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

The GNMT v2 model is similar to the one discussed in the Google's Neural Machine\nTranslation System: Bridging the Gap between Human and Machine\nTranslation paper.

The most important difference between the two models is in the attention\nmechanism. In our model, the output from the first LSTM layer of the decoder\ngoes into the attention module, then the re-weighted context is concatenated\nwith inputs to all subsequent LSTM layers in the decoder at the current\ntimestep.

The same attention mechanism is also implemented in the default\nGNMT-like models from\nTensorFlow Neural Machine Translation Tutorial\nand\nNVIDIA OpenSeq2Seq Toolkit.

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

The following image shows the GNMT model architecture:

$\"TrainingLoss\"$

Default configuration

The following features were implemented in this model:

\n
general:
\n
- encoder and decoder are using shared embeddings
- data-parallel multi-GPU training
- dynamic loss scaling with backoff for Tensor Cores (mixed precision) training
- trained with label smoothing loss (smoothing factor 0.1)
\n
\n
encoder:
\n
- 4-layer LSTM, hidden size 1024, first layer is bidirectional, the rest are\nunidirectional
- with residual connections starting from 3rd layer
- dropout is applied on input to all LSTM layers, probability of dropout is\nset to 0.2
- hidden state of LSTM layers is initialized with zeros
- weights and bias of LSTM layers is initialized with uniform (-0.1, 0.1)\nDistribution
\n
\n
decoder:
\n
- 4-layer unidirectional LSTM with hidden size 1024 and fully-connected\nclassifier
- with residual connections starting from 3rd layer
- dropout is applied on input to all LSTM layers, probability of dropout is\nset to 0.2
- hidden state of LSTM layers is initialized with the last hidden state from\nencoder
- weights and bias of LSTM layers is initialized with uniform (-0.1, 0.1)\ndistribution
- weights and bias of fully-connected classifier is initialized with\nuniform (-0.1, 0.1) distribution
\n
\n
attention:
\n
- normalized Bahdanau attention
- output from first LSTM layer of decoder goes into attention,\nthen re-weighted context is concatenated with the input to all subsequent\nLSTM layers of the decoder at the current timestep
- linear transform of keys and queries is initialized with uniform (-0.1, 0.1), normalization scalar is initialized with 1.0 / sqrt(1024), normalization bias is initialized with zero
\n
\n
inference:
\n
- beam search with default beam size of 5
- with coverage penalty and length normalization, coverage penalty factor is\nset to 0.1, length normalization factor is set to 0.6 and length\nnormalization constant is set to 5.0
- de-tokenized BLEU computed by SacreBLEU
- motivation for choosing SacreBLEU
\n

When comparing the BLEU score, there are various tokenization approaches and\nBLEU calculation methodologies; therefore, ensure you align similar metrics.

Code from this repository can be used to train a larger, 8-layer GNMT v2 model.\nOur experiments show that a 4-layer model is significantly faster to train and\nyields comparable accuracy on the public\nWMT16 English-German\ndataset. The number of LSTM layers is controlled by the --num_layers parameter\nin the nmt.py script.

Feature support matrix

The following features are supported by this model.

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

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

Feature	GNMT TF
Automatic Mixed Precision	yes

Features

The following features are supported by this model.

Automatic Mixed Precision (AMP) - Computation graphs can be modified by TensorFlow on runtime to support mixed precision training. 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.

This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full mixed precision methodology in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.

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

For information about:

How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.
APEX tools for mixed precision training, see the NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch.

Enabling mixed precision

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

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

Enable TF-AMP graph rewrite:

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

Enable Automated Mixed Precision:

