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Changelog

June 2020

Updated performance tables to include A100 results

March 2019

Initial release

May 2019

Test scripts updated

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The SSD320 v1.2 model is based on the SSD: Single Shot MultiBox Detector paper, which describes SSD as \"a method for detecting objects in images using a single deep neural network\".\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.5x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

Our implementation is based on the existing model from the TensorFlow models repository.\nThe network was altered in order to improve accuracy and increase throughput. Changes include:

Replacing the VGG backbone with the more popular ResNet50.
Adding multi-scale detection to the backbone using Feature Pyramid Networks.
Replacing the original hard negative mining loss function with Focal Loss.
Decreasing the input size to 320 x 320.

Default configuration

We trained the model for 12500 steps (27 epochs) with the following setup:

SGDR with cosine decay learning rate
Learning rate base = 0.16
Momentum = 0.9
Warm-up learning rate = 0.0693312
Warm-up steps = 1000
Batch size per GPU = 32
Number of GPUs = 8

Feature support matrix

The following features are supported by this model:

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

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

Feature	Transformer-XL
Automatic mixed precision (AMP)	Yes
Horovod Multi-GPU (NCCL)	Yes

Features

TF-AMP - a\ntool that enables Tensor Core-accelerated training. Refer to the Enabling\nmixed precision section for more details.

Horovod - Horovod\nis a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet.\nThe goal of Horovod is to make distributed deep learning fast and easy to use.\nFor more information about how to get started with Horovod, see the Horovod:\nOfficial repository.

Multi-GPU training with Horovod - our model\nuses Horovod to implement efficient multi-GPU training with NCCL. For details,\nsee example sources in this repository or see the TensorFlow\ntutorial.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a\ncomputational method.\nMixed precision training offers significant\ncomputational speedup by performing operations in half-precision format while\nstoring minimal information in single-precision to retain as much information\nas possible in critical parts of the network. Since the introduction of Tensor\nCores in Volta, and following with both the\nTuring and Ampere architectures, significant training speedups are experienced by switching to\nmixed precision -- up to 3x overall speedup on the most arithmetically intense\nmodel architectures. Using mixed precision training previously required two\nsteps:

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

This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enablethe full\nmixed precision methodology\nin your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe TensorFlow framework code makes all necessary model changes internally.

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

For information about:

How to train using mixed precision, see the Mixed Precision\nTraining paper and Training With Mixed\nPrecision\ndocumentation.
Techniques used for mixed precision training, see the Mixed-Precision\nTraining of Deep Neural\nNetworks\nblog.
How to access and enable AMP for TensorFlow, see Using\nTF-AMP\nfrom the TensorFlow User Guide.

Enabling mixed precision

Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP)\nextension which casts variables to half-precision upon retrieval, while storing variables\nin single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation,\na loss scaling\nstep must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by\nusing simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed\nprecision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations.\nFirst, programmers need not modify network model code, reducing development and maintenance effort.\nSecond, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.

To enable mixed precision, you can simply add the values to the environmental variables inside your training script:

Enable TF-AMP graph rewrite:

os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n

Enable Automated Mixed Precision:

os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n

Enabling TF32

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.

