.65 | 224.03 | 1.01 | 1.01 | 1.00 | 1.00 |\n\nWhere:\n\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\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=2 -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**Note:** When using a container based on [PyTorch 24.0x](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes), you may encounter random NCCL timeout errors. To address this issue, consider the following adjustments:\n\n- Reduce the `num_workers`: Decreasing the number of data loader workers can help minimize these errors.\n- Set `pin_memory` to `False`: Disabling pinned memory may reduce the likelihood of timeouts.\n- Switch to the `gloo` backend: As a workaround, you can set the distributed training backend to `gloo` to avoid NCCL-related timeouts.\n\nYou can implement these settings by adding flags like `--train#dataloader#num_workers 0` or `--train#dataloader#pin_memory false`.\n\n##### Execute inference\n\n```\nCUDA_LAUNCH_BLOCKING=1 python -m monai.bundle run --config_file configs/inference.json\n```\n\n##### Evaluate FROC metric\n\n```\ncd scripts && source evaluate_froc.sh\n```\n\n##### Export checkpoint to TorchScript file\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.json\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.json --precision --dynamic_batchsize \"[1, 400, 600]\"\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] He, Kaiming, et al, \"Deep Residual Learning for Image Recognition.\" In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016. \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",{"_1361":1362,"_1363":1364,"_1365":1366,"_1367":1368},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[471,472,473,474,475],{"_476":1013,"_84":1014,"_275":1015,"_480":194,"_481":18,"_483":1016,"_485":1017},{"_476":802,"_84":803,"_275":804,"_480":31,"_481":805,"_483":806,"_485":807},{"_476":702,"_84":703,"_275":704,"_480":31,"_481":54,"_483":705,"_485":706},{"_476":487,"_84":488,"_275":489,"_480":31,"_481":490,"_483":491,"_485":492},{"_476":477,"_84":478,"_275":479,"_480":31,"_481":482,"_483":484,"_485":486},"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,[620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654],[493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509],{"_510":616,"_324":616,"_275":276,"_483":617},{"_510":611,"_324":611,"_275":276,"_483":612},{"_510":607,"_324":607,"_275":276,"_483":608},{"_510":602,"_324":602,"_275":276,"_483":603},{"_510":597,"_324":597,"_275":276,"_483":598},{"_510":585,"_324":585,"_275":276,"_483":586},{"_510":573,"_324":573,"_275":276,"_483":574},{"_510":568,"_324":568,"_275":276,"_483":569},{"_510":557,"_324":557,"_275":276,"_483":558},{"_510":552,"_324":552,"_275":276,"_483":553},{"_510":547,"_324":547,"_275":276,"_483":548},{"_510":542,"_324":542,"_275":276,"_483":543},{"_510":537,"_324":537,"_275":276,"_483":538},{"_510":532,"_324":532,"_275":276,"_483":533},{"_510":527,"_324":527,"_275":276,"_483":528},{"_510":520,"_324":520,"_275":276,"_483":521},{"_510":511,"_324":511,"_275":276,"_483":512},"hostName","amazon-adsystem.com",[513,514],{"_84":519,"_516":511,"_517":31,"_275":518},{"_84":515,"_516":511,"_517":31,"_275":518},"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",[522,523],{"_84":526,"_516":520,"_517":31,"_275":525},{"_84":524,"_516":520,"_517":31,"_275":525},"IDE","This domain is owned by Doubleclick (Google). 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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.",[742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762],[707,708,709,710,711],{"_510":602,"_324":602,"_275":276,"_483":733},{"_510":568,"_324":568,"_275":276,"_483":728},{"_510":721,"_324":721,"_275":276,"_483":722},{"_510":717,"_324":717,"_275":276,"_483":718},{"_510":712,"_324":712,"_275":276,"_483":713},"nvcrm.my.site.com",[714],{"_84":715,"_516":712,"_517":31,"_275":716},"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",[719],{"_84":720,"_516":717,"_517":31,"_275":551},"edge","github.com",[723,724],{"_84":727,"_516":721,"_517":31,"_275":726},{"_84":725,"_516":721,"_517":31,"_275":726},"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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Name	Size	Updated	Actions




LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download
testing.csv	432 B	February 28, 2025 UTC	curl wget Download
training.csv	19.73 MB	February 28, 2025 UTC	curl wget Download
validation.csv	1.96 MB	February 28, 2025 UTC	curl wget Download