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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\nMachine Translation 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\ntime step.

The same attention mechanism is also implemented in the default GNMT-like\nmodels from TensorFlow Neural Machine Translation\nTutorial and NVIDIA OpenSeq2Seq\nToolkit.

Model architecture

$\"ModelArchitecture\"$

Default configuration

The following features were implemented in this model:

general:\n
- encoder and decoder are using shared embeddings
- data-parallel multi-GPU training
- dynamic loss scaling with backoff for Tensor Cores (mixed precision)\ntraining
- trained with label smoothing loss (smoothing factor 0.1)
\n
encoder:\n
- 4-layer LSTM, hidden size 1024, first layer is bidirectional, the rest are\nunidirectional
- with residual connections starting from 3rd layer
- uses standard PyTorch nn.LSTM 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
decoder:\n
- 4-layer unidirectional LSTM with hidden size 1024 and fully-connected\nclassifier
- with residual connections starting from 3rd layer
- uses standard PyTorch nn.LSTM 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
- weights and bias of fully-connected classifier is initialized with\nuniform(-0.1,0.1) distribution
\n
attention:\n
- normalized Bahdanau attention
- output from first LSTM layer of decoder goes into attention, then\nre-weighted context is concatenated with the input to all subsequent LSTM\nlayers of the decoder at the current timestep
- linear transform of keys and queries is initialized with uniform(-0.1,\n0.1), normalization scalar is initialized with 1.0/sqrt(1024),\nnormalization bias is initialized with zero
\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\nSacreBLEU
- motivation for choosing\nSacreBLEU
\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 WMT16\nEnglish-German dataset. The\nnumber of LSTM layers is controlled by the --num-layers parameter in the\ntrain.py training 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\n\n\n\n

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

Feature	GNMT v2
Apex AMP	Yes
Apex DistributedDataParallel	Yes

Features

Apex AMP - a tool that enables Tensor\nCore-accelerated training. Refer to the Enabling mixed\nprecision section for more details.

Apex\nDistributedDataParallel -\na module wrapper that enables easy multiprocess distributed data parallel\ntraining, similar to\ntorch.nn.parallel.DistributedDataParallel.\nDistributedDataParallel is optimized for use with\nNCCL. It achieves high performance by\noverlapping communication with computation during backward() and bucketing\nsmaller gradient transfers to reduce the total number of transfers required.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a\ncomputational method.\nMixed precision training offers significant\ncomputational speedup by performing operations in half-precision format, while\nstoring minimal information in single-precision to retain as much information\nas possible in critical parts of the network. Since the introduction of Tensor\nCores in Volta, and following with\nboth the Turing and Ampere architectures, significant training speedups are\nexperienced by switching to mixed precision -- up to 3x overall speedup on the\nmost arithmetically intense model architectures. Using mixed precision training\npreviously required two steps:

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

The ability to train deep learning networks with lower precision was introduced\nin the Pascal architecture and first supported in CUDA\n8 in the NVIDIA Deep\nLearning SDK.

For information about:

How to train using mixed precision, see the Mixed Precision\nTraining paper and Training With Mixed\nPrecision\ndocumentation.
Techniques used for mixed precision training, see the Mixed-Precision\nTraining of Deep Neural\nNetworks\nblog.
APEX tools for mixed precision training, see the NVIDIA Apex: Tools for Easy\nMixed-Precision Training in\nPyTorch\n.

Enabling mixed precision

Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision\n(AMP), library from APEX that casts variables\nto half-precision upon retrieval, while storing variables in single-precision\nformat. Furthermore, to preserve small gradient magnitudes in backpropagation,\na loss\nscaling\nstep must be included when applying gradients. In PyTorch, loss scaling can be\neasily applied by using scale_loss() method provided by AMP. The scaling\nvalue to be used can be\ndynamic or fixed.

For an in-depth walk through on AMP, check out sample usage\nhere.\nAPEX is a PyTorch extension that contains\nutility libraries, such as AMP, which require minimal network code changes to\nleverage Tensor Cores performance.

The following steps were needed to enable mixed precision training in GNMT:

Import AMP from APEX (file: seq2seq/train/trainer.py):

from apex import amp\n

Initialize AMP and wrap the model and the optimizer (file:\nseq2seq/train/trainer.py, class: Seq2SeqTrainer):

self.model, self.optimizer = amp.initialize(\n    self.model,\n    self.optimizer,\n    cast_model_outputs=torch.float16,\n    keep_batchnorm_fp32=False,\n    opt_level='O2')\n

Apply scale_loss context manager (file: seq2seq/train/fp_optimizers.py,\nclass: AMPOptimizer):

with amp.scale_loss(loss, optimizer) as scaled_loss:\n    scaled_loss.backward()\n

Apply gradient clipping on single precision master weights (file:\nseq2seq/train/fp_optimizers.py, class: AMPOptimizer):

if self.grad_clip != float('inf'):\n    clip_grad_norm_(amp.master_params(optimizer), self.grad_clip)\n

Enabling TF32

TensorFloat-32 (TF32) is the new math mode in NVIDIA\nA100 GPUs for handling the\nmatrix math also called tensor operations. TF32 running on Tensor Cores in A100\nGPUs can provide up to 10x speedups compared to single-precision floating-point\nmath (FP32) on Volta GPUs.

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

For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates\nAI Training, HPC up to\n20x\nblog post.