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

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

","resourceVersions",[170,171,172,173,174,175],"resourceVersionsTotal",5,"hasMoreVersions","versionMetadata",{"_13":14,"_142":-5,"_43":143,"_144":-5,"_145":146,"_147":-5,"_148":149,"_150":151,"_152":26,"_153":-5,"_154":26,"_15":16,"_155":64,"_156":157,"_158":64,"_159":160,"_161":64,"_162":163,"_164":64,"_165":166,"_167":64,"_168":169},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/-/resources/ssd_for_tensorflow/-","version","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/ssd_for_tensorflow:20.06.2","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/-/resources/ssd_for_tensorflow/20.06.2/version-history","accessCheckResult",{"_141":64},"hasRelatedCollections","context",{"_138":139,"_140":-7},1783560023118,[],[],[131,132,133,134,135,136],{"_39":53,"_41":61,"_43":57,"_45":58,"_47":62,"_49":50},{"_39":53,"_41":60,"_43":57,"_45":58,"_47":137,"_49":50},{"_39":53,"_41":56,"_43":57,"_45":58,"_47":59,"_49":50},{"_39":53,"_41":54,"_43":57,"_45":58,"_47":137,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":48,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},65,"orgName","nvidia","teamName","allowed","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1632262135849],"updatedDate","totalSizeInBytes",7581582,"totalFileCount",533,"isSigned","malwareScanStatus","isScanned","hasReleaseNotes","setup","The following section list the requirements in order to start training the SSD320 v1.2 model.\n\n### Requirements\nThis repository contains `Dockerfile` which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following software:\n* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n* [TensorFlow 20.06-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) (or later) NGC container\n* GPU-based architecture:\n * [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n * [NVIDIA Turing](https://www.nvidia.com/en-us/geforce/turing/)\n * [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the\nfollowing sections from the NVIDIA GPU Cloud Documentation and the Deep Learning\nDocumentation:\n* [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n* [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/dgx/user-guide/index.html#accessing_registry)\n* [Running TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running)","hasSetup","quickStartGuide","To train your model using mixed precision or TF32 with tensor cores or using TF32, FP32,\nperform the following steps using the default parameters of the SSD320 v1.2 model on the\n[COCO 2017](http://cocodataset.org/#download) dataset.\n\n### 1. Clone the repository.\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/TensorFlow/Detection/SSD\n```\n\n### 2. Build the SSD320 v1.2 TensorFlow NGC container.\n\n```\ndocker build . -t nvidia_ssd\n```\n\n### 3. Download and preprocess the dataset. \n\nExtract the COCO 2017 dataset with:\n\n```\ndownload_all.sh nvidia_ssd \n```\n\nData will be downloaded, preprocessed to tfrecords format and saved in the `` directory (on the host).\nMoreover the script will download pre-trained RN50 checkpoint in the `` directory\n\n### 4. Launch the NGC container to run training/inference.\n```\nnvidia-docker run --rm -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -v :/data/coco2017_tfrecords -v :/checkpoints --ipc=host nvidia_ssd\n```\n\n### 5. Start training.\n\nThe `./examples` directory provides several sample scripts for various GPU settings and act as wrappers around\n`object_detection/model_main.py` script. The example scripts can be modified by arguments: \n- A path to directory for checkpoints\n- A path to directory for configs\n- Additional arguments to `object_detection/model_main.py`\n\nIf you want to run 8 GPUs, training with tensor cores acceleration and save checkpoints in `/checkpoints` directory, run:\n\n```\nbash ./examples/SSD320_FP16_8GPU.sh /checkpoints\n```\n\n### 6. Start validation/evaluation.\n\nThe `model_main.py` training script automatically runs validation during training.\nThe results from the validation are printed to `stdout`.\n\nPycocotools' open-sourced scripts provides a consistent way to evaluate models on the COCO dataset.\nWe are using these scripts during validation to measure models performance in AP metric.\nMetrics below are evaluated using pycocotools' methodology,\nin the following format:during validation to measure models performance in AP metric.\nMetrics below are evaluated using pycocotools' methodology, in the following format:\n```\n Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.273\n Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.423\n Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.291\n Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.024\n Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.218\n Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.451\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.257\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398\n Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.427\n Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.070\n Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418\n Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.645\n```\nThe metric reported in our results is present in the first row.