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

Enabling TF32

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

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

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

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

","resourceVersions",[232,233],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_211":28,"_212":-5,"_52":117,"_114":-5,"_118":213,"_120":-5,"_214":116,"_125":16,"_122":34,"_215":-5,"_121":34,"_216":217,"_218":91,"_219":220,"_221":91,"_222":223,"_224":91,"_225":226,"_227":91,"_228":229,"_230":91,"_123":231},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/resources/gnmt_tf1/-","-","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/dle/gnmt_tf1:20.06.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/resources/gnmt_tf1/-/version-history","accessCheckResult",{"_210":91},"hasRelatedCollections","context",{"_206":207,"_208":209},1787644243370,[],[],[199,200,201,202,203,204,205],{"_48":63,"_50":74,"_52":65,"_54":66,"_56":75,"_58":59},{"_48":63,"_50":72,"_52":65,"_54":66,"_56":73,"_58":59},{"_48":63,"_50":70,"_52":65,"_54":66,"_56":71,"_58":59},{"_48":63,"_50":68,"_52":65,"_54":66,"_56":69,"_58":59},{"_48":63,"_50":64,"_52":65,"_54":66,"_56":67,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":62,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","dle","allowed","versionId","description",["D",1663857301570],"totalSizeInBytes","malwareScanStatus","releaseNotes","### Changelog\n1. Mar 18, 2019\n * Initial release\n2. June, 2019\n * Performance improvements\n3. June, 2020\n * Updated performance tables to include A100 results\n\n### Known issues\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 GNMT v2 model.\n\n### Requirements\n\nThis repository contains Dockerfile which extends the TensorFlow 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- [TensorFlow 20.06-py3 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow)\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/geforce/turing/)\n - [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the 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 TensorFlow](https://docs.nvidia.com/deeplearning/dgx/tensorflow-release-notes/running.html#running).\n\nFor those unable to use the TensorFlow 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,\nperform the following steps using the default parameters of the GNMT v2 model\non the WMT16 English German dataset.\nFor the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n**1. Clone the repository.**\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/TensorFlow/Translation/GNMT\n```\n\n**2. Build the GNMT v2 TensorFlow container.**\n```\nbash scripts/docker/build.sh\n```\n\n**3. Start an interactive session in the NGC container to run.** training/inference.\n```\nbash scripts/docker/interactive.sh\n```\n\n**4. Download and preprocess the dataset.**\n\nData will be downloaded to the `data` directory (on the host). The `data`\ndirectory is mounted to the `/workspace/gnmt/data` location in the Docker\ncontainer.\n```\nbash scripts/wmt16_en_de.sh\n```\n\n**5. Start training.**\n\nAll results and logs are saved to the `results` directory (on the host) or to\nthe `/workspace/gnmt/results` directory (in the container). The training script\nsaves the checkpoint after every training epoch and after every 2000 training steps\nwithin each epoch. You can modify the results directory using the `--output_dir`\nargument.\n\nTo launch mixed precision training on 1 GPU, run:\n\n```\npython nmt.py --output_dir=results --batch_size=128 --learning_rate=5e-4 --amp\n```\n\nTo launch mixed precision training on 8 GPUs, run:\n\n```\npython nmt.py --output_dir=results --batch_size=1024 --num_gpus=8 --learning_rate=2e-3 --amp\n```\n\nTo launch FP32 (TF32 on NVIDIA Ampere GPUs) training on 1 GPU, run:\n\n```\npython nmt.py --output_dir=results --batch_size=128 --learning_rate=5e-4\n```\n\nTo launch FP32 (TF32 on NVIDIA Ampere GPUs) training on 8 GPUs, run:\n\n```\npython nmt.py --output_dir=results --batch_size=1024 --num_gpus=8 --learning_rate=2e-3\n```\n\n**6. Start evaluation.**\n\nThe training process automatically runs evaluation and outputs the BLEU score\nafter each training epoch. Additionally, after the training is done, you can\nmanually run inference on test dataset with the checkpoint saved during the\ntraining.\n\nTo launch mixed precision inference on 1 GPU, run:\n\n```\npython nmt.py --output_dir=results --infer_batch_size=128 --mode=infer --amp\n```\n\nTo launch FP32 (TF32 on NVIDIA Ampere GPUs) inference on 1 GPU, run:\n\n```\npython nmt.py --output_dir=results --infer_batch_size=128 --mode=infer\n```\n\n**7. Start translation.**\n\nAfter the training is done, you can translate custom sentences with the checkpoint saved during the training.\n\n```bash\necho \"The quick brown fox jumps over the lazy dog\" >file.txt\npython nmt.py --output_dir=results --mode=translate --translate-file=file.txt\ncat file.txt.trans\n```\n```\nDer schnelle braune Fuchs springt über den faulen Hund\n```","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training\nand inference, and the training results.\n\n### Scripts and sample code\nIn the root directory, the most important files are:\n\n* `nmt.py`: serves as the entry point to launch the training\n* `Dockerfile`: container with the basic set of dependencies to run GNMT v2\n* `requirements.txt`: set of extra requirements for running GNMT v2\n* `attention_wrapper.py`, `gnmt_model.py`, `model.py`: model definition\n* `estimator.py`: functions for training and inference\n\nIn the `script` directory, the most important files are:\n* `translate.py`: wrapped on `nmt.py` for benchmarking and running inference\n* `parse_log.py`: script for retrieving information in JSON format from the training log\n* `wmt16_en_de.sh`: script for downloading and preprocessing the dataset\n\nIn the `script/docker` directory, the files are:\n* `build.sh`: script for building the GNMT container\n* `interactive.sh`: script for running the GNMT container interactively\n\n### Parameters\n\nThe most useful arguments are as follows:\n\n```\n --learning_rate LEARNING_RATE\n Learning rate.\n --warmup_steps WARMUP_STEPS\n How many steps we inverse-decay learning.\n --max_train_epochs MAX_TRAIN_EPOCHS\n Max number of epochs.\n --target_bleu TARGET_BLEU\n Target bleu.\n --data_dir DATA_DIR Training/eval data directory.\n --translate_file TRANSLATE_FILE\n File to translate, works only with translate mode\n --output_dir OUTPUT_DIR\n Store log/model files.\n --batch_size BATCH_SIZE\n Total batch size.\n --log_step_count_steps LOG_STEP_COUNT_STEPS\n The frequency, in number of global steps, that the\n global step and the loss will be logged during training\n --num_gpus NUM_GPUS Number of gpus in each worker.\n --random_seed RANDOM_SEED\n Random seed (>0, set a specific seed).\n --ckpt CKPT Checkpoint file to load a model for inference.\n (defaults to newest checkpoint)\n --infer_batch_size INFER_BATCH_SIZE\n Batch size for inference mode.\n --beam_width BEAM_WIDTH\n beam width when using beam search decoder. If 0, use\n standard decoder with greedy helper.\n --amp use amp for training and inference\n --mode {train_and_eval,infer,translate}\n```\n\n### Command-line options\n\nTo see the full list of available options and their descriptions, use the `-h`\nor `--help` command line option, for example:\n\n```\npython nmt.py --help\n```\n\n### Getting the data\n\nThe GNMT v2 model was trained on the\n[WMT16 English-German](http://www.statmt.org/wmt16/translation-task.html)\ndataset and newstest2014 is used as a testing dataset.\n\nThis repository contains the `scripts/wmt16_en_de.sh` download script which\nautomatically downloads and preprocesses the training and test datasets. By\ndefault, data is downloaded to the `data` directory.\n\nOur download script is very similar to the `wmt16_en_de.sh` script from the\n[tensorflow/nmt](https://github.com/tensorflow/nmt/blob/master/nmt/scripts/wmt16_en_de.sh)\nrepository. Our download script contains an extra preprocessing step, which\ndiscards all pairs of sentences which can't be decoded by latin-1 encoder.\nThe `scripts/wmt16_en_de.sh` script uses the\n[subword-nmt](https://github.com/rsennrich/subword-nmt)\npackage to segment text into subword units (Byte Pair Encodings - [BPE](https://en.wikipedia.org/wiki/Byte_pair_encoding)). By default, the script builds\nthe shared vocabulary of 32,000 tokens.\n\nIn order to test with other datasets, the scripts need to be customized accordingly.\n\n#### Dataset guidelines\n\nThe process of downloading and preprocessing the data can be found in the\n`scripts/wmt16_en_de.sh` script.\n\nInitially, data is downloaded from [www.statmt.org](www.statmt.org). Then, `europarl-v7`,\n`commoncrawl` and `news-commentary` corpora are concatenated to form the\ntraining dataset, similarly `newstest2015` and `newstest2016` are concatenated\nto form the validation dataset. Raw data is preprocessed with\n[Moses](https://github.com/moses-smt/mosesdecoder), first by launching [Moses\ntokenizer](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl)\n(tokenizer breaks up text into individual words), then by launching\n[clean-corpus-n.perl](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/training/clean-corpus-n.perl)\nwhich removes invalid sentences and does initial filtering by sequence length.