This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from NGC

The ResNet-50 v1.5 model is a modified version of the original ResNet-50 v1 model.

The difference between v1 and v1.5 is in the bottleneck blocks which require downsampling. ResNet v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.

This difference makes ResNet-50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec).

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 3.5x 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

The model architecture was present in Deep Residual Learning for Image Recognition paper. The main advantage of the model is the usage of residual layers as a building block that helps with gradient propagation during training.

$\"ResidualLayer\"$

Image source: Deep Residual Learning for Image Recognition

Default configuration

Optimizer

SGD with momentum (0.875)
Learning rate = 0.256 for 256 batch size, for other batch sizes we linearly scale the learning rate
Learning rate schedule - we use cosine LR schedule
Linear warmup of the learning rate during the first 5 epochs according to Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.
Weight decay: 3.0517578125e-05 (1/32768)
We do not apply WD on batch norm trainable parameters (gamma/bias)
Label Smoothing: 0.1
We train for:\n
- 50 Epochs - configuration that reaches 75.9% top1 accuracy
- 90 Epochs - 90 epochs is a standard for ResNet-50
- 250 Epochs - best possible accuracy. For 250 epoch training we also use MixUp regularization.
\n

Data augmentation

For training:

Normalization
Random resized crop to 224x224
Scale from 8% to 100%
Aspect ratio from 3/4 to 4/3
Random horizontal flip

For inference:

Normalization
Scale to 256x256
Center crop to 224x224

Feature support matrix

\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	ResNet-50 MXNet
DALI	yes
Horovod Multi-GPU	yes

Features

The following features are supported by this model.

NVIDIA DALI\nNVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, to accelerate the pre-processing of the input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications.

Horovod Multi-GPU\nHorovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the Horovod: Official repository.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:

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

The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.

For information about:

How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.

Enabling mixed precision

Using the Gluon API, ensure you perform the following steps to convert a model that supports computation with float16.