\n\nTo evaluate a checkpointed model saved in the previous step, you can use script from examples directory.\nIf you want to run inference with tensor cores acceleration, run:\n\n```\nbash examples/SSD320_evaluate.sh \n```","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Scripts and sample code\n\n* `Dockerfile`: a container with the basic set of dependencies to run SSD\n\nIn the `model/research/object_detection` directory, the most important files are:\n\n* `model_main.py`: serves as the entry point to launch the training and inference\n* `models/ssd_resnet_v1_fpn_feature_extractor.py`: implementation of the model\n* `metrics/coco_tools.py`: implementation of mAP metric\n* `utils/exp_utils.py`: utility functions for running training and benchmarking\n\n### Parameters\n\nThe complete list of available parameters for the `model/research/object_detection/model_main.py` script contains:\n\n```\n./object_detection/model_main.py:\n --[no]allow_xla: Enable XLA compilation\n (default: 'false')\n --checkpoint_dir: Path to directory holding a checkpoint. If `checkpoint_dir` is provided, this binary operates in\n eval-only mode, writing resulting metrics to `model_dir`.\n --eval_count: How many times the evaluation should be run\n (default: '1')\n (an integer)\n --[no]eval_training_data: If training data should be evaluated for this job. Note that one call only use this in eval-\n only mode, and `checkpoint_dir` must be supplied.\n (default: 'false')\n --hparams_overrides: Hyperparameter overrides, represented as a string containing comma-separated hparam_name=value\n pairs.\n --model_dir: Path to output model directory where event and checkpoint files will be written.\n --num_train_steps: Number of train steps.\n (an integer)\n --pipeline_config_path: Path to pipeline config file.\n --raport_file: Path to dlloger json\n (default: 'summary.json')\n --[no]run_once: If running in eval-only mode, whether to run just one round of eval vs running continuously (default).\n (default: 'false')\n --sample_1_of_n_eval_examples: Will sample one of every n eval input examples, where n is provided.\n (default: '1')\n (an integer)\n --sample_1_of_n_eval_on_train_examples: Will sample one of every n train input examples for evaluation, where n is\n provided. This is only used if `eval_training_data` is True.\n (default: '5')\n (an integer)\n```\n\n### Command line options\nThe SSD model training is conducted by the script from the object_detection library, `model_main.py`.\nOur experiments were done with settings described in the `examples` directory.\nIf you would like to get more details about available arguments, please run:\n\n```\npython object_detection/model_main.py --help\n```\n\n### Getting the data\n\nThe SSD320 v1.2 model was trained on the COCO 2017 dataset. The val2017 validation set was used as a validation dataset.\nThe `download_data.sh` script will preprocess the data to tfrecords format.\n\nThis repository contains the `download_dataset.sh` script which will automatically download and preprocess the training,\nvalidation and test datasets. By default, data will be downloaded to the `/data/coco2017_tfrecords` directory.\n\n### Training process\nTraining the SSD model is implemented in the `object_detection/model_main.py` script. \n\nAll training parameters are set in the config files. Because evaluation is relatively time consuming,\nit does not run every epoch. By default, evaluation is executed only once at the end of the training.\nThe model is evaluated using pycocotools distributed with the COCO dataset.\nThe number of evaluations can be changed using the `eval_count` parameter.\n\nTo run training with tensor cores, use `./examples/SSD320_FP16_{1,4,8}GPU.sh` scripts. For more details see [Enabling mixed precision](#enabling-mixed-precision) section below.\n\n#### Data preprocessing\nBefore we feed data to the model, both during training and inference, we perform:\n*\tNormalization\n*\tEncoding bounding boxes\n* Resize to 320x320\n\n#### Data augmentation\nDuring training we perform the following augmentation techniques:\n*\tRandom crop\n*\tRandom horizontal flip\n*\tColor jitter\n\n### Enabling mixed precision\n[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format,\nwhile storing minimal information in single-precision to retain as much information as possible in critical parts of the network.\nSince the introduction of [tensor cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architectures,\nsignificant training speedups are experienced by switching to mixed precision --\nup to 3x overall speedup on the most arithmetically intense model architectures.