\n\nSecond stage of preprocessing is done by launching the\n`scripts/filter_dataset.py` script, which discards all pairs of sentences that\ncan't be decoded by latin-1 encoder.\n\nThird state of preprocessing uses the\n[subword-nmt](https://github.com/rsennrich/subword-nmt) package. First it\nbuilds shared [byte pair\nencoding](https://en.wikipedia.org/wiki/Byte_pair_encoding) vocabulary with\n32,000 merge operations (command `subword-nmt learn-bpe`), then it applies\ngenerated vocabulary to training, validation and test corpora (command\n`subword-nmt apply-bpe`).\n\n### Training process\n\nThe training configuration can be launched by running the `nmt.py` script.\nBy default, the training script saves the checkpoint after every training epoch\nand after every 2000 training steps within each epoch.\nResults are stored in the `results` directory.\n\nThe training script launches data-parallel training on multiple GPUs. We have\ntested reliance on up to 8 GPUs on a single node.\n\nAfter each training epoch, the script runs an evaluation and outputs a BLEU\nscore on the test dataset (*newstest2014*). BLEU is computed by the\n[SacreBLEU](https://github.com/awslabs/sockeye/tree/master/sockeye_contrib/sacrebleu)\npackage. Logs from the training and evaluation are saved to the `results`\ndirectory.\n\nThe training script automatically runs testing after each training epoch. The\nresults from the testing are printed to the standard output and saved to the\nlog files.\n\nThe summary after each training epoch is printed in the following format:\n\n```\ntraining time for epoch 1: 29.37 mins (2918.36 sent/sec, 139640.48 tokens/sec)\n[...]\nbleu is 20.50000\neval time for epoch 1: 1.57 mins (78.48 sent/sec, 4283.88 tokens/sec)\n```\nThe BLEU score is computed on the test dataset.\nPerformance is reported in total sentences per second and in total tokens per\nsecond. The performance result is averaged over an entire training epoch and\nsummed over all GPUs participating in the training.\n\nTo view all available options for training, run `python nmt.py --help`.\n\n### Inference process\n\nValidation and translation can be run by launching the `nmt.py` script, although, it requires a\npre-trained model checkpoint and tokenized input (for validation) and non-tokenized input (for translation).\n\n#### Validation process\n\nThe `nmt.py` script supports batched validation (`--mode=infer` flag). By\ndefault, it launches beam search with beam size of 5, coverage penalty term and\nlength normalization term. Greedy decoding can be enabled by setting the\n`--beam_width=1` flag for the `nmt.py` inference script. To control the\nbatch size use the `--infer_batch_size` flag.\n\nTo view all available options for validation, run `python nmt.py --help`.\n\n#### Translation process\n\nThe `nmt.py` script supports batched translation (`--mode=translate` flag). By\ndefault, it launches beam search with beam size of 5, coverage penalty term and\nlength normalization term. Greedy decoding can be enabled by setting the\n`--beam_width=1` flag for the `nmt.py` prediction script. To control the\nbatch size use the `--infer_batch_size` flag.\n\nThe input file may contain many sentences, each on a new line. The file can be specified\nby the `--translate_file ` flag. This script will create a new file called `.trans`,\nwith translation of the input file.\n\nTo view all available options for translation, run `python nmt.py --help`.","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA's latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\n\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 training performance, run:\n\n* `python nmt.py --output_dir=results --max_train_epochs=1 --num_gpus --batch_size --amp` for mixed precision\n* `python nmt.py --output_dir=results --max_train_epochs=1 --num_gpus --batch_size ` for FP32/TF32\n\nThe log file will contain training performance in the following format:\n\n```\ntraining time for epoch 1: 25.75 mins (3625.19 sent/sec, 173461.27 tokens/sec)\n```\n\n#### Inference performance benchmark\n\nTo benchmark inference performance, run the `scripts/translate.py` script:\n\n* For FP32/TF32:\n `python scripts/translate.py --output_dir=/path/to/trained/model --beam_width --infer_batch_size `\n\n* For mixed precision\n `python scripts/translate.py --output_dir=/path/to/trained/model --amp --beam_width --infer_batch_size `\n\nThe benchmark requires a checkpoint from a fully trained model.\n\n### Results\n\nThe following sections provide details on how we achieved our performance and\naccuracy in training and inference.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the `examples/DGXA100_{TF32,AMP}_8GPU.sh`\ntraining script in the tensorflow-20.06-tf1-py3 NGC container\non NVIDIA DGX A100 (8x A100 40GB) GPUs.\n\n| **GPUs** | **Batch size / GPU** |**Accuracy - mixed precision (BLEU)** | **Accuracy - TF32 (BLEU)** | **Time to train - mixed precision** | **Time to train - TF32** | **Time to train speedup (TF32 to mixed precision)** |\n| --- | --- | ----- | ----- | -------- | -------- | ---- |\n| 8 | 128 | 25.1 | 24.31 | 96 min | 139 min | 1.45 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `nmt.py` script in the\ntensorflow-19.07-py3 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.\n\n| **GPUs** | **Batch size / GPU** |**Accuracy - mixed precision (BLEU)** | **Accuracy - FP32 (BLEU)** | **Time to train - mixed precision** | **Time to train - FP32** | **Time to train speedup (FP32 to mixed precision)** |\n| --- | --- | ----- | ----- | -------- | -------- | ---- |\n| 1 | 128 | 24.90 | 24.84 | 763 min | 1237 min | 1.62 |\n| 8 | 128 | 24.33 | 24.34 | 168 min | 237 min | 1.41 |\n\nIn the following plot, the BLEU scores after each training epoch for different\nconfigurations are displayed.\n\n![BLEUScore](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow/Translation/GNMT//img/bleu_score.png)\n\n\n##### Training stability test\n\nThe GNMT v2 model was trained for 6 epochs, starting from 6 different initial\nrandom seeds. After each training epoch, the model was evaluated on the test\ndataset and the BLEU score was recorded. The training was performed in the\ntensorflow-20.06-tf1-py3 NGC container.\n\nIn the following tables, the BLEU scores after each training epoch for different\ninitial random seeds are displayed.\n\n###### NVIDIA DGX A100 with 8 Ampere A100 40GB GPUs with TF32.\n\n| Epoch | Average | Standard deviation | Minimum | Median | Maximum |\n| ----- | ------- | ------------------ | ------- | ------ | ------- |\n| 1 | 20.272 | 0.165 | 19.760 | 20.295 | 20.480 |\n| 2 | 21.911 | 0.145 | 21.650 | 21.910 | 22.230 |\n| 3 | 22.731 | 0.140 | 22.490 | 22.725 | 23.020 |\n| 4 | 23.142 | 0.164 | 22.930 | 23.090 | 23.440 |\n| 5 | 23.967 | 0.137 | 23.760 | 23.940 | 24.260 |\n| 6 | 24.358 | 0.143 | 24.120 | 24.360 | 24.610 |\n\n###### NVIDIA DGX-1 with 8 Tesla V100 16GB GPUs with FP32.\n\n| Epoch | Average | Standard deviation | Minimum | Median | Maximum |\n| ----- | ------- | ------------------ | ------- | ------ | ------- |\n| 1 | 20.259 | 0.225 | 19.820 | 20.300 | 20.590 |\n| 2 | 21.954 | 0.194 | 21.540 | 21.955 | 22.370 |\n| 3 | 22.729 | 0.150 | 22.480 | 22.695 | 23.110 |\n| 4 | 23.218 | 0.210 | 22.820 | 23.225 | 23.470 |\n| 5 | 23.921 | 0.114 | 23.680 | 23.910 | 24.080 |\n| 6 | 24.381 | 0.131 | 24.160 | 24.375 | 24.590 |\n\n#### Inference accuracy results\n\n##### Inference accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `scripts/translate.py` script in the tensorflow-19.07-py3 NGC container on NVIDIA DGX-1 8x V100 16GB GPUs.\n\n* For mixed precision: `python scripts/translate.py --output_dir=/path/to/trained/model --beam_width 1,2,5 --infer_batch_size 128 --amp`\n\n* For FP32: `python scripts/translate.py --output_dir=/path/to/trained/model --beam_width 1,2,5 --infer_batch_size 128`\n\n| **Batch size** | **Beam size** | **Mixed precision BLEU** | **FP32 BLEU** |\n|:---:|:---:|:---:|:---:|\n|128|1|23.80|23.80|\n|128|2|24.58|24.59|\n|128|5|25.10|25.09|\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the `examples/DGXA100_{TF32,AMP}_{1,8}GPU.sh`\ntraining script in the tensorflow-20.06-tf1-py3 NGC container\non NVIDIA DGX A100 (8x A100 40GB) GPUs.\nPerformance numbers (in items/images per second)\nwere averaged over an entire training epoch.\n\n| **GPUs** | **Batch size / GPU** | **Throughput - mixed precision (tokens/s)** | **Throughput - TF32 (tokens/s)** | **Throughput speedup (TF32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - TF32** |\n| --- | --- | ------- | ------- | ---- | ---- | ---- |\n| 1 | 128 | 29 911 | 31 110 | 0.96 | 1.00 | 1.00 |\n| 8 | 128 | 181 384 | 175 292 | 1.03 | 6.06 | 5.63 |\n\nTo achieve these same results, follow the steps in the\n[Quick Start Guide](#quick-start-guide).\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `nmt.py` script in the tensorflow-19.07-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs.\nPerformance numbers (in tokens per second) were averaged over an entire\ntraining epoch.\n\n| **GPUs** | **Batch size / GPU** | **Throughput - mixed precision (tokens/s)** | **Throughput - FP32 (tokens/s)** | **Throughput speedup (FP32 - mixed precision)** | **Weak scaling - mixed precision** | **Weak scaling - FP32** |\n| --- | --- | ------- | ------ | ---- | ---- | ---- |\n| 1 | 128 | 23 011 | 14 106 | 1.63 | 1.00 | 1.00 |\n| 8 | 128 | 138 106 | 93 688 | 1.47 | 6.00 | 6.64 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n#### Inference performance results\n\nThe benchmark requires a checkpoint from a fully trained model.