\n
Cast Gluon Block's parameters and expected input type to float16 by calling the cast method of the Block representing the network.
\n
```
net = net.cast('float16')\n
```
\n
\n
Ensure the data input to the network is of float16 type. If your DataLoader or Iterator produces output in another datatype, then you have to cast your data. There are different ways you can do this. The easiest way is to use the astype method of NDArrays.
\n
```
data = data.astype('float16', copy=False)\n
```
\n
\n
If you are using images and DataLoader, you can also use a Cast transform. It is preferable to use multi_precision mode of optimizer when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios.
\n
```
optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)\n
```
\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",[235,236,237],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_214":29,"_215":-5,"_53":117,"_114":-5,"_118":216,"_120":-5,"_217":116,"_125":18,"_122":35,"_218":-5,"_121":35,"_219":220,"_221":90,"_222":223,"_224":90,"_225":226,"_227":90,"_228":229,"_230":90,"_231":232,"_233":90,"_123":234},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/resources/resnet_mxnet/-","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/dle/resnet_mxnet:20.12.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/resources/resnet_mxnet/20.12.0/version-history","accessCheckResult",{"_213":90},"hasRelatedCollections","context",{"_209":210,"_211":212},1787729899999,[],[],[202,203,204,205,206,207,208],{"_49":63,"_51":74,"_53":65,"_55":66,"_57":69,"_58":59},{"_49":63,"_51":72,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":65,"_55":66,"_57":71,"_58":59},{"_49":63,"_51":68,"_53":65,"_55":66,"_57":69,"_58":59},{"_49":63,"_51":64,"_53":65,"_55":66,"_57":67,"_58":59},{"_49":60,"_51":61,"_53":54,"_55":56,"_57":62,"_58":59},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":16,"_58":59},"orgName","nvidia","teamName","dle","allowed","versionId","description",["D",1663864789234],"totalSizeInBytes","malwareScanStatus","releaseNotes","### Changelog\n\n1. Dec, 2018\n * Initial release (based on https://github.com/apache/incubator-mxnet/tree/v1.8.x/example/image-classification)\n2. June, 2019\n * Code refactor\n * Label smoothing\n * Cosine LR schedule\n * MixUp regularization\n * Better configurations\n3. February, 2021\n * DGX-A100 performance results\n * Container version upgraded to 20.12\n\n### Known Issues\n\nThere are no known issues with this model.","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to start training the ResNet-50 v1.5 model.\n\n### Requirements\n\nThis repository contains Dockerfile which extends the MXNet NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [MXNet 22.10-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia%2Fmxnet)\nSupported GPUs:\n- [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n- [NVIDIA Turing architecture](https://www.nvidia.com/en-us/design-visualization/technologies/turing-architecture/)\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 following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:\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/frameworks/user-guide/index.html#accessing_registry)\n- [Running MXNet](https://docs.nvidia.com/deeplearning/frameworks/mxnet-release-notes/running.html#running)\n\nFor those unable to use the MXNet NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the ResNet-50 model on the ImageNet 1k dataset. For the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n\n```bash\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/MxNet/Classification/RN50v1.5\n```\n\n2. Build the ResNet-50 MXNet NGC container.\n\nAfter Docker is set up, you can build the ResNet-50 image with:\n\n```bash\ndocker build . -t nvidia_rn50_mx\n```\n\n3. Start an interactive session in the NGC container to run preprocessing/training/inference.\n\n```bash\nnvidia-docker run --rm -it --ipc=host -v :/data/imagenet/train-val-recordio-passthrough nvidia_rn50_mx\n```\n\n4. Download the data.\n\n* Download the images from `http://image-net.org/download-images`.\n* Extract the training and validation data:\n\n ```bash\n mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train\n tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar\n find . -name \"*.tar\" | while read NAME ; do mkdir -p \"${NAME%.tar}\"; tar -xvf \"${NAME}\" -C \"${NAME%.tar}\"; rm -f \"${NAME}\"; done\n cd ..\n mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar\nwget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash\n ```\n\n5. Preprocess the ImageNet 1k dataset.\n\n```bash\n./scripts/prepare_imagenet.sh \n```\n\n6. Start training.\n\n```bash\n./runner -n -b \n```\n\n7. Start validation/evaluation.\n\n```bash\n./runner -n -b --load --mode val\n```\n\n8. Start inference/predictions.\n\n```bash\n./runner --load --mode pred --data-pred \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\nIn the root directory, the most important files are:\n* `runner`: A wrapper on the `train.py` script which is the main executable script for training/validation/predicting.\n* `benchmark.py`: A script for benchmarking.\n* `Dockerfile`: Container to build the container.\n* `fit.py`: A file containing most of the training and validation logic.\n* `data.py`: Data loading and preprocessing code.\n* `dali.py`: Data loading and preprocessing code using DALI.\n* `models.py`: The model architecture.\n* `report.py`: A file containing JSON report structure and description of fields.\n\nIn the `scripts` directory, the most important files are:\n* `prepare_imagenet.sh`: A script that converts raw dataset format to RecordIO format.\n\n### Parameters\n\nThe complete list of available parameters contains:\n```\nModel:\n --arch {resnetv1,resnetv15,resnextv1,resnextv15,xception}\n model architecture (default: resnetv15)\n --num-layers NUM_LAYERS\n number of layers in the neural network, required by\n some networks such as resnet (default: 50)\n --num-groups NUM_GROUPS\n number of groups for grouped convolutions, required by\n some networks such as resnext (default: 32)\n --num-classes NUM_CLASSES\n the number of classes (default: 1000)\n --batchnorm-eps BATCHNORM_EPS\n the amount added to the batchnorm variance to prevent\n output explosion. (default: 1e-05)\n --batchnorm-mom BATCHNORM_MOM\n the leaky-integrator factor controling the batchnorm\n mean and variance. (default: 0.9)\n --fuse-bn-relu FUSE_BN_RELU\n have batchnorm kernel perform activation relu\n (default: 0)\n --fuse-bn-add-relu FUSE_BN_ADD_RELU\n have batchnorm kernel perform add followed by\n activation relu (default: 0)\n\nTraining:\n --mode {train_val,train,val,pred}\n mode (default: train_val)\n --seed SEED random seed (default: None)\n --gpus GPUS list of gpus to run, e.g. 0 or 0,2,5 (default: [0])\n --kv-store {device,horovod}\n key-value store type (default: device)\n --dtype {float32,float16}\n precision (default: float16)\n --amp If enabled, turn on AMP (Automatic Mixed Precision)\n (default: False)\n --batch-size BATCH_SIZE\n the batch size (default: 192)\n --num-epochs NUM_EPOCHS\n number of epochs (default: 90)\n --lr LR initial learning rate (default: 0.1)\n --lr-schedule {multistep,cosine}\n learning rate schedule (default: cosine)\n --lr-factor LR_FACTOR\n the ratio to reduce lr on each step (default: 0.256)\n --lr-steps LR_STEPS the epochs to reduce the lr, e.g. 30,60 (default: [])\n --warmup-epochs WARMUP_EPOCHS\n the epochs to ramp-up lr to scaled large-batch value\n (default: 5)\n --optimizer OPTIMIZER\n the optimizer type (default: sgd)\n --mom MOM momentum for sgd (default: 0.875)\n --wd WD weight decay for sgd (default: 3.0517578125e-05)\n --label-smoothing LABEL_SMOOTHING\n label smoothing factor (default: 0.1)\n --mixup MIXUP alpha parameter for mixup (if 0 then mixup is not\n applied) (default: 0)\n --disp-batches DISP_BATCHES\n show progress for every n batches (default: 20)\n --model-prefix MODEL_PREFIX\n model checkpoint prefix (default: model)\n --save-frequency SAVE_FREQUENCY\n frequency of saving model in epochs (--model-prefix\n must be specified). If -1 then save only best model.