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

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Aside from these dependencies, ensure you\nhave the following components:\n* [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n* [PyTorch 20.06-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch)\n* GPU architecture:\n * [NVIDIA Volta](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n * [NVIDIA Turing](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\nfollowing sections from the NVIDIA GPU Cloud Documentation and the Deep\nLearning DGX Documentation:\n\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/dgx/user-guide/index.html#accessing_registry),\n* [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running).\n\nFor those unable to use the Pytorch NGC container, to set up the required\nenvironment or create your own container, see the versioned [NVIDIA Container\nSupport\nMatrix](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\nFP32, perform the following steps using the default parameters of the GNMT v2\nmodel on the WMT16 English German dataset. For the specifics concerning\ntraining and inference, see the [Advanced](#advanced) section.\n\n**1. Clone the repository.**\n\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/PyTorch/Translation/GNMT\n```\n\n**2. Build the GNMT v2 Docker container.**\n\n```\nbash scripts/docker/build.sh\n```\n\n**3. Start an interactive session in the container to run training/inference.**\n\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\n```\nbash scripts/wmt16_en_de.sh\n```\n\n**5. Start training.**\n\nThe training script saves only one checkpoint with the lowest value of the loss\nfunction on the validation dataset. All results and logs are saved to the\n`gnmt` directory (on the host) or to the `/workspace/gnmt/gnmt` directory\n(in the container). By default, the `train.py` script will launch mixed\nprecision training with Tensor Cores. You can change this behavior by setting:\n* the `--math fp32` flag to launch single precision training (for NVIDIA Volta\n and NVIDIA Turing architectures) or\n* the `--math tf32` flag to launch TF32 training with Tensor Cores (for NVIDIA\n Ampere architecture) \n\nfor the `train.py` training script.\n\nTo launch mixed precision training on 1, 4 or 8 GPUs, run:\n\n```\npython3 -m torch.distributed.launch --nproc_per_node=<#GPUs> train.py --seed 2 --train-global-batch-size 1024\n```\n\nTo launch mixed precision training on 16 GPUs, run:\n\n```\npython3 -m torch.distributed.launch --nproc_per_node=16 train.py --seed 2 --train-global-batch-size 2048\n```\n\nBy default, the training script will launch training with batch size 128 per\nGPU. If `--train-global-batch-size` is specified and larger than 128 times the\nnumber of GPUs available for the training then the training script will\naccumulate gradients over consecutive iterations and then perform the weight\nupdate. For example, 1 GPU training with `--train-global-batch-size 1024` will\naccumulate gradients over 8 iterations before doing the weight update with\naccumulated gradients.\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 the test dataset with the checkpoint saved during the\ntraining.\n\nTo launch FP16 inference on the `newstest2014.en` test set, run:\n\n```\npython3 translate.py \\\n --input data/wmt16_de_en/newstest2014.en \\\n --reference data/wmt16_de_en/newstest2014.de \\\n --output /tmp/output \\\n --model gnmt/model_best.pth\n```\n\nThe script will load the checkpoint specified by the `--model` option, then it\nwill launch inference on the file specified by the `--input` option, and\ncompute BLEU score against the reference translation specified by the\n`--reference` option. Outputs will be stored to the location specified by the\n`--output` option.\n\nAdditionally, one can pass the input text directly from the command-line:\n\n```\npython3 translate.py \\\n --input-text \"The quick brown fox jumps over the lazy dog\" \\\n --model gnmt/model_best.pth\n```\n\nTranslated output will be printed to the console:\n\n```\n(...)\n0: Translated output:\nDer schnelle braune Fuchs springt über den faulen Hund\n```\n\nBy default, the `translate.py` script will launch FP16 inference with Tensor\nCores. You can change this behavior by setting:\n* the `--math fp32` flag to launch single precision inference (for NVIDIA Volta\n and NVIDIA Turing architectures) or\n* the `--math tf32` flag to launch TF32 inference with Tensor Cores (for NVIDIA\n Ampere architecture)\n\nfor the `translate.py` inference script.","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* `train.py`: serves as the entry point to launch the training\n* `translate.py`: serves as the entry point to launch inference\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\nThe `seq2seq/model` directory contains the implementation of GNMT v2 building\nblocks:\n\n* `attention.py`: implementation of normalized Bahdanau attention\n* `encoder.py`: implementation of recurrent encoder\n* `decoder.py`: implementation of recurrent decoder with attention\n* `seq2seq_base.py`: base class for seq2seq models\n* `gnmt.py`: implementation of GNMT v2 model\n\nThe `seq2seq/train` directory encapsulates the necessary tools to execute\ntraining:\n\n* `trainer.py`: implementation of training loop\n* `smoothing.py`: implementation of cross-entropy with label smoothing\n* `lr_scheduler.py`: implementation of exponential learning rate warmup and\n step decay\n* `fp_optimizers.py`: implementation of optimizers for various floating point\n precisions\n\nThe `seq2seq/inference` directory contains scripts required to run inference:\n\n* `beam_search.py`: implementation of beam search with length normalization and\n length penalty\n* `translator.py`: implementation of auto-regressive inference\n\nThe `seq2seq/data` directory contains implementation of components needed for\ndata loading:\n\n* `dataset.py`: implementation of text datasets\n* `sampler.py`: implementation of batch samplers with bucketing by sequence\n length\n* `tokenizer.py`: implementation