[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10,"_11":12},"actionData","errors","root",{"_88":256,"_257":258,"_259":260,"_261":262,"_263":264,"_265":30,"_266":267,"_268":269,"_270":30,"_271":30,"_272":30,"_273":30,"_274":275,"_276":-5,"_277":278,"_279":280,"_281":282,"_283":115,"_284":117,"_285":286,"_109":-5,"_287":30,"_288":-5,"_289":290,"_139":291,"_292":293,"_294":295,"_296":297,"_298":299,"_300":30,"_25":26,"_27":301,"_29":30,"_31":302,"_33":303,"_35":304},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/route",{"_124":125,"_126":127,"_128":129,"_130":131,"_132":30,"_133":134,"_135":30,"_136":30,"_137":138,"_139":21,"_140":-5,"_141":142,"_143":-5,"_144":145,"_146":147,"_148":30,"_149":150,"_25":26,"_27":151,"_29":30,"_31":152,"_33":153,"_35":154},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/_access/route",{"_85":86,"_87":-5,"_88":89,"_90":91,"_25":26,"_27":92,"_29":30,"_31":93,"_33":94,"_35":95},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/file-browser/route",{"_13":14,"_15":16,"_17":16,"_18":19,"_20":21,"_22":21,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32,"_33":34,"_35":36},"fileTree",[72],"totalFiles",1,"page","pageSize",50,"unresolvedVersion","21.07.1","resolvedVersion","expandedFolders",[],"traceId","97196673868bc81b93408d0551f69a88","traceStartTime",1784002870197,"isStaleResponse",false,"notifications",[],"toasts",[],"apiCalls",[37,38,39,40,41,42,43],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":64,"_48":61,"_50":62,"_52":65,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":58,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"apiTag","subscription-api","path","/v2/users/me/subscriptions","status","error","httpStatus",401,"durationMs",4,"retryCount",0,"oms-api","/v2/users/me",3,"models-api","/v2/resources/org/nvidia/team/dle/quartznet_pyt/collections?page-number=0&page-size=1","success",200,51,"/v2/resources/nvidia/dle/quartznet_pyt/versions/21.07.1/files?page-size=50&page-number=0",59,"/v2/resources/nvidia/dle/quartznet_pyt/versions/21.07.1",54,"/v2/resources/nvidia/dle/quartznet_pyt/versions?page-size=100&page-number=0&sort-order=CREATED_DATE_DESC",78,"/v2/resources/nvidia/dle/quartznet_pyt?resolve-labels=true&remove-unresolved-labels=true",52,{"_73":74,"_74":75},"type","file",{"_76":77,"_78":77,"_46":77,"_79":80,"_81":82,"_83":84},"id","quartznet_pyt.md","name","size",43659,"formattedSize","42.64 KB","updatedAt","2022-11-04T01:07:07.099Z","canDownload",true,"accessDeniedReason","appConfig",{"_107":108,"_109":110},"debugAccessInfo",{"_103":86,"_104":105,"_106":30},1784002870129,[],[],[96,97,98,99,100,101],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"pending","canGuestAccess","requiredProducts",[],"hasLicenseRequirement","urls",{"_112":113,"_114":115,"_116":117,"_118":119,"_120":121,"_122":123},"user",{"_111":30},"loggedIn","build","https://build.nvidia.com","ngcApi","https://api.ngc.nvidia.com","ngcHelm","https://helm.ngc.nvidia.com","ngcRegistry","https://registry.ngc.nvidia.com","orgUi","https://org.ngc.nvidia.com","ngcUi","https://ngc.nvidia.com","resource",{"_76":204,"_205":206,"_207":208,"_209":210,"_162":163,"_164":165,"_211":212,"_168":213,"_214":215,"_173":216,"_217":218,"_219":21,"_220":-7,"_221":-7,"_222":223,"_224":225,"_226":86,"_227":80,"_228":-5,"_229":-7,"_230":-7,"_231":-7,"_232":-7,"_233":-7,"_234":-7,"_235":236,"_237":238,"_239":240,"_241":242,"_243":244,"_245":30},"html","

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

This repository provides an implementation of the QuartzNet model in PyTorch from the paper QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions.\nThe QuartzNet model is an end-to-end neural acoustic model for automatic speech recognition (ASR), that provides high accuracy at a low memory footprint. The QuartzNet architecture of convolutional layers was designed to facilitate fast GPU inference, by allowing whole sub-blocks to be fused into a single GPU kernel. This is important for meeting strict real-time requirements of ASR systems in deployment.

This repository is a PyTorch implementation of QuartzNet and provides scripts to train the QuartzNet 10x5 model from scratch on the LibriSpeech dataset to achieve the greedy decoding results improved upon the original paper.\nThe repository is self-contained and includes data preparation scripts, training, and inference scripts.\nBoth training and inference scripts offer the option to use Automatic Mixed Precision (AMP) to benefit from Tensor Cores for better performance.

In addition to providing the hyperparameters for training a model checkpoint, we publish a thorough inference analysis across different NVIDIA GPU platforms, for example, DGX-2, NVIDIA A100 GPU, and T4.

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results [1.4]x 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

QuartzNet is an end-to-end neural acoustic model that is based on efficient, time-channel separable convolutions (Figure 1).\nIn the audio processing stage, each frame is transformed into mel-scale spectrogram features, which the acoustic model takes as input and outputs a probability distribution over the vocabulary for each frame.

\n $\"QuartzNet$ \n

\n Figure 1. Architecture of QuartzNet (source)\n \n

Default configuration

The following features were implemented in this model:

GPU-supported feature extraction with data augmentation options SpecAugment and Cutout using the DALI library
offline and online Speed Perturbation using the DALI library
data-parallel multi-GPU training and evaluation
AMP with dynamic loss scaling for Tensor Core training
FP16 inference

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	QuartzNet
Apex AMP	Yes
DALI	Yes

Features

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. For details, see example sources in this repository or see the DALI documentation.

Automatic Mixed Precision (AMP)\nComputation graphs can be modified by PyTorch on runtime to support mixed precision training. A detailed explanation of mixed precision can be found in the next section.

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 previously required two steps:

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

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.
APEX tools for mixed precision training, see the NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch.

Enabling mixed precision

For training, mixed precision can be enabled by setting the flag: train.py --amp. When using bash helper scripts, mixed precision can be enabled with the environment variable AMP=true, for example, AMP=true bash scripts/train.sh, AMP=true bash scripts/inference.sh, etc.

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.

Glossary

Time-channel separable (TCS) convolution\nA module composed mainly of two convolutional layers: a 1D depthwise convolutional layer,\nand a pointwise convolutional layer (Figure 2). The former operates across K time frames, and the latter across all channels. By decoupling time and channel axes, the separable module uses less parameters and calculates the result faster, than it would otherwise would.

\n $\"Time-channel$ \n

\n Figure 2. Time-channel separable (TCS) convolutional module: (a) basic design, (b) TCS with a group shuffle layer, added to increase cross-group interchange\n

Automatic Speech Recognition (ASR)\nUses both an acoustic model and a language model to output the transcript of an input audio signal.

Acoustic model\nAssigns a probability distribution over a vocabulary of characters given an audio frame. Typically, a large part of the entire ASR model.

Language model\nAssigns a probability distribution over a sequence of words. Given a sequence of words, it assigns a probability to the whole sequence.

Pre-training\nTraining a model on vast amounts of data on the same (or different) task to build general understandings.

Language support and NeMo compatibility

This repository allows to train and run models in laguages other than English.

During inference, QuartzNet models trained with NVIDIA NeMo can also be used, for instance one of pre-trained models\nfor Catalan, French, German, Italian, Mandarin Chinese, Polish, Russian or Spanish available on NGC.\nTo download automatically, run:

bash scripts/download_quartznet.sh [ca|fr|de|it|zh|pl|ru|es]\n

Pre-trained models can be explicitly converted from the .nemo checkpoint format to .pt and vice versa.\nFor more details, run:

python nemo_dle_model_converter.py --help\n

","resourceVersions",[196,197,198],"resourceVersionsTotal",2,"hasMoreVersions","versionMetadata",{"_167":21,"_168":-5,"_48":169,"_170":-5,"_171":172,"_173":-5,"_174":80,"_175":16,"_176":30,"_177":-5,"_178":30,"_179":180,"_181":86,"_182":183,"_184":86,"_185":186,"_187":86,"_188":189,"_190":86,"_191":192,"_193":86,"_194":195},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/resources/quartznet_pyt/-","version","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/dle/quartznet_pyt:21.07.1","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/resources/quartznet_pyt/21.07.1/version-history","accessCheckResult",{"_166":86},"hasRelatedCollections","context",{"_162":163,"_164":165},1784002870170,[],[],[155,156,157,158,159,160,161],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":64,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":58,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"orgName","nvidia","teamName","dle","allowed","versionId","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1667524022604],"updatedDate","totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","We're constantly refining and improving our performance on AI and HPC workloads even on the same hardware with frequent updates to our software stack. For our latest performance data, refer to these pages for [AI](#https://developer.nvidia.com/deep-learning-performance-training-inference) and [HPC](#https://developer.nvidia.com/hpc-application-performance) benchmarks.\n\n### Changelog\n\nSeptember 2021\n- Initial release\n\n### Known issues\n\nThere are no known issues in this release.","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to start training the QuartzNet model.\n\n### Requirements\n\nThis repository contains Dockerfile which extends the PyTorch 21.07-py3 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- [PyTorch 21.07-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch)\n- Supported 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 PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)\n\nFurther required Python packages are listed in `requirements.txt`, which are automatically installed with the built Docker container. To manually install them, run:\n```bash\npip install -r requirements.txt\n```\n\nFor those unable to use the PyTorch 21.07-py3 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 QuartzNet model on the LibriSpeech dataset. For the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n ```bash\n git clone https://github.com/NVIDIA/DeepLearningExamples\n cd DeepLearningExamples/PyTorch/SpeechRecognition/QuartzNet\n ```\n\n2. Build the QuartzNet PyTorch NGC container.\n ```bash\n bash scripts/docker/build.sh\n ```\n\n3. Start an interactive session in the NGC container to prepare the dataset, or run training/inference.\n Specify a local mountpoint for the dataset with the `DATA_DIR` variable:\n ```bash\n DATA_DIR= bash scripts/docker/launch.sh\n ```\n\n4. Download and preprocess the dataset.\n No GPU is required for data download and preprocessing.\n It can take several hours to complete, and requires over 250GB of free disk space.\n\n This repository provides scripts to download and extract LibriSpeech [http://www.openslr.org/12](http://www.openslr.org/12). The dataset contains 1000 hours of 16kHz read English speech derived from public domain audiobooks from the LibriVox project and has been carefully segmented and aligned. For more information, see the [LIBRISPEECH: AN ASR CORPUS BASED ON PUBLIC DOMAIN AUDIO BOOKS](http://www.danielpovey.com/files/2015_icassp_librispeech.pdf) paper.\n\n Inside the container, download and extract the datasets into the required format for later training and inference:\n ```bash\n bash scripts/download_librispeech.sh\n ```\n After the data download is complete, the following folders should exist:\n ```bash\n datasets/LibriSpeech/\n |-- dev-clean\n |-- dev-other\n |-- test-clean\n |-- test-other\n |-- train-clean-100\n |-- train-clean-360\n |-- train-other-500\n ```\n\n Since `/datasets/` is mounted to `DATA_DIR` on the host, after the dataset is downloaded it will be accessible from outside of the container at `$DATA_DIR/LibriSpeech`.\n\n Next, convert the data into WAV files:\n ```bash\n bash scripts/preprocess_librispeech.sh\n ```\n\n After the data is converted, the following additional files and folders should exist:\n ```bash\n datasets/LibriSpeech/\n |-- dev-clean-wav\n |-- dev-other-wav\n |-- librispeech-train-clean-100-wav.json\n |-- librispeech-train-clean-360-wav.json\n |-- librispeech-train-other-500-wav.json\n |-- librispeech-dev-clean-wav.json\n |-- librispeech-dev-other-wav.json\n |-- librispeech-test-clean-wav.json\n |-- librispeech-test-other-wav.json\n |-- test-clean-wav\n |-- test-other-wav\n |-- train-clean-100-wav\n |-- train-clean-360-wav\n |-- train-other-500-wav\n ```\n\n5. Start training.\n Inside the container, use the following script to start training.\n Make sure the downloaded and preprocessed dataset is located at `$DATA_DIR/LibriSpeech` on the host, which is mounted as `/datasets/LibriSpeech` inside the container.\n\n ```bash\n [OPTION1=value1 OPTION2=value2 ...] bash scripts/train.sh\n ```\n By default, automatic precision is disabled, batch size is 144 over two gradient accumulation steps, and the recipe is run on a total of 8 GPUs. The hyperparameters are tuned for a GPU with at least 32GB of memory and will require adjustment for different configurations (for example, by lowering the batch size and using more gradient accumulation steps).\n\n Options are being passed as environment variables. More details on the available options can be found in the [Parameters](#parameters) and [Training process](#training-process) sections.\n\n6. Start validation/evaluation.\n Inside the container, use the following script to run evaluation.\n Make sure the downloaded and preprocessed dataset is located at `$DATA_DIR/LibriSpeech` on the host, which is mounted as `/datasets/LibriSpeech` inside the container.\n ```bash\n [OPTION1=value1 OPTION2=value2 ...] bash scripts/evaluation.sh [OPTIONS]\n ```\n\n By default, this will use full precision, a batch size of 64, and run on a single GPU.\n\n Options are being passed as environment variables. More details on the available options can be found in the [Parameters](#parameters) and [Evaluation process](#evaluation-process) sections.\n\n7. Start inference/predictions.\n Inside the container, use the following script to run inference.\n Make sure the downloaded and preprocessed dataset is located at `$DATA_DIR/LibriSpeech` on the host, which is mounted as `/datasets/LibriSpeech` inside the container.\n A pretrained model checkpoint can be downloaded from [NGC model repository](https://ngc.nvidia.com/catalog/models), manually or automatically using `scripts/download_quartznet.sh`.\n\n ```bash\n [OPTION1=value1 OPTION2=value2 ...] bash scripts/inference.sh\n ```\n\n By default, this will use single precision, a batch size of 64, and run on a single GPU.\n\n Options are being passed as environment variables. More details on the available options can be found in the [Parameters](#parameters) and [Inference process](#inference-process) sections.\n\nNow that you have your model trained and evaluated, you can choose to compare your training results with our [Training accuracy results](#training-accuracy-results). You can also choose to benchmark your performance to [Training performance benchmark](#training-performance-results), or [Inference performance benchmark](#inference-performance-results). Following the steps in these sections will ensure that you achieve the same accuracy and performance results as stated in the [Results](#results) section.","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```\nquartznet\n|-- common # data pre-processing, logging, etc.\n|-- configs # model configurations\n|-- Dockerfile # container with the basic set of dependencies to run QuartzNet\n|-- inference.py # entry point for inference\n|-- quartznet # model-specific code\n|-- scripts # one-click scripts required for running various supported functionalities\n│ |-- docker # contains the scripts for building and launching the container\n│ |-- download_librispeech.sh # downloads LibriSpeech dataset\n│ |-- evaluation.sh # runs evaluation using the `inference.py` script\n│ |-- inference_benchmark.sh # runs the inference benchmark using the `inference_benchmark.py` script\n│ |-- inference.sh # runs inference using the `inference.py` script\n│ |-- preprocess_librispeech.sh # preprocess LibriSpeech raw data files for training and inference\n│ |-- train_benchmark.sh # runs the training performance benchmark using the `train.py` script\n│ |-- train.sh # runs training using the `train.py` script\n|-- train.py # entry point for training\n|-- utils # data downloading and common routines\n```\n\n### Parameters\n\nParameters should be set as environment variables.\n\nThe complete list of available parameters for `scripts/train.sh` script contains:\n```bash\nDATA_DIR: directory of dataset. (default: '/datasets/LibriSpeech')\nMODEL_CONFIG: relative path to model configuration. (default: 'configs/quartznet10x5dr_speedp_online_speca.yaml')\nOUTPUT_DIR: directory for results, logs, and created checkpoints. (default: '/results')\nCHECKPOINT: a specific model checkpoint to continue training from. To resume training from the last checkpoint, see the RESUME option.