\n If 0 then do not save anything. (default: -1)\n --begin-epoch BEGIN_EPOCH\n start the model from an epoch (default: 0)\n --load LOAD checkpoint to load (default: None)\n --test-io test reading speed without training (default: False)\n --test-io-mode {train,val}\n data to test (default: train)\n --log LOG file where to save the log from the experiment\n (default: log.log)\n --dllogger-log DLLOGGER_LOG\n file where to save the dllogger log from the\n experiment (default: dllogger_log.log)\n --workspace WORKSPACE\n path to directory where results will be stored\n (default: ./)\n --no-metrics do not calculate evaluation metrics (for benchmarking)\n (default: False)\n --benchmark-iters BENCHMARK_ITERS\n run only benchmark-iters iterations from each epoch\n (default: None)\n\nData:\n --data-train DATA_TRAIN\n the training data (default: None)\n --data-train-idx DATA_TRAIN_IDX\n the index of training data (default: )\n --data-val DATA_VAL the validation data (default: None)\n --data-val-idx DATA_VAL_IDX\n the index of validation data (default: )\n --data-pred DATA_PRED\n the image on which run inference (only for pred mode)\n (default: None)\n --data-backend {dali-gpu,dali-cpu,mxnet,synthetic}\n set data loading & augmentation backend (default:\n dali-gpu)\n --image-shape IMAGE_SHAPE\n the image shape feed into the network (default: [3,\n 224, 224])\n --rgb-mean RGB_MEAN a tuple of size 3 for the mean rgb (default: [123.68,\n 116.779, 103.939])\n --rgb-std RGB_STD a tuple of size 3 for the std rgb (default: [58.393,\n 57.12, 57.375])\n --input-layout {NCHW,NHWC}\n the layout of the input data (default: NCHW)\n --conv-layout {NCHW,NHWC}\n the layout of the data assumed by the conv operation\n (default: NCHW)\n --batchnorm-layout {NCHW,NHWC}\n the layout of the data assumed by the batchnorm\n operation (default: NCHW)\n --pooling-layout {NCHW,NHWC}\n the layout of the data assumed by the pooling\n operation (default: NCHW)\n --num-examples NUM_EXAMPLES\n the number of training examples (doesn't work with\n mxnet data backend) (default: 1281167)\n --data-val-resize DATA_VAL_RESIZE\n base length of shorter edge for validation dataset\n (default: 256)\n\nDALI data backend:\n entire group applies only to dali data backend\n\n --dali-separ-val each process will perform independent validation on\n whole val-set (default: False)\n --dali-threads DALI_THREADS\n number of threadsper GPU for DALI (default: 3)\n --dali-validation-threads DALI_VALIDATION_THREADS\n number of threadsper GPU for DALI for validation\n (default: 10)\n --dali-prefetch-queue DALI_PREFETCH_QUEUE\n DALI prefetch queue depth (default: 2)\n --dali-nvjpeg-memory-padding DALI_NVJPEG_MEMORY_PADDING\n Memory padding value for nvJPEG (in MB) (default: 64)\n --dali-fuse-decoder DALI_FUSE_DECODER\n 0 or 1 whether to fuse decoder or not (default: 1)\n\nMXNet data backend:\n entire group applies only to mxnet data backend\n\n --data-mxnet-threads DATA_MXNET_THREADS\n number of threads for data decoding for mxnet data\n backend (default: 40)\n --random-crop RANDOM_CROP\n if or not randomly crop the image (default: 0)\n --random-mirror RANDOM_MIRROR\n if or not randomly flip horizontally (default: 1)\n --max-random-h MAX_RANDOM_H\n max change of hue, whose range is [0, 180] (default:\n 0)\n --max-random-s MAX_RANDOM_S\n max change of saturation, whose range is [0, 255]\n (default: 0)\n --max-random-l MAX_RANDOM_L\n max change of intensity, whose range is [0, 255]\n (default: 0)\n --min-random-aspect-ratio MIN_RANDOM_ASPECT_RATIO\n min value of aspect ratio, whose value is either None\n or a positive value. (default: 0.75)\n --max-random-aspect-ratio MAX_RANDOM_ASPECT_RATIO\n max value of aspect ratio. If min_random_aspect_ratio\n is None, the aspect ratio range is\n [1-max_random_aspect_ratio,\n 1+max_random_aspect_ratio], otherwise it is\n [min_random_aspect_ratio, max_random_aspect_ratio].\n (default: 1.33)\n --max-random-rotate-angle MAX_RANDOM_ROTATE_ANGLE\n max angle to rotate, whose range is [0, 360] (default:\n 0)\n --max-random-shear-ratio MAX_RANDOM_SHEAR_RATIO\n max ratio to shear, whose range is [0, 1] (default: 0)\n --max-random-scale MAX_RANDOM_SCALE\n max ratio to scale (default: 1)\n --min-random-scale MIN_RANDOM_SCALE\n min ratio to scale, should >= img_size/input_shape.\n otherwise use --pad-size (default: 1)\n --max-random-area MAX_RANDOM_AREA\n max area to crop in random resized crop, whose range\n is [0, 1] (default: 1)\n --min-random-area MIN_RANDOM_AREA\n min area to crop in random resized crop, whose range\n is [0, 1] (default: 0.05)\n --min-crop-size MIN_CROP_SIZE\n Crop both width and height into a random size in\n [min_crop_size, max_crop_size] (default: -1)\n --max-crop-size MAX_CROP_SIZE\n Crop both width and height into a random size in\n [min_crop_size, max_crop_size] (default: -1)\n --brightness BRIGHTNESS\n brightness jittering, whose range is [0, 1] (default:\n 0)\n --contrast CONTRAST contrast jittering, whose range is [0, 1] (default: 0)\n --saturation SATURATION\n saturation jittering, whose range is [0, 1] (default:\n 0)\n --pca-noise PCA_NOISE\n pca noise, whose range is [0, 1] (default: 0)\n --random-resized-crop RANDOM_RESIZED_CROP\n whether to use random resized crop (default: 1)\n```\n\n### Command-line options\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command line option: \n\n`./runner --help` and `python train.py --help`\n\n`./runner` acts as a wrapper on `train.py` and all additional flags will be passed to `train.py`.\n\n### Getting the data\n\nThe MXNet ResNet-50 v1.5 script operates on ImageNet 1k, a widely popular image classification dataset from ILSVRC challenge. You can download the images from `http://image-net.org/download-images`.\n\nThe recommended data format is\n[RecordIO](https://mxnet.apache.org/versions/1.7.0/api/architecture/note_data_loading), which\nconcatenates multiple examples into seekable binary files for better read\nefficiency. MXNet provides a tool called `im2rec.py` located in the `/opt/mxnet/tools/` directory.\nThe tool converts individual images into `.rec` files.\n\nTo prepare a RecordIO file containing ImageNet data, we first need to create `.lst` files\nwhich consist of the labels and image paths. We assume that the original images were\ndownloaded to `/data/imagenet/raw/train-jpeg` and `/data/imagenet/raw/val-jpeg`.\n\n```bash\npython /opt/mxnet/tools/im2rec.py --list --recursive train /data/imagenet/raw/train-jpeg\npython /opt/mxnet/tools/im2rec.py --list --recursive val /data/imagenet/raw/val-jpeg\n```\n\nNext, we generate the `.rec` (RecordIO files with data) and `.idx` (indexes required by DALI\nto speed up data loading) files. To obtain the best training accuracy we do not preprocess the images when creating the RecordIO file.\n\n```bash\npython /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 train /data/imagenet/raw/train-jpeg\npython /opt/mxnet/tools/im2rec.py --pass-through --num-thread 40 val /data/imagenet/raw/val-jpeg\n```\n\n#### Dataset guidelines\n\nThe process of loading, normalizing, and augmenting the data contained in the dataset can be found in the `data.py` and `dali.py` files.\n\nThe data is read from RecordIO format, which concatenates multiple examples into seekable binary files for better read efficiency.\n\nData augmentation techniques are described in the [Default configuration](#default-configuration) section.\n\n#### Multi-dataset\n\nIn most cases, to train a model on a different dataset, no changes in the code are required, but the dataset has to be converted into RecordIO format.\n\nTo convert a custom dataset, follow the steps from [Getting the data](#getting-the-data) section, and refer to the `scripts/prepare_dataset.py` script.\n\n### Training process\n\nTo start training, run:\n`./runner -n -b --data-root --dtype `\n\nBy default, the training script runs the validation after each epoch:\n* The best checkpoint will be stored in the `model_best.params` file in the working directory.\n* The log from training will be saved in the `log.log` file in the working directory.\n* The JSON report with statistics will be saved in the `report.json` file in the working directory.\n\nIf ImageNet is mounted in the `/data/imagenet/train-val-recordio-passthrough` directory, you don't have to specify the `--data-root` flag.\n\n### Inference process\n\nTo start validation, run:\n`./runner -n -b --data-root --dtype --mode val`\n\nBy default: \n* The log from validation will be saved in the `log.log` file in the working directory.\n* The JSON report with statistics will be saved in the `report.json` file in the working directory.","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\n\nTo benchmark training and inference, run:\n`python benchmark.py -n -b --data-root --dtype -o `\n\n* To control the benchmark length per epoch, use the `-i` flag (defaults to 100 iterations).\n* To control the number of epochs, use the `-e` flag.\n* To control the number of warmup epochs (epochs which are not taken into account), use the `-w` flag.\n* To limit the length of the dataset, use the `--num-examples` flag.\n\nBy default, the same parameters as in `./runner` will be used. Additional flags will be passed to `./runner`.\n\n#### Training performance benchmark\n\nTo benchmark only training, use the `--mode train` flag.\n\n#### Inference performance benchmark\n\nTo benchmark only inference, use the `--mode val` flag.