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
\n
encoder and decoder are using shared embeddings
\n
\n
data-parallel multi-GPU training
\n
\n
dynamic loss scaling with backoff for Tensor Cores (mixed precision) training
\n
\n
trained with label smoothing loss (smoothing factor 0.1)
\n
\n
encoder:
\n
\n
4-layer LSTM, hidden size 1024, first layer is bidirectional, the rest are\nunidirectional
\n
\n
with residual connections starting from 3rd layer
\n
\n
dropout is applied on input to all LSTM layers, probability of dropout is\nset to 0.2
\n
\n
hidden state of LSTM layers is initialized with zeros
\n
\n
weights and bias of LSTM layers is initialized with uniform (-0.1, 0.1)\nDistribution
\n
\n
decoder:
\n
\n
4-layer unidirectional LSTM with hidden size 1024 and fully-connected\nclassifier
\n
\n
with residual connections starting from 3rd layer
\n
\n
dropout is applied on input to all LSTM layers, probability of dropout is\nset to 0.2
\n
\n
hidden state of LSTM layers is initialized with the last hidden state from\nencoder
\n
\n
weights and bias of LSTM layers is initialized with uniform (-0.1, 0.1)\ndistribution
\n
\n
weights and bias of fully-connected classifier is initialized with\nuniform (-0.1, 0.1) distribution
\n
\n
attention:
\n
\n
normalized Bahdanau attention
\n
\n
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
\n
\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
\n
beam search with default beam size of 5
\n
\n
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
\n
\n
de-tokenized BLEU computed by SacreBLEU
\n
\n
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",[254,255,256,257,258],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_232":29,"_233":-5,"_53":120,"_117":-5,"_121":234,"_123":-5,"_235":137,"_128":140,"_125":35,"_236":-5,"_237":-5,"_124":35,"_238":239,"_240":90,"_241":242,"_243":90,"_244":245,"_246":90,"_247":248,"_249":90,"_250":251,"_252":90,"_126":253},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/-/resources/gnmt_v2_for_tensorflow/-","imageReference","registry","targetPath","cliDownloadCommand","ngc registry resource download-version nvidia/gnmt_v2_for_tensorflow:2","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/-/resources/gnmt_v2_for_tensorflow/2/version-history","accessCheckResult",{"_231":90},"hasRelatedCollections","showNimMetadataTab","context",{"_228":229,"_230":-7},[],[],[222,223,224,225,226,227],{"_49":63,"_51":73,"_53":65,"_55":66,"_57":74,"_59":60},{"_49":63,"_51":70,"_53":65,"_55":66,"_57":71,"_59":60},{"_49":63,"_51":68,"_53":65,"_55":66,"_57":69,"_59":60},{"_49":63,"_51":64,"_53":65,"_55":66,"_57":67,"_59":60},{"_49":61,"_51":62,"_53":169,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60},"orgName","nvidia","teamName","allowed","versionId","description",["D",1578425608176],"totalSizeInBytes","digest","malwareScanStatus","releaseNotes","### Changelog\n1. Mar 18, 2019\n * Initial release\n2. June, 2019\n * Performance improvements\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://github.com/NVIDIA/DeepLearningExamples/raw/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",[],{"_85":135,"_116":263,"_117":-5,"_125":35,"_124":35,"_126":262,"_240":90},{"_85":149,"_116":149,"_117":-5,"_125":35,"_124":35,"_126":262,"_240":90},{"_85":143,"_116":143,"_117":-5,"_125":35,"_124":35,"_126":261,"_240":90},{"_85":29,"_116":29,"_117":-5,"_125":35,"_124":35,"_126":260,"_240":90},{"_85":115,"_116":115,"_117":-5,"_125":35,"_124":35,"_126":259,"_240":90},[],[],[],[],"Latest (20.06.1)","nvidia/gnmt_v2_for_tensorflow","key","nvidia/resources/gnmt_v2_for_tensorflow","resourceId","gnmt_v2_for_tensorflow","shortDescription","The GNMT v2 model is an improved version of the first Google's Neural Machine Translation System with a modified attention mechanism.","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](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://github.com/NVIDIA/DeepLearningExamples/raw/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 TensorFlow",["D",1680631691932],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/resources/gnmt_v2_for_tensorflow","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[304,305,306,307,308],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_272":328,"_310":329,"_312":330,"_314":87},{"_272":324,"_310":325,"_312":326,"_314":87},{"_272":320,"_310":321,"_312":322,"_314":87},{"_272":317,"_310":318,"_312":319,"_314":87},{"_272":309,"_310":311,"_312":313,"_314":87},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_315":316,"_310":311},"category","field","application","Natural Language Processing","uscs_natural_language_processing",{"_315":316,"_310":318},"Deep Learning Examples","pltfm_deep_learning_examples",{"_315":323,"_310":321},"platform","Conversational AI","soln_conversational_ai",{"_315":327,"_310":325},"technology","AI","soln_ai",{"_315":327,"_310":329},{"_174":1464,"_176":1465},"ngcApps",[1417],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1398":1399,"_1400":1401,"_1402":1403,"_1404":1405,"_1406":342,"_109":368,"_1407":1408,"_1409":60,"_1410":90,"_1411":90,"_1412":1413,"_1414":90,"_1415":1416},"amplitude",{"_1395":1396,"_1003":-7,"_1397":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1389":1390,"_1391":1392,"_1393":1394},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1792950893738,"1.28.1","defaultSearchSuggestions",{"_1273":449,"_1274":1275,"_1276":1277,"_1278":1279},"announcements",[],"consentState",{"_405":1263,"_1264":35,"_1265":35,"_1266":90,"_1267":1268},"consentConfig",{"_387":388,"_389":390,"_391":392,"_393":394,"_395":396,"_397":398,"_399":400,"_401":402,"_403":404,"_405":406},"showContactAdmin",[],[],[381,382,383,384,385,386],{"_49":63,"_51":73,"_53":169,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":169,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":169,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":169,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":169,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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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","research.nvidia.com","_twpid","_gat_gtag_UA_141699104_1","_gat_gtag_UA_53775284_6","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. 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	Files	Compressed Size
20.06.1	47	799.62 KB	August 4, 2021 UTC
20.06.0	47	799.27 KB	August 14, 2020 UTC
2Selected	37	772.02 KB	January 7, 2020 UTC
1	35	410.44 KB	March 19, 2019 UTC