of tokenizer (maps integer vocabulary indices\n to text)\n\n### Parameters\nTraining\n\nThe complete list of available parameters for the `train.py` training script\ncontains:\n\n```\ndataset setup:\n --dataset-dir DATASET_DIR\n path to the directory with training/test data\n (default: data/wmt16_de_en)\n --src-lang SRC_LANG source language (default: en)\n --tgt-lang TGT_LANG target language (default: de)\n --vocab VOCAB path to the vocabulary file (relative to DATASET_DIR\n directory) (default: vocab.bpe.32000)\n -bpe BPE_CODES, --bpe-codes BPE_CODES\n path to the file with bpe codes (relative to\n DATASET_DIR directory) (default: bpe.32000)\n --train-src TRAIN_SRC\n path to the training source data file (relative to\n DATASET_DIR directory) (default:\n train.tok.clean.bpe.32000.en)\n --train-tgt TRAIN_TGT\n path to the training target data file (relative to\n DATASET_DIR directory) (default:\n train.tok.clean.bpe.32000.de)\n --val-src VAL_SRC path to the validation source data file (relative to\n DATASET_DIR directory) (default:\n newstest_dev.tok.clean.bpe.32000.en)\n --val-tgt VAL_TGT path to the validation target data file (relative to\n DATASET_DIR directory) (default:\n newstest_dev.tok.clean.bpe.32000.de)\n --test-src TEST_SRC path to the test source data file (relative to\n DATASET_DIR directory) (default:\n newstest2014.tok.bpe.32000.en)\n --test-tgt TEST_TGT path to the test target data file (relative to\n DATASET_DIR directory) (default: newstest2014.de)\n --train-max-size TRAIN_MAX_SIZE\n use at most TRAIN_MAX_SIZE elements from training\n dataset (useful for benchmarking), by default uses\n entire dataset (default: None)\n\nresults setup:\n --save-dir SAVE_DIR path to directory with results, it will be\n automatically created if it does not exist (default:\n gnmt)\n --print-freq PRINT_FREQ\n print log every PRINT_FREQ batches (default: 10)\n\nmodel setup:\n --hidden-size HIDDEN_SIZE\n hidden size of the model (default: 1024)\n --num-layers NUM_LAYERS\n number of RNN layers in encoder and in decoder\n (default: 4)\n --dropout DROPOUT dropout applied to input of RNN cells (default: 0.2)\n --share-embedding use shared embeddings for encoder and decoder (use '--\n no-share-embedding' to disable) (default: True)\n --smoothing SMOOTHING\n label smoothing, if equal to zero model will use\n CrossEntropyLoss, if not zero model will be trained\n with label smoothing loss (default: 0.1)\n\ngeneral setup:\n --math {fp16,fp32,tf32,manual_fp16}\n precision (default: fp16)\n --seed SEED master seed for random number generators, if \"seed\" is\n undefined then the master seed will be sampled from\n random.SystemRandom() (default: None)\n --prealloc-mode {off,once,always}\n controls preallocation (default: always)\n --dllog-file DLLOG_FILE\n Name of the DLLogger output file (default:\n train_log.json)\n --eval run validation and test after every epoch (use '--no-\n eval' to disable) (default: True)\n --env print info about execution env (use '--no-env' to\n disable) (default: True)\n --cuda enables cuda (use '--no-cuda' to disable) (default:\n True)\n --cudnn enables cudnn (use '--no-cudnn' to disable) (default:\n True)\n --log-all-ranks enables logging from all distributed ranks, if\n disabled then only logs from rank 0 are reported (use\n '--no-log-all-ranks' to disable) (default: True)\n\ntraining setup:\n --train-batch-size TRAIN_BATCH_SIZE\n training batch size per worker (default: 128)\n --train-global-batch-size TRAIN_GLOBAL_BATCH_SIZE\n global training batch size, this argument does not\n have to be defined, if it is defined it will be used\n to automatically compute train_iter_size using the\n equation: train_iter_size = train_global_batch_size //\n (train_batch_size * world_size) (default: None)\n --train-iter-size N training iter size, training loop will accumulate\n gradients over N iterations and execute optimizer\n every N steps (default: 1)\n --epochs EPOCHS max number of training epochs (default: 6)\n --grad-clip GRAD_CLIP\n enables gradient clipping and sets maximum norm of\n gradients (default: 5.0)\n --train-max-length TRAIN_MAX_LENGTH\n maximum sequence length for training (including\n special BOS and EOS tokens) (default: 50)\n --train-min-length TRAIN_MIN_LENGTH\n minimum sequence length for training (including\n special BOS and EOS tokens) (default: 0)\n --train-loader-workers TRAIN_LOADER_WORKERS\n number of workers for training data loading (default:\n 2)\n --batching {random,sharding,bucketing}\n select batching algorithm (default: bucketing)\n --shard-size SHARD_SIZE\n shard size for \"sharding\" batching algorithm, in\n multiples of global batch size (default: 80)\n --num-buckets NUM_BUCKETS\n number of buckets for \"bucketing\" batching algorithm\n (default: 5)\n\noptimizer setup:\n --optimizer OPTIMIZER\n training optimizer (default: Adam)\n --lr LR learning rate (default: 0.002)\n --optimizer-extra OPTIMIZER_EXTRA\n extra options for the optimizer (default: {})\n\nmixed precision loss scaling setup:\n --init-scale INIT_SCALE\n initial loss scale (default: 8192)\n --upscale-interval UPSCALE_INTERVAL\n loss upscaling interval (default: 128)\n\nlearning rate scheduler setup:\n --warmup-steps WARMUP_STEPS\n number of learning rate warmup iterations (default:\n 200)\n --remain-steps REMAIN_STEPS\n starting iteration for learning rate decay (default:\n 0.666)\n --decay-interval DECAY_INTERVAL\n interval between learning rate decay steps (default:\n None)\n --decay-steps DECAY_STEPS\n max number of learning rate decay steps (default: 4)\n --decay-factor DECAY_FACTOR\n learning rate decay factor (default: 0.5)\n\nvalidation setup:\n --val-batch-size VAL_BATCH_SIZE\n batch size for validation (default: 64)\n --val-max-length VAL_MAX_LENGTH\n maximum sequence length for validation (including\n special BOS and EOS tokens) (default: 125)\n --val-min-length VAL_MIN_LENGTH\n minimum sequence length for validation (including\n special BOS and EOS tokens) (default: 0)\n --val-loader-workers VAL_LOADER_WORKERS\n number of workers for validation