\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 80GB)\n\n**90 epochs configuration**\n\nOur results were obtained by running 8 times the `./runner -n -b 512 --dtype float32` script for TF32 and the `./runner -n -b 512` script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.\n\n| **GPUs** | **Accuracy - mixed precision** | **Accuracy - TF32** | **Time to train - mixed precision** | **Time to train - TF32** | **Time to train - speedup** |\n|:---:|:---:|:---:|:--:|:---:|:---:|\n|1|77.185|77.184|8.75|29.39|3.36|\n|8|77.185|77.184|1.14|3.82|3.35|\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\n**90 epochs configuration**\n\nOur results were obtained by running the `./runner -n -b 96 --dtype float32` training script for FP32 and the `./runner -n -b 192` training script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.\n\n| **GPUs** | **Accuracy - mixed precision** | **Accuracy - FP32** | **Time to train - mixed precision** | **Time to train - FP32** | **Time to train - speedup** |\n|:---:|:---:|:---:|:---:|:---:|:---:|\n|1|77.342|77.160|24.2|84.5|3.49|\n|4|77.196|77.290|6.0|21.4|3.59|\n|8|77.150|77.313|3.0|10.7|3.54|\n\n##### Training stability test\n\nOur results were obtained by running the following commands 8 times with different seeds.\n\n* For 50 epochs\n * `./runner -n 8 -b 96 --dtype float32 --num-epochs 50` for FP32\n * `./runner -n 8 -b 192 --num-epochs 50` for mixed precision\n\n* For 90 epochs\n * `./runner -n 8 -b 96 --dtype float32` for FP32\n * `./runner -n 8 -b 192` for mixed precision\n\n* For 250 epochs\n * `./runner -n 8 -b 96 --dtype float32 --num-epochs 250 --mixup 0.2` for FP32\n * `./runner -n 8 -b 192 --num-epochs 250 --mixup 0.2` for mixed precision\n\n| **# of epochs** | **mixed precision avg top1** | **FP32 avg top1** | **mixed precision standard deviation** | **FP32 standard deviation** | **mixed precision minimum top1** | **FP32 minimum top1** | **mixed precision maximum top1** | **FP32 maximum top1** |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|50|76.308|76.329|0.00073|0.00094|76.230|76.234|76.440|76.470|\n|90|77.150|77.313|0.00098|0.00085|76.972|77.228|77.266|77.474|\n|250|78.460|78.483|0.00078|0.00065|78.284|78.404|78.560|78.598|\n\n**Plots for 250 epoch configuration**\nHere are example graphs of FP32 and mixed precision training on 8 GPU 250 epochs configuration:\n\n![TrainingLoss](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/MxNet/Classification/RN50v1.5//img/dgx1-16g_250e_training_loss.png)\n\n\n![TrainingAccuracy](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/MxNet/Classification/RN50v1.5//img/dgx1-16g_250e_validation_top1.png)\n\n\n![ValidationAccuracy](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/MxNet/Classification/RN50v1.5//img/dgx1-16g_250e_validation_top5.png)\n\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\nThe following results were obtained by running the \n`python benchmark.py -n 1,4,8 -b 512 --dtype float32 -o benchmark_report_tf32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for TF32 and the\n`python benchmark.py -n 1,4,8 -b 512 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for mixed precision in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.\n\nTraining performance reported as Total IPS (data + compute time taken into account).\nWeak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.\n\n| **GPUs** | **Throughput - mixed precision** | **Throughput - TF32** | **Throughput speedup (TF32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - TF32** |\n|:---:|:---:|:---:|:---:|:---:|:---:|\n|1|3410.52 |1055.78 |2.18 |1.00 |1.00 |\n|4|13442.66 |4182.30 |3.24 |3.97 |3.96 |\n|8|26673.72|8247.44 |3.23 |7.82 |7.81 |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nThe following results were obtained by running the\n`python benchmark.py -n 1,2,4,8 -b 192 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for mixed precision and the\n`python benchmark.py -n 1,2,4,8 -b 96 --dtype float32 -o benchmark_report_fp32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.\n\nTraining performance reported as Total IPS (data + compute time taken into account).\nWeak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.\n\n| **GPUs** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** |\n|:---:|:---:|:---:|:---:|:---:|:---:|\n|1|1376 |384 |3.58 |1.00 |1.00 |\n|2|2768 |763 |3.62 |2.01 |1.98 |\n|4|5357 |1513 |3.54 |3.89 |3.94 |\n|8|10723 |3005 |3.56 |7.79 |7.82 |\n\n##### Training performance: NVIDIA DGX-2 (16x V100 32GB)\n\nThe following results were obtained by running the\n`python benchmark.py -n 1,2,4,8,16 -b 256 --dtype float16 -o benchmark_report_fp16.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for mixed precision and the\n`python benchmark.py -n 1,2,4,8,16 -b 128 --dtype float32 -o benchmark_report_fp32.json -i 500 -e 3 -w 1 --num-examples 32000 --mode train` script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-2 with (16x V100 32GB) GPUs.\n\nTraining performance reported as Total IPS (data + compute time taken into account).\nWeak scaling is calculated as a ratio of speed for given number of GPUs to speed for 1 GPU.\n\n| **GPUs** | **Throughput - mixed precision** | **Throughput - FP32** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** |\n|:---:|:---:|:---:|:---:|:---:|:---:|\n|1 |1492 |417 |3.57 |1.00 |1.00 |\n|2 |2935 |821 |3.57 |1.96 |1.96 |\n|4 |5726 |1623 |3.52 |3.83 |3.92 |\n|8 |11368|3223 |3.52 |7.61 |7.72 |\n|16|21484|6338 |3.38 |14.39|15.19|\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)\n\nThe following results were obtained by running the\n`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val` script for mixed precision and the\n`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_tf32.json -i 500 -e 3 -w 1 --mode val` script for TF32 in the mxnet-22.10-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs.\n\nInference performance reported as Total IPS (data + compute time taken into account).\nReported mixed precision speedups are relative to TF32 numbers for corresponding configuration.\n\n| **Batch size** | **Throughput (img/sec) - mixed precision** | **Throughput - speedup** | **Avg latency (ms) - mixed precision** | **Avg latency - speedup** | **50% latency (ms) - mixed precision** | **50% latency - speedup** | **90% latency (ms) - mixed precision** | **90% latency - speedup** | **95% latency (ms) - mixed precision** | **95% latency - speedup** | **99% latency (ms) - mixed precision** | **99% latency - speedup** |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| 1 | 1431.99 | 1.9 | 0.7 | 1.9 | 0.68 | 1.95 | 0.71 | 1.9 | 0.84 | 1.65 | 0.88 | 1.7 | \n | 2 | 2530.66 | 2.19 | 0.79 | 2.19 | 0.74 | 2.31 | 0.86 | 2.05 | 0.93 | 2.0 | 2.0 | 0.97 | \n | 4 | 3680.74 | 2.11 | 1.09 | 2.11 | 0.92 | 2.49 | 1.21 | 1.98 | 1.64 | 1.51 | 6.03 | 0.45 | \n | 8 | 2593.88 | 1.11 | 3.08 | 1.11 | 2.89 | 1.17 | 4.09 | 0.89 | 4.72 | 0.8 | 9.85 | 0.55 | \n | 16 | 4340.08 | 1.52 | 3.69 | 1.52 | 3.31 | 1.68 | 4.73 | 1.24 | 6.3 | 0.95 | 12.31 | 0.54 | \n | 32 | 6808.22 | 2.1 | 4.7 | 2.1 | 4.0 | 2.46 | 6.44 | 1.58 | 9.01 | 1.15 | 15.88 | 0.68 | \n | 64 | 7659.96 | 2.21 | 8.36 | 2.21 | 7.44 | 2.48 | 10.76 | 1.75 | 13.91 | 1.37 | 21.96 | 0.9 | \n | 128 | 8017.67 | 2.23 | 15.96 | 2.23 | 15.0 | 2.37 | 18.95 | 1.9 | 21.65 | 1.67 | 30.36 | 1.23 | \n | 192 | 8240.8 | 2.26 | 23.3 | 2.26 | 22.49 | 2.33 | 25.65 | 2.07 | 27.54 | 1.94 | 37.19 | 1.5 | \n | 256 | 7909.62 | 2.15 | 32.37 | 2.15 | 31.66 | 2.2 | 34.27 | 2.05 | 37.02 | 1.9 | 42.83 | 1.66 | \n | 512 | 7213.43 | 2.07 | 70.98 | 2.07 | 70.48 | 2.08 | 73.21 | 2.04 | 74.38 | 2.03 | 79.15 | 1.99 |\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nThe following results were obtained by running the\n`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val` script for mixed precision and the\n`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_fp32.json -i 500 -e 3 -w 1 --mode val` script for FP32 in the mxnet-20.12-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.\n\nInference performance reported as Total IPS (data + compute time taken into account).\nReported mixed precision speedups are relative to FP32 numbers for corresponding configuration.