\nUsing [mixed precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) training previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Manually adding loss scaling to preserve small gradient values.\n \nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code.\nAMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16,\nand the loss scaling is automatically applied inside of supported optimizers.\nAMP can be configured to work with the existing `tf.contrib` loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization.\nIt accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html) documentation.\n- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA's latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\nTraining benchmark was run in various scenarios on V100 16G GPU. For each scenario, batch size was set to 32. \n\nTo benchmark training, run:\n\n```\nbash examples/SSD320_{PREC}_{NGPU}GPU_BENCHMARK.sh\n```\n\nWhere the `{NGPU}` defines number of GPUs used in benchmark, and the `{PREC}` defines precision.\nThe benchmark runs training with only 1200 steps and computes average training speed of last 300 steps.\n\n#### Inference performance benchmark\nInference benchmark was run with various batch-sizes on V100 16G GPU.\nFor inference we are using single GPU setting. Examples are taken from the validation dataset.\n\nTo benchmark inference, run:\n\n```\nbash examples/SSD320_FP{16,32}_inference.sh --batch_size --checkpoint_dir \n```\n\nBatch size for the inference benchmark is controlled by the `--batch_size` argument,\nwhile the checkpoint is provided to the script with the `--checkpoint_dir` argument.\n\nThe benchmark script provides extra arguments for extra control over the experiment.\nWe were using default values for the extra arguments during the experiments. For more details about them, please run:\n\n```\nbash examples/SSD320_FP16_inference.sh --help\n```\n\n### Results\n\nThe following sections provide details on how we achieved our performance and accuracy in training and inference.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the `./examples/SSD320_FP{16,32}_{1,4,8}GPU.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX A100 (8x A100 40GB) GPUs.\n\nAll the results are obtained with batch size set to 32.\n\n| **Number of GPUs** | **Mixed precision mAP** | **Training time with mixed precision** | **TF32 mAP** | **Training time with TF32** |\n|:------------------:|:-----------------------:|:--------------------------------------:|:------------:|:---------------------------:|\n| 1 | 0.279 | 4h 48min | 0.280 | 6h 40min |\n| 4 | 0.280 | 1h 20min | 0.279 | 1h 53min |\n| 8 | 0.281 | 0h 53min | 0.282 | 1h 05min |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `./examples/SSD320_FP{16,32}_{1,4,8}GPU.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.\nAll the results are obtained with batch size set to 32.\n\n| **Number of GPUs** | **Mixed precision mAP** | **Training time with mixed precision** | **FP32 mAP** | **Training time with FP32** |\n|:------------------:|:-----------------------:|:--------------------------------------:|:------------:|:---------------------------:|\n| 1 | 0.279 | 7h 36min | 0.278 | 10h 38min |\n| 4 | 0.277 | 2h 18min | 0.279 | 2h 58min |\n| 8 | 0.280 | 1h 28min | 0.282 | 1h 55min |\n\nHere are example graphs of TF32, FP32 and FP16 training on 8 GPU configuration:\n\n![TrainingLoss](./https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow/Detection/SSD/img/training_loss.png)\n\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running:\n\n```\npython bash examples/SSD320_FP*GPU_BENCHMARK.sh\n```\n\nscripts in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX A100 (8x A100 40GB) GPUs.\n\n| **Number of GPUs** | **Batch size per GPU** | **Mixed precision img/s** | **TF32 img/s** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with mixed precision** | **Multi-gpu weak scaling with TF32** |\n|:------------------:|:----------------------:|:-------------------------:|:--------------:|:---------------------------------:|:-----------------------------------------------:|:------------------------------------:|\n| 1 | 32 | 180.55 | 123.48 | 1.46 | 1.00 | 1.00 |\n| 4 | 32 | 624.35 | 449.17 | 1.39 | 3.46 | 3.64 |\n| 8 | 32 | 1008.46 | 779.96 | 1.29 | 5.59 | 6.32 |\n\nTo achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.\n\nThose results can be improved when [XLA](https://www.tensorflow.org/xla) is used \nin conjunction with mixed precision, delivering up to 2x speedup over FP32 on a single GPU (\\~179 img/s).\nHowever XLA is still considered experimental.