data loading\n (default: 0)\n\ntest setup:\n --test-batch-size TEST_BATCH_SIZE\n batch size for test (default: 128)\n --test-max-length TEST_MAX_LENGTH\n maximum sequence length for test (including special\n BOS and EOS tokens) (default: 150)\n --test-min-length TEST_MIN_LENGTH\n minimum sequence length for test (including special\n BOS and EOS tokens) (default: 0)\n --beam-size BEAM_SIZE\n beam size (default: 5)\n --len-norm-factor LEN_NORM_FACTOR\n length normalization factor (default: 0.6)\n --cov-penalty-factor COV_PENALTY_FACTOR\n coverage penalty factor (default: 0.1)\n --len-norm-const LEN_NORM_CONST\n length normalization constant (default: 5.0)\n --intra-epoch-eval N evaluate within training epoch, this option will\n enable extra N equally spaced evaluations executed\n during each training epoch (default: 0)\n --test-loader-workers TEST_LOADER_WORKERS\n number of workers for test data loading (default: 0)\n\ncheckpointing setup:\n --start-epoch START_EPOCH\n manually set initial epoch counter (default: 0)\n --resume PATH resumes training from checkpoint from PATH (default:\n None)\n --save-all saves checkpoint after every epoch (default: False)\n --save-freq SAVE_FREQ\n save checkpoint every SAVE_FREQ batches (default:\n 5000)\n --keep-checkpoints KEEP_CHECKPOINTS\n keep only last KEEP_CHECKPOINTS checkpoints, affects\n only checkpoints controlled by --save-freq option\n (default: 0)\n\nbenchmark setup:\n --target-perf TARGET_PERF\n target training performance (in tokens per second)\n (default: None)\n --target-bleu TARGET_BLEU\n target accuracy (default: None)\n```\n\nInference\n\nThe complete list of available parameters for the `translate.py` inference\nscript contains:\n\n```\ndata setup:\n -o OUTPUT, --output OUTPUT\n full path to the output file if not specified, then\n the output will be printed (default: None)\n -r REFERENCE, --reference REFERENCE\n full path to the file with reference translations (for\n sacrebleu, raw text) (default: None)\n -m MODEL, --model MODEL\n full path to the model checkpoint file (default: None)\n --synthetic use synthetic dataset (default: False)\n --synthetic-batches SYNTHETIC_BATCHES\n number of synthetic batches to generate (default: 64)\n --synthetic-vocab SYNTHETIC_VOCAB\n size of synthetic vocabulary (default: 32320)\n --synthetic-len SYNTHETIC_LEN\n sequence length of synthetic samples (default: 50)\n -i INPUT, --input INPUT\n full path to the input file (raw text) (default: None)\n -t INPUT_TEXT [INPUT_TEXT ...], --input-text INPUT_TEXT [INPUT_TEXT ...]\n raw input text (default: None)\n --sort sorts dataset by sequence length (use '--no-sort' to\n disable) (default: False)\n\ninference setup:\n --batch-size BATCH_SIZE [BATCH_SIZE ...]\n batch size per GPU (default: [128])\n --beam-size BEAM_SIZE [BEAM_SIZE ...]\n beam size (default: [5])\n --max-seq-len MAX_SEQ_LEN\n maximum generated sequence length (default: 80)\n --len-norm-factor LEN_NORM_FACTOR\n length normalization factor (default: 0.6)\n --cov-penalty-factor COV_PENALTY_FACTOR\n coverage penalty factor (default: 0.1)\n --len-norm-const LEN_NORM_CONST\n length normalization constant (default: 5.0)\n\ngeneral setup:\n --math {fp16,fp32,tf32} [{fp16,fp32,tf32} ...]\n precision (default: ['fp16'])\n --env print info about execution env (use '--no-env' to\n disable) (default: False)\n --bleu compares with reference translation and computes BLEU\n (use '--no-bleu' to disable) (default: True)\n --cuda enables cuda (use '--no-cuda' to disable) (default:\n True)\n --cudnn enables cudnn (use '--no-cudnn' to disable) (default:\n True)\n --batch-first uses (batch, seq, feature) data format for RNNs\n (default: True)\n --seq-first uses (seq, batch, feature) data format for RNNs\n (default: True)\n --save-dir SAVE_DIR path to directory with results, it will be\n automatically created if it does not exist (default:\n gnmt)\n --dllog-file DLLOG_FILE\n Name of the DLLogger output file (default:\n eval_log.json)\n --print-freq PRINT_FREQ, -p PRINT_FREQ\n print log every PRINT_FREQ batches (default: 1)\n\nbenchmark setup:\n --target-perf TARGET_PERF\n target inference performance (in tokens per second)\n (default: None)\n --target-bleu TARGET_BLEU\n target accuracy (default: None)\n --repeat REPEAT [REPEAT ...]\n loops over the dataset REPEAT times, flag accepts\n multiple arguments, one for each specified batch size\n (default: [1])\n --warmup WARMUP warmup iterations for performance counters (default:\n 0)\n --percentiles PERCENTILES [PERCENTILES ...]\n Percentiles for confidence intervals for\n throughput/latency benchmarks (default: (90, 95, 99))\n --tables print accuracy, throughput and latency results in\n tables (use '--no-tables' to disable) (default: False)\n```\n\n### Command-line options\nTo see the full list of available options and their descriptions, use the `-h`\nor `--help` command line option. For example, for training:\n\n```\npython3 train.py --help\n\nusage: train.py [-h] [--dataset-dir DATASET_DIR] [--src-lang SRC_LANG]\n [--tgt-lang TGT_LANG] [--vocab VOCAB] [-bpe BPE_CODES]\n [--train-src TRAIN_SRC] [--train-tgt TRAIN_TGT]\n [--val-src VAL_SRC] [--val-tgt VAL_TGT] [--test-src TEST_SRC]\n [--test-tgt TEST_TGT] [--save-dir SAVE_DIR]\n [--print-freq PRINT_FREQ] [--hidden-size HIDDEN_SIZE]\n [--num-layers NUM_LAYERS] [--dropout DROPOUT]\n [--share-embedding] [--smoothing SMOOTHING]\n [--math {fp16,fp32,tf32,manual_fp16}] [--seed SEED]\n [--prealloc-mode {off,once,always}] [--dllog-file DLLOG_FILE]\n [--eval] [--env] [--cuda] [--cudnn] [--log-all-ranks]\n [--train-max-size TRAIN_MAX_SIZE]\n [--train-batch-size TRAIN_BATCH_SIZE]\n [--train-global-batch-size TRAIN_GLOBAL_BATCH_SIZE]\n [--train-iter-size N] [--epochs EPOCHS]\n [--grad-clip GRAD_CLIP] [--train-max-length TRAIN_MAX_LENGTH]\n [--train-min-length TRAIN_MIN_LENGTH]\n [--train-loader-workers TRAIN_LOADER_WORKERS]\n [--batching {random,sharding,bucketing}]\n [--shard-size SHARD_SIZE] [--num-buckets NUM_BUCKETS]\n [--optimizer OPTIMIZER] [--lr LR]\n [--optimizer-extra OPTIMIZER_EXTRA] [--init-scale