\n\n| **Batch size** | **Throughput (img/sec) - mixed precision** | **Throughput - speedup** | **Avg latency (ms) - mixed precision** | **Avg latency - speedup** | **50% latency (ms) - mixed precision** | **50% latency - speedup** | **90% latency (ms) - mixed precision** | **90% latency - speedup** | **95% latency (ms) - mixed precision** | **95% latency - speedup** | **99% latency (ms) - mixed precision** | **99% latency - speedup** | \n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| 1 | 286 | 1.27 | 3.48 | 1.27 | 3.45 | 1.27 | 3.61 | 1.26| 3.68 | 1.26| 3.86 | 1.24| \n| 2 | 519 | 1.34 | 3.84 | 1.34 | 3.77 | 1.35 | 4.05 | 1.31| 4.16 | 1.29| 4.59 | 1.27| \n| 4 | 910 | 1.60 | 4.39 | 1.60 | 4.35 | 1.61 | 4.59 | 1.56| 4.66 | 1.56| 5.19 | 1.47| \n| 8 | 1642| 2.20 | 4.87 | 2.20 | 4.68 | 2.29 | 5.35 | 2.05| 6.01 | 1.84| 11.06| 1.04| \n| 16 | 2359| 2.55 | 6.78 | 2.55 | 6.49 | 2.66 | 7.07 | 2.48| 8.33 | 2.12| 13.89| 1.30| \n| 32 | 2902| 2.86 | 11.02| 2.86 | 10.43| 3.02 | 12.25| 2.60| 13.88| 2.31| 21.41| 1.55| \n| 64 | 3234| 2.74 | 19.78| 2.74 | 18.89| 2.86 | 22.50| 2.44| 25.38| 2.17| 30.78| 1.81| \n| 128 | 3362| 2.69 | 38.06| 2.69 | 37.20| 2.75 | 42.32| 2.44| 45.12| 2.30| 50.59| 2.07| \n| 192 | 3178| 2.52 | 60.40| 2.52 | 59.62| 2.55 | 65.56| 2.35| 68.16| 2.25| 73.72| 2.10| \n| 256 | 3057| 2.38 | 83.73| 2.38 | 82.77| 2.40 | 92.26| 2.24| 92.26| 2.17|100.84| 2.23| \n\n##### Inference performance: NVIDIA T4\n\nThe following results were obtained by running the\n`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float16 -o inferbenchmark_report_fp16.json -i 500 -e 3 -w 1 --mode val` script for mixed precision and the\n`python benchmark.py -n 1 -b 1,2,4,8,16,32,64,128,192,256 --dtype float32 -o inferbenchmark_report_fp32.json -i 500 -e 3 -w 1 --mode val` script for FP32 in the mxnet-20.12-py3 NGC container on an NVIDIA T4 GPU.\n\nInference performance reported as Total IPS (data + compute time taken into account).\nReported mixed precision speedups are relative to FP32 numbers for corresponding configuration.\n\n| **Batch size** | **Throughput (img/sec) - mixed precision** | **Throughput - speedup** | **Avg latency (ms) - mixed precision** | **Avg latency - speedup** | **50% latency (ms) - mixed precision** | **50% latency - speedup** | **90% latency (ms) - mixed precision** | **90% latency - speedup** | **95% latency (ms) - mixed precision** | **95% latency - speedup** | **99% latency (ms) - mixed precision** | **99% latency - speedup** |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| 1 | 131 | 1.11 | 7.61 | 1.17 | 7.10 | 0.97 | 10.28 | 0.92 | 11.35 | 0.95 | 15.05 | 0.96 |\n| 2 | 277 | 1.48 | 7.20 | 1.53 | 7.30 | 1.19 | 7.74 | 1.48 | 8.82 | 1.49 | 12.09 | 1.58 | \n| 4 | 374 | 1.47 | 10.67| 1.50 | 10.20| 1.40 | 13.51 | 1.09 | 14.82 | 1.03 | 22.36 | 0.74 |\n| 8 | 672 | 2.21 | 11.90| 2.23 | 11.21| 2.21 | 14.54 | 1.74 | 17.24 | 1.48 | 28.65 | 0.92 |\n| 16 | 1267| 3.57 | 12.62| 3.58 | 12.02| 3.59 | 14.02 | 3.13 | 16.02 | 2.76 | 22.28 | 2.01 |\n| 32 | 1473| 3.85 | 21.71| 3.86 | 21.67| 3.76 | 22.63 | 3.64 | 22.98 | 3.60 | 23.85 | 3.52 |\n| 64 | 1561| 3.70 | 40.98| 3.70 | 40.87| 3.64 | 41.98 | 3.57 | 42.56 | 3.53 | 43.85 | 3.46 | \n| 128 | 1555| 3.60 | 82.26| 3.60 | 81.86| 3.57 | 83.87 | 3.51 | 84.63 | 3.49 | 96.56 | 3.09 |\n| 192 | 1545| 3.64 |124.26| 3.64 |123.67| 3.61 |125.76 | 3.58 |126.73 | 3.56 |143.27 | 3.19 |\n| 256 | 1559| 3.71 |164.15| 3.71 |163.97| 3.71 |166.28 | 3.70 |167.01 | 3.70 |168.54 | 3.69 |","hasPerformance",[],{"_85":240,"_113":241,"_114":-5,"_122":35,"_121":35,"_123":239,"_221":90},{"_85":131,"_113":131,"_114":-5,"_122":35,"_121":35,"_123":239,"_221":90},{"_85":29,"_113":29,"_114":-5,"_122":35,"_121":35,"_123":238,"_221":90},[],[],"-","Latest (22.10.0)","nvidia/dle/resnet_mxnet","key","nvidia/dle/resources/resnet_mxnet","resourceId","resnet_mxnet","shortDescription","With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.","This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/resnet-mxnet)\n\n\nThe ResNet-50 v1.5 model is a modified version of the [original ResNet-50 v1 model](https://arxiv.org/abs/1512.03385).\n\nThe difference between v1 and v1.5 is in the bottleneck blocks which require downsampling. ResNet v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.\n\nThis difference makes ResNet-50 v1.5 slightly more accurate (\\~0.5% top1) than v1, but comes with a small performance drawback (\\~5% imgs/sec).\n\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 3.5x 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\n### Model architecture\n\nThe model architecture was present in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) paper. The main advantage of the model is the usage of residual layers as a building block that helps with gradient propagation during training.\n\n![ResidualLayer](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/MxNet/Classification/RN50v1.5//img/residual_diagram.png)\n\n\n_Image source: [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)_\n\n### Default configuration\n\n**Optimizer**\n\n* SGD with momentum (0.875)\n* Learning rate = 0.256 for 256 batch size, for other batch sizes we linearly scale the learning rate\n* Learning rate schedule - we use cosine LR schedule\n* Linear warmup of the learning rate during the first 5 epochs according to [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677).\n* Weight decay: 3.0517578125e-05 (1/32768)\n* We do not apply WD on batch norm trainable parameters (gamma/bias)\n* Label Smoothing: 0.1\n* We train for:\n * 50 Epochs - configuration that reaches 75.9% top1 accuracy\n * 90 Epochs - 90 epochs is a standard for ResNet-50\n * 250 Epochs - best possible accuracy. For 250 epoch training we also use [MixUp regularization](https://arxiv.org/pdf/1710.09412.pdf).\n\n**Data augmentation**\n\nFor training:\n* Normalization\n* Random resized crop to 224x224\n* Scale from 8% to 100%\n* Aspect ratio from 3/4 to 4/3\n* Random horizontal flip\n\nFor inference:\n* Normalization\n* Scale to 256x256\n* Center crop to 224x224\n\n### Feature support matrix\n\n| **Feature** | **ResNet-50 MXNet** |\n|:---:|:--------:|\n|[DALI](https://docs.nvidia.com/deeplearning/sdk/dali-release-notes/index.html)|yes|\n|Horovod Multi-GPU|yes|\n\n#### Features\n\nThe following features are supported by this model.\n\n**NVIDIA DALI**\nNVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, to accelerate the pre-processing of the input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications.\n\n**Horovod Multi-GPU**\nHorovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the [Horovod: Official repository](https://github.com/horovod/horovod).\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Adding loss scaling to preserve small gradient values.\n\nThe ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in [CUDA 8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep Learning SDK.\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/performance/mixed-precision-training/index.html) documentation.\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.\n\n#### Enabling mixed precision\n\nUsing the Gluon API, ensure you perform the following steps to convert a model that supports computation with float16.\n\n1. Cast Gluon Block's parameters and expected input type to float16 by calling the cast method of the Block representing the network.\n\n ```python\n net = net.cast('float16')\n ```\n\n2. Ensure the data input to the network is of float16 type. If your DataLoader or Iterator produces output in another datatype, then you have to cast your data. There are different ways you can do this. The easiest way is to use the `astype` method of NDArrays.\n\n ```python\n data = data.astype('float16', copy=False)\n ```\n\n3. If you are using images and DataLoader, you can also use a Cast transform. It is preferable to use `multi_precision` mode of optimizer when training in float16. This mode of optimizer maintains a master copy of the weights in float32 even when the training (forward and backward pass) is in float16. This helps increase precision of the weight updates and can lead to faster convergence in some scenarios.\n\n ```python\n optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)\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","ResNet50 v1.5 for MXNet",["D",1670944640298],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/resnet_mxnet","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_156":1260,"_158":1261},"ngcApps",[1213],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1194":1195,"_1196":1197,"_1198":1199,"_1200":1201,"_1202":292,"_109":318,"_1203":1204,"_1205":59,"_1206":90,"_1207":90,"_1208":1209,"_1210":90,"_1211":1212},"amplitude",{"_1191":1192,"_832":-7,"_1193":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1185":1186,"_1187":1188,"_1189":1190},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787729899889,"1.12.0","defaultSearchSuggestions",{"_1072":400,"_1073":1074,"_1075":1076,"_1077":1078},"announcements",[],"consentState",{"_357":1062,"_1063":90,"_1064":35,"_1065":35,"_1066":1067},"consentConfig",{"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358},"showContactAdmin",1787729899890,[],[],[332,333,334,335,336,337],{"_49":63,"_51":74,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":70,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":68,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":151,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":338,"_58":59},4,"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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