\n\nTo launch the inference benchmark in mixed precision on 1 GPU, run:\n\n```\npython scripts/translate.py --output_dir=/path/to/trained/model --beam_width 1,2,5 --infer_batch_size 1,2,4,8,32,128,512 --amp\n```\n\nTo launch the inference benchmark in FP32/TF32 on 1 GPU, run:\n\n```\npython scripts/translate.py --output_dir=/path/to/trained/model --beam_width 1,2,5 --infer_batch_size 1,2,4,8,32,128,512\n```\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)\n\nOur results were obtained by running the\n`python scripts/translate.py --infer_batch_size 1,2,4,8,32,128,512 --beam_width 1,2,5 {--amp}`\ninferencing benchmarking script in the tensorflow-20.06-tf1-py3 NGC container\non NVIDIA DGX A100 (1x A100 40GB) GPU.\n\nFP16\n\n| **Batch size** | **Beam width** | **Bleu** | **Sentences/sec** | **Tokens/sec** | **Latency Avg** | **Latency 50%** | **Latency 90%** | **Latency 95%** | **Latency 99%** | **Latency 100%** |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| 1 | 1 | 23.80 | 13.67 | 737.89 | 73.15 | 67.69 | 121.98 | 137.20 | 162.74 | 201.06 |\n| 1 | 2 | 24.58 | 13.40 | 721.18 | 74.65 | 69.12 | 123.99 | 138.82 | 169.58 | 198.49 |\n| 1 | 5 | 25.10 | 12.12 | 647.78 | 82.53 | 76.53 | 136.35 | 152.59 | 196.09 | 216.55 |\n| 2 | 1 | 23.80 | 21.55 | 1163.16 | 92.82 | 88.15 | 139.88 | 152.49 | 185.18 | 208.35 |\n| 2 | 2 | 24.58 | 21.07 | 1134.42 | 94.91 | 89.62 | 142.08 | 158.12 | 188.00 | 205.08 |\n| 2 | 5 | 25.10 | 19.59 | 1047.21 | 102.10 | 96.20 | 152.36 | 172.46 | 211.96 | 219.87 |\n| 4 | 1 | 23.80 | 36.98 | 1996.27 | 108.16 | 105.07 | 150.42 | 161.56 | 200.99 | 205.87 |\n| 4 | 2 | 24.57 | 34.92 | 1880.48 | 114.53 | 111.42 | 160.29 | 177.14 | 205.32 | 211.80 |\n| 4 | 5 | 25.10 | 31.56 | 1687.34 | 126.74 | 122.06 | 179.68 | 201.38 | 225.08 | 229.14 |\n| 8 | 1 | 23.80 | 64.52 | 3482.81 | 123.99 | 122.89 | 159.89 | 174.66 | 201.12 | 205.59 |\n| 8 | 2 | 24.57 | 59.04 | 3178.17 | 135.50 | 135.23 | 180.50 | 191.66 | 214.95 | 216.84 |\n| 8 | 5 | 25.09 | 55.51 | 2967.82 | 144.11 | 141.98 | 198.39 | 218.88 | 223.55 | 225.61 |\n| 32 | 1 | 23.80 | 193.54 | 10447.04 | 165.34 | 163.56 | 211.67 | 215.37 | 221.07 | 221.14 |\n| 32 | 2 | 24.57 | 182.00 | 9798.09 | 175.82 | 176.04 | 220.33 | 224.25 | 226.45 | 227.05 |\n| 32 | 5 | 25.10 | 141.63 | 7572.02 | 225.94 | 225.59 | 278.38 | 279.56 | 281.61 | 282.13 |\n| 128 | 1 | 23.80 | 556.57 | 30042.59 | 229.98 | 226.81 | 259.05 | 260.26 | 260.74 | 260.85 |\n| 128 | 2 | 24.57 | 400.02 | 21535.38 | 319.98 | 328.23 | 351.31 | 352.82 | 353.01 | 353.06 |\n| 128 | 5 | 25.10 | 235.14 | 12570.95 | 544.35 | 576.62 | 581.95 | 582.64 | 583.61 | 583.85 |\n| 512 | 1 | 23.80 | 903.83 | 48786.58 | 566.48 | 570.44 | 579.74 | 580.66 | 581.39 | 581.57 |\n| 512 | 2 | 24.58 | 588.63 | 31689.07 | 869.81 | 894.90 | 902.65 | 902.85 | 903.00 | 903.04 |\n| 512 | 5 | 25.10 | 285.86 | 15283.40 | 1791.06 | 1835.19 | 1844.29 | 1845.59 | 1846.63 | 1846.89 |\n\nTF32\n\n| **Batch size** | **Beam width** | **Bleu** | **Sentences/sec** | **Tokens/sec** | **Latency Avg** | **Latency 50%** | **Latency 90%** | **Latency 95%** | **Latency 99%** | **Latency 100%** |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| 1 | 1 | 23.82 | 13.25 | 715.47 | 75.45 | 69.81 | 125.63 | 141.89 | 169.70 | 209.78 |\n| 1 | 2 | 24.59 | 13.21 | 711.16 | 75.72 | 70.06 | 124.75 | 140.20 | 173.23 | 201.39 |\n| 1 | 5 | 25.08 | 12.38 | 661.99 | 80.76 | 74.90 | 131.93 | 148.91 | 187.05 | 208.39 |\n| 2 | 1 | 23.82 | 21.61 | 1166.56 | 92.55 | 87.25 | 139.54 | 151.77 | 180.24 | 209.05 |\n| 2 | 2 | 24.59 | 21.24 | 1143.63 | 94.17 | 88.78 | 139.70 | 156.61 | 189.09 | 205.06 |\n| 2 | 5 | 25.10 | 19.49 | 1042.17 | 102.62 | 96.14 | 153.38 | 172.89 | 213.99 | 219.54 |\n| 4 | 1 | 23.81 | 35.84 | 1934.49 | 111.62 | 108.73 | 154.52 | 165.42 | 207.88 | 211.29 |\n| 4 | 2 | 24.58 | 34.71 | 1869.20 | 115.24 | 111.24 | 161.24 | 177.73 | 208.12 | 212.74 |\n| 4 | 5 | 25.09 | 32.24 | 1723.86 | 124.07 | 119.35 | 177.54 | 196.69 | 221.10 | 223.52 |\n| 8 | 1 | 23.80 | 64.08 | 3459.74 | 124.84 | 123.61 | 161.92 | 177.06 | 205.47 | 206.47 |\n| 8 | 2 | 24.61 | 59.31 | 3193.52 | 134.89 | 133.44 | 182.92 | 192.71 | 216.04 | 218.78 |\n| 8 | 5 | 25.10 | 56.60 | 3026.29 | 141.35 | 138.61 | 194.52 | 213.65 | 220.24 | 221.45 |\n| 32 | 1 | 23.80 | 195.31 | 10544.22 | 163.85 | 162.80 | 212.71 | 215.41 | 216.92 | 217.34 |\n| 32 | 2 | 24.61 | 185.66 | 9996.59 | 172.36 | 171.07 | 216.46 | 221.64 | 223.68 | 225.25 |\n| 32 | 5 | 25.11 | 147.24 | 7872.61 | 217.34 | 214.97 | 269.75 | 270.71 | 271.44 | 272.87 |\n| 128 | 1 | 23.81 | 576.54 | 31123.19 | 222.02 | 219.25 | 249.44 | 249.75 | 249.88 | 249.91 |\n| 128 | 2 | 24.57 | 419.87 | 22609.82 | 304.86 | 314.47 | 332.18 | 334.13 | 336.22 | 336.74 |\n| 128 | 5 | 25.10 | 245.76 | 13138.84 | 520.83 | 552.68 | 558.89 | 559.09 | 559.13 | 559.13 |\n| 512 | 1 | 23.80 | 966.24 | 52156.34 | 529.89 | 534.82 | 558.30 | 559.33 | 560.16 | 560.36 |\n| 512 | 2 | 24.58 | 642.41 | 34590.81 | 797.00 | 812.40 | 824.23 | 825.92 | 827.27 | 827.61 |\n| 512 | 5 | 25.10 | 289.33 | 15468.09 | 1769.61 | 1817.19 | 1849.83 | 1855.17 | 1859.45 | 1860.51 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nOur results were obtained by running the\n`python scripts/translate.py --infer_batch_size 1,2,4,8,32,128,512 --beam_width 1,2,5 {--amp}`\ninferencing benchmarking script in the tensorflow-20.06-tf1-py3 NGC container\non NVIDIA DGX-1 with (1x V100 16GB) GPU.\n\nFP16\n\n| **Batch size** | **Sequence length** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |\n|------------|-----------------|-----|-----|-----|-----|-----|\n| 1 | 1 | 23.78 | 9.06 | 489.00 | 110.41 | 102.80 | 183.54 | 206.33 | 242.44 | 306.21 |\n| 1 | 2 | 24.58 | 8.68 | 467.35 | 115.22 | 107.17 | 188.75 | 212.36 | 258.15 | 306.15 |\n| 1 | 5 | 25.09 | 8.39 | 448.32 | 119.25 | 109.79 | 195.68 | 220.56 | 276.41 | 325.65 |\n| 2 | 1 | 23.82 | 14.59 | 787.70 | 137.04 | 129.38 | 206.35 | 224.94 | 267.30 | 318.60 |\n| 2 | 2 | 24.57 | 14.44 | 777.60 | 138.51 | 131.07 | 206.67 | 228.95 | 275.56 | 311.23 |\n| 2 | 5 | 25.11 | 13.78 | 736.99 | 145.11 | 136.76 | 216.01 | 243.24 | 299.28 | 315.88 |\n| 4 | 1 | 23.82 | 23.79 | 1284.24 | 168.14 | 164.13 | 234.70 | 248.42 | 308.38 | 325.46 |\n| 4 | 2 | 24.59 | 22.67 | 1220.66 | 176.45 | 171.40 | 243.76 | 271.92 | 314.79 | 330.19 |\n| 4 | 5 | 25.08 | 22.33 | 1194.00 | 179.12 | 174.04 | 253.36 | 281.88 | 318.76 | 340.01 |\n| 8 | 1 | 23.81 | 43.33 | 2338.68 | 184.63 | 183.25 | 237.66 | 266.73 | 305.89 | 315.03 |\n| 8 | 2 | 24.60 | 39.12 | 2106.44 | 204.49 | 200.96 | 276.05 | 294.53 | 327.61 | 335.50 |\n| 8 | 5 | 25.10 | 37.16 | 1987.05 | 215.26 | 210.92 | 295.65 | 323.83 | 337.09 | 343.03 |\n| 32 | 1 | 23.82 | 129.52 | 6992.15 | 247.06 | 245.81 | 317.71 | 325.54 | 330.09 | 335.04 |\n| 32 | 2 | 24.55 | 123.28 | 6637.86 | 259.57 | 261.07 | 319.13 | 333.45 | 338.75 | 342.57 |\n| 32 | 5 | 25.05 | 88.74 | 4744.33 | 360.61 | 359.27 | 446.65 | 448.40 | 455.93 | 461.86 |\n| 128 | 1 | 23.80 | 332.81 | 17964.83 | 384.60 | 382.14 | 434.46 | 436.71 | 439.64 | 440.37 |\n| 128 | 2 | 24.59 | 262.87 | 14153.59 | 486.93 | 506.45 | 528.87 | 530.90 | 533.09 | 533.64 |\n| 128 | 5 | 25.08 | 143.91 | 7695.36 | 889.42 | 932.93 | 965.67 | 966.26 | 966.53 | 966.59 |\n| 512 | 1 | 23.80 | 613.57 | 33126.42 | 834.46 | 848.06 | 868.21 | 869.04 | 869.70 | 869.86 |\n| 512 | 2 | 24.59 | 387.72 | 20879.62 | 1320.54 | 1343.05 | 1354.40 | 1356.50 | 1358.19 | 1358.61 |\n| 512 | 5 | 25.10 | 199.48 | 10664.34 | 2566.67 | 2628.50 | 2642.59 | 2644.73 | 2646.44 | 2646.86 |\n\nFP32\n\n| **Batch size** | **Sequence length** | **Throughput Avg** | **Latency Avg** | **Latency 90%** |**Latency 95%** |**Latency 99%** |\n|------------|-----------------|-----|-----|-----|-----|-----|\n| 1 | 1 | 23.80 | 8.37 | 451.86 | 119.46 | 111.26 | 199.36 | 224.49 | 269.03 | 330.72 |\n| 1 | 2 | 24.59 | 8.83 | 475.11 | 113.31 | 104.54 | 187.79 | 210.64 | 260.42 | 317.45 |\n| 1 | 5 | 25.09 | 7.74 | 413.92 | 129.15 | 119.44 | 212.84 | 239.52 | 305.47 | 349.09 |\n| 2 | 1 | 23.80 | 13.96 | 753.79 | 143.22 | 135.73 | 213.96 | 235.89 | 284.62 | 330.71 |\n| 2 | 2 | 24.59 | 12.96 | 697.63 | 154.33 | 145.01 | 230.88 | 255.31 | 306.71 | 340.36 |\n| 2 | 5 | 25.09 | 12.67 | 677.23 | 157.88 | 148.24 | 236.50 | 266.91 | 322.94 | 349.55 |\n| 4 | 1 | 23.80 | 22.42 | 1209.97 | 178.44 | 172.70 | 247.51 | 266.07 | 326.95 | 343.86 |\n| 4 | 2 | 24.59 | 20.55 | 1106.07 | 194.68 | 188.83 | 271.75 | 295.08 | 345.76 | 364.00 |\n| 4 | 5 | 25.09 | 21.19 | 1132.58 | 188.81 | 182.77 | 268.18 | 298.53 | 331.96 | 357.36 |\n| 8 | 1 | 23.80 | 39.32 | 2122.26 | 203.48 | 201.89 | 263.28 | 286.71 | 332.70 | 348.93 |\n| 8 | 2 | 24.59 | 37.51 | 2019.43 | 213.26 | 211.55 | 283.67 | 302.28 | 338.47 | 356.51 |\n| 8 | 5 | 25.09 | 31.69 | 1694.02 | 252.46 | 245.33 | 348.95 | 378.16 | 392.72 | 401.73 |\n| 32 | 1 | 23.80 | 118.51 | 6396.93 | 270.02 | 269.22 | 337.17 | 352.12 | 361.36 | 361.40 |\n| 32 | 2 | 24.59 | 100.23 | 5395.33 | 319.28 | 318.89 | 399.80 | 403.12 | 414.51 | 423.41 |\n| 32 | 5 | 25.09 | 68.59 | 3666.77 | 466.55 | 466.84 | 581.77 | 586.42 | 589.04 | 593.41 |\n| 128 | 1 | 23.80 | 256.49 | 13845.09 | 499.04 | 492.36 | 562.12 | 567.20 | 571.18 | 572.18 |\n| 128 | 2 | 24.59 | 176.83 | 9519.12 | 723.86 | 754.89 | 792.12 | 793.86 | 796.44 | 797.09 |\n| 128 | 5 | 25.09 | 96.21 | 5143.17 | 1330.48 | 1420.94 | 1427.91 | 1431.02 | 1435.23 | 1436.28 |\n| 512 | 1 | 23.80 | 366.07 | 19759.97 | 1398.63 | 1421.81 | 1457.81 | 1461.04 | 1463.63 | 1464.27 |\n| 512 | 2 | 24.59 | 225.48 | 12137.77 | 2270.75 | 2323.62 | 2338.62 | 2340.94 | 2342.80 | 2343.27 |\n| 512 | 5 | 25.09 | 106.02 | 5667.78 | 4829.31 | 4946.65 | 4956.15 | 4957.85 | 4959.21 | 4959.55 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA T4\n\nOur results were obtained by running the `scripts/translate.py` script in the tensorflow-19.07-py3 NGC container on NVIDIA T4.