INIT_SCALE]\n [--upscale-interval UPSCALE_INTERVAL]\n [--warmup-steps WARMUP_STEPS] [--remain-steps REMAIN_STEPS]\n [--decay-interval DECAY_INTERVAL] [--decay-steps DECAY_STEPS]\n [--decay-factor DECAY_FACTOR]\n [--val-batch-size VAL_BATCH_SIZE]\n [--val-max-length VAL_MAX_LENGTH]\n [--val-min-length VAL_MIN_LENGTH]\n [--val-loader-workers VAL_LOADER_WORKERS]\n [--test-batch-size TEST_BATCH_SIZE]\n [--test-max-length TEST_MAX_LENGTH]\n [--test-min-length TEST_MIN_LENGTH] [--beam-size BEAM_SIZE]\n [--len-norm-factor LEN_NORM_FACTOR]\n [--cov-penalty-factor COV_PENALTY_FACTOR]\n [--len-norm-const LEN_NORM_CONST] [--intra-epoch-eval N]\n [--test-loader-workers TEST_LOADER_WORKERS]\n [--start-epoch START_EPOCH] [--resume PATH] [--save-all]\n [--save-freq SAVE_FREQ] [--keep-checkpoints KEEP_CHECKPOINTS]\n [--target-perf TARGET_PERF] [--target-bleu TARGET_BLEU]\n [--local_rank LOCAL_RANK]\n```\nFor example, for inference:\n\n```\npython3 translate.py --help\n\nusage: translate.py [-h] [-o OUTPUT] [-r REFERENCE] [-m MODEL] [--synthetic]\n [--synthetic-batches SYNTHETIC_BATCHES]\n [--synthetic-vocab SYNTHETIC_VOCAB]\n [--synthetic-len SYNTHETIC_LEN]\n [-i INPUT | -t INPUT_TEXT [INPUT_TEXT ...]] [--sort]\n [--batch-size BATCH_SIZE [BATCH_SIZE ...]]\n [--beam-size BEAM_SIZE [BEAM_SIZE ...]]\n [--max-seq-len MAX_SEQ_LEN]\n [--len-norm-factor LEN_NORM_FACTOR]\n [--cov-penalty-factor COV_PENALTY_FACTOR]\n [--len-norm-const LEN_NORM_CONST]\n [--math {fp16,fp32,tf32} [{fp16,fp32,tf32} ...]] [--env]\n [--bleu] [--cuda] [--cudnn] [--batch-first | --seq-first]\n [--save-dir SAVE_DIR] [--dllog-file DLLOG_FILE]\n [--print-freq PRINT_FREQ] [--target-perf TARGET_PERF]\n [--target-bleu TARGET_BLEU] [--repeat REPEAT [REPEAT ...]]\n [--warmup WARMUP]\n [--percentiles PERCENTILES [PERCENTILES ...]] [--tables]\n [--local_rank LOCAL_RANK]\n```\n\n### Getting the data\nThe GNMT v2 model was trained on the [WMT16\nEnglish-German](http://www.statmt.org/wmt16/translation-task.html) dataset.\nConcatenation of the newstest2015 and newstest2016 test sets are used as a\nvalidation dataset and the 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, validation and test\ndatasets. By default, 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) package to segment text\ninto subword units (Byte Pair Encodings -\n[BPE](https://en.wikipedia.org/wiki/Byte_pair_encoding)). By default, the\nscript builds the shared vocabulary of 32,000 tokens.\n\nIn order to test with other datasets, the script needs to be customized\naccordingly.\n\n#### Dataset guidelines\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\n`europarl-v7`, `commoncrawl` and `news-commentary` corpora are concatenated to\nform the training dataset, similarly `newstest2015` and `newstest2016` are\nconcatenated to 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\nThe default training configuration can be launched by running the `train.py`\ntraining script. By default, the training script saves only one checkpoint with\nthe lowest value of the loss function on the validation dataset. An evaluation\nis then performed after each training epoch. Results are stored in the\n`gnmt` directory.\n\nThe training script launches data-parallel training with batch size 128 per GPU\non all available GPUs. We have tested reliance on up to 16 GPUs on a single\nnode.\nAfter each training epoch, the script runs an evaluation on the validation\ndataset and outputs a BLEU score on the test dataset (newstest2014). BLEU is\ncomputed by the [SacreBLEU](https://github.com/mjpost/sacreBLEU) package. Logs\nfrom the training and evaluation are saved to the `gnmt` directory.\n\nThe summary after each training epoch is printed in the following format:\n\n```\n0: Summary: Epoch: 3\tTraining Loss: 3.1336\tValidation Loss: 2.9587\tTest BLEU: 23.18\n0: Performance: Epoch: 3\tTraining: 418772 Tok/s\tValidation: 1445331 Tok/s\n```\n\nThe training loss is averaged over an entire training epoch, the validation\nloss is averaged over the validation dataset and the BLEU score is computed on\nthe test dataset. Performance is reported in total tokens per second. The\nresult is averaged over an entire training epoch and summed over all GPUs\nparticipating in the training.\n\nBy default, the `train.py` script will launch mixed precision training with\nTensor Cores. You can change this behavior by setting:\n* the `--math fp32` flag to launch single precision training (for NVIDIA Volta\n and NVIDIA Turing architectures) or\n* the `--math tf32` flag to launch TF32 training with Tensor Cores (for NVIDIA\n Ampere architecture)\n\nfor the `train.py` training script.\n\nTo view all available options for training, run `python3 train.py --help`.\n\n### Inference process\nInference can be run by launching the `translate.py` inference script,\nalthough, it requires a pre-trained model checkpoint and tokenized input.\n\nThe inference script, `translate.py`, supports batched inference. By default,\nit launches beam search with beam size of 5, coverage penalty term and length\nnormalization term. Greedy decoding can be enabled by setting the beam size to\n1.\n\nTo view all available options for inference, run `python3 translate.py --help`.","hasAdvanced","performance","The performance measurements in this document were conducted at the time of\npublication and may not reflect the performance achieved from NVIDIA's latest\nsoftware release. For the most up-to-date performance measurements, go to\n[NVIDIA Data Center Deep Learning Product\nPerformance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\nThe following section shows how to run benchmarks measuring the model\nperformance in training and inference modes.\n\n#### Training performance benchmark\nTraining is launched on batches of text data, different batches have different\nsequence lengths (number of tokens in the longest sequence). Sequence length\nand batch efficiency (ratio of non-pad tokens to total number of tokens) affect\nperformance of the training, therefore it's recommended to run the training on\na large chunk of training dataset to get a stable and reliable average training\nperformance. Ideally at least one full epoch of training should be launched to\nget a good estimate of training performance.