\nRESUME: resume training from the last checkpoint found in OUTPUT_DIR, or from scratch if there are no checkpoints (default: true)\nCUDNN_BENCHMARK: boolean that indicates whether to enable cudnn benchmark mode for using more optimized kernels. (default: true)\nNUM_GPUS: number of GPUs to use. (default: 8)\nAMP: if set to `true`, enables automatic mixed precision (default: false)\nGPU_BATCH_SIZE: batch size for every forward/backward pass. The effective batch size might be higher, if gradient accumulation is enabled (default: 72)\nGRAD_ACCUMULATION: number of forward/backward passes until the optimizer updates weights. (default: 2)\nLEARNING_RATE: initial learning rate. (default: 0.01)\nMIN_LEARNING_RATE: minimum learning rate, despite LR scheduling (default: 1e-5)\nLR_POLICY: how to decay LR (default: exponential)\nLR_EXP_GAMMA: decay factor for the exponential LR schedule (default: 0.981)\nEMA: decay factor for exponential averages of checkpoints (default: 0.999)\nSEED: seed for random number generator and used for ensuring reproducibility. (default: 0)\nEPOCHS: number of training epochs. (default: 440)\nWARMUP_EPOCHS: number of initial epoch of linearly increasing LR. (default: 2)\nHOLD_EPOCHS: number of epochs to hold maximum LR after warmup. (default: 140)\nSAVE_FREQUENCY: number of epochs between saving the model to disk. (default: 10)\nEPOCHS_THIS_JOB: run training for this number of epochs. Does not affect LR schedule like the EPOCHS parameter. (default: 0)\nDALI_DEVICE: device to run the DALI pipeline on for calculation of filterbanks. Valid choices: cpu, gpu, none. (default: gpu)\nPAD_TO_MAX_DURATION: pad all sequences with zeros to maximum length. (default: false)\nEVAL_FREQUENCY: number of steps between evaluations on the validation set. (default: 544)\nPREDICTION_FREQUENCY: the number of steps between writing a sample prediction to stdout. (default: 544)\nTRAIN_MANIFESTS: lists of .json training set files\nVAL_MANIFESTS: lists of .json validation set files\n\n```\n\nThe complete list of available parameters for `scripts/inference.sh` script contains:\n```bash\nDATA_DIR: directory of dataset. (default: '/datasets/LibriSpeech')\nMODEL_CONFIG: model configuration. (default: 'configs/quartznet10x5dr_speedp-online_speca.yaml')\nOUTPUT_DIR: directory for results and logs. (default: '/results')\nCHECKPOINT: model checkpoint path. (required)\nDATASET: name of the LibriSpeech subset to use. (default: 'dev-clean')\nLOG_FILE: path to the DLLogger .json logfile. (default: '')\nCUDNN_BENCHMARK: enable cudnn benchmark mode for using more optimized kernels. (default: false)\nMAX_DURATION: filter out recordings shorter then MAX_DURATION seconds. (default: \"\")\nPAD_TO_MAX_DURATION: pad all sequences with zeros to maximum length. (default: false)\nNUM_GPUS: number of GPUs to use. Note that with > 1 GPUs WER results might be inaccurate due to the batching policy. (default: 1)\nNUM_STEPS: number of batches to evaluate, loop the dataset if necessary. (default: 0)\nNUM_WARMUP_STEPS: number of initial steps before measuring performance. (default: 0)\nAMP: enable FP16 inference with AMP. (default: false)\nBATCH_SIZE: data batch size. (default: 64)\nEMA: Attempt to load exponentially averaged weights from a checkpoint. (default: true)\nSEED: seed for random number generator and used for ensuring reproducibility. (default: 0)\nDALI_DEVICE: device to run the DALI pipeline on for calculation of filterbanks. Valid choices: cpu, gpu, none. (default: gpu)\nCPU: run inference on CPU. (default: false)\nLOGITS_FILE: dump logit matrices to a file. (default: \"\")\nPREDICTION_FILE: save predictions to a file. (default: \"${OUTPUT_DIR}/${DATASET}.predictions\")\n```\n\nThe complete list of available parameters for `scripts/evaluation.sh` is the same as `scripts/inference.sh`. Only the defaults have changed.\n```bash\nPREDICTION_FILE: (default: \"\")\nDATASET: (default: \"test-other\")\n```\n\nThe `scripts/inference_benchmark.sh` script pads all input to a fixed duration and computes the mean, 90%, 95%, 99% percentile of latency for the specified number of inference steps. Latency is measured in milliseconds per batch. The `scripts/inference_benchmark.sh` measures latency for a single GPU and loops over a number of batch sizes and durations. It extends `scripts/inference.sh` and changes the defaults with:\n```bash\nBATCH_SIZE_SEQ: batch sizes to measure with. (default: \"1 2 4 8 16\")\nMAX_DURATION_SEQ: input durations (in seconds) to measure with (default: \"2 7 16.7\")\nCUDNN_BENCHMARK: (default: true)\nPAD_TO_MAX_DURATION: (default: true)\nNUM_WARMUP_STEPS: (default: 10)\nNUM_STEPS: (default: 500)\nDALI_DEVICE: (default: \"cpu\")\n```\n\nThe `scripts/train_benchmark.sh` script pads all input to the same length according to the input argument `MAX_DURATION` and measures average training latency and throughput performance. Latency is measured in seconds per batch, throughput in sequences per second.\nTraining performance is measured with online speed perturbation and NVIDIA cuDNN benchmark mode enabled.\nThe script `scripts/train_benchmark.sh` loops over a number of batch sizes and GPU counts.\nIt extends `scripts/train.sh`, the complete list of available parameters for `scripts/train_benchmark.sh` script contains:\n```bash\nACC_BATCH_SIZE: accumulated (effective) batch size to measure with. (default: \"144\")\nGRAD_ACC_SEQ: the sequence of gradient accumulation settings to measure with. (default: \"4 2\")\nNUM_GPUS_SEQ: number of GPUs to run the training on. (default: \"1 4 8\")\nMODEL_CONFIG: (default: \"configs/quartznet10x5dr_speedp-online_train-benchmark.yaml\")\nTRAIN_MANIFESTS: (default: \"$DATA_DIR/librispeech-train-clean-100-wav.json\")\nRESUME: (default: false)\nEPOCHS_THIS_JOB: (default: 2)\nEPOCHS: (default: 100000)\nSAVE_FREQUENCY: (default: 100000)\nEVAL_FREQUENCY: (default: 100000)\nGRAD_ACCUMULATION_STEPS: (default: 1)\nPAD_TO_MAX_DURATION: (default: true)\nEMA: (default: 0)\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, for example:\n```bash\npython train.py --help\npython inference.py --help\n```\n\n### Getting the data\n\nQuartzNet is trained on the LibriSpeech dataset. We use the concatenation of `train-clean-100`, `train-clean-360`, and `train-other-500` for training and `dev-clean` for validation.\n\nThis repository contains the `scripts/download_librispeech.sh` and `scripts/preprocess_librispeech.sh` scripts that automatically downloads and preprocesses the training, test, and development datasets. By default, data is downloaded to the `/datasets/LibriSpeech` directory. A minimum of 250GB free space is required for download and preprocessing; the final preprocessed dataset is approximately 100GB.\n\n#### Dataset guidelines\n\nThe `scripts/preprocess_librispeech.sh` script converts the input audio files to WAV format with a sample rate of 16kHz. The target transcripts are stripped from whitespace characters, then lower-cased. No offline augmentations are stored on the disk - these are computed online with the DALI library without any impact on training time.\n\nAfter preprocessing, the script creates JSON metadata files with output file paths, sample rate, target transcript and other metadata. These JSON files are used by the training script to identify training and validation datasets.\n\nThe QuartzNet model was tuned on audio signals with a sample rate of 16kHz. If you wish to use a different sampling rate, then some hyperparameters might need to be changed - specifically, the window size and step size.\n\n#### Multi-dataset\n\nTraining scripts in this repository treat the training subsets of LibriSpeech (`train-clean-100`, `train-clean-360`, `train-other-500`) as three independent training datasets.\nIn order to add more datasets, follow the format of LibriSpeech, adjust the provided pre-processing scripts to generate metadata JSON files, and point them with the `TRAIN_MANIFESTS` variable to the `scripts/train.sh` script.\n\n### Training process\n\nTraining is performed using the `train.py` script along with parameters defined in `scripts/train.sh`.\nThe `scripts/train.sh` script runs a job on a single node that trains the QuartzNet model from scratch using LibriSpeech as training data. To make training more efficient, we discard audio samples longer than 16.7 seconds from the training dataset, the total number of these samples is less than 1%. Such filtering does not degrade accuracy, but it allows us to decrease the number of time steps in a batch, which requires less GPU memory and increases training speed.\nApart from the default arguments as listed in the [Parameters](#parameters) section, by default the training script:\n\n* Runs on 8 GPUs with at least 32GB of memory and training/evaluation batch size 48, split over three gradient accumulation steps\n* Uses TF32 precision (A100 GPU) or FP32 (other GPUs)\n* Trains on the concatenation of all 3 LibriSpeech training datasets and evaluates on the LibriSpeech dev-clean dataset\n* Maintains an exponential moving average of parameters for evaluation\n* Has cuDNN benchmark enabled\n* Runs for 260 epochs\n* Uses an initial learning rate of 0.02 and an exponential learning rate decay\n* Saves a checkpoint every 10 epochs\n* Automatically removes old checkpoints and preserves milestone checkpoints\n* Runs evaluation on the development dataset every epoch and at the end of training\n* Maintains a separate checkpoint with the lowest WER on development set\n* Prints out training progress every iteration to `stdout`\n* Creates a DLLogger log file and a TensorBoard log\n* Calculates speed perturbation online during training\n* Uses `SpecAugment` in data pre-processing\n* Filters out audio samples longer than 16.7 seconds\n* Pads each batch so its length is divisible by 16\n* Uses time-channel separable convolutions as described in the paper\n* Uses weight decay of 0.001\n* Uses [Novograd](https://arxiv.org/pdf/1905.11286.pdf) as optimizer with betas=(0.95, 0)\n\nEnabling AMP permits batch size 144 with one gradient accumulation step. Since each batch has to be padded to the longest sequence, all GPUs have to wait for the slowest one, and two accumulation steps are slightly faster.\n\nThe current training setup improves upon the greedy WER [Results](#results) of the QuartzNet paper.\n\n### Inference process\n\nInference is performed using the `inference.py` script along with parameters defined in `scripts/inference.sh`.\nThe `scripts/inference.sh` script runs the job on a single GPU, taking a pre-trained QuartzNet model checkpoint and running it on the specified dataset.\nApart from the default arguments as listed in the [Parameters](#parameters) section, by default, the inference script:\n\n* Evaluates on the LibriSpeech dev-clean dataset and prints out the final word error rate\n* Uses a batch size of 64\n* Creates a log file with progress and results which will be stored in the `results` folder\n* Pads each batch so its length would be divisible by 16\n* Does not use data augmentation\n* Does greedy decoding and optionally saves the transcriptions in the results folder\n* Has the option to save the model output tensors for more complex decoding, for example, beam search\n* Has cuDNN benchmark disabled\n\nTo view all available options for inference, run `python inference.py --help`.","hasAdvanced","performance","### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nTo benchmark the training performance with a number of specific configurations, run:\n```bash\nGRAD_ACC_SEQ= NUM_GPUS_SEQ= bash scripts/train_benchmark.sh\n```\nfor example:\n```bash\nGRAD_ACC_SEQ=\"12 24\" NUM_GPUS_SEQ=\"4 8\" bash scripts/train_benchmark.sh\n```\n\nThis invocation will measure performance in four setups (two different batch sizes for every single forward/backward pass times two hardware setups).\n\nBy default, this script makes forward/backward pre-allocation passes with all possible audio lengths\nenabling immediate stabilization of training step times in the cuDNN benchmark mode,\nand trains for two epochs on the `train-clean-100` subset of LibriSpeech.