\n\nReported mixed precision speedups are relative to FP32 numbers for corresponding configuration.\n\n| **Batch size** | **Beam size** | **Mixed precision tokens/s** | **Speedup** | **Mixed precision average latency (ms)** | **Average latency speedup** | **Mixed precision latency 50% (ms)** | **Latency 50% speedup** | **Mixed precision latency 90% (ms)** | **Latency 90% speedup** | **Mixed precision latency 95% (ms)** | **Latency 95% speedup** | **Mixed precision latency 99% (ms)** | **Latency 99% speedup** | **Mixed precision latency 100% (ms)** | **Latency 100% speedup** |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 1 | 1 | 643 | 1.278 | 84 | 1.278 | 78 | 1.279 | 138 | 1.309 | 154 | 1.312 | 180 | 1.304 | 220 | 1.296 |\n| 1 | 2 | 584 | 1.693 | 92 | 1.692 | 86 | 1.686 | 150 | 1.743 | 168 | 1.737 | 201 | 1.770 | 236 | 1.742 |\n| 1 | 5 | 552 | 1.702 | 97 | 1.701 | 90 | 1.696 | 158 | 1.746 | 176 | 1.738 | 218 | 1.769 | 244 | 1.742 |\n| 2 | 1 | 948 | 1.776 | 114 | 1.776 | 108 | 1.769 | 170 | 1.803 | 184 | 1.807 | 218 | 1.783 | 241 | 1.794 |\n| 2 | 2 | 912 | 1.761 | 118 | 1.760 | 112 | 1.763 | 175 | 1.776 | 192 | 1.781 | 226 | 1.770 | 246 | 1.776 |\n| 2 | 5 | 832 | 1.900 | 128 | 1.900 | 121 | 1.910 | 192 | 1.912 | 214 | 1.922 | 258 | 1.922 | 266 | 1.905 |\n| 4 | 1 | 1596 | 1.792 | 135 | 1.792 | 132 | 1.791 | 187 | 1.799 | 197 | 1.815 | 241 | 1.784 | 245 | 1.796 |\n| 4 | 2 | 1495 | 1.928 | 144 | 1.927 | 141 | 1.926 | 201 | 1.927 | 216 | 1.936 | 250 | 1.956 | 264 | 1.890 |\n| 4 | 5 | 1308 | 1.702 | 164 | 1.702 | 159 | 1.702 | 230 | 1.722 | 251 | 1.742 | 283 | 1.708 | 288 | 1.699 |\n| 8 | 1 | 2720 | 1.981 | 159 | 1.981 | 158 | 1.992 | 204 | 1.975 | 219 | 1.986 | 249 | 1.987 | 252 | 1.966 |\n| 8 | 2 | 2554 | 1.809 | 169 | 1.808 | 168 | 1.829 | 224 | 1.797 | 237 | 1.783 | 260 | 1.807 | 262 | 1.802 |\n| 8 | 5 | 1979 | 1.768 | 216 | 1.768 | 213 | 1.780 | 292 | 1.797 | 319 | 1.793 | 334 | 1.760 | 336 | 1.769 |\n| 32 | 1 | 7449 | 1.775 | 232 | 1.774 | 231 | 1.777 | 292 | 1.789 | 300 | 1.760 | 301 | 1.768 | 301 | 1.768 |\n| 32 | 2 | 5569 | 1.670 | 309 | 1.669 | 311 | 1.672 | 389 | 1.652 | 392 | 1.665 | 401 | 1.651 | 404 | 1.644 |\n| 32 | 5 | 3079 | 1.867 | 556 | 1.867 | 555 | 1.865 | 692 | 1.858 | 695 | 1.860 | 702 | 1.847 | 703 | 1.847 |\n| 128 | 1 | 12986 | 1.662 | 532 | 1.662 | 529 | 1.667 | 607 | 1.643 | 608 | 1.645 | 609 | 1.647 | 609 | 1.647 |\n| 128 | 2 | 7856 | 1.734 | 878 | 1.734 | 911 | 1.755 | 966 | 1.742 | 967 | 1.741 | 968 | 1.744 | 968 | 1.744 |\n| 128 | 5 | 3361 | 1.683 | 2036 | 1.682 | 2186 | 1.678 | 2210 | 1.673 | 2210 | 1.674 | 2211 | 1.674 | 2211 | 1.674 |\n| 512 | 1 | 14932 | 1.825 | 1851 | 1.825 | 1889 | 1.808 | 1927 | 1.801 | 1928 | 1.800 | 1929 | 1.800 | 1930 | 1.799 |\n| 512 | 2 | 8109 | 1.786 | 3400 | 1.786 | 3505 | 1.783 | 3520 | 1.782 | 3523 | 1.781 | 3525 | 1.781 | 3525 | 1.781 |\n| 512 | 5 | 3370 | 1.802 | 8123 | 1.801 | 8376 | 1.798 | 8391 | 1.804 | 8394 | 1.804 | 8396 | 1.805 | 8397 | 1.805 |","hasPerformance",[],{"_86":183,"_113":235,"_114":-5,"_122":34,"_121":34,"_123":234,"_218":91},{"_86":28,"_113":28,"_114":-5,"_122":34,"_121":34,"_123":234,"_218":91},[],"Latest (20.06.0)","nvidia/dle/gnmt_tf1","key","nvidia/dle/resources/gnmt_tf1","resourceId","gnmt_tf1","shortDescription","The GNMT v2 model is an improved version of the first Google's Neural Machine Translation System with a modified attention mechanism.","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/gnmt-tf1)\n\n\nThe GNMT v2 model is similar to the one discussed in the [Google's Neural Machine\nTranslation System: Bridging the Gap between Human and Machine\nTranslation](https://arxiv.org/abs/1609.08144) paper.\n\nThe most important difference between the two models is in the attention\nmechanism. In our model, the output from the first LSTM layer of the decoder\ngoes into the attention module, then the re-weighted context is concatenated\nwith inputs to all subsequent LSTM layers in the decoder at the current\ntimestep.\n\nThe same attention mechanism is also implemented in the default\nGNMT-like models from\n[TensorFlow Neural Machine Translation Tutorial](https://github.com/tensorflow/nmt)\nand\n[NVIDIA OpenSeq2Seq Toolkit](https://github.com/NVIDIA/OpenSeq2Seq).\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 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n### Model architecture\n\nThe following image shows the GNMT model architecture:\n\n![TrainingLoss](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/TensorFlow/Translation/GNMT//img/diagram.png)\n\n\n### Default configuration\n\nThe following features were implemented in this model:\n\n* general:\n * encoder and decoder are using shared embeddings\n * data-parallel multi-GPU training\n * dynamic loss scaling with backoff for Tensor Cores (mixed precision) training\n * trained with label smoothing loss (smoothing factor 0.1)\n\n* encoder:\n * 4-layer LSTM, hidden size 1024, first layer is bidirectional, the rest are\n unidirectional\n * with residual connections starting from 3rd layer\n * dropout is applied on input to all LSTM layers, probability of dropout is\n set to 0.2\n * hidden state of LSTM layers is initialized with zeros\n * weights and bias of LSTM layers is initialized with uniform (-0.1, 0.1)\n Distribution\n\n* decoder:\n * 4-layer unidirectional LSTM with hidden size 1024 and fully-connected\n classifier\n * with residual connections starting from 3rd layer\n * dropout is applied on input to all LSTM layers, probability of dropout is\n set to 0.2\n * hidden state of LSTM layers is initialized with the last hidden state from\n encoder\n * weights and bias of LSTM layers is initialized with uniform (-0.1, 0.1)\n distribution\n * weights and bias of fully-connected classifier is initialized with\n uniform (-0.1, 0.1) distribution\n\n* attention:\n * normalized Bahdanau attention\n * output from first LSTM layer of decoder goes into attention,\n then re-weighted context is concatenated with the input to all subsequent\n LSTM layers of the decoder at the current timestep\n * linear transform of keys and queries is initialized with uniform (-0.1, 0.1), normalization scalar is initialized with 1.0 / sqrt(1024), normalization bias is initialized with zero\n\n* inference:\n * beam search with default beam size of 5\n * with coverage penalty and length normalization, coverage penalty factor is\n set to 0.1, length normalization factor is set to 0.6 and length\n normalization constant is set to 5.0\n * de-tokenized BLEU computed by [SacreBLEU](https://github.com/awslabs/sockeye/tree/master/sockeye_contrib/sacrebleu)\n * [motivation](https://github.com/awslabs/sockeye/tree/master/sockeye_contrib/sacrebleu#motivation) for choosing SacreBLEU\n\nWhen comparing the BLEU score, there are various tokenization approaches and\nBLEU calculation methodologies; therefore, ensure you align similar metrics.\n\nCode from this repository can be used to train a larger, 8-layer GNMT v2 model.\nOur experiments show that a 4-layer model is significantly faster to train and\nyields comparable accuracy on the public\n[WMT16 English-German](http://www.statmt.org/wmt16/translation-task.html)\ndataset. The number of LSTM layers is controlled by the `--num_layers` parameter\nin the `nmt.py` script.\n\n### Feature support matrix\n\nThe following features are supported by this model.\n\n| **Feature** | **GNMT TF** |\n|:---:|:--------:|\n| Automatic Mixed Precision | yes |\n\n#### Features\n\nThe following features are supported by this model.\n\n* Automatic Mixed Precision (AMP) - Computation graphs can be modified by TensorFlow on runtime to support mixed precision training. 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\nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/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- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n- 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\nMixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.\n\nTo enable mixed precision, you can simply add the values to the environmental variables inside your training script:\n- Enable TF-AMP graph rewrite:\n ```\n os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n ```\n\n- Enable Automated Mixed Precision:\n ```\n os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n ```\n\n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.