Under certain privacy laws, you have the right to direct us not to \"sell\" or \"share\" your personal information for targeted advertising. To opt-out of the \"sale\" and \"sharing\" of personal information through cookies, you must opt-out of optional cookies using the toggles below. To opt out of the \"sale\" and \"sharing\" of data collected by other means (e.g., online forms) you must also update your data sharing preferences through the NVIDIA Preference Center.

Click on the different category headings below to find out more and change the settings according to your preference. You cannot opt out of Required Cookies as they are deployed to ensure the proper functioning of our website (such as prompting the cookie banner and remembering your settings, etc.). By clicking \"Save and Accept\" or \"Decline All\" at the bottom, you consent to the use of cookies and other tools as described in our Cookie Policy in accordance with your settings and accept our Terms of Service (which contains important waivers). For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1054":1055,"_1056":1057,"_1058":1059,"_1060":1061},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[359,360,361,362,363],{"_364":744,"_113":745,"_215":746,"_368":90,"_369":18,"_371":747,"_373":748},{"_364":647,"_113":648,"_215":649,"_368":35,"_369":62,"_371":650,"_373":651},{"_364":555,"_113":556,"_215":557,"_368":35,"_369":338,"_371":558,"_373":559},{"_364":375,"_113":376,"_215":377,"_368":35,"_369":378,"_371":379,"_373":380},{"_364":365,"_113":366,"_215":367,"_368":35,"_369":370,"_371":372,"_373":374},"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,[497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518],[381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397],{"_398":492,"_250":492,"_215":400,"_371":493},{"_398":488,"_250":488,"_215":400,"_371":489},{"_398":483,"_250":483,"_215":400,"_371":484},{"_398":479,"_250":479,"_215":400,"_371":480},{"_398":474,"_250":474,"_215":400,"_371":475},{"_398":469,"_250":469,"_215":400,"_371":470},{"_398":461,"_250":461,"_215":400,"_371":462},{"_398":453,"_250":453,"_215":400,"_371":454},{"_398":448,"_250":448,"_215":400,"_371":449},{"_398":443,"_250":443,"_215":400,"_371":444},{"_398":438,"_250":438,"_215":400,"_371":439},{"_398":433,"_250":433,"_215":400,"_371":434},{"_398":428,"_250":428,"_215":400,"_371":429},{"_398":421,"_250":421,"_215":400,"_371":422},{"_398":416,"_250":416,"_215":400,"_371":417},{"_398":409,"_250":409,"_215":400,"_371":410},{"_398":399,"_250":399,"_215":400,"_371":401},"hostName","amazon-adsystem.com","",[402,403],{"_113":408,"_405":399,"_406":35,"_215":407},{"_113":404,"_405":399,"_406":35,"_215":407},"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",[411,412],{"_113":415,"_405":409,"_406":35,"_215":414},{"_113":413,"_405":409,"_406":35,"_215":414},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[418],{"_113":419,"_405":416,"_406":35,"_215":420},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[423,424],{"_113":427,"_405":421,"_406":35,"_215":426},{"_113":425,"_405":421,"_406":35,"_215":426},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[430],{"_113":431,"_405":428,"_406":35,"_215":432},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[435],{"_113":436,"_405":433,"_406":35,"_215":437},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[440],{"_113":441,"_405":438,"_406":35,"_215":442},"sp","Testing","google.com",[445],{"_113":446,"_405":443,"_406":35,"_215":447},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[450],{"_113":451,"_405":448,"_406":35,"_215":452},"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",[455,456],{"_113":459,"_405":453,"_406":35,"_215":460},{"_113":457,"_405":453,"_406":35,"_215":458},"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_*","This domain is owned by X (formerly Twitter). The main business activity is: Social Networking Services. Where X (formerly 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.","youtube.com",[463,464],{"_113":467,"_405":461,"_406":35,"_215":468},{"_113":465,"_405":461,"_406":35,"_215":466},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-*","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","demdex.net",[471],{"_113":472,"_405":469,"_406":35,"_215":473},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[476],{"_113":477,"_405":474,"_406":35,"_215":478},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[481],{"_113":482,"_405":479,"_406":35,"_215":442},"R","6sc.co",[485],{"_113":486,"_405":483,"_406":35,"_215":487},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. It is primarily used for tracking and targeting in marketing automation and advertising.","hushly.com",[490],{"_113":491,"_405":488,"_406":35,"_215":442},"_hly_gvid","t.contentsquare.net",[494],{"_113":495,"_405":492,"_406":90,"_215":496},"_cs_*","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.",{"_113":553,"_405":520,"_406":35,"_215":554},{"_113":551,"_405":520,"_406":35,"_215":552},{"_113":549,"_405":520,"_406":35,"_215":550},{"_113":431,"_405":520,"_406":35,"_215":548},{"_113":525,"_405":520,"_406":90,"_215":527},{"_113":547,"_405":520,"_406":35,"_215":400},{"_113":546,"_405":523,"_406":35,"_215":400},{"_113":544,"_405":520,"_406":35,"_215":545},{"_113":542,"_405":520,"_406":35,"_215":543},{"_113":541,"_405":520,"_406":35,"_215":400},{"_113":539,"_405":520,"_406":35,"_215":540},{"_113":525,"_405":538,"_406":90,"_215":527},{"_113":536,"_405":523,"_406":35,"_215":537},{"_113":536,"_405":526,"_406":35,"_215":537},{"_113":495,"_405":520,"_406":35,"_215":496},{"_113":534,"_405":520,"_406":35,"_215":535},{"_113":441,"_405":532,"_406":35,"_215":533},{"_113":530,"_405":520,"_406":35,"_215":531},{"_113":528,"_405":520,"_406":35,"_215":529},{"_113":525,"_405":526,"_406":90,"_215":527},{"_113":522,"_405":523,"_406":35,"_215":524},{"_113":519,"_405":520,"_406":90,"_215":521},"AMCVS_xxxxxAdobeOrg","nvidia.com","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.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","AMP_*","build.nvidia.com","Supports Amplitude product analytics.","_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.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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.","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.",[589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613],[560,561,562,563,564],{"_398":474,"_250":474,"_215":400,"_371":584},{"_398":448,"_250":448,"_215":400,"_371":579},{"_398":574,"_250":574,"_215":400,"_371":575},{"_398":569,"_250":569,"_215":400,"_371":570},{"_398":565,"_250":565,"_215":400,"_371":566},"nvidia.partners",[567],{"_113":568,"_405":565,"_406":35,"_215":442},"edge","app.hushly.com",[571],{"_113":572,"_405":569,"_406":35,"_215":573},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[576],{"_113":577,"_405":574,"_406":35,"_215":578},"_octo","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform",[580,581],{"_113":583,"_405":448,"_406":35,"_215":452},{"_113":582,"_405":448,"_406":35,"_215":452},"lidc","li_gc",[585,586],{"_113":588,"_405":474,"_406":35,"_215":478},{"_113":587,"_405":474,"_406":35,"_215":478},"imid_*","imid",{"_113":614,"_405":523,"_406":35,"_215":616},{"_113":645,"_405":520,"_406":35,"_215":646},{"_113":614,"_405":644,"_406":35,"_215":616},{"_113":642,"_405":520,"_406":90,"_215":643},{"_113":640,"_405":520,"_406":35,"_215":641},{"_113":639,"_405":622,"_406":90,"_215":400},{"_113":614,"_405":618,"_406":35,"_215":616},{"_113":637,"_405":520,"_406":35,"_215":638},{"_113":614,"_405":526,"_406":35,"_215":616},{"_113":625,"_405":523,"_406":35,"_215":400},{"_113":614,"_405":636,"_406":35,"_215":616},{"_113":634,"_405":520,"_406":35,"_215":635},{"_113":631,"_405":632,"_406":35,"_215":633},{"_113":629,"_405":630,"_406":35,"_215":400},{"_113":614,"_405":626,"_406":35,"_215":616},{"_113":628,"_405":520,"_406":35,"_215":400},{"_113":614,"_405":627,"_406":35,"_215":616},{"_113":625,"_405":626,"_406":35,"_215":400},{"_113":624,"_405":622,"_406":90,"_215":400},{"_113":614,"_405":623,"_406":35,"_215":616},{"_113":614,"_405":622,"_406":35,"_215":616},{"_113":614,"_405":621,"_406":35,"_215":616},{"_113":620,"_405":520,"_406":90,"_215":616},{"_113":617,"_405":618,"_406":90,"_215":619},{"_113":614,"_405":615,"_406":35,"_215":616},"_gd_*","info.nvidia.com","This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700],[652,653,654,655],{"_398":667,"_250":667,"_215":400,"_371":668},{"_398":662,"_250":662,"_215":400,"_371":663},{"_398":461,"_250":461,"_215":400,"_371":659},{"_398":574,"_250":574,"_215":400,"_371":656},[657],{"_113":658,"_405":574,"_406":90,"_215":578},"_gh_sess",[660],{"_113":661,"_405":461,"_406":90,"_215":468},"YSC","bizible.com",[664],{"_113":665,"_405":662,"_406":35,"_215":666},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[669],{"_113":670,"_405":667,"_406":35,"_215":671},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_113":742,"_405":520,"_406":35,"_215":743},{"_113":727,"_405":741,"_406":90,"_215":728},{"_113":710,"_405":523,"_406":35,"_215":635},{"_113":710,"_405":623,"_406":35,"_215":635},{"_113":739,"_405":520,"_406":35,"_215":740},{"_113":727,"_405":526,"_406":90,"_215":728},{"_113":737,"_405":520,"_406":35,"_215":738},{"_113":727,"_405":523,"_406":90,"_215":728},{"_113":735,"_405":622,"_406":35,"_215":736},{"_113":727,"_405":644,"_406":90,"_215":728},{"_113":707,"_405":523,"_406":35,"_215":709},{"_113":733,"_405":520,"_406":90,"_215":734},{"_113":731,"_405":520,"_406":35,"_215":732},{"_113":729,"_405":520,"_406":90,"_215":730},{"_113":727,"_405":618,"_406":90,"_215":728},{"_113":725,"_405":520,"_406":35,"_215":726},{"_113":723,"_405":520,"_406":35,"_215":724},{"_113":721,"_405":520,"_406":90,"_215":722},{"_113":719,"_405":627,"_406":35,"_215":720},{"_113":707,"_405":618,"_406":35,"_215":709},{"_113":717,"_405":520,"_406":35,"_215":718},{"_113":715,"_405":520,"_406":90,"_215":716},{"_113":713,"_405":520,"_406":35,"_215":714},{"_113":711,"_405":520,"_406":35,"_215":712},{"_113":710,"_405":520,"_406":35,"_215":635},{"_113":707,"_405":708,"_406":35,"_215":709},{"_113":705,"_405":520,"_406":35,"_215":706},{"_113":703,"_405":520,"_406":90,"_215":704},{"_113":701,"_405":520,"_406":35,"_215":702},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","ekfls-2025-03-27","This