\n\nThe following commands will launch one epoch of training:\n\nTo launch mixed precision training on 1, 4 or 8 GPUs, run:\n```\npython3 -m torch.distributed.launch --nproc_per_node=<#GPUs> train.py --seed 2 --train-global-batch-size 1024 --epochs 1 --math fp16\n```\n\nTo launch mixed precision training on 16 GPUs, run:\n```\npython3 -m torch.distributed.launch --nproc_per_node=16 train.py --seed 2 --train-global-batch-size 2048 --epochs 1 --math fp16\n```\n\nChange `--math fp16` to `--math fp32` to launch single precision training (for\nNVIDIA Volta and NVIDIA Turing architectures) or to `--math tf32` to launch\nTF32 training with Tensor Cores (for NVIDIA Ampere architecture).\n\nAfter the training is completed, the `train.py` script prints a summary to\nstandard output. Performance results are printed in the following format:\n\n```\n(...)\n0: Performance: Epoch: 0\tTraining: 418926 Tok/s\tValidation: 1430828 Tok/s\n(...)\n```\n\n`Training: 418926 Tok/s` represents training throughput averaged over an entire\ntraining epoch and summed over all GPUs participating in the training.\n\n#### Inference performance benchmark\nThe inference performance and accuracy benchmarks require a checkpoint from a\nfully trained model.\n\nCommand to launch the inference accuracy benchmark on NVIDIA Volta or on NVIDIA\nTuring architectures:\n\n```\npython3 translate.py \\\n --model gnmt/model_best.pth \\\n --input data/wmt16_de_en/newstest2014.en \\\n --reference data/wmt16_de_en/newstest2014.de \\\n --output /tmp/output \\\n --math fp16 fp32 \\\n --batch-size 128 \\\n --beam-size 1 2 5 \\\n --tables\n```\n\nCommand to launch the inference accuracy benchmark on NVIDIA Ampere architecture:\n\n```\npython3 translate.py \\\n --model gnmt/model_best.pth \\\n --input data/wmt16_de_en/newstest2014.en \\\n --reference data/wmt16_de_en/newstest2014.de \\\n --output /tmp/output \\\n --math fp16 tf32 \\\n --batch-size 128 \\\n --beam-size 1 2 5 \\\n --tables\n```\n\nCommand to launch the inference throughput and latency benchmarks on NVIDIA\nVolta or NVIDIA Turing architectures:\n\n```\npython3 translate.py \\\n --model gnmt/model_best.pth \\\n --input data/wmt16_de_en/newstest2014.en \\\n --reference data/wmt16_de_en/newstest2014.de \\\n --output /tmp/output \\\n --math fp16 fp32 \\\n --batch-size 1 2 4 8 32 128 512 \\\n --repeat 1 1 1 1 2 8 16 \\\n --beam-size 1 2 5 \\\n --warmup 5 \\\n --tables\n```\n\nCommand to launch the inference throughput and latency benchmarks on NVIDIA\nAmpere architecture:\n\n```\npython3 translate.py \\\n --model gnmt/model_best.pth \\\n --input data/wmt16_de_en/newstest2014.en \\\n --reference data/wmt16_de_en/newstest2014.de \\\n --output /tmp/output \\\n --math fp16 tf32 \\\n --batch-size 1 2 4 8 32 128 512 \\\n --repeat 1 1 1 1 2 8 16 \\\n --beam-size 1 2 5 \\\n --warmup 5 \\\n --tables\n```\n\n### Results\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)\nOur results were obtained by running the `train.py` script with the default\nbatch size = 128 per GPU in the pytorch-20.06-py3 NGC container on NVIDIA DGX\nA100 with 8x A100 40GB GPUs.\n\nCommand to launch the training:\n\n```\npython3 -m torch.distributed.launch --nproc_per_node=<#GPUs> train.py --seed 2 --train-global-batch-size 1024 --math fp16\n```\n\nChange `--math fp16` to `--math tf32` to launch TF32 training with Tensor Cores.\n\n| **GPUs** | **Batch Size / GPU** | **Accuracy - TF32 (BLEU)** | **Accuracy - Mixed precision (BLEU)** | **Time to Train - TF32 (minutes)** | **Time to Train - Mixed precision (minutes)** | **Time to Train Speedup (TF32 to Mixed precision)** |\n| --- | --- | ----- | ----- | ----- | ------ | ---- |\n| 8 | 128 | 24.46 | 24.60 | 34.7 | 22.7 | 1.53 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\nOur results were obtained by running the `train.py` script with the default\nbatch size = 128 per GPU in the pytorch-20.06-py3 NGC container on NVIDIA DGX-1\nwith 8x V100 16GB GPUs.\n\nCommand to launch the training:\n\n```\npython3 -m torch.distributed.launch --nproc_per_node=<#GPUs> train.py --seed 2 --train-global-batch-size 1024 --math fp16\n```\n\nChange `--math fp16` to `--math fp32` to launch single precision training.\n\n| **GPUs** | **Batch Size / GPU** | **Accuracy - FP32 (BLEU)** | **Accuracy - Mixed precision (BLEU)** | **Time to Train - FP32 (minutes)** | **Time to Train - Mixed precision (minutes)** | **Time to Train Speedup (FP32 to Mixed precision)** |\n| --- | --- | ----- | ----- | ----- | ------ | ---- |\n| 1 | 128 | 24.41 | 24.42 | 810.0 | 224.0 | 3.62 |\n| 4 | 128 | 24.40 | 24.33 | 218.2 | 69.5 | 3.14 |\n| 8 | 128 | 24.45 | 24.38 | 112.0 | 38.6 | 2.90 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n##### Training accuracy: NVIDIA DGX-2H (16x V100 32GB)\nOur results were obtained by running the `train.py` script with the default\nbatch size = 128 per GPU in the pytorch-20.06-py3 NGC container on NVIDIA DGX-2H\nwith 16x V100 32GB GPUs.\n\nTo launch mixed precision training on 16 GPUs, run:\n```\npython3 -m torch.distributed.launch --nproc_per_node=16 train.py --seed 2 --train-global-batch-size 2048 --math fp16\n```\n\nChange `--math fp16` to `--math fp32` to launch single precision training.\n\n| **GPUs** | **Batch Size / GPU** | **Accuracy - FP32 (BLEU)** | **Accuracy - Mixed precision (BLEU)** | **Time to Train - FP32 (minutes)** | **Time to Train - Mixed precision (minutes)** | **Time to Train Speedup (FP32 to Mixed precision)** |\n| --- | --- | ----- | ----- | ------ | ----- | ---- |\n| 16 | 128 | 24.41 | 24.38 | 52.1 | 19.4 | 2.69 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n![TrainingLoss](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/Translation/GNMT//img/training_loss.png)\n\n\n##### Training stability test\nThe GNMT v2 model was trained for 6 epochs, starting from 32 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\npytorch-20.06-py3 Docker container on NVIDIA DGX A100 with 8x A100 40GB GPUs.