\n\n#### Inference performance benchmark\n\nTo benchmark the inference performance on a specific batch size and audio length, run:\n\n```bash\nBATCH_SIZE_SEQ= MAX_DURATION_SEQ= bash scripts/inference_benchmark.sh\n```\n\nfor example:\n```bash\nBATCH_SIZE_SEQ=\"24 48\" MAX_DURATION_SEQ=\"2 7 16.7\" bash scripts/inference_benchmark.sh\n```\n\nThe script runs on a single GPU and evaluates on the dataset of fixed-length utterances shorter than `MAX_DURATION` and padded to that duration.\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\nOur results were obtained by running the `scripts/train.sh` training script in the PyTorch 21.07-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs.\n\n| Number of GPUs | Batch size per GPU | Precision | dev-clean WER | dev-other WER | test-clean WER | test-other WER | Time to train |\n|-----|-----|-------|-------|-------|------|-------|------|\n| 8 | 144 | mixed | 3.47 | 10.84 | 3.69 | 10.69 | 34 h |\n\nThe table reports word error rate (WER) of the acoustic model with greedy decoding on all LibriSpeech dev and test datasets for mixed precision training.\n\n##### Training stability test\n\nThe following table compares greedy decoding word error rates across 8 different training runs with different seeds for mixed precision training.\n\n| DGX A100 80GB, FP16, 8x GPU | Seed #1 | Seed #2 | Seed #3 | Seed #4 | Seed #5 | Seed #6 | Seed #7 | Seed #8 | Mean | Std |\n|-----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|-------:|------:|\n| dev-clean | 3.57 | 3.48 | 3.54 | 3.48 | 3.47 | 3.69 | 3.51 | 3.59 | 3.54 | 0.07 |\n| dev-other | 10.68 | 10.78 | 10.47 | 10.72 | 10.84 | 11.03 | 10.67 | 10.86 | 10.76 | 0.15 |\n| test-clean | 3.70 | 3.82 | 3.79 | 3.84 | 3.69 | 4.03 | 3.82 | 3.80 | 3.81 | 0.10 |\n| test-other | 10.75 | 10.62 | 10.54 | 10.90 | 10.69 | 11.14 | 10.41 | 10.82 | 10.73 | 0.21 |\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running:\n```bash\nAMP=true NUM_GPUS_SEQ=\"1\" GRAD_ACC_SEQ=\"16 24\" bash scripts/train_benchmark.sh\nAMP=true NUM_GPUS_SEQ=\"4\" GRAD_ACC_SEQ=\"4 6\" bash scripts/train_benchmark.sh\nAMP=true NUM_GPUS_SEQ=\"8\" GRAD_ACC_SEQ=\"2 3\" bash scripts/train_benchmark.sh\nAMP=false NUM_GPUS_SEQ=\"1\" GRAD_ACC_SEQ=\"16 24\" bash scripts/train_benchmark.sh\nAMP=false NUM_GPUS_SEQ=\"4\" GRAD_ACC_SEQ=\"4 6\" bash scripts/train_benchmark.sh\nAMP=false NUM_GPUS_SEQ=\"8\" GRAD_ACC_SEQ=\"2 3\" bash scripts/train_benchmark.sh\n```\nin the PyTorch 21.07-py3 NGC container on NVIDIA DGX A100 with (8x A100 80GB) GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch.\n\n| Batch size / GPU | Grad accumulation | GPUs | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 to mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision |\n|-----:|-----:|-------:|----------:|-------:|--------:|-----:|------:|\n| 48 | 24 | 1 | 78.89 | 89.69 | 1.14 | 1.00 | 1.00 |\n| 72 | 16 | 1 | 79.01 | 88.70 | 1.12 | 1.00 | 1.00 |\n| 48 | 6 | 4 | 303.16 | 343.06 | 1.13 | 3.84 | 3.82 |\n| 72 | 4 | 4 | 304.47 | 341.95 | 1.12 | 3.85 | 3.86 |\n| 48 | 3 | 8 | 576.37 | 644.27 | 1.12 | 7.31 | 7.18 |\n| 72 | 2 | 8 | 583.31 | 651.60 | 1.12 | 7.38 | 7.35 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training performance: NVIDIA DGX-2 (16x V100 32GB)\n\nOur results were obtained by running:\n```bash\nAMP=true NUM_GPUS_SEQ=\"1\" GRAD_ACC_SEQ=\"24 48\" bash scripts/train_benchmark.sh\nAMP=true NUM_GPUS_SEQ=\"4\" GRAD_ACC_SEQ=\"6 12\" bash scripts/train_benchmark.sh\nAMP=true NUM_GPUS_SEQ=\"8\" GRAD_ACC_SEQ=\"3 6\" bash scripts/train_benchmark.sh\nAMP=true NUM_GPUS_SEQ=\"16\" GRAD_ACC_SEQ=\"3\" bash scripts/train_benchmark.sh\nAMP=false NUM_GPUS_SEQ=\"1\" GRAD_ACC_SEQ=\"48\" bash scripts/train_benchmark.sh\nAMP=false NUM_GPUS_SEQ=\"4\" GRAD_ACC_SEQ=\"12\" bash scripts/train_benchmark.sh\nAMP=false NUM_GPUS_SEQ=\"8\" GRAD_ACC_SEQ=\"6\" bash scripts/train_benchmark.sh\nAMP=false NUM_GPUS_SEQ=\"16\" GRAD_ACC_SEQ=\"3\" bash scripts/train_benchmark.sh\n```\nin the PyTorch 21.07-py3 NGC container on NVIDIA DGX-2 with (16x V100 32GB) GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch.\n\n| Batch size / GPU | Grad accumulation | GPUs | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 to mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision |\n|-----:|-----:|-------:|-------:|-------:|------:|------:|------:|\n| 24 | 48 | 1 | 44.65 | 67.95 | 1.52 | 1.00 | 1.00 |\n| 48 | 24 | 1 | - | 67.49 | - | 1.00 | 1.00 |\n| 24 | 12 | 4 | 170.18 | 258.56 | 1.52 | 3.81 | 3.81 |\n| 48 | 6 | 4 | - | 254.58 | - | - | 3.77 |\n| 24 | 6 | 8 | 330.53 | 495.52 | 1.50 | 7.40 | 7.29 |\n| 48 | 3 | 8 | - | 477.87 | - | - | 7.08 |\n| 24 | 3 | 16 | 616.51 | 872.99 | 1.42 | 13.81 | 12.85 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)\n\nOur results were obtained by running:\n```bash\nbash AMP=false scripts/inference_benchmark.sh\nbash AMP=true scripts/inference_benchmark.sh\n```\n\nin the PyTorch 21.07-py3 NGC container on NVIDIA DGX A100 (1x A100 80GB) GPU.\nPerformance numbers (latency in milliseconds per batch) were averaged over 500 iterations.\n\n| | | FP16 Latency (ms) Percentiles | | | | TF32 Latency (ms) Percentiles | | | | FP16/TF32 speed up |\n|-----:|---------------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|\n| BS | Duration (s) | 90% | 95% | 99% | Avg | 90% | 95% | 99% | Avg | Avg |\n| 1 | 2.0 | 35.51 | 36.36 | 55.57 | 35.71 | 33.23 | 33.86 | 40.05 | 33.23 | 0.93 |\n| 2 | 2.0 | 38.05 | 38.91 | 52.67 | 38.21 | 34.17 | 35.17 | 39.32 | 33.73 | 0.88 |\n| 4 | 2.0 | 38.43 | 38.98 | 45.44 | 37.78 | 35.02 | 36.00 | 44.10 | 34.75 | 0.92 |\n| 8 | 2.0 | 38.63 | 39.37 | 45.43 | 37.94 | 35.49 | 36.70 | 45.94 | 34.53 | 0.91 |\n| 16 | 2.0 | 42.33 | 44.58 | 61.02 | 40.28 | 35.66 | 36.93 | 45.38 | 34.78 | 0.86 |\n| 1 | 7.0 | 37.72 | 38.54 | 42.56 | 37.28 | 33.23 | 34.16 | 40.54 | 33.13 | 0.89 |\n| 2 | 7.0 | 39.44 | 41.35 | 53.62 | 38.56 | 35.15 | 35.81 | 41.83 | 34.82 | 0.90 |\n| 4 | 7.0 | 38.39 | 39.48 | 45.01 | 37.98 | 37.54 | 38.51 | 42.67 | 36.12 | 0.95 |\n| 8 | 7.0 | 40.82 | 41.76 | 54.20 | 39.43 | 37.67 | 39.97 | 45.24 | 36.12 | 0.92 |\n| 16 | 7.0 | 42.80 | 44.80 | 56.92 | 41.52 | 40.66 | 41.96 | 53.24 | 39.24 | 0.95 |\n| 1 | 16.7 | 38.22 | 38.98 | 44.15 | 37.80 | 33.89 | 34.98 | 42.66 | 33.23 | 0.88 |\n| 2 | 16.7 | 39.84 | 41.09 | 52.50 | 39.34 | 35.86 | 37.16 | 42.04 | 34.39 | 0.87 |\n| 4 | 16.7 | 41.02 | 42.64 | 54.96 | 39.50 | 35.98 | 37.02 | 39.30 | 34.87 | 0.88 |\n| 8 | 16.7 | 40.93 | 42.06 | 56.26 | 39.36 | 40.93 | 42.06 | 45.50 | 39.34 | 1.00 |\n| 16 | 16.7 | 57.21 | 58.65 | 71.33 | 57.78 | 62.74 | 63.82 | 71.13 | 61.49 | 1.06 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-2 (1x V100 32GB)\n\nOur results were obtained by running:\n```bash\nbash AMP=false scripts/inference_benchmark.sh\nbash AMP=true scripts/inference_benchmark.sh\n```\n\nin the PyTorch 21.07-py3 NGC container on NVIDIA DGX-2 with (1x V100 32GB) GPU.\nPerformance numbers (latency in milliseconds per batch) were averaged over 500 iterations.\n\n| | | FP16 Latency (ms) Percentiles | | | | FP32 Latency (ms) Percentiles | | | | FP16/FP32 speed up |\n|-----:|---------------:|------:|------:|------:|------:|-------:|-------:|-------:|-------:|------:|\n| BS | Duration (s) | 90% | 95% | 99% | Avg | 90% | 95% | 99% | Avg | Avg |\n| 1 | 2.0 | 36.89 | 38.16 | 41.80 | 35.85 | 33.44 | 33.78 | 38.09 | 33.01 | 0.92 |\n| 2 | 2.0 | 40.47 | 41.33 | 45.70 | 40.02 | 32.62 | 33.27 | 36.38 | 32.09 | 0.80 |\n| 4 | 2.0 | 41.50 | 42.85 | 49.65 | 41.12 | 34.56 | 34.83 | 37.10 | 34.04 | 0.83 |\n| 8 | 2.0 | 49.87 | 50.48 | 51.99 | 49.19 | 34.90 | 35.17 | 36.57 | 34.27 | 0.70 |\n| 16 | 2.0 | 46.39 | 46.77 | 47.87 | 40.04 | 45.37 | 45.89 | 47.52 | 44.46 | 1.11 |\n| 1 | 7.0 | 48.83 | 49.16 | 52.22 | 48.26 | 33.87 | 34.50 | 36.45 | 33.24 | 0.69 |\n| 2 | 7.0 | 41.48 | 41.82 | 45.07 | 41.03 | 42.32 | 42.66 | 43.86 | 41.79 | 1.02 |\n| 4 | 7.0 | 42.48 | 43.25 | 47.29 | 41.56 | 37.20 | 38.18 | 39.74 | 36.46 | 0.88 |\n| 8 | 7.0 | 39.78 | 40.49 | 44.73 | 38.89 | 46.84 | 47.17 | 48.07 | 44.78 | 1.15 |\n| 16 | 7.0 | 49.85 | 50.56 | 53.04 | 44.95 | 60.21 | 60.68 | 64.92 | 57.94 | 1.29 |\n| 1 | 16.7 | 40.80 | 41.16 | 42.96 | 40.52 | 42.04 | 42.53 | 44.59 | 37.08 | 0.92 |\n| 2 | 16.7 | 41.37 | 41.69 | 43.74 | 40.85 | 35.61 | 36.49 | 40.32 | 34.68 | 0.85 |\n| 4 | 16.7 | 50.22 | 51.07 | 54.13 | 49.51 | 40.95 | 41.38 | 44.09 | 40.39 | 0.82 |\n| 8 | 16.7 | 44.93 | 45.38 | 49.24 | 44.16 | 62.54 | 62.92 | 65.95 | 61.86 | 1.40 |\n| 16 | 16.7 | 70.74 | 71.56 | 75.16 | 69.87 | 102.52 | 103.57 | 108.20 | 101.57 | 1.45 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).","hasPerformance","policies",[],{"_76":202,"_78":203,"_170":-5,"_176":30,"_178":30,"_194":201,"_181":86},{"_76":21,"_78":21,"_170":-5,"_176":30,"_178":30,"_194":201,"_181":86},{"_76":199,"_78":199,"_170":-5,"_176":30,"_178":30,"_194":200,"_181":86},"21.07.0",[],[],"-","Latest (21.07.1)","nvidia/dle/quartznet_pyt","key","nvidia/dle/resources/quartznet_pyt","resourceType","resources","resourceId","quartznet_pyt","shortDescription","End-to-end neural acoustic model for automatic speech recognition providing high accuracy at a low memory footprint.","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/quartznet-pyt)\n\n\nThis repository provides an implementation of the QuartzNet model in PyTorch from the paper [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://arxiv.org/pdf/1910.10261).\nThe QuartzNet model is an end-to-end neural acoustic model for automatic speech recognition (ASR), that provides high accuracy at a low memory footprint. The QuartzNet architecture of convolutional layers was designed to facilitate fast GPU inference, by allowing whole sub-blocks to be fused into a single GPU kernel. This is important for meeting strict real-time requirements of ASR systems in deployment.\n\nThis repository is a PyTorch implementation of QuartzNet and provides scripts to train the QuartzNet 10x5 model from scratch on the [LibriSpeech](http://www.openslr.org/12) dataset to achieve the greedy decoding results improved upon the original paper.\nThe repository is self-contained and includes data preparation scripts, training, and inference scripts.\nBoth training and inference scripts offer the option to use Automatic Mixed Precision (AMP) to benefit from Tensor Cores for better performance.\n\nIn addition to providing the hyperparameters for training a model checkpoint, we publish a thorough inference analysis across different NVIDIA GPU platforms, for example, DGX-2, NVIDIA A100 GPU, and T4.\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 [1.4]x 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\nQuartzNet is an end-to-end neural acoustic model that is based on efficient, time-channel separable convolutions (Figure 1).\nIn the audio processing stage, each frame is transformed into mel-scale spectrogram features, which the acoustic model takes as input and outputs a probability distribution over the vocabulary for each frame.\n\n