\n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.","displayName","GNMT v2 for TensorFlow1",["D",1680632071460],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/gnmt_tf1","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[275],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_244":276,"_277":278,"_279":280,"_281":88},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_282":283,"_277":278},"category","field","application",{"_152":1261,"_154":1262},"ngcApps",[1214],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1195":1196,"_1197":1198,"_1199":1200,"_1201":1202,"_1203":295,"_110":321,"_1204":1205,"_1206":59,"_1207":91,"_1208":91,"_1209":1210,"_1211":91,"_1212":1213},"amplitude",{"_1192":1193,"_833":-7,"_1194":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1186":1187,"_1188":1189,"_1190":1191},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787644243232,"1.12.0","defaultSearchSuggestions",{"_1073":401,"_1074":1075,"_1076":1077,"_1078":1079},"announcements",[],"consentState",{"_358":1063,"_1064":91,"_1065":34,"_1066":34,"_1067":1068},"consentConfig",{"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359},"showContactAdmin",1787644243233,[],[],[335,336,337,338,339],{"_48":63,"_50":74,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":147,"_54":-5,"_56":-5,"_58":59},"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",{"_1055":1056,"_1057":1058,"_1059":1060,"_1061":1062},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[360,361,362,363,364],{"_365":745,"_113":746,"_212":747,"_369":91,"_370":16,"_372":748,"_374":749},{"_365":648,"_113":649,"_212":650,"_369":34,"_370":57,"_372":651,"_374":652},{"_365":556,"_113":557,"_212":558,"_369":34,"_370":62,"_372":559,"_374":560},{"_365":376,"_113":377,"_212":378,"_369":34,"_370":379,"_372":380,"_374":381},{"_365":366,"_113":367,"_212":368,"_369":34,"_370":371,"_372":373,"_374":375},"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,[498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519],[382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398],{"_399":493,"_244":493,"_212":401,"_372":494},{"_399":489,"_244":489,"_212":401,"_372":490},{"_399":484,"_244":484,"_212":401,"_372":485},{"_399":480,"_244":480,"_212":401,"_372":481},{"_399":475,"_244":475,"_212":401,"_372":476},{"_399":470,"_244":470,"_212":401,"_372":471},{"_399":462,"_244":462,"_212":401,"_372":463},{"_399":454,"_244":454,"_212":401,"_372":455},{"_399":449,"_244":449,"_212":401,"_372":450},{"_399":444,"_244":444,"_212":401,"_372":445},{"_399":439,"_244":439,"_212":401,"_372":440},{"_399":434,"_244":434,"_212":401,"_372":435},{"_399":429,"_244":429,"_212":401,"_372":430},{"_399":422,"_244":422,"_212":401,"_372":423},{"_399":417,"_244":417,"_212":401,"_372":418},{"_399":410,"_244":410,"_212":401,"_372":411},{"_399":400,"_244":400,"_212":401,"_372":402},"hostName","amazon-adsystem.com","",[403,404],{"_113":409,"_406":400,"_407":34,"_212":408},{"_113":405,"_406":400,"_407":34,"_212":408},"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",[412,413],{"_113":416,"_406":410,"_407":34,"_212":415},{"_113":414,"_406":410,"_407":34,"_212":415},"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",[419],{"_113":420,"_406":417,"_407":34,"_212":421},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[424,425],{"_113":428,"_406":422,"_407":34,"_212":427},{"_113":426,"_406":422,"_407":34,"_212":427},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[431],{"_113":432,"_406":429,"_407":34,"_212":433},"_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",[436],{"_113":437,"_406":434,"_407":34,"_212":438},"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",[441],{"_113":442,"_406":439,"_407":34,"_212":443},"sp","Testing","google.com",[446],{"_113":447,"_406":444,"_407":34,"_212":448},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[451],{"_113":452,"_406":449,"_407":34,"_212":453},"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",[456,457],{"_113":460,"_406":454,"_407":34,"_212":461},{"_113":458,"_406":454,"_407":34,"_212":459},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_*","This domain is owned by X (formerly Twitter). The main business activity is: Social Networking Services. Where X (formerly Twitter) acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","youtube.com",[464,465],{"_113":468,"_406":462,"_407":34,"_212":469},{"_113":466,"_406":462,"_407":34,"_212":467},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-*","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","demdex.net",[472],{"_113":473,"_406":470,"_407":34,"_212":474},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[477],{"_113":478,"_406":475,"_407":34,"_212":479},"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",[482],{"_113":483,"_406":480,"_407":34,"_212":443},"R","6sc.co",[486],{"_113":487,"_406":484,"_407":34,"_212":488},"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",[491],{"_113":492,"_406":489,"_407":34,"_212":443},"_hly_gvid","t.contentsquare.net",[495],{"_113":496,"_406":493,"_407":91,"_212":497},"_cs_*","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.",{"_113":554,"_406":521,"_407":34,"_212":555},{"_113":552,"_406":521,"_407":34,"_212":553},{"_113":550,"_406":521,"_407":34,"_212":551},{"_113":432,"_406":521,"_407":34,"_212":549},{"_113":526,"_406":521,"_407":91,"_212":528},{"_113":548,"_406":521,"_407":34,"_212":401},{"_113":547,"_406":524,"_407":34,"_212":401},{"_113":545,"_406":521,"_407":34,"_212":546},{"_113":543,"_406":521,"_407":34,"_212":544},{"_113":542,"_406":521,"_407":34,"_212":401},{"_113":540,"_406":521,"_407":34,"_212":541},{"_113":526,"_406":539,"_407":91,"_212":528},{"_113":537,"_406":524,"_407":34,"_212":538},{"_113":537,"_406":527,"_407":34,"_212":538},{"_113":496,"_406":521,"_407":34,"_212":497},{"_113":535,"_406":521,"_407":34,"_212":536},{"_113":442,"_406":533,"_407":34,"_212":534},{"_113":531,"_406":521,"_407":34,"_212":532},{"_113":529,"_406":521,"_407":34,"_212":530},{"_113":526,"_406":527,"_407":91,"_212":528},{"_113":523,"_406":524,"_407":34,"_212":525},{"_113":520,"_406":521,"_407":91,"_212":522},"AMCVS_xxxxxAdobeOrg","nvidia.com","This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","AMP_*","build.nvidia.com","Supports Amplitude product analytics.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614],[561,562,563,564,565],{"_399":475,"_244":475,"_212":401,"_372":585},{"_399":449,"_244":449,"_212":401,"_372":580},{"_399":575,"_244":575,"_212":401,"_372":576},{"_399":570,"_244":570,"_212":401,"_372":571},{"_399":566,"_244":566,"_212":401,"_372":567},"nvidia.partners",[568],{"_113":569,"_406":566,"_407":34,"_212":443},"edge","app.hushly.com",[572],{"_113":573,"_406":570,"_407":34,"_212":574},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[577],{"_113":578,"_406":575,"_407":34,"_212":579},"_octo","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform",[581,582],{"_113":584,"_406":449,"_407":34,"_212":453},{"_113":583,"_406":449,"_407":34,"_212":453},"lidc","li_gc",[586,587],{"_113":589,"_406":475,"_407":34,"_212":479},{"_113":588,"_406":475,"_407":34,"_212":479},"imid_*","imid",{"_113":615,"_406":524,"_407":34,"_212":617},{"_113":646,"_406":521,"_407":34,"_212":647},{"_113":615,"_406":645,"_407":34,"_212":617},{"_113":643,"_406":521,"_407":91,"_212":644},{"_113":641,"_406":521,"_407":34,"_212":642},{"_113":640,"_406":623,"_407":91,"_212":401},{"_113":615,"_406":619,"_407":34,"_212":617},{"_113":638,"_406":521,"_407":34,"_212":639},{"_113":615,"_406":527,"_407":34,"_212":617},{"_113":626,"_406":524,"_407":34,"_212":401},{"_113":615,"_406":637,"_407":34,"_212":617},{"_113":635,"_406":521,"_407":34,"_212":636},{"_113":632,"_406":633,"_407":34,"_212":634},{"_113":630,"_406":631,"_407":34,"_212":401},{"_113":615,"_406":627,"_407":34,"_212":617},{"_113":629,"_406":521,"_407":34,"_212":401},{"_113":615,"_406":628,"_407":34,"_212":617},{"_113":626,"_406":627,"_407":34,"_212":401},{"_113":625,"_406":623,"_407":91,"_212":401},{"_113":615,"_406":624,"_407":34,"_212":617},{"_113":615,"_406":623,"_407":34,"_212":617},{"_113":615,"_406":622,"_407":34,"_212":617},{"_113":621,"_406":521,"_407":91,"_212":617},{"_113":618,"_406":619,"_407":91,"_212":620},{"_113":615,"_406":616,"_407":34,"_212":617},"_gd_*","info.nvidia.com","This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701],[653,654,655,656],{"_399":668,"_244":668,"_212":401,"_372":669},{"_399":663,"_244":663,"_212":401,"_372":664},{"_399":462,"_244":462,"_212":401,"_372":660},{"_399":575,"_244":575,"_212":401,"_372":657},[658],{"_113":659,"_406":575,"_407":91,"_212":579},"_gh_sess",[661],{"_113":662,"_406":462,"_407":91,"_212":469},"YSC","bizible.com",[665],{"_113":666,"_406":663,"_407":34,"_212":667},"_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",[670],{"_113":671,"_406":668,"_407":34,"_212":672},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_113":743,"_406":521,"_407":34,"_212":744},{"_113":728,"_406":742,"_407":91,"_212":729},{"_113":711,"_406":524,"_407":34,"_212":636},{"_113":711,"_406":624,"_407":34,"_212":636},{"_113":740,"_406":521,"_407":34,"_212":741},{"_113":728,"_406":527,"_407":91,"_212":729},{"_113":738,"_406":521,"_407":34,"_212":739},{"_113":728,"_406":524,"_407":91,"_212":729},{"_113":736,"_406":623,"_407":34,"_212":737},{"_113":728,"_406":645,"_407":91,"_212":729},{"_113":708,"_406":524,"_407":34,"_212":710},{"_113":734,"_406":521,"_407":91,"_212":735},{"_113":732,"_406":521,"_407":34,"_212":733},{"_113":730,"_406":521,"_407":91,"_212":731},{"_113":728,"_406":619,"_407":91,"_212":729},{"_113":726,"_406":521,"_407":34,"_212":727},{"_113":724,"_406":521,"_407":34,"_212":725},{"_113":722,"_406":521,"_407":91,"_212":723},{"_113":720,"_406":628,"_407":34,"_212":721},{"_113":708,"_406":619,"_407":34,"_212":710},{"_113":718,"_406":521,"_407":34,"_212":719},{"_113":716,"_406":521,"_407":91,"_212":717},{"_113":714,"_406":521,"_407":34,"_212":715},{"_113":712,"_406":521,"_407":34,"_212":713},{"_113":711,"_406":521,"_407":34,"_212":636},{"_113":708,"_406":709,"_407":34,"_212":710},{"_113":706,"_406":521,"_407":34,"_212":707},{"_113":704,"_406":521,"_407":91,"_212":705},{"_113":702,"_406":521,"_407":34,"_212":703},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","ekfls-2025-03-27","This