\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running:\n\n```\npython bash examples/SSD320_FP*GPU_BENCHMARK.sh\n```\n\nscripts in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX-1 with V100 16G GPUs. \n\n| **Number of GPUs** | **Batch size per GPU** | **Mixed precision img/s** | **FP32 img/s** | **Speed-up with mixed precision** | **Multi-gpu weak scaling with mixed precision** | **Multi-gpu weak scaling with FP32** |\n|:------------------:|:----------------------:|:-------------------------:|:--------------:|:---------------------------------:|:-----------------------------------------------:|:------------------------------------:|\n| 1 | 32 | 127.96 | 84.96 | 1.51 | 1.00 | 1.00 |\n| 4 | 32 | 396.38 | 283.30 | 1.40 | 3.10 | 3.33 |\n| 8 | 32 | 676.83 | 501.30 | 1.35 | 5.29 | 5.90 |\n\nTo achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.\n\nThose results can be improved when [XLA](https://www.tensorflow.org/xla) is used \nin conjunction with mixed precision, delivering up to 2x speedup over FP32 on a single GPU (\\~179 img/s).\nHowever XLA is still considered experimental.\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)\n\nOur results were obtained by running the `examples/SSD320_FP{16,32}_inference.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX A100 (1x A100 40GB) GPU.\n\nFP16\n\n| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |\n|------------|----------------|-------|-------|-------|-------|\n| 1 | 40.88 | 24.46 | 25.76 | 26.47 | 27.91 |\n| 2 | 49.26 | 40.60 | 42.09 | 42.61 | 45.26 |\n| 4 | 58.81 | 68.01 | 73.12 | 76.02 | 80.38 |\n| 8 | 69.13 |115.73 |121.58 |123.87 |129.00 |\n| 16 | 78.10 |204.85 |212.40 |216.38 |225.80 |\n| 32 | 76.19 |420.00 |437.24 |443.21 |479.80 |\n| 64 | 77.92 |821.37 |840.82 |867.62 |1204.64|\n\nTF32\n\n| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |\n|------------|----------------|-------|-------|-------|-------|\n| 1 | 36.93 | 27.08 | 29.10 | 29.89 | 32.24 |\n| 2 | 44.03 | 45.42 | 48.67 | 49.56 | 51.12 |\n| 4 | 54.65 | 73.20 | 77.50 | 78.89 | 85.81 |\n| 8 | 62.96 |127.06 |137.04 |141.64 |152.92 |\n| 16 | 71.48 |223.83 |231.36 |233.35 |247.51 |\n| 32 | 73.11 |437.71 |450.86 |455.14 |467.11 |\n| 64 | 73.74 |867.88 |898.99 |912.07 |1077.13|\n\nTo achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nOur results were obtained by running the `examples/SSD320_FP{16,32}_inference.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPU.\n\nFP16\n\n| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |\n|------------|----------------|-------|-------|-------|-------|\n| 1 | 28.34 | 35.29 | 38.09 | 39.06 | 41.07 |\n| 2 | 41.21 | 48.54 | 52.77 | 54.45 | 57.10 |\n| 4 | 55.41 | 72.19 | 75.44 | 76.99 | 84.15 |\n| 8 | 61.83 |129.39 |133.37 |136.89 |145.69 |\n| 16 | 66.36 |241.12 |246.05 |249.47 |259.79 |\n| 32 | 65.01 |492.21 |510.01 |516.45 |526.83 |\n| 64 | 64.75 |988.47 |1012.11|1026.19|1290.54|\n\nFP32\n\n| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |\n|------------|----------------|-------|-------|-------|-------|\n| 1 | 29.15 | 34.31 | 36.26 | 37.63 | 39.95 |\n| 2 | 41.20 | 48.54 | 53.08 | 54.47 | 57.32 |\n| 4 | 50.72 | 78.86 | 82.49 | 84.08 | 92.15 |\n| 8 | 55.72 |143.57 |147.20 |148.92 |152.44 |\n| 16 | 59.41 |269.32 |278.30 |281.06 |286.54 |\n| 32 | 59.81 |534.99 |542.49 |551.58 |572.16 |\n| 64 | 58.93 |1085.96|1111.20|1118.21|1253.74|\n\nTo achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.\n\n##### Inference performance: NVIDIA T4\n\nOur results were obtained by running the `examples/SSD320_FP{16,32}_inference.sh` script in the TensorFlow-20.06-py3 NGC container on NVIDIA T4.\n\nFP16\n\n| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |\n|------------|----------------|-------|-------|-------|-------|\n| 1 | 19.29 | 51.90 | 53.77 | 54.95 | 59.21 |\n| 2 | 30.36 | 66.04 | 70.13 | 71.49 | 73.97 |\n| 4 | 37.71 |106.21 |111.32 |113.04 |118.03 |\n| 8 | 40.95 |195.49 |201.66 |204.00 |210.32 |\n| 16 | 41.04 |390.05 |399.73 |402.88 |410.02 |\n| 32 | 40.36 |794.48 |815.81 |825.39 |841.45 |\n| 64 | 40.27 |1590.98|1631.00|1642.22|1838.95|\n\nFP32\n\n| **Batch size** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |\n|------------|----------------|-------|-------|-------|-------|\n| 1 | 14.30 | 69.99 | 72.30 | 73.29 | 76.35 |\n| 2 | 20.04 | 99.87 |104.50 |106.03 |108.15 |\n| 4 | 25.01 |159.99 |163.00 |164.13 |168.63 |\n| 8 | 28.42 |281.58 |286.57 |289.01 |294.37 |\n| 16 | 32.56 |492.08 |501.98 |505.29 |509.95 |\n| 32 | 34.14 |939.11 |961.35 |968.26 |983.77 |\n| 64 | 33.47 |1915.36|1971.90|1992.24|2030.54|\n\nTo achieve same results, follow the [Quick start guide](#quick-start-guide) outlined above.","hasPerformance","policies",[],{"_176":187,"_178":188,"_144":-5,"_152":26,"_154":26,"_168":186,"_155":64},{"_176":14,"_178":14,"_144":-5,"_152":26,"_154":26,"_168":186,"_155":64},{"_176":184,"_178":184,"_144":-5,"_152":26,"_154":26,"_168":185,"_155":64},{"_176":182,"_178":182,"_144":-5,"_152":26,"_154":26,"_168":183,"_155":64},{"_176":180,"_178":180,"_144":-5,"_152":26,"_154":26,"_168":181,"_155":64},{"_176":177,"_178":177,"_144":-5,"_152":26,"_154":26,"_168":179,"_155":64},"id","1","name",[],"2",[],"20.06.0",[],"20.06.1",[],[],"-","Latest (20.06.2)","nvidia/ssd_for_tensorflow","key","nvidia/resources/ssd_for_tensorflow","resourceId","ssd_for_tensorflow","shortDescription","With a ResNet-50 backbone and a number of architectural modifications, this version provides better accuracy and performance.","The SSD320 v1.2 model is based on the [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper, which describes SSD as \"a method for detecting objects in images using a single deep neural network\".