cookie name is associated with readme.io which is used to help maintain data for the API reference support page.","s_getNewRepeat","This cookie is used to determine whether a visitor is a new visitor or a repeat visitor to the site, and is used for analytical purposes.","_hly_sid","This cookie is likely associated with website analytics, as the prefix '_hly_' suggests a connection to Holistics, a business intelligence and analytics platform.","docs.nvidia.com","_hjSession_xxxxxx","A cookie that holds the current session data. This ensues that subsequent requests within the session window will be attributed to the same Hotjar session.","C0001","Required Cookies","These cookies enable core functionality such as security, network management, and accessibility. These cookies are required for the site to function and cannot be turned off.",[889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961],[749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773],{"_398":886,"_250":886,"_215":400,"_371":887},{"_398":880,"_250":880,"_215":400,"_371":881},{"_398":877,"_250":877,"_215":400,"_371":878},{"_398":865,"_250":865,"_215":400,"_371":866},{"_398":862,"_250":862,"_215":400,"_371":863},{"_398":859,"_250":859,"_215":400,"_371":860},{"_398":854,"_250":854,"_215":400,"_371":855},{"_398":461,"_250":461,"_215":400,"_371":851},{"_398":848,"_250":848,"_215":400,"_371":849},{"_398":453,"_250":453,"_215":400,"_371":846},{"_398":448,"_250":448,"_215":400,"_371":844},{"_398":839,"_250":839,"_215":400,"_371":840},{"_398":834,"_250":834,"_215":400,"_371":835},{"_398":829,"_250":829,"_215":400,"_371":830},{"_398":826,"_250":826,"_215":400,"_371":827},{"_398":574,"_250":574,"_215":400,"_371":823},{"_398":433,"_250":433,"_215":400,"_371":820},{"_398":569,"_250":569,"_215":400,"_371":813},{"_398":565,"_250":565,"_215":400,"_371":807},{"_398":801,"_250":801,"_215":400,"_371":802},{"_398":796,"_250":796,"_215":400,"_371":797},{"_398":791,"_250":791,"_215":400,"_371":792},{"_398":787,"_250":787,"_215":400,"_371":788},{"_398":782,"_250":782,"_215":400,"_371":783},{"_398":774,"_250":774,"_215":400,"_371":775},"polyfill.io",[776,777],{"_113":780,"_405":774,"_406":35,"_215":781},{"_113":778,"_405":774,"_406":35,"_215":779},"cf_ob_info","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information.","cf_use_ob","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information. ","www.recaptcha.net",[784],{"_113":785,"_405":782,"_406":35,"_215":786},"_GRECAPTCHA","This domain is used by Google reCAPTCHA, which provides spam protection services to websites by detecting if a user is a human or a bot.","api2.hcaptcha.com",[789],{"_113":790,"_405":787,"_406":35,"_215":442},"__cflb","instagram.com",[793],{"_113":794,"_405":791,"_406":35,"_215":795},"csrftoken","Instagram is a social networking service owned by Facebook, Inc. It's designed for sharing photos and videos from a smartphone. The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[798],{"_113":799,"_405":796,"_406":35,"_215":800},"__cf_bm","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.","nvidiacorp.us-5.evergage.com",[803,804],{"_113":806,"_405":801,"_406":35,"_215":442},{"_113":805,"_405":801,"_406":35,"_215":442},"AWSALBTG","AWSALBTGCORS",[808,809],{"_113":811,"_405":565,"_406":35,"_215":812},{"_113":810,"_405":565,"_406":35,"_215":442},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.",[814,815,816],{"_113":819,"_405":569,"_406":35,"_215":442},{"_113":818,"_405":569,"_406":90,"_215":442},{"_113":817,"_405":569,"_406":35,"_215":442},"AWSALBCORS","JSESSIONID","AWSALB",[821],{"_113":799,"_405":433,"_406":35,"_215":822},"This is a CloudFoundry cookie",[824],{"_113":825,"_405":574,"_406":35,"_215":578},"logged_in","hcaptcha.com",[828],{"_113":799,"_405":826,"_406":35,"_215":822},"tracking.intentsify.io",[831],{"_113":832,"_405":829,"_406":35,"_215":833},"userId","Used to track usage of the embedded video player.","static.reo.dev",[836],{"_113":837,"_405":834,"_406":35,"_215":838},"cookietest","Common cookie name could have a number of different origins. Where this is first party and a session cookie, its most likely to do with checking to see if the browser is set to block or allow cookies.","www.datadoghq-browser-agent.com",[841],{"_113":842,"_405":839,"_406":35,"_215":843},"dd_cookie_test_","dd_cookie_test",[845],{"_113":799,"_405":448,"_406":35,"_215":822},[847],{"_113":799,"_405":453,"_406":35,"_215":822},"supabase.co",[850],{"_113":799,"_405":848,"_406":35,"_215":822},[852],{"_113":853,"_405":461,"_406":35,"_215":468},"VISITOR_PRIVACY_METADATA","dosa-prd.azurewebsites.net",[856],{"_113":857,"_405":854,"_406":35,"_215":858},"access_token","This cookie is used to obtain the permissions for users of the portal to allow for API access in order to retrieve information required to perform portal functions.","w.hcaptcha.com",[861],{"_113":799,"_405":859,"_406":35,"_215":822},"cdn.readme.io",[864],{"_113":799,"_405":862,"_406":35,"_215":822},"queue-it.net",[867,868,869,870,871],{"_113":876,"_405":865,"_406":35,"_215":400},{"_113":875,"_405":865,"_406":35,"_215":400},{"_113":874,"_405":865,"_406":35,"_215":400},{"_113":873,"_405":865,"_406":35,"_215":400},{"_113":872,"_405":865,"_406":35,"_215":400},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","x.com",[879],{"_113":799,"_405":877,"_406":35,"_215":800},"api.hcaptcha.com",[882,883],{"_113":885,"_405":880,"_406":35,"_215":442},{"_113":790,"_405":880,"_406":35,"_215":884},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[888],{"_113":799,"_405":886,"_406":35,"_215":822},{"_113":1022,"_405":520,"_406":35,"_215":1053},{"_113":1051,"_405":615,"_406":90,"_215":1052},{"_113":1044,"_405":1013,"_406":90,"_215":1045},{"_113":1050,"_405":708,"_406":90,"_215":400},{"_113":1048,"_405":520,"_406":35,"_215":1049},{"_113":806,"_405":1034,"_406":35,"_215":973},{"_113":981,"_405":1043,"_406":90,"_215":983},{"_113":805,"_405":972,"_406":35,"_215":973},{"_113":984,"_405":1043,"_406":35,"_215":985},{"_113":1046,"_405":520,"_406":90,"_215":1047},{"_113":1044,"_405":1020,"_406":90,"_215":1045},{"_113":978,"_405":1043,"_406":35,"_215":980},{"_113":1041,"_405":520,"_406":35,"_215":1042},{"_113":805,"_405":964,"_406":35,"_215":973},{"_113":969,"_405":1040,"_406":35,"_215":971},{"_113":817,"_405":970,"_406":35,"_215":986},{"_113":819,"_405":970,"_406":35,"_215":1037},{"_113":984,"_405":979,"_406":35,"_215":985},{"_113":1038,"_405":618,"_406":35,"_215":1039},{"_113":819,"_405":644,"_406":35,"_215":1037},{"_113":1035,"_405":520,"_406":35,"_215":1036},{"_113":811,"_405":644,"_406":35,"_215":1030},{"_113":1007,"_405":623,"_406":90,"_215":400},{"_113":805,"_405":1034,"_406":35,"_215":973},{"_113":1032,"_405":972,"_406":90,"_215":1033},{"_113":996,"_405":520,"_406":35,"_215":987},{"_113":969,"_405":520,"_406":35,"_215":971},{"_113":1031,"_405":532,"_406":35,"_215":400},{"_113":811,"_405":520,"_406":35,"_215":1030},{"_113":819,"_405":964,"_406":35,"_215":1029},{"_113":1027,"_405":627,"_406":90,"_215":1028},{"_113":1025,"_405":520,"_406":90,"_215":1026},{"_113":1024,"_405":520,"_406":35,"_215":400},{"_113":1022,"_405":970,"_406":35,"_215":1023},{"_113":1021,"_405":532,"_406":35,"_215":400},{"_113":1012,"_405":1020,"_406":90,"_215":1014},{"_113":1018,"_405":623,"_406":35,"_215":1019},{"_113":1016,"_405":708,"_406":35,"_215":1017},{"_113":1015,"_405":526,"_406":35,"_215":400},{"_113":1012,"_405":1013,"_406":90,"_215":1014},{"_113":1010,"_405":979,"_406":90,"_215":1011},{"_113":1008,"_405":520,"_406":35,"_215":1009},{"_113":1007,"_405":523,"_406":90,"_215":400},{"_113":978,"_405":982,"_406":35,"_215":980},{"_113":810,"_405":997,"_406":35,"_215":987},{"_113":1005,"_405":523,"_406":35,"_215":1006},{"_113":1003,"_405":520,"_406":35,"_215":1004},{"_113":1001,"_405":520,"_406":35,"_215":1002},{"_113":1000,"_405":520,"_406":35,"_215":400},{"_113":842,"_405":523,"_406":35,"_215":999},{"_113":996,"_405":644,"_406":35,"_215":987},{"_113":799,"_405":615,"_406":35,"_215":998},{"_113":996,"_405":997,"_406":35,"_215":987},{"_113":995,"_405":532,"_406":35,"_215":400},{"_113":994,"_405":520,"_406":35,"_215":967},{"_113":992,"_405":979,"_406":35,"_215":993},{"_113":805,"_405":990,"_406":35,"_215":991},{"_113":989,"_405":532,"_406":35,"_215":400},{"_113":988,"_405":520,"_406":35,"_215":400},{"_113":981,"_405":979,"_406":90,"_215":983},{"_113":810,"_405":644,"_406":35,"_215":987},{"_113":817,"_405":644,"_406":35,"_215":986},{"_113":984,"_405":982,"_406":35,"_215":985},{"_113":981,"_405":982,"_406":90,"_215":983},{"_113":978,"_405":979,"_406":35,"_215":980},{"_113":976,"_405":972,"_406":90,"_215":977},{"_113":974,"_405":630,"_406":35,"_215":975},{"_113":806,"_405":972,"_406":35,"_215":973},{"_113":969,"_405":970,"_406":35,"_215":971},{"_113":799,"_405":622,"_406":35,"_215":968},{"_113":966,"_405":520,"_406":35,"_215":967},{"_113":817,"_405":964,"_406":35,"_215":965},{"_113":962,"_405":520,"_406":35,"_215":963},"LoginHints","Keeps users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","LSKey-c$CookieConsentPolicy","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","OptanonAlertBoxClosed","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","docs.ngc.nvidia.com","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking \"Accept All\", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on \"Manage Settings.\" By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.