cookie name is associated with readme.io which is used to help maintain data for the API reference support page.","s_getNewRepeat","This cookie is used to determine whether a visitor is a new visitor or a repeat visitor to the site, and is used for analytical purposes.","_hly_sid","This cookie is likely associated with website analytics, as the prefix '_hly_' suggests a connection to Holistics, a business intelligence and analytics platform.","docs.nvidia.com","_hjSession_xxxxxx","A cookie that holds the current session data. This ensues that subsequent requests within the session window will be attributed to the same Hotjar session.","C0001","Required Cookies","These cookies enable core functionality such as security, network management, and accessibility. These cookies are required for the site to function and cannot be turned off.",[890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962],[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],{"_399":887,"_244":887,"_212":401,"_372":888},{"_399":881,"_244":881,"_212":401,"_372":882},{"_399":878,"_244":878,"_212":401,"_372":879},{"_399":866,"_244":866,"_212":401,"_372":867},{"_399":863,"_244":863,"_212":401,"_372":864},{"_399":860,"_244":860,"_212":401,"_372":861},{"_399":855,"_244":855,"_212":401,"_372":856},{"_399":462,"_244":462,"_212":401,"_372":852},{"_399":849,"_244":849,"_212":401,"_372":850},{"_399":454,"_244":454,"_212":401,"_372":847},{"_399":449,"_244":449,"_212":401,"_372":845},{"_399":840,"_244":840,"_212":401,"_372":841},{"_399":835,"_244":835,"_212":401,"_372":836},{"_399":830,"_244":830,"_212":401,"_372":831},{"_399":827,"_244":827,"_212":401,"_372":828},{"_399":575,"_244":575,"_212":401,"_372":824},{"_399":434,"_244":434,"_212":401,"_372":821},{"_399":570,"_244":570,"_212":401,"_372":814},{"_399":566,"_244":566,"_212":401,"_372":808},{"_399":802,"_244":802,"_212":401,"_372":803},{"_399":797,"_244":797,"_212":401,"_372":798},{"_399":792,"_244":792,"_212":401,"_372":793},{"_399":788,"_244":788,"_212":401,"_372":789},{"_399":783,"_244":783,"_212":401,"_372":784},{"_399":775,"_244":775,"_212":401,"_372":776},"polyfill.io",[777,778],{"_113":781,"_406":775,"_407":34,"_212":782},{"_113":779,"_406":775,"_407":34,"_212":780},"cf_ob_info","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information.","cf_use_ob","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information. ","www.recaptcha.net",[785],{"_113":786,"_406":783,"_407":34,"_212":787},"_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",[790],{"_113":791,"_406":788,"_407":34,"_212":443},"__cflb","instagram.com",[794],{"_113":795,"_406":792,"_407":34,"_212":796},"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",[799],{"_113":800,"_406":797,"_407":34,"_212":801},"__cf_bm","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.","nvidiacorp.us-5.evergage.com",[804,805],{"_113":807,"_406":802,"_407":34,"_212":443},{"_113":806,"_406":802,"_407":34,"_212":443},"AWSALBTG","AWSALBTGCORS",[809,810],{"_113":812,"_406":566,"_407":34,"_212":813},{"_113":811,"_406":566,"_407":34,"_212":443},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.",[815,816,817],{"_113":820,"_406":570,"_407":34,"_212":443},{"_113":819,"_406":570,"_407":91,"_212":443},{"_113":818,"_406":570,"_407":34,"_212":443},"AWSALBCORS","JSESSIONID","AWSALB",[822],{"_113":800,"_406":434,"_407":34,"_212":823},"This is a CloudFoundry cookie",[825],{"_113":826,"_406":575,"_407":34,"_212":579},"logged_in","hcaptcha.com",[829],{"_113":800,"_406":827,"_407":34,"_212":823},"tracking.intentsify.io",[832],{"_113":833,"_406":830,"_407":34,"_212":834},"userId","Used to track usage of the embedded video player.","static.reo.dev",[837],{"_113":838,"_406":835,"_407":34,"_212":839},"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",[842],{"_113":843,"_406":840,"_407":34,"_212":844},"dd_cookie_test_","dd_cookie_test",[846],{"_113":800,"_406":449,"_407":34,"_212":823},[848],{"_113":800,"_406":454,"_407":34,"_212":823},"supabase.co",[851],{"_113":800,"_406":849,"_407":34,"_212":823},[853],{"_113":854,"_406":462,"_407":34,"_212":469},"VISITOR_PRIVACY_METADATA","dosa-prd.azurewebsites.net",[857],{"_113":858,"_406":855,"_407":34,"_212":859},"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",[862],{"_113":800,"_406":860,"_407":34,"_212":823},"cdn.readme.io",[865],{"_113":800,"_406":863,"_407":34,"_212":823},"queue-it.net",[868,869,870,871,872],{"_113":877,"_406":866,"_407":34,"_212":401},{"_113":876,"_406":866,"_407":34,"_212":401},{"_113":875,"_406":866,"_407":34,"_212":401},{"_113":874,"_406":866,"_407":34,"_212":401},{"_113":873,"_406":866,"_407":34,"_212":401},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","x.com",[880],{"_113":800,"_406":878,"_407":34,"_212":801},"api.hcaptcha.com",[883,884],{"_113":886,"_406":881,"_407":34,"_212":443},{"_113":791,"_406":881,"_407":34,"_212":885},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[889],{"_113":800,"_406":887,"_407":34,"_212":823},{"_113":1023,"_406":521,"_407":34,"_212":1054},{"_113":1052,"_406":616,"_407":91,"_212":1053},{"_113":1045,"_406":1014,"_407":91,"_212":1046},{"_113":1051,"_406":709,"_407":91,"_212":401},{"_113":1049,"_406":521,"_407":34,"_212":1050},{"_113":807,"_406":1035,"_407":34,"_212":974},{"_113":982,"_406":1044,"_407":91,"_212":984},{"_113":806,"_406":973,"_407":34,"_212":974},{"_113":985,"_406":1044,"_407":34,"_212":986},{"_113":1047,"_406":521,"_407":91,"_212":1048},{"_113":1045,"_406":1021,"_407":91,"_212":1046},{"_113":979,"_406":1044,"_407":34,"_212":981},{"_113":1042,"_406":521,"_407":34,"_212":1043},{"_113":806,"_406":965,"_407":34,"_212":974},{"_113":970,"_406":1041,"_407":34,"_212":972},{"_113":818,"_406":971,"_407":34,"_212":987},{"_113":820,"_406":971,"_407":34,"_212":1038},{"_113":985,"_406":980,"_407":34,"_212":986},{"_113":1039,"_406":619,"_407":34,"_212":1040},{"_113":820,"_406":645,"_407":34,"_212":1038},{"_113":1036,"_406":521,"_407":34,"_212":1037},{"_113":812,"_406":645,"_407":34,"_212":1031},{"_113":1008,"_406":624,"_407":91,"_212":401},{"_113":806,"_406":1035,"_407":34,"_212":974},{"_113":1033,"_406":973,"_407":91,"_212":1034},{"_113":997,"_406":521,"_407":34,"_212":988},{"_113":970,"_406":521,"_407":34,"_212":972},{"_113":1032,"_406":533,"_407":34,"_212":401},{"_113":812,"_406":521,"_407":34,"_212":1031},{"_113":820,"_406":965,"_407":34,"_212":1030},{"_113":1028,"_406":628,"_407":91,"_212":1029},{"_113":1026,"_406":521,"_407":91,"_212":1027},{"_113":1025,"_406":521,"_407":34,"_212":401},{"_113":1023,"_406":971,"_407":34,"_212":1024},{"_113":1022,"_406":533,"_407":34,"_212":401},{"_113":1013,"_406":1021,"_407":91,"_212":1015},{"_113":1019,"_406":624,"_407":34,"_212":1020},{"_113":1017,"_406":709,"_407":34,"_212":1018},{"_113":1016,"_406":527,"_407":34,"_212":401},{"_113":1013,"_406":1014,"_407":91,"_212":1015},{"_113":1011,"_406":980,"_407":91,"_212":1012},{"_113":1009,"_406":521,"_407":34,"_212":1010},{"_113":1008,"_406":524,"_407":91,"_212":401},{"_113":979,"_406":983,"_407":34,"_212":981},{"_113":811,"_406":998,"_407":34,"_212":988},{"_113":1006,"_406":524,"_407":34,"_212":1007},{"_113":1004,"_406":521,"_407":34,"_212":1005},{"_113":1002,"_406":521,"_407":34,"_212":1003},{"_113":1001,"_406":521,"_407":34,"_212":401},{"_113":843,"_406":524,"_407":34,"_212":1000},{"_113":997,"_406":645,"_407":34,"_212":988},{"_113":800,"_406":616,"_407":34,"_212":999},{"_113":997,"_406":998,"_407":34,"_212":988},{"_113":996,"_406":533,"_407":34,"_212":401},{"_113":995,"_406":521,"_407":34,"_212":968},{"_113":993,"_406":980,"_407":34,"_212":994},{"_113":806,"_406":991,"_407":34,"_212":992},{"_113":990,"_406":533,"_407":34,"_212":401},{"_113":989,"_406":521,"_407":34,"_212":401},{"_113":982,"_406":980,"_407":91,"_212":984},{"_113":811,"_406":645,"_407":34,"_212":988},{"_113":818,"_406":645,"_407":34,"_212":987},{"_113":985,"_406":983,"_407":34,"_212":986},{"_113":982,"_406":983,"_407":91,"_212":984},{"_113":979,"_406":980,"_407":34,"_212":981},{"_113":977,"_406":973,"_407":91,"_212":978},{"_113":975,"_406":631,"_407":34,"_212":976},{"_113":807,"_406":973,"_407":34,"_212":974},{"_113":970,"_406":971,"_407":34,"_212":972},{"_113":800,"_406":623,"_407":34,"_212":969},{"_113":967,"_406":521,"_407":34,"_212":968},{"_113":818,"_406":965,"_407":34,"_212":966},{"_113":963,"_406":521,"_407":34,"_212":964},"LoginHints","Keeps users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","LSKey-c$CookieConsentPolicy","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","OptanonAlertBoxClosed","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","docs.ngc.nvidia.com","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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