\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.5x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n### Model architecture\n\nOur implementation is based on the existing [model from the TensorFlow models repository](https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config).\nThe network was altered in order to improve accuracy and increase throughput. Changes include:\n- Replacing the VGG backbone with the more popular ResNet50.\n- Adding multi-scale detection to the backbone using [Feature Pyramid Networks](https://arxiv.org/pdf/1612.03144.pdf).\n- Replacing the original hard negative mining loss function with [Focal Loss](https://arxiv.org/pdf/1708.02002.pdf).\n- Decreasing the input size to 320 x 320.\n\n### Default configuration\nWe trained the model for 12500 steps (27 epochs) with the following setup:\n- [SGDR](https://arxiv.org/pdf/1608.03983.pdf) with cosine decay learning rate\n- Learning rate base = 0.16 \n- Momentum = 0.9\n- Warm-up learning rate = 0.0693312\n- Warm-up steps = 1000\n- Batch size per GPU = 32\n- Number of GPUs = 8\n\n### Feature support matrix\n\nThe following features are supported by this model:\n\n| **Feature** | **Transformer-XL** |\n|:------------|-------------------:|\n|[Automatic mixed precision (AMP)](https://nvidia.github.io/apex/amp.html) | Yes |\n|[Horovod Multi-GPU (NCCL)](https://github.com/horovod/horovod) | Yes |\n\n#### Features\n\n[TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) - a \ntool that enables Tensor Core-accelerated training. Refer to the [Enabling\nmixed precision](#enabling-mixed-precision) section for more details.\n\n[Horovod](https://github.com/horovod/horovod) - Horovod \nis a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet.\nThe goal of Horovod is to make distributed deep learning fast and easy to use.\nFor more information about how to get started with Horovod, see the [Horovod:\nOfficial repository](https://github.com/horovod/horovod).\n\n[Multi-GPU training with Horovod](https://github.com/horovod/horovod/#usage) - our model \nuses Horovod to implement efficient multi-GPU training with NCCL. For details,\nsee example sources in this repository or see the [TensorFlow\ntutorial](https://github.com/horovod/horovod/#usage).\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a\ncomputational method.\n[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant\ncomputational speedup by performing operations in half-precision format while\nstoring minimal information in single-precision to retain as much information\nas possible in critical parts of the network. Since the introduction of [Tensor\nCores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the\nTuring and Ampere architectures, significant training speedups are experienced by switching to\nmixed precision -- up to 3x overall speedup on the most arithmetically intense\nmodel architectures. Using mixed precision training previously required two\nsteps:\n\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enablethe full\n[mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow)\nin your existing TensorFlow model code. AMP enables mixed precision training on Volta and Turing GPUs automatically.\nThe TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16,\nand the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work\nwith the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment\nvariable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically\nrewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\n\n* How to train using mixed precision, see the [Mixed Precision\n Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed\n Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)\n documentation.\n* Techniques used for mixed precision training, see the [Mixed-Precision\n Training of Deep Neural\n Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)\n blog.\n* How to access and enable AMP for TensorFlow, see [Using\n TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp)\n from the TensorFlow User Guide. \n\n#### Enabling mixed precision\n\nMixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP)\nextension which casts variables to half-precision upon retrieval, while storing variables\nin single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation,\na [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling)\nstep must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by\nusing simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed\nprecision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations.\nFirst, programmers need not modify network model code, reducing development and maintenance effort.