\n $\"QuartzNet$ \n

\n Figure 1. Architecture of QuartzNet (source)\n \n

\n\n### Default configuration\n\nThe following features were implemented in this model:\n* GPU-supported feature extraction with data augmentation options [SpecAugment](https://arxiv.org/abs/1904.08779) and [Cutout](https://arxiv.org/pdf/1708.04552.pdf) using the DALI library\n* offline and online [Speed Perturbation](https://www.danielpovey.com/files/2015_interspeech_augmentation.pdf) using the DALI library\n* data-parallel multi-GPU training and evaluation\n* AMP with dynamic loss scaling for Tensor Core training\n* FP16 inference\n\n### Feature support matrix\n\n| **Feature** | **QuartzNet** |\n|---------------|---------------|\n|[Apex AMP](https://nvidia.github.io/apex/amp.html) | Yes |\n|[DALI](https://docs.nvidia.com/deeplearning/dali/release-notes/index.html) | Yes |\n\n#### Features\n\n**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. For details, see example sources in this repository or see the [DALI documentation](https://docs.nvidia.com/deeplearning/dali/index.html).\n\n**Automatic Mixed Precision (AMP)**\nComputation graphs can be modified by PyTorch on runtime to support mixed precision training. A detailed explanation of mixed precision can be found in the next section.\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](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Adding loss scaling to preserve small gradient values.\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- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).\n\n#### Enabling mixed precision\n\nFor training, mixed precision can be enabled by setting the flag: `train.py --amp`. When using bash helper scripts, mixed precision can be enabled with the environment variable `AMP=true`, for example, `AMP=true bash scripts/train.sh`, `AMP=true bash scripts/inference.sh`, etc.\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.\n\n### Glossary\n\n**Time-channel separable (TCS) convolution**\nA module composed mainly of two convolutional layers: a 1D depthwise convolutional layer,\nand a pointwise convolutional layer (Figure 2). The former operates across K time frames, and the latter across all channels. By decoupling time and channel axes, the separable module uses less parameters and calculates the result faster, than it would otherwise would.\n\n

\n $\"Time-channel$ \n

\n Figure 2. Time-channel separable (TCS) convolutional module: (a) basic design, (b) TCS with a group shuffle layer, added to increase cross-group interchange\n