","gpc","

We have detected the Global Privacy Control (GPC) signal and recorded your rejection of all optional cookies on this site for this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. To opt out of non-cookie personal information \"sales\" / \"sharing\" for targeted advertising purposes, please visit the NVIDIA Preference Center. Please see our Privacy Policy for more information on our privacy practices.

","gpcLoggedIn","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies. You can manage these settings in the NVIDIA NVIDIA Preference Center. Please see our Privacy Policy for more information.

","gpcLoggedInOverridden","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies which overrides at least one of your previous settings. You can manage them in the NVIDIA Preference Center. Please see our Privacy Policy for more information.

",{"_744":18,"_647":59,"_555":59,"_375":59,"_365":59},"hasConsented","gpcApplied","showBanner","geolocation",{"_1068":1069,"_1070":1071},"country","US","state","OR","query","categories",[1091,1092,1093,1094,1095],"optionUrls",[1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090],"total",12,"/orgs/nim/deepseek-ai/containers/deepseek-r1/-","/orgs/nim/nvidia/containers/llama-3.1-nemotron-70b-instruct/-","/orgs/nvidia/-/containers/pytorch/-","/orgs/nvidia/omniverse/collections/kit/-","/orgs/nvidia/-/collections/deepstream_sdk/-","/orgs/nvidia/omniverse/collections/kit-appstreaming-collection/-","/orgs/nvidia/research/models/stylegan3/-","/orgs/nvidia/tao/models/peoplenet/-","/orgs/nvidia/tao/models/trafficcamnet/-","/orgs/nvidia/riva/resources/riva_quickstart/-","/orgs/nvidia/-/helm-charts/gpu-operator/-","/orgs/nvidia/-/helm-charts/k8s-nim-operator/-",{"_21":1169,"_1097":1170,"_1099":59},{"_21":1134,"_1097":1135,"_1099":62},{"_21":1117,"_1097":1118,"_1099":1119},{"_21":22,"_1097":1110,"_1099":1111},{"_21":1096,"_1097":1098,"_1099":20},"helm-charts","items",[1100,1101],"startIndex",{"_21":1096,"_250":1107,"_209":210,"_211":-7,"_245":1108,"_253":1104,"_278":1109,"_1106":1089},{"_21":1096,"_250":1102,"_209":210,"_211":-7,"_245":1103,"_253":1104,"_278":1105,"_1106":1090},"NVIDIA NIM Operator","k8s-nim-operator","NVIDIA",[],"url","GPU Operator","gpu-operator",[],[1112],9,{"_21":22,"_250":1113,"_209":210,"_211":1114,"_245":1115,"_253":1104,"_278":1116,"_1106":1088},"Riva Skills Quick Start","riva","riva_quickstart",[],"models",[1120,1121,1122],6,{"_21":1117,"_250":1130,"_209":210,"_211":1131,"_245":1132,"_253":1104,"_278":1133,"_1106":1085},{"_21":1117,"_250":1127,"_209":210,"_211":1124,"_245":1128,"_253":1104,"_278":1129,"_1106":1086},{"_21":1117,"_250":1123,"_209":210,"_211":1124,"_245":1125,"_253":1104,"_278":1126,"_1106":1087},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1136,1137,1138],{"_21":1134,"_250":1166,"_209":210,"_211":1140,"_245":1167,"_253":1104,"_278":1168,"_1106":1082},{"_21":1134,"_250":1163,"_209":210,"_211":-7,"_245":1164,"_253":1104,"_278":1165,"_1106":1083},{"_21":1134,"_250":1139,"_209":210,"_211":1140,"_245":1141,"_253":1104,"_278":1142,"_1106":1084},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1143,1144,1145],{"_113":1157,"_250":1158,"_247":1159,"_257":1160,"_1150":1161},{"_113":1140,"_250":1153,"_247":1154,"_257":1149,"_1150":1155},{"_113":1146,"_250":1147,"_247":1148,"_257":1149,"_1150":1151},"omniverse-dev","NVIDIA Developer Program","Join the Developer Program for access to free tools, support, and tech resources.","https://store-images.s-microsoft.com/image/apps.33936.6f1bcf42-55d0-45d1-b4b6-f08c68a50bbe.e91ca707-db64-49d6-9761-4781590a9685.7ba64fc5-7c44-4864-a5e6-7bde05126935","supportedFeatures",[1152],"UNIFIED_CATALOG","NVIDIA Omniverse","NVIDIA Omniverse is a platform of APIs, services, and software development kits (SDKs) that enable developers to build generative AI-enabled tools and applications.",[1152,1156],"SHAREABLE_TO_USER_GROUPS","nv-ai-enterprise","NVIDIA AI Enterprise","Accelerate your AI agent development","https://assets.ngc.nvidia.com/products/catalog/images/product-NVAIE.jpg",[1152,1156,1162],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1143,1144,1145],"containers",[1171,1172,1173],{"_21":1169,"_250":1181,"_209":1178,"_211":1182,"_245":1183,"_253":1104,"_278":1184,"_1106":1079},{"_21":1169,"_250":1177,"_209":1178,"_211":210,"_245":1179,"_253":1104,"_278":1180,"_1106":1080},{"_21":1169,"_250":1174,"_209":210,"_211":-7,"_245":1175,"_253":1104,"_278":1176,"_1106":1081},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[],"DeepSeek-R1","deepseek-ai","deepseek-r1",[],"ownerOrgs",[],"memberOrgs",[],"externalOrgs",[],"apiKey","2b44f86fea50e90ffd2db29ee2028419","consentGranted","applicationId","4c857e4f-5ffa-45fa-b75e-af773c22f0ad","clientToken","pubb6927db33dae60ba5031a186cde0242a","site","datadoghq.com","service","catalog-ui","env","sessionSampleRate",100,"sessionReplaySampleRate","compressIntakeRequests","storeContextsAcrossPages","defaultPrivacyLevel","mask-user-input","trackViewsManually","trackingConsent","not-granted",{"_1214":1215,"_1216":311,"_1217":35,"_1218":1219,"_1097":1220,"_1221":1222,"_1223":1215,"_1224":35,"_1225":35,"_1226":35,"_1227":1228},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1229,1230,1231,1232,1233,1234,1235],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1227":1257,"_51":1258,"_1221":1259},{"_1250":1251,"_1227":1252,"_51":1253,"_1223":1157},{"_1227":1248,"_51":1249,"_1221":1222},{"_1227":1245,"_51":1246,"_1221":1247},{"_1227":1242,"_51":1243,"_1221":1244},{"_1227":1239,"_51":1240,"_1221":1241},{"_1227":1236,"_51":1237,"_1221":1238},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1254],"Enterprise Catalog","/enterprise",[1255,1256],"NVAIE","READ","Explore Catalog","/","common-home",{"_161":162,"_163":164,"_165":166,"_167":168,"_169":170,"_171":172},{"_160":35}]

	Files	Compressed Size		Download
22.10.0	1	41.76 KB	December 13, 2022 UTC
20.12.0Selected	1	41.46 KB	September 22, 2022 UTC