\nThe following table summarizes the results of the stability test.\n\nIn the following table, the BLEU scores after each training epoch for different\ninitial random seeds are displayed.\n\n| **Epoch** | **Average** | **Standard deviation** | **Minimum** | **Maximum** | **Median** |\n| --- | ------ | ----- | ------ | ------ | ------ |\n| 1 | 19.959 | 0.238 | 19.410 | 20.390 | 19.970 |\n| 2 | 21.772 | 0.293 | 20.960 | 22.280 | 21.820 |\n| 3 | 22.435 | 0.264 | 21.740 | 22.870 | 22.465 |\n| 4 | 23.167 | 0.166 | 22.870 | 23.620 | 23.195 |\n| 5 | 24.233 | 0.149 | 23.820 | 24.530 | 24.235 |\n| 6 | 24.416 | 0.131 | 24.140 | 24.660 | 24.390 |\n\n#### Training throughput results\n\n##### Training throughput: NVIDIA DGX A100 (8x A100 40GB)\nOur results were obtained by running the `train.py` training script in the\npytorch-20.06-py3 NGC container on NVIDIA DGX A100 with 8x A100 40GB GPUs.\nThroughput performance numbers (in tokens per second) were averaged over an\nentire training epoch.\n\n| **GPUs** | **Batch size / GPU** | **Throughput - TF32 (tok/s)** | **Throughput - Mixed precision (tok/s)** | **Throughput speedup (TF32 to Mixed precision)** | **Strong Scaling - TF32** | **Strong Scaling - Mixed precision** |\n| --- | --- | ------ | ------ | ----- | ----- | ----- |\n| 1 | 128 | 83214 | 140909 | 1.693 | 1.000 | 1.000 |\n| 4 | 128 | 278576 | 463144 | 1.663 | 3.348 | 3.287 |\n| 8 | 128 | 519952 | 822024 | 1.581 | 6.248 | 5.834 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n##### Training throughput: NVIDIA DGX-1 (8x V100 16GB)\nOur results were obtained by running the `train.py` training script in the\npytorch-20.06-py3 NGC container on NVIDIA DGX-1 with 8x V100 16GB GPUs.\nThroughput performance numbers (in tokens per second) were averaged over an\nentire training epoch.\n\n| **GPUs** | **Batch size / GPU** | **Throughput - FP32 (tok/s)** | **Throughput - Mixed precision (tok/s)** | **Throughput speedup (FP32 to Mixed precision)** | **Strong Scaling - FP32** | **Strong Scaling - Mixed precision** |\n| --- | --- | ------ | ------ | ----- | ----- | ----- |\n| 1 | 128 | 21860 | 76438 | 3.497 | 1.000 | 1.000 |\n| 4 | 128 | 80224 | 249168 | 3.106 | 3.670 | 3.260 |\n| 8 | 128 | 154168 | 447832 | 2.905 | 7.053 | 5.859 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n##### Training throughput: NVIDIA DGX-2H (16x V100 32GB)\nOur results were obtained by running the `train.py` training script in the\npytorch-20.06-py3 NGC container on NVIDIA DGX-2H with 16x V100 32GB GPUs.\nThroughput performance numbers (in tokens per second) were averaged over an\nentire training epoch.\n\n| **GPUs** | **Batch size / GPU** | **Throughput - FP32 (tok/s)** | **Throughput - Mixed precision (tok/s)** | **Throughput speedup (FP32 to Mixed precision)** | **Strong Scaling - FP32** | **Strong Scaling - Mixed precision** |\n| --- | --- | ------ | ------ | ----- | ------ | ------ |\n| 1 | 128 | 25583 | 87829 | 3.433 | 1.000 | 1.000 |\n| 4 | 128 | 91400 | 290640 | 3.180 | 3.573 | 3.309 |\n| 8 | 128 | 176616 | 522008 | 2.956 | 6.904 | 5.943 |\n| 16 | 128 | 351792 | 1010880 | 2.874 | 13.751 | 11.510 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n#### Inference accuracy results\n\n##### Inference accuracy: NVIDIA A100 40GB\nOur results were obtained by running the `translate.py` script in the\npytorch-20.06-py3 NGC Docker container with NVIDIA A100 40GB GPU. Full\ncommand to launch the inference accuracy benchmark was provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n| **Batch Size** | **Beam Size** | **Accuracy - TF32 (BLEU)** | **Accuracy - FP16 (BLEU)** |\n| -------------: | ------------: | -------------------------: | -------------------------: |\n| 128 | 1 | 23.07 | 23.07 |\n| 128 | 2 | 23.81 | 23.81 |\n| 128 | 5 | 24.41 | 24.43 |\n\n##### Inference accuracy: NVIDIA Tesla V100 16GB\nOur results were obtained by running the `translate.py` script in the\npytorch-20.06-py3 NGC Docker container with NVIDIA Tesla V100 16GB GPU. Full\ncommand to launch the inference accuracy benchmark was provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n| **Batch Size** | **Beam Size** | **Accuracy - FP32 (BLEU)** | **Accuracy - FP16 (BLEU)** |\n| -------------: | ------------: | -------------------------: | -------------------------: |\n| 128 | 1 | 23.07 | 23.07 |\n| 128 | 2 | 23.81 | 23.79 |\n| 128 | 5 | 24.40 | 24.43 |\n\n##### Inference accuracy: NVIDIA T4\nOur results were obtained by running the `translate.py` script in the\npytorch-20.06-py3 NGC Docker container with NVIDIA Tesla T4. Full command to\nlaunch the inference accuracy benchmark was provided in the [Inference\nperformance benchmark](#inference-performance-benchmark) section.\n\n| **Batch Size** | **Beam Size** | **Accuracy - FP32 (BLEU)** | **Accuracy - FP16 (BLEU)** |\n| -------------: | ------------: | -------------------------: | -------------------------: |\n| 128 | 1 | 23.07 | 23.08 |\n| 128 | 2 | 23.81 | 23.80 |\n| 128 | 5 | 24.40 | 24.39 |\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n#### Inference throughput results\nTables presented in this section show the average inference throughput (columns\n**Avg (tok/s)**) and inference throughput for various confidence intervals\n(columns **N% (ms)**, where `N` denotes the confidence interval). Inference\nthroughput is measured in tokens per second. Speedups reported in FP16\nsubsections are relative to FP32 (for NVIDIA Volta and NVIDIA Turing) and\nrelative to TF32 (for NVIDIA Ampere) numbers for corresponding configuration.\n\n##### Inference throughput: NVIDIA A100 40GB\nOur results were obtained by running the `translate.py` script in the\npytorch-20.06-py3 NGC Docker container with NVIDIA A100 40GB.