\n\n**Automatic Speech Recognition (ASR)**\nUses both an acoustic model and a language model to output the transcript of an input audio signal.\n\n**Acoustic model**\nAssigns a probability distribution over a vocabulary of characters given an audio frame. Typically, a large part of the entire ASR model.\n\n**Language model**\nAssigns a probability distribution over a sequence of words. Given a sequence of words, it assigns a probability to the whole sequence.\n\n**Pre-training**\nTraining a model on vast amounts of data on the same (or different) task to build general understandings.\n\n### Language support and NeMo compatibility\n\nThis repository allows to train and run models in laguages other than English.\n\nDuring inference, QuartzNet models trained with [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) can also be used, for instance one of pre-trained models\nfor Catalan, French, German, Italian, Mandarin Chinese, Polish, Russian or Spanish available on [NGC](https://ngc.nvidia.com/).\nTo download automatically, run:\n```bash\nbash scripts/download_quartznet.sh [ca|fr|de|it|zh|pl|ru|es]\n```\n\nPre-trained models can be explicitly converted from the `.nemo` checkpoint format to `.pt` and vice versa.\nFor more details, run:\n```bash\npython nemo_dle_model_converter.py --help\n```","displayName","QuartzNet for PyTorch",["D",1680632047428],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/quartznet_pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_214":247,"_248":249,"_250":251,"_252":253},"Automatic Speech Recognition","value","uscs_automatic_speech_recognition","filter",{"_254":255,"_248":249},"category","label","field","application",{"_107":1432,"_109":1433},"ngcApps",[1385],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1366":1367,"_1368":1369,"_1370":1371,"_1372":1373,"_1374":267,"_139":291,"_1375":1376,"_1377":55,"_1378":86,"_1379":86,"_1380":1381,"_1382":86,"_1383":1384},"amplitude",{"_1363":1364,"_965":-7,"_1365":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1357":1358,"_1359":1360,"_1361":1362},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1240":373,"_1241":1242,"_1243":1244,"_1245":1246},"announcements",[],"consentState",{"_330":1230,"_1231":86,"_1232":30,"_1233":30,"_1234":1235},"consentConfig",{"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331},"showContactAdmin",1784002870079,[],[],[305,306,307,308,309,310],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":311,"_54":55},10,"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",{"_1222":1223,"_1224":1225,"_1226":1227,"_1228":1229},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[332,333,334,335,336],{"_337":874,"_78":875,"_168":876,"_341":86,"_342":16,"_344":877,"_346":878},{"_337":664,"_78":665,"_168":666,"_341":30,"_342":58,"_344":667,"_346":668},{"_337":564,"_78":565,"_168":566,"_341":30,"_342":53,"_344":567,"_346":568},{"_337":348,"_78":349,"_168":350,"_341":30,"_342":351,"_344":352,"_346":353},{"_337":338,"_78":339,"_168":340,"_341":30,"_342":343,"_344":345,"_346":347},"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,[482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516],[354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370],{"_371":478,"_214":478,"_168":373,"_344":479},{"_371":473,"_214":473,"_168":373,"_344":474},{"_371":469,"_214":469,"_168":373,"_344":470},{"_371":464,"_214":464,"_168":373,"_344":465},{"_371":459,"_214":459,"_168":373,"_344":460},{"_371":447,"_214":447,"_168":373,"_344":448},{"_371":435,"_214":435,"_168":373,"_344":436},{"_371":430,"_214":430,"_168":373,"_344":431},{"_371":419,"_214":419,"_168":373,"_344":420},{"_371":414,"_214":414,"_168":373,"_344":415},{"_371":409,"_214":409,"_168":373,"_344":410},{"_371":404,"_214":404,"_168":373,"_344":405},{"_371":399,"_214":399,"_168":373,"_344":400},{"_371":394,"_214":394,"_168":373,"_344":395},{"_371":389,"_214":389,"_168":373,"_344":390},{"_371":382,"_214":382,"_168":373,"_344":383},{"_371":372,"_214":372,"_168":373,"_344":374},"hostName","amazon-adsystem.com","",[375,376],{"_78":381,"_378":372,"_379":30,"_168":380},{"_78":377,"_378":372,"_379":30,"_168":380},"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",[384,385],{"_78":388,"_378":382,"_379":30,"_168":387},{"_78":386,"_378":382,"_379":30,"_168":387},"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",[391],{"_78":392,"_378":389,"_379":30,"_168":393},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[396],{"_78":397,"_378":394,"_379":30,"_168":398},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[401],{"_78":402,"_378":399,"_379":30,"_168":403},"_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",[406],{"_78":407,"_378":404,"_379":30,"_168":408},"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",[411],{"_78":412,"_378":409,"_379":30,"_168":413},"sp","Testing","google.com",[416],{"_78":417,"_378":414,"_379":30,"_168":418},"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.","tags.srv.stackadapt.com",[421,422,423,424],{"_78":429,"_378":419,"_379":30,"_168":426},{"_78":428,"_378":419,"_379":30,"_168":426},{"_78":427,"_378":419,"_379":30,"_168":426},{"_78":425,"_378":419,"_379":30,"_168":426},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[432],{"_78":433,"_378":430,"_379":30,"_168":434},"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",[437,438,439,440],{"_78":446,"_378":435,"_379":30,"_168":442},{"_78":444,"_378":435,"_379":30,"_168":445},{"_78":443,"_378":435,"_379":30,"_168":442},{"_78":441,"_378":435,"_379":30,"_168":442},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[449,450,451,452],{"_78":458,"_378":447,"_379":30,"_168":456},{"_78":457,"_378":447,"_379":30,"_168":456},{"_78":455,"_378":447,"_379":30,"_168":456},{"_78":453,"_378":447,"_379":30,"_168":454},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. 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.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[461],{"_78":462,"_378":459,"_379":30,"_168":463},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[466],{"_78":467,"_378":464,"_379":30,"_168":468},"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",[471],{"_78":472,"_378":469,"_379":30,"_168":413},"R","6sc.co",[475],{"_78":476,"_378":473,"_379":30,"_168":477},"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",[480],{"_78":481,"_378":478,"_379":30,"_168":413},"_hly_gvid",{"_78":563,"_378":518,"_379":30,"_168":537},{"_78":561,"_378":518,"_379":30,"_168":562},{"_78":425,"_378":522,"_379":30,"_168":551},{"_78":559,"_378":518,"_379":30,"_168":560},{"_78":428,"_378":522,"_379":30,"_168":525},{"_78":557,"_378":518,"_379":30,"_168":558},{"_78":402,"_378":518,"_379":30,"_168":556},{"_78":534,"_378":555,"_379":30,"_168":535},{"_78":427,"_378":522,"_379":30,"_168":554},{"_78":427,"_378":524,"_379":30,"_168":554},{"_78":429,"_378":522,"_379":30,"_168":538},{"_78":553,"_378":518,"_379":30,"_168":537},{"_78":532,"_378":552,"_379":30,"_168":373},{"_78":425,"_378":524,"_379":30,"_168":551},{"_78":550,"_378":518,"_379":30,"_168":373},{"_78":532,"_378":522,"_379":30,"_168":373},{"_78":548,"_378":518,"_379":30,"_168":549},{"_78":546,"_378":518,"_379":30,"_168":547},{"_78":545,"_378":518,"_379":30,"_168":373},{"_78":543,"_378":518,"_379":30,"_168":544},{"_78":541,"_378":518,"_379":30,"_168":542},{"_78":540,"_378":518,"_379":30,"_168":373},{"_78":534,"_378":522,"_379":30,"_168":535},{"_78":534,"_378":539,"_379":30,"_168":535},{"_78":429,"_378":524,"_379":30,"_168":538},{"_78":536,"_378":518,"_379":30,"_168":537},{"_78":534,"_378":518,"_379":30,"_168":535},{"_78":532,"_378":524,"_379":30,"_168":533},{"_78":530,"_378":518,"_379":30,"_168":531},{"_78":528,"_378":518,"_379":30,"_168":529},{"_78":526,"_378":518,"_379":30,"_168":527},{"_78":428,"_378":524,"_379":30,"_168":525},{"_78":521,"_378":522,"_379":30,"_168":523},{"_78":520,"_378":518,"_379":30,"_168":373},{"_78":517,"_378":518,"_379":86,"_168":519},"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.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.","_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","_twpid","This cookie is likely used to track user identity by assigning a unique user ID when the user engages with the website. It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624],[569,570,571,572,573],{"_371":464,"_214":464,"_168":373,"_344":595},{"_371":430,"_214":430,"_168":373,"_344":590},{"_371":583,"_214":583,"_168":373,"_344":584},{"_371":579,"_214":579,"_168":373,"_344":580},{"_371":574,"_214":574,"_168":373,"_344":575},"nvcrm.my.site.com",[576],{"_78":577,"_378":574,"_379":30,"_168":578},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[581],{"_78":582,"_378":579,"_379":30,"_168":413},"edge","github.com",[585,586],{"_78":589,"_378":583,"_379":30,"_168":588},{"_78":587,"_378":583,"_379":30,"_168":588},"logged_in","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform","_octo",[591,592],{"_78":594,"_378":430,"_379":30,"_168":434},{"_78":593,"_378":430,"_379":30,"_168":434},"lidc","li_gc",[596,597,598,599],{"_78":603,"_378":464,"_379":30,"_168":468},{"_78":602,"_378":464,"_379":30,"_168":468},{"_78":601,"_378":464,"_379":30,"_168":468},{"_78":600,"_378":464,"_379":30,"_168":468},"imid_secure","imid_created_secure","imid","imid_created",{"_78":663,"_378":518,"_379":30,"_168":660},{"_78":661,"_378":518,"_379":30,"_168":662},{"_78":659,"_378":518,"_379":30,"_168":660},{"_78":657,"_378":518,"_379":86,"_168":658},{"_78":655,"_378":626,"_379":30,"_168":656},{"_78":654,"_378":635,"_379":86,"_168":373},{"_78":652,"_378":626,"_379":30,"_168":653},{"_78":650,"_378":518,"_379":30,"_168":651},{"_78":647,"_378":648,"_379":30,"_168":649},{"_78":646,"_378":522,"_379":30,"_168":373},{"_78":644,"_378":518,"_379":30,"_168":645},{"_78":641,"_378":642,"_379":30,"_168":643},{"_78":639,"_378":640,"_379":30,"_168":373},{"_78":638,"_378":626,"_379":30,"_168":373},{"_78":637,"_378":518,"_379":30,"_168":373},{"_78":636,"_378":522,"_379":30,"_168":373},{"_78":634,"_378":635,"_379":86,"_168":373},{"_78":632,"_378":626,"_379":30,"_168":633},{"_78":630,"_378":518,"_379":86,"_168":631},{"_78":628,"_378":518,"_379":30,"_168":629},{"_78":625,"_378":626,"_379":86,"_168":627},"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.","_biz_kvpA","This cookie is likely related to business analytics and key-value pairs for storing user preferences, which may be used to enhance functionality and user experience on the website.","_gdxxxxxxxxxxxxx","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.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792],[669,670,671,672,673,674],{"_371":695,"_214":695,"_168":373,"_344":696},{"_371":690,"_214":690,"_168":373,"_344":691},{"_371":686,"_214":686,"_168":373,"_344":687},{"_371":447,"_214":447,"_168":373,"_344":683},{"_371":583,"_214":583,"_168":373,"_344":680},{"_371":394,"_214":394,"_168":373,"_344":675},[676,677],{"_78":679,"_378":394,"_379":30,"_168":398},{"_78":678,"_378":394,"_379":30,"_168":398},"ncid","VisitorID",[681],{"_78":682,"_378":583,"_379":86,"_168":588},"_gh_sess",[684],{"_78":685,"_378":447,"_379":86,"_168":456},"YSC","t.contentsquare.net",[688],{"_78":689,"_378":686,"_379":86,"_168":542},"_cs_same_site","bizible.com",[692],{"_78":693,"_378":690,"_379":30,"_168":694},"_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",[697],{"_78":698,"_378":695,"_379":30,"_168":699},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":872,"_378":518,"_379":30,"_168":873},{"_78":811,"_378":522,"_379":30,"_168":631},{"_78":845,"_378":626,"_379":86,"_168":846},{"_78":871,"_378":518,"_379":30,"_168":848},{"_78":870,"_378":518,"_379":30,"_168":848},{"_78":843,"_378":648,"_379":86,"_168":844},{"_78":796,"_378":522,"_379":30,"_168":373},{"_78":826,"_378":522,"_379":30,"_168":631},{"_78":811,"_378":626,"_379":30,"_168":631},{"_78":811,"_378":539,"_379":30,"_168":823},{"_78":813,"_378":522,"_379":30,"_168":645},{"_78":811,"_378":866,"_379":30,"_168":631},{"_78":799,"_378":539,"_379":30,"_168":631},{"_78":813,"_378":816,"_379":30,"_168":645},{"_78":868,"_378":518,"_379":30,"_168":869},{"_78":867,"_378":518,"_379":30,"_168":542},{"_78":826,"_378":866,"_379":30,"_168":631},{"_78":811,"_378":808,"_379":30,"_168":823},{"_78":799,"_378":866,"_379":30,"_168":631},{"_78":843,"_378":539,"_379":86,"_168":844},{"_78":851,"_378":626,"_379":30,"_168":373},{"_78":865,"_378":518,"_379":30,"_168":537},{"_78":799,"_378":626,"_379":30,"_168":631},{"_78":826,"_378":635,"_379":30,"_168":857},{"_78":863,"_378":518,"_379":30,"_168":864},{"_78":826,"_378":808,"_379":30,"_168":857},{"_78":826,"_378":835,"_379":30,"_168":631},{"_78":861,"_378":518,"_379":86,"_168":862},{"_78":826,"_378":539,"_379":30,"_168":631},{"_78":811,"_378":835,"_379":30,"_168":631},{"_78":843,"_378":522,"_379":86,"_168":844},{"_78":811,"_378":635,"_379":30,"_168":823},{"_78":807,"_378":626,"_379":30,"_168":373},{"_78":859,"_378":635,"_379":30,"_168":860},{"_78":843,"_378":835,"_379":86,"_168":844},{"_78":804,"_378":858,"_379":30,"_168":805},{"_78":826,"_378":524,"_379":30,"_168":857},{"_78":804,"_378":522,"_379":30,"_168":805},{"_78":855,"_378":518,"_379":86,"_168":856},{"_78":851,"_378":648,"_379":30,"_168":373},{"_78":854,"_378":626,"_379":86,"_168":825},{"_78":852,"_378":518,"_379":86,"_168":853},{"_78":826,"_378":816,"_379":30,"_168":631},{"_78":851,"_378":522,"_379":30,"_168":373},{"_78":849,"_378":518,"_379":30,"_168":850},{"_78":799,"_378":816,"_379":30,"_168":631},{"_78":847,"_378":518,"_379":30,"_168":848},{"_78":810,"_378":522,"_379":30,"_168":373},{"_78":845,"_378":518,"_379":86,"_168":846},{"_78":843,"_378":626,"_379":86,"_168":844},{"_78":841,"_378":518,"_379":30,"_168":842},{"_78":795,"_378":522,"_379":30,"_168":373},{"_78":821,"_378":518,"_379":30,"_168":822},{"_78":839,"_378":518,"_379":86,"_168":840},{"_78":836,"_378":837,"_379":30,"_168":838},{"_78":804,"_378":626,"_379":30,"_168":805},{"_78":799,"_378":835,"_379":30,"_168":631},{"_78":799,"_378":524,"_379":30,"_168":809},{"_78":826,"_378":812,"_379":30,"_168":631},{"_78":834,"_378":518,"_379":30,"_168":542},{"_78":799,"_378":812,"_379":30,"_168":631},{"_78":806,"_378":522,"_379":30,"_168":373},{"_78":832,"_378":518,"_379":30,"_168":833},{"_78":829,"_378":830,"_379":30,"_168":831},{"_78":827,"_378":518,"_379":86,"_168":828},{"_78":796,"_378":626,"_379":30,"_168":373},{"_78":826,"_378":626,"_379":30,"_168":631},{"_78":824,"_378":626,"_379":86,"_168":825},{"_78":799,"_378":635,"_379":30,"_168":809},{"_78":811,"_378":524,"_379":30,"_168":823},{"_78":821,"_378":648,"_379":86,"_168":822},{"_78":819,"_378":518,"_379":86,"_168":820},{"_78":817,"_378":518,"_379":30,"_168":818},{"_78":811,"_378":816,"_379":30,"_168":631},{"_78":814,"_378":518,"_379":30,"_168":815},{"_78":806,"_378":626,"_379":30,"_168":373},{"_78":813,"_378":518,"_379":30,"_168":645},{"_78":810,"_378":626,"_379":30,"_168":373},{"_78":811,"_378":812,"_379":30,"_168":631},{"_78":807,"_378":522,"_379":30,"_168":373},{"_78":810,"_378":648,"_379":30,"_168":373},{"_78":795,"_378":648,"_379":30,"_168":373},{"_78":799,"_378":808,"_379":30,"_168":809},{"_78":807,"_378":648,"_379":30,"_168":373},{"_78":806,"_378":648,"_379":30,"_168":373},{"_78":804,"_378":552,"_379":30,"_168":805},{"_78":802,"_378":518,"_379":30,"_168":803},{"_78":800,"_378":518,"_379":30,"_168":801},{"_78":799,"_378":522,"_379":30,"_168":631},{"_78":797,"_378":518,"_379":86,"_168":798},{"_78":796,"_378":648,"_379":30,"_168":373},{"_78":795,"_378":626,"_379":30,"_168":373},{"_78":793,"_378":518,"_379":30,"_168":794},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","__sec__token","__sec__cid","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","_gd_visitor","_biz_dfsA","This