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Changelog

Dec, 2018

Initial release (based on https://github.com/apache/incubator-mxnet/tree/master/example/image-classification)

June, 2019

Code refactor
Label smoothing
Cosine LR schedule
MixUp regularization
Better configurations

Known Issues

There are no known issues with this model.

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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:
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.

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.

Cast Gluon Block's parameters and expected input type to float16 by calling the cast method of the Block representing the network.

net = net.cast('float16')\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.

data = data.astype('float16', copy=False)\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.

optimizer = mx.optimizer.create('sgd', multi_precision=True, lr=0.01)\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.

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Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [MXNet 20.12-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 256 --dtype float32` script for TF32 and the `./runner -n -b 256` script for mixed precision in the mxnet-20.12-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|14.6|31.26|2.13|\n|8|77.185|77.184|1.8|4.0|2.12|\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-20.12-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://github.com/NVIDIA/DeepLearningExamples/raw/master/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_training_loss.png)\n\n\n![TrainingAccuracy](https://github.com/NVIDIA/DeepLearningExamples/raw/master/MxNet/Classification/RN50v1.5/img/dgx1-16g_250e_validation_top1.png)\n\n\n![ValidationAccuracy](https://github.com/NVIDIA/DeepLearningExamples/raw/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 1,2,4,8 -b 256 --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 1,2,4,8 -b 256 --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-20.12-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|2180 |1022 |2.18 |1.00 |1.00 |\n|2|4332 |2032 |2.13 |1.98 |1.98 |\n|4|8587 |4035 |2.12 |3.93 |3.94 |\n|8|16925|8001 |2.11 |7.76 |7.82 |\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-20.12-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 | 463 | 1.72 | 2.15 | 1.72 | 2.10 | 1.58 | 2.23 | 1.58 | 2.39 | 1.56 | 2.94 | 1.79 |\n| 2 | 880 | 1.62 | 2.27 | 1.62 | 2.14 | 1.66 | 2.52 | 1.54 | 2.73 | 1.50 | 3.70 | 1.42 |\n| 4 | 1668| 1.76 | 2.39 | 1.76 | 2.21 | 1.86 | 2.70 | 1.66 | 3.30 | 1.44 | 5.72 | 1.01 |\n| 8 | 2522| 1.75 | 3.17 | 1.75 | 2.74 | 2.00 | 4.26 | 1.35 | 5.36 | 1.10 | 10.43| 0.65 |\n| 16 | 3704| 1.90 | 4.31 | 1.90 | 3.83 | 2.13 | 6.00 | 1.43 | 7.20 | 1.24 | 12.77| 0.85 |\n| 32 | 2964| 1.51 | 10.79| 1.51 | 10.45| 1.52 | 14.52| 1.37 | 16.07| 1.32 | 22.76| 1.21 |\n| 64 | 4547| 1.80 | 14.07| 1.80 | 13.75| 1.82 | 17.16| 1.67 | 19.04| 1.59 | 28.12| 1.28 |\n| 128 | 5530| 1.94 | 23.14| 1.94 | 23.63| 1.82 | 29.04| 1.71 | 32.75| 1.56 | 41.45| 1.34 |\n| 192 | 6198| 2.19 | 30.97| 2.19 | 31.02| 2.21 | 40.04| 1.81 | 44.03| 1.68 | 51.44| 1.51 |\n| 256 | 6120| 2.19 | 41.82| 2.19 | 42.01| 2.19 | 50.72| 1.89 | 55.09| 1.77 | 63.08| 1.60 |\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","policies",[],{"_183":196,"_185":197,"_149":-5,"_157":26,"_160":26,"_174":195,"_161":65},{"_183":194,"_185":194,"_149":-5,"_157":26,"_160":26,"_174":195,"_161":65},{"_183":192,"_185":192,"_149":-5,"_157":26,"_160":26,"_174":193,"_161":65},{"_183":190,"_185":190,"_149":-5,"_157":26,"_160":26,"_174":191,"_161":65},{"_183":188,"_185":188,"_149":-5,"_157":26,"_160":26,"_174":189,"_161":65},{"_183":14,"_185":14,"_149":-5,"_157":26,"_160":26,"_174":187,"_161":65},{"_183":184,"_185":184,"_149":-5,"_157":26,"_160":26,"_174":186,"_161":65},"id","1","name",[],[],"20.12.0",[],"20.12.2",[],"20.12.3",[],"20.12.4",[],"-","Latest (20.12.4)","nvidia/resnet_50_v1_5_for_mxnet","key","nvidia/resources/resnet_50_v1_5_for_mxnet","resourceId","resnet_50_v1_5_for_mxnet","shortDescription","With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.","The 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://github.com/NVIDIA/DeepLearningExamples/raw/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","ResNet v1.5 for MXNet",["D",1680631542619],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/resnet_50_v1_5_for_mxnet","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",245160,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[240,241],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_206":251,"_243":252,"_245":253,"_247":248},{"_206":242,"_243":244,"_245":246,"_247":248},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_249":250,"_243":244},"category","label","field","platform","Computer Vision","soln_computer_vision",{"_249":254,"_243":252},"technology",{"_86":1390,"_88":1391},"ngcApps",[1343],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1324":1325,"_1326":1327,"_1328":1329,"_1330":1331,"_1332":266,"_117":292,"_1333":1334,"_1335":50,"_1336":65,"_1337":65,"_1338":1339,"_1340":65,"_1341":1342},"amplitude",{"_1321":1322,"_929":-7,"_1323":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1315":1316,"_1317":1318,"_1319":1320},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1792469696196,"1.24.0","defaultSearchSuggestions",{"_1199":373,"_1200":1201,"_1202":1203,"_1204":1205},"announcements",[],"consentState",{"_329":1189,"_1190":26,"_1191":26,"_1192":65,"_1193":1194},"consentConfig",{"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330},"showContactAdmin",[],[],[305,306,307,308,309,310],{"_39":53,"_41":62,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":60,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":58,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":48,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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If you do not allow these cookies, then some or all of these services may not function properly.",4,[603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633],[569,570,571,572,573,574],{"_371":454,"_206":454,"_147":373,"_343":598},{"_371":429,"_206":429,"_147":373,"_343":593},{"_371":588,"_206":588,"_147":373,"_343":589},{"_371":583,"_206":583,"_147":373,"_343":584},{"_371":579,"_206":579,"_147":373,"_343":580},{"_371":575,"_206":575,"_147":373,"_343":576},"nvcrm.my.site.com",[577],{"_185":578,"_378":575,"_379":26,"_147":373},"BrowserId","nvidia.partners",[581],{"_185":582,"_378":579,"_379":26,"_147":415},"edge","app.hushly.com",[585],{"_185":586,"_378":583,"_379":26,"_147":587},"*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",[590],{"_185":591,"_378":588,"_379":26,"_147":592},"_octo","This domain is owned by Github. 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