\nFull command to launch the inference throughput benchmark was provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n**FP16**\n\n|**Batch Size**|**Beam Size**|**Avg (tok/s)**|**Speedup**|**90% (tok/s)**|**Speedup**|**95% (tok/s)**|**Speedup**|**99% (tok/s)**|**Speedup**|\n|-------------:|------------:|--------------:|----------:|--------------:|----------:|--------------:|----------:|--------------:|----------:|\n| 1| 1| 1291.6| 1.031| 1195.7| 1.029| 1165.8| 1.029| 1104.7| 1.030|\n| 1| 2| 882.7| 1.019| 803.4| 1.015| 769.2| 1.015| 696.7| 1.017|\n| 1| 5| 848.3| 1.042| 753.0| 1.037| 715.0| 1.043| 636.4| 1.033|\n| 2| 1| 2060.5| 1.034| 1700.8| 1.032| 1621.8| 1.032| 1487.4| 1.022|\n| 2| 2| 1445.7| 1.026| 1197.6| 1.024| 1132.5| 1.023| 1043.7| 1.033|\n| 2| 5| 1402.3| 1.063| 1152.4| 1.056| 1100.5| 1.053| 992.9| 1.053|\n| 4| 1| 3465.6| 1.046| 2838.3| 1.040| 2672.7| 1.043| 2392.8| 1.043|\n| 4| 2| 2425.4| 1.041| 2002.5| 1.028| 1898.3| 1.033| 1690.2| 1.028|\n| 4| 5| 2364.4| 1.075| 1930.0| 1.067| 1822.0| 1.065| 1626.1| 1.058|\n| 8| 1| 6151.1| 1.099| 5078.0| 1.087| 4786.5| 1.096| 4206.9| 1.090|\n| 8| 2| 4241.9| 1.075| 3494.1| 1.066| 3293.6| 1.066| 2970.9| 1.064|\n| 8| 5| 4117.7| 1.118| 3430.9| 1.103| 3224.5| 1.104| 2833.5| 1.110|\n| 32| 1| 18830.4| 1.147| 16210.0| 1.152| 15563.9| 1.138| 13973.2| 1.135|\n| 32| 2| 12698.2| 1.133| 10812.3| 1.114| 10256.1| 1.145| 9330.2| 1.101|\n| 32| 5| 11802.6| 1.355| 9998.8| 1.318| 9671.6| 1.329| 9058.4| 1.335|\n| 128| 1| 53394.5| 1.350| 48867.6| 1.342| 46898.5| 1.414| 40670.6| 1.305|\n| 128| 2| 34876.4| 1.483| 31687.4| 1.491| 30025.4| 1.505| 27677.1| 1.421|\n| 128| 5| 28201.3| 1.986| 25660.5| 1.997| 24306.0| 1.967| 23326.2| 2.007|\n| 512| 1| 119675.3| 1.904| 112400.5| 1.971| 109694.8| 1.927| 108781.3| 1.919|\n| 512| 2| 74514.7| 2.126| 69578.9| 2.209| 69348.1| 2.210| 69253.7| 2.212|\n| 512| 5| 47003.2| 2.760| 43348.2| 2.893| 43080.3| 2.884| 42878.4| 2.881|\n\n##### Inference throughput: NVIDIA T4\nOur results were obtained by running the `translate.py` script in the\npytorch-20.06-py3 NGC Docker container with NVIDIA T4.\nFull command to launch the inference throughput benchmark was provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n**FP16**\n\n|**Batch Size**|**Beam Size**|**Avg (tok/s)**|**Speedup**|**90% (tok/s)**|**Speedup**|**95% (tok/s)**|**Speedup**|**99% (tok/s)**|**Speedup**|\n|-------------:|------------:|--------------:|----------:|--------------:|----------:|--------------:|----------:|--------------:|----------:|\n| 1| 1| 1133.8| 1.266| 1059.1| 1.253| 1036.6| 1.251| 989.5| 1.242|\n| 1| 2| 793.9| 1.169| 728.3| 1.165| 698.1| 1.163| 637.1| 1.157|\n| 1| 5| 766.8| 1.343| 685.6| 1.335| 649.3| 1.335| 584.1| 1.318|\n| 2| 1| 1759.8| 1.233| 1461.6| 1.239| 1402.3| 1.242| 1302.1| 1.242|\n| 2| 2| 1313.3| 1.186| 1088.7| 1.185| 1031.6| 1.180| 953.2| 1.178|\n| 2| 5| 1257.2| 1.301| 1034.1| 1.316| 990.3| 1.313| 886.3| 1.265|\n| 4| 1| 2974.0| 1.261| 2440.3| 1.255| 2294.6| 1.257| 2087.7| 1.261|\n| 4| 2| 2204.7| 1.320| 1826.3| 1.283| 1718.9| 1.260| 1548.4| 1.260|\n| 4| 5| 2106.1| 1.340| 1727.8| 1.345| 1625.7| 1.353| 1467.7| 1.346|\n| 8| 1| 5076.6| 1.423| 4207.9| 1.367| 3904.4| 1.360| 3475.3| 1.355|\n| 8| 2| 3761.7| 1.311| 3108.1| 1.285| 2931.6| 1.300| 2628.7| 1.300|\n| 8| 5| 3578.2| 1.660| 2998.2| 1.614| 2812.1| 1.609| 2447.6| 1.523|\n| 32| 1| 14637.8| 1.636| 12702.5| 1.644| 12070.3| 1.634| 11036.9| 1.647|\n| 32| 2| 10627.3| 1.818| 9198.3| 1.818| 8431.6| 1.725| 8000.0| 1.773|\n| 32| 5| 8205.7| 2.598| 7117.6| 2.476| 6825.2| 2.497| 6293.2| 2.437|\n| 128| 1| 33800.5| 2.755| 30824.5| 2.816| 27685.2| 2.661| 26580.9| 2.694|\n| 128| 2| 20829.4| 2.795| 18665.2| 2.778| 17372.1| 2.639| 16820.5| 2.821|\n| 128| 5| 11753.9| 3.309| 10658.1| 3.273| 10308.7| 3.205| 9630.7| 3.328|\n| 512| 1| 44474.6| 3.327| 40108.1| 3.394| 39816.6| 3.378| 39708.0| 3.381|\n| 512| 2| 26057.9| 3.295| 23197.3| 3.294| 23019.8| 3.284| 22951.4| 3.284|\n| 512| 5| 12161.5| 3.428| 10777.5| 3.418| 10733.1| 3.414| 10710.5| 3.420|\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.\n\n#### Inference latency results\nTables presented in this section show the average inference latency (columns **Avg\n(ms)**) and inference latency for various confidence intervals (columns **N%\n(ms)**, where `N` denotes the confidence interval). Inference latency is\nmeasured in milliseconds. Speedups reported in FP16 subsections are relative to\nFP32 (for NVIDIA Volta and NVIDIA Turing) and relative to TF32 (for NVIDIA\nAmpere) numbers for corresponding configuration.\n\n##### Inference latency: NVIDIA A100 40GB\nOur results were obtained by running the `translate.py` script in the\npytorch-20.06-py3 NGC Docker container with NVIDIA A100 40GB.\nFull command to launch the inference latency benchmark was provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n**FP16**\n\n|**Batch Size**|**Beam Size**|**Avg (ms)**|**Speedup**|**90% (ms)**|**Speedup**|**95% (ms)**|**Speedup**|**99% (ms)**|**Speedup**|\n|-------------:|------------:|-----------:|----------:|-----------:|----------:|-----------:|----------:|-----------:|----------:|\n| 1| 1| 44.69| 1.032| 74.04| 1.035| 84.61| 1.034| 99.14| 1.042|\n| 1| 2| 64.76| 1.020| 105.18| 1.018| 118.92| 1.019| 139.42| 1.023|\n| 1| 5| 67.06| 1.043| 107.56| 1.049| 121.82| 1.054| 143.85| 1.054|\n| 2| 1| 56.57| 1.034| 85.59| 1.037| 92.55| 1.038| 107.59| 1.046|\n| 2| 2| 80.22| 1.027| 119.22| 1.027| 128.43| 1.030| 150.06| 1.028|\n| 2| 5| 82.54| 1.063| 121.37| 1.067| 132.35| 1.069| 156.34| 1.059|\n| 4| 1| 67.29| 1.047| 92.69| 1.048| 100.08| 1.056| 112.63| 1.064|\n| 4| 2| 95.86| 1.041| 129.83| 1.040| 139.48| 1.044| 162.34| 1.045|\n| 4| 5| 98.34| 1.075| 133.83| 1.076| 142.70| 1.068| 168.30| 1.075|\n| 8| 1| 75.60| 1.099| 97.87| 1.103| 104.13| 1.099| 117.40| 1.102|\n| 8| 2| 109.38| 1.074| 137.71| 1.079| 147.69| 1.069| 168.79| 1.065|\n| 8| 5| 112.71| 1.116| 143.50| 1.104| 153.17| 1.118| 172.60| 1.113|\n| 32| 1| 98.40| 1.146| 117.02| 1.153| 123.42| 1.150| 129.01| 1.128|\n| 32| 2| 145.87| 1.133| 171.71| 1.159| 184.01| 1.127| 188.64| 1.141|\n| 32| 5| 156.82| 1.357| 189.10| 1.374| 194.95| 1.392| 196.65| 1.419|\n| 128| 1| 137.97| 1.350| 150.04| 1.348| 151.52| 1.349| 154.52| 1.434|\n| 128| 2| 211.58| 1.484| 232.96| 1.490| 237.46| 1.505| 239.86| 1.567|\n| 128| 5| 261.44| 1.990| 288.54| 2.017| 291.63| 2.052| 298.73| 2.136|\n| 512| 1| 245.93| 1.906| 262.51| 1.998| 264.24| 1.999| 