cookie is associated with CloudFlare, used to speed up page load times. It is not used to track users but rather to limit the number of requests that have to be made to doubleclick.net.","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","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).","__sec_tid","__sec__fid","crowdsource.nvidia.com","This cookie name is associated with 6sense which is used to collect visitor data related to the user’s visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.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.","__sec__ghost","_gd_svisitor","info.nvidia.com","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.","research.nvidia.com","_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.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.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.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","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.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_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.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","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.","_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.","nv.referrerHref","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","_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.","__sec_crid","_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.","s_tp","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","This _gd_session cookie is used to collect visitor data related to the user's visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.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.","catalog.ngc.nvidia.com","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_sq","Adobe Site Catalyst cookie, stores information about the previous link clicked within the site","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.","AMP_92dea1e784","nvidianews.nvidia.com","_cs_s","_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.","nv.buttonReferrer","nv.referrerId","_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.",[1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115],[879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903],{"_371":1022,"_214":1022,"_168":373,"_344":1023},{"_371":1016,"_214":1016,"_168":373,"_344":1017},{"_371":1004,"_214":1004,"_168":373,"_344":1005},{"_371":1001,"_214":1001,"_168":373,"_344":1002},{"_371":998,"_214":998,"_168":373,"_344":999},{"_371":993,"_214":993,"_168":373,"_344":994},{"_371":988,"_214":988,"_168":373,"_344":989},{"_371":447,"_214":447,"_168":373,"_344":985},{"_371":981,"_214":981,"_168":373,"_344":982},{"_371":435,"_214":435,"_168":373,"_344":979},{"_371":430,"_214":430,"_168":373,"_344":977},{"_371":972,"_214":972,"_168":373,"_344":973},{"_371":967,"_214":967,"_168":373,"_344":968},{"_371":962,"_214":962,"_168":373,"_344":963},{"_371":959,"_214":959,"_168":373,"_344":960},{"_371":404,"_214":404,"_168":373,"_344":957},{"_371":949,"_214":949,"_168":373,"_344":950},{"_371":579,"_214":579,"_168":373,"_344":943},{"_371":937,"_214":937,"_168":373,"_344":938},{"_371":933,"_214":933,"_168":373,"_344":934},{"_371":928,"_214":928,"_168":373,"_344":929},{"_371":924,"_214":924,"_168":373,"_344":925},{"_371":919,"_214":919,"_168":373,"_344":920},{"_371":574,"_214":574,"_168":373,"_344":912},{"_371":904,"_214":904,"_168":373,"_344":905},"pexels.com",[906,907],{"_78":910,"_378":904,"_379":30,"_168":911},{"_78":908,"_378":904,"_379":86,"_168":909},"_cfuvid","Pexels provides high quality and completely free stock photos licensed under the Pexels license. These photos are often used for personal and commercial purposes.","__cf_bm","This is a CloudFoundry cookie",[913,914],{"_78":917,"_378":574,"_379":30,"_168":918},{"_78":915,"_378":574,"_379":30,"_168":916},"CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","LSKey-c$CookieConsentPolicy","Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","www.recaptcha.net",[921],{"_78":922,"_378":919,"_379":30,"_168":923},"_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",[926],{"_78":927,"_378":924,"_379":30,"_168":413},"__cflb","instagram.com",[930],{"_78":931,"_378":928,"_379":30,"_168":932},"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",[935],{"_78":910,"_378":933,"_379":30,"_168":936},"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",[939,940],{"_78":942,"_378":937,"_379":30,"_168":413},{"_78":941,"_378":937,"_379":30,"_168":413},"AWSALBTG","AWSALBTGCORS",[944,945],{"_78":947,"_378":579,"_379":30,"_168":948},{"_78":946,"_378":579,"_379":30,"_168":413},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.","app.hushly.com",[951,952,953],{"_78":956,"_378":949,"_379":30,"_168":413},{"_78":955,"_378":949,"_379":86,"_168":413},{"_78":954,"_378":949,"_379":30,"_168":413},"AWSALBCORS","JSESSIONID","AWSALB",[958],{"_78":910,"_378":404,"_379":30,"_168":911},"hcaptcha.com",[961],{"_78":910,"_378":959,"_379":30,"_168":911},"tracking.intentsify.io",[964],{"_78":965,"_378":962,"_379":30,"_168":966},"userId","Used to track usage of the embedded video player.","static.reo.dev",[969],{"_78":970,"_378":967,"_379":30,"_168":971},"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",[974],{"_78":975,"_378":972,"_379":30,"_168":976},"dd_cookie_test_","dd_cookie_test",[978],{"_78":910,"_378":430,"_379":30,"_168":911},[980],{"_78":910,"_378":435,"_379":30,"_168":911},"mynvidia.force.com",[983,984],{"_78":917,"_378":981,"_379":30,"_168":918},{"_78":915,"_378":981,"_379":30,"_168":916},[986],{"_78":987,"_378":447,"_379":30,"_168":456},"VISITOR_PRIVACY_METADATA","prod.cloudmatchbeta.nvidiagrid.net",[990],{"_78":991,"_378":988,"_379":86,"_168":992},"SERVERID","Usually used for load balancing. Identifies the server that delivered the last page to the browser. Associated with the HAProxy Load Balancer software.","dosa-prd.azurewebsites.net",[995],{"_78":996,"_378":993,"_379":30,"_168":997},"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",[1000],{"_78":910,"_378":998,"_379":30,"_168":911},"cdn.readme.io",[1003],{"_78":910,"_378":1001,"_379":30,"_168":911},"queue-it.net",[1006,1007,1008,1009,1010],{"_78":1015,"_378":1004,"_379":30,"_168":373},{"_78":1014,"_378":1004,"_379":30,"_168":373},{"_78":1013,"_378":1004,"_379":30,"_168":373},{"_78":1012,"_378":1004,"_379":30,"_168":373},{"_78":1011,"_378":1004,"_379":30,"_168":373},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","api.hcaptcha.com",[1018,1019],{"_78":1021,"_378":1016,"_379":30,"_168":413},{"_78":927,"_378":1016,"_379":30,"_168":1020},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[1024,1025],{"_78":910,"_378":1022,"_379":30,"_168":911},{"_78":908,"_378":1022,"_379":86,"_168":1026},"This domain is owned by Vimeo. The main business activity is: Video Hosting/Sharing",{"_78":1212,"_378":1182,"_379":86,"_168":1213},{"_78":1141,"_378":518,"_379":30,"_168":1221},{"_78":1219,"_378":812,"_379":86,"_168":1220},{"_78":1208,"_378":1175,"_379":86,"_168":1209},{"_78":1218,"_378":552,"_379":86,"_168":373},{"_78":1217,"_378":518,"_379":86,"_168":537},{"_78":1215,"_378":518,"_379":30,"_168":1216},{"_78":942,"_378":1196,"_379":30,"_168":1127},{"_78":1168,"_378":858,"_379":86,"_168":1214},{"_78":1212,"_378":1175,"_379":86,"_168":1213},{"_78":1134,"_378":1207,"_379":86,"_168":1136},{"_78":941,"_378":1126,"_379":30,"_168":1127},{"_78":915,"_378":1207,"_379":30,"_168":916},{"_78":1210,"_378":518,"_379":86,"_168":1211},{"_78":1208,"_378":1182,"_379":86,"_168":1209},{"_78":917,"_378":1207,"_379":30,"_168":1133},{"_78":1205,"_378":518,"_379":30,"_168":1206},{"_78":941,"_378":1118,"_379":30,"_168":1127},{"_78":1123,"_378":1142,"_379":30,"_168":1125},{"_78":1203,"_378":518,"_379":30,"_168":1204},{"_78":954,"_378":1124,"_379":30,"_168":1139},{"_78":942,"_378":1118,"_379":30,"_168":1127},{"_78":956,"_378":1124,"_379":30,"_168":1200},{"_78":915,"_378":1132,"_379":30,"_168":916},{"_78":1201,"_378":626,"_379":30,"_168":1202},{"_78":942,"_378":1147,"_379":30,"_168":1148},{"_78":956,"_378":835,"_379":30,"_168":1200},{"_78":1198,"_378":518,"_379":30,"_168":1199},{"_78":947,"_378":835,"_379":30,"_168":1192},{"_78":1165,"_378":816,"_379":86,"_168":373},{"_78":1197,"_378":866,"_379":86,"_168":373},{"_78":941,"_378":1196,"_379":30,"_168":1127},{"_78":1194,"_378":1126,"_379":86,"_168":1195},{"_78":1153,"_378":518,"_379":30,"_168":1140},{"_78":1123,"_378":518,"_379":30,"_168":1125},{"_78":1193,"_378":1146,"_379":30,"_168":373},{"_78":947,"_378":518,"_379":30,"_168":1192},{"_78":956,"_378":1118,"_379":30,"_168":1191},{"_78":1189,"_378":837,"_379":86,"_168":1190},{"_78":1187,"_378":866,"_379":86,"_168":1188},{"_78":1185,"_378":518,"_379":86,"_168":1186},{"_78":1184,"_378":518,"_379":30,"_168":373},{"_78":1141,"_378":1124,"_379":30,"_168":1143},{"_78":1183,"_378":1146,"_379":30,"_168":373},{"_78":1174,"_378":1182,"_379":86,"_168":1176},{"_78":1180,"_378":816,"_379":30,"_168":1181},{"_78":1178,"_378":552,"_379":30,"_168":1179},{"_78":1177,"_378":539,"_379":30,"_168":373},{"_78":1174,"_378":1175,"_379":86,"_168":1176},{"_78":1172,"_378":518,"_379":30,"_168":1173},{"_78":1170,"_378":1132,"_379":86,"_168":1171},{"_78":1168,"_378":539,"_379":86,"_168":1169},{"_78":1166,"_378":518,"_379":30,"_168":1167},{"_78":1165,"_378":522,"_379":86,"_168":373},{"_78":917,"_378":1135,"_379":30,"_168":1133},{"_78":946,"_378":1154,"_379":30,"_168":1140},{"_78":1163,"_378":522,"_379":30,"_168":1164},{"_78":1162,"_378":518,"_379":30,"_168":660},{"_78":975,"_378":858,"_379":30,"_168":1156},{"_78":1160,"_378":518,"_379":30,"_168":1161},{"_78":1158,"_378":518,"_379":30,"_168":1159},{"_78":1157,"_378":518,"_379":30,"_168":373},{"_78":975,"_378":522,"_379":30,"_168":1156},{"_78":1153,"_378":835,"_379":30,"_168":1140},{"_78":910,"_378":812,"_379":30,"_168":1155},{"_78":1153,"_378":1154,"_379":30,"_168":1140},{"_78":1152,"_378":1146,"_379":30,"_168":373},{"_78":1151,"_378":518,"_379":30,"_168":1121},{"_78":1149,"_378":1132,"_379":30,"_168":1150},{"_78":941,"_378":1147,"_379":30,"_168":1148},{"_78":1145,"_378":1146,"_379":30,"_168":373},{"_78":1144,"_378":518,"_379":30,"_168":373},{"_78":1134,"_378":1132,"_379":86,"_168":1136},{"_78":946,"_378":518,"_379":30,"_168":1140},{"_78":1141,"_378":1142,"_379":30,"_168":1143},{"_78":946,"_378":835,"_379":30,"_168":1140},{"_78":954,"_378":835,"_379":30,"_168":1139},{"_78":1137,"_378":518,"_379":30,"_168":1138},{"_78":915,"_378":1135,"_379":30,"_168":916},{"_78":1134,"_378":1135,"_379":86,"_168":1136},{"_78":917,"_378":1132,"_379":30,"_168":1133},{"_78":1130,"_378":1126,"_379":86,"_168":1131},{"_78":1128,"_378":640,"_379":30,"_168":1129},{"_78":942,"_378":1126,"_379":30,"_168":1127},{"_78":1123,"_378":1124,"_379":30,"_168":1125},{"_78":910,"_378":635,"_379":30,"_168":1122},{"_78":1120,"_378":518,"_379":30,"_168":1121},{"_78":954,"_378":1118,"_379":30,"_168":1119},{"_78":1116,"_378":518,"_379":30,"_168":1117},"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.","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.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","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.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","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.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","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.","_biz_pendingA","_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.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","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","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.","session","This cookie is typically used to maintain an anonymized user session by the server.","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","login_session","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.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","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.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","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.