","gpc","

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

","gpcLoggedIn","

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

","gpcLoggedInOverridden","

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

",{"_745":16,"_648":59,"_556":59,"_376":59,"_366":59},"hasConsented","gpcApplied","showBanner","geolocation",{"_1069":1070,"_1071":1072},"country","US","state","OR","query","categories",[1092,1093,1094,1095,1096],"optionUrls",[1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091],"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/-",{"_20":1170,"_1098":1171,"_1100":59},{"_20":1135,"_1098":1136,"_1100":57},{"_20":1118,"_1098":1119,"_1100":1120},{"_20":21,"_1098":1111,"_1100":1112},{"_20":1097,"_1098":1099,"_1100":19},"helm-charts","items",[1101,1102],"startIndex",{"_20":1097,"_244":1108,"_206":207,"_208":-7,"_239":1109,"_247":1105,"_272":1110,"_1107":1090},{"_20":1097,"_244":1103,"_206":207,"_208":-7,"_239":1104,"_247":1105,"_272":1106,"_1107":1091},"NVIDIA NIM Operator","k8s-nim-operator","NVIDIA",[],"url","GPU Operator","gpu-operator",[],[1113],9,{"_20":21,"_244":1114,"_206":207,"_208":1115,"_239":1116,"_247":1105,"_272":1117,"_1107":1089},"Riva Skills Quick Start","riva","riva_quickstart",[],"models",[1121,1122,1123],6,{"_20":1118,"_244":1131,"_206":207,"_208":1132,"_239":1133,"_247":1105,"_272":1134,"_1107":1086},{"_20":1118,"_244":1128,"_206":207,"_208":1125,"_239":1129,"_247":1105,"_272":1130,"_1107":1087},{"_20":1118,"_244":1124,"_206":207,"_208":1125,"_239":1126,"_247":1105,"_272":1127,"_1107":1088},"TrafficCamNet","tao","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1137,1138,1139],{"_20":1135,"_244":1167,"_206":207,"_208":1141,"_239":1168,"_247":1105,"_272":1169,"_1107":1083},{"_20":1135,"_244":1164,"_206":207,"_208":-7,"_239":1165,"_247":1105,"_272":1166,"_1107":1084},{"_20":1135,"_244":1140,"_206":207,"_208":1141,"_239":1142,"_247":1105,"_272":1143,"_1107":1085},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1144,1145,1146],{"_113":1158,"_244":1159,"_241":1160,"_251":1161,"_1151":1162},{"_113":1141,"_244":1154,"_241":1155,"_251":1150,"_1151":1156},{"_113":1147,"_244":1148,"_241":1149,"_251":1150,"_1151":1152},"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",[1153],"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.",[1153,1157],"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",[1153,1157,1163],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1144,1145,1146],"containers",[1172,1173,1174],{"_20":1170,"_244":1182,"_206":1179,"_208":1183,"_239":1184,"_247":1105,"_272":1185,"_1107":1080},{"_20":1170,"_244":1178,"_206":1179,"_208":207,"_239":1180,"_247":1105,"_272":1181,"_1107":1081},{"_20":1170,"_244":1175,"_206":207,"_208":-7,"_239":1176,"_247":1105,"_272":1177,"_1107":1082},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[],"DeepSeek-R1","deepseek-ai","deepseek-r1",[],"ownerOrgs",[],"memberOrgs",[],"externalOrgs",[],"apiKey","2b44f86fea50e90ffd2db29ee2028419","consentGranted","applicationId","4c857e4f-5ffa-45fa-b75e-af773c22f0ad","clientToken","pubb6927db33dae60ba5031a186cde0242a","site","datadoghq.com","service","catalog-ui","env","sessionSampleRate",100,"sessionReplaySampleRate","compressIntakeRequests","storeContextsAcrossPages","defaultPrivacyLevel","mask-user-input","trackViewsManually","trackingConsent","not-granted",{"_1215":1216,"_1217":314,"_1218":34,"_1219":1220,"_1098":1221,"_1222":1223,"_1224":1216,"_1225":34,"_1226":34,"_1227":34,"_1228":1229},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1230,1231,1232,1233,1234,1235,1236],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1228":1258,"_50":1259,"_1222":1260},{"_1251":1252,"_1228":1253,"_50":1254,"_1224":1158},{"_1228":1249,"_50":1250,"_1222":1223},{"_1228":1246,"_50":1247,"_1222":1248},{"_1228":1243,"_50":1244,"_1222":1245},{"_1228":1240,"_50":1241,"_1222":1242},{"_1228":1237,"_50":1238,"_1222":1239},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1255],"Enterprise Catalog","/enterprise",[1256,1257],"NVAIE","READ","Explore Catalog","/","common-home",{"_157":158,"_159":160,"_161":162,"_163":164,"_165":166,"_167":168},{"_156":34}]