\nSecond, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.\n\nTo enable mixed precision, you can simply add the values to the environmental variables inside your training script:\n- Enable TF-AMP graph rewrite:\n ```\n os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n ```\n \n- Enable Automated Mixed Precision:\n ```\n os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n ```\n\n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. \n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.","displayName","SSD v1.2 for TensorFlow",["D",1680632747098],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/ssd_for_tensorflow","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[229,230],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_197":240,"_232":241,"_234":242,"_236":237},{"_197":231,"_232":233,"_234":235,"_236":237},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_238":239,"_232":233},"category","label","field","platform","Computer Vision","soln_computer_vision",{"_238":243,"_232":241},"technology",{"_84":1420,"_86":1421},"ngcApps",[1373],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1354":1355,"_1356":1357,"_1358":1359,"_1360":1361,"_1362":255,"_115":279,"_1363":1364,"_1365":50,"_1366":64,"_1367":64,"_1368":1369,"_1370":64,"_1371":1372},"amplitude",{"_1351":1352,"_953":-7,"_1353":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1345":1346,"_1347":1348,"_1349":1350},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1228":359,"_1229":1230,"_1231":1232,"_1233":1234},"announcements",[],"consentState",{"_317":1218,"_1219":64,"_1220":26,"_1221":26,"_1222":1223},"consentConfig",{"_299":300,"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318},"showContactAdmin",1783560023051,[],[],[293,294,295,296,297,298],{"_39":53,"_41":61,"_43":55,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":60,"_43":55,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":56,"_43":55,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":55,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":55,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":55,"_45":-5,"_47":-5,"_49":50},"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.",[468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502],[340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356],{"_357":464,"_197":464,"_142":359,"_331":465},{"_357":459,"_197":459,"_142":359,"_331":460},{"_357":455,"_197":455,"_142":359,"_331":456},{"_357":450,"_197":450,"_142":359,"_331":451},{"_357":445,"_197":445,"_142":359,"_331":446},{"_357":433,"_197":433,"_142":359,"_331":434},{"_357":421,"_197":421,"_142":359,"_331":422},{"_357":416,"_197":416,"_142":359,"_331":417},{"_357":405,"_197":405,"_142":359,"_331":406},{"_357":400,"_197":400,"_142":359,"_331":401},{"_357":395,"_197":395,"_142":359,"_331":396},{"_357":390,"_197":390,"_142":359,"_331":391},{"_357":385,"_197":385,"_142":359,"_331":386},{"_357":380,"_197":380,"_142":359,"_331":381},{"_357":375,"_197":375,"_142":359,"_331":376},{"_357":368,"_197":368,"_142":359,"_331":369},{"_357":358,"_197":358,"_142":359,"_331":360},"hostName","amazon-adsystem.com","",[361,362],{"_178":367,"_364":358,"_365":26,"_142":366},{"_178":363,"_364":358,"_365":26,"_142":366},"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",[370,371],{"_178":374,"_364":368,"_365":26,"_142":373},{"_178":372,"_364":368,"_365":26,"_142":373},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[397],{"_178":398,"_364":395,"_365":26,"_142":399},"sp","Testing","google.com",[402],{"_178":403,"_364":400,"_365":26,"_142":404},"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. 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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",[423,424,425,426],{"_178":432,"_364":421,"_365":26,"_142":428},{"_178":430,"_364":421,"_365":26,"_142":431},{"_178":429,"_364":421,"_365":26,"_142":428},{"_178":427,"_364":421,"_365":26,"_142":428},"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",[435,436,437,438],{"_178":444,"_364":433,"_365":26,"_142":442},{"_178":443,"_364":433,"_365":26,"_142":442},{"_178":441,"_364":433,"_365":26,"_142":442},{"_178":439,"_364":433,"_365":26,"_142":440},"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.",4,[591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611],[556,557,558,559,560],{"_357":450,"_197":450,"_142":359,"_331":582},{"_357":416,"_197":416,"_142":359,"_331":577},{"_357":570,"_197":570,"_142":359,"_331":571},{"_357":566,"_197":566,"_142":359,"_331":567},{"_357":561,"_197":561,"_142":359,"_331":562},"nvcrm.my.site.com",[563],{"_178":564,"_364":561,"_365":26,"_142":565},"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",[568],{"_178":569,"_364":566,"_365":26,"_142":399},"edge","github.com",[572,573],{"_178":576,"_364":570,"_365":26,"_142":575},{"_178":574,"_364":570,"_365":26,"_142":575},"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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