",{"_874":16,"_664":55,"_564":55,"_348":55,"_338":55},"hasConsented","gpcApplied","showBanner","geolocation",{"_1236":1237,"_1238":1239},"country","US","state","OR","query","categories",[1259,1260,1261,1262,1263],"optionUrls",[1247,1248,1249,1250,1251,1252,1253,1254,1255,1256,1257,1258],"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/-",{"_207":1337,"_1265":1338,"_1267":55},{"_207":1302,"_1265":1303,"_1267":58},{"_207":1285,"_1265":1286,"_1267":1287},{"_207":208,"_1265":1278,"_1267":1279},{"_207":1264,"_1265":1266,"_1267":311},"helm-charts","items",[1268,1269],"startIndex",{"_207":1264,"_214":1275,"_162":163,"_164":-7,"_209":1276,"_217":1272,"_243":1277,"_1274":1257},{"_207":1264,"_214":1270,"_162":163,"_164":-7,"_209":1271,"_217":1272,"_243":1273,"_1274":1258},"NVIDIA NIM Operator","k8s-nim-operator","NVIDIA",[],"url","GPU Operator","gpu-operator",[],[1280],9,{"_207":208,"_214":1281,"_162":163,"_164":1282,"_209":1283,"_217":1272,"_243":1284,"_1274":1256},"Riva Skills Quick Start","riva","riva_quickstart",[],"models",[1288,1289,1290],6,{"_207":1285,"_214":1298,"_162":163,"_164":1299,"_209":1300,"_217":1272,"_243":1301,"_1274":1253},{"_207":1285,"_214":1295,"_162":163,"_164":1292,"_209":1296,"_217":1272,"_243":1297,"_1274":1254},{"_207":1285,"_214":1291,"_162":163,"_164":1292,"_209":1293,"_217":1272,"_243":1294,"_1274":1255},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1304,1305,1306],{"_207":1302,"_214":1334,"_162":163,"_164":1308,"_209":1335,"_217":1272,"_243":1336,"_1274":1250},{"_207":1302,"_214":1331,"_162":163,"_164":-7,"_209":1332,"_217":1272,"_243":1333,"_1274":1251},{"_207":1302,"_214":1307,"_162":163,"_164":1308,"_209":1309,"_217":1272,"_243":1310,"_1274":1252},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1311,1312,1313],{"_78":1325,"_214":1326,"_211":1327,"_222":1328,"_1318":1329},{"_78":1308,"_214":1321,"_211":1322,"_222":1317,"_1318":1323},{"_78":1314,"_214":1315,"_211":1316,"_222":1317,"_1318":1319},"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",[1320],"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.",[1320,1324],"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",[1320,1324,1330],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1311,1312,1313],"containers",[1339,1340,1341],{"_207":1337,"_214":1353,"_162":1346,"_164":1354,"_209":1355,"_217":1272,"_243":1356,"_1274":1247},{"_207":1337,"_214":1345,"_162":1346,"_164":163,"_209":1347,"_217":1272,"_243":1348,"_1274":1248},{"_207":1337,"_214":1342,"_162":163,"_164":-7,"_209":1343,"_217":1272,"_243":1344,"_1274":1249},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[1349,1311],{"_78":1350,"_214":1315,"_211":1316,"_222":1351,"_1318":1352},"nim-dev","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[1320],"DeepSeek-R1","deepseek-ai","deepseek-r1",[1349,1311],"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",{"_1386":1387,"_1388":286,"_1389":30,"_1390":1391,"_1265":1392,"_1393":1394,"_1395":1387,"_1396":30,"_1397":30,"_1398":30,"_1399":1400},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1401,1402,1403,1404,1405,1406,1407],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1399":1429,"_46":1430,"_1393":1431},{"_1422":1423,"_1399":1424,"_46":1425,"_1395":1325},{"_1399":1420,"_46":1421,"_1393":1394},{"_1399":1417,"_46":1418,"_1393":1419},{"_1399":1414,"_46":1415,"_1393":1416},{"_1399":1411,"_46":1412,"_1393":1413},{"_1399":1408,"_46":1409,"_1393":1410},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1426],"Enterprise Catalog","/enterprise",[1427,1428],"NVAIE","READ","Explore Catalog","/","common-home",{"_112":113,"_114":115,"_116":117,"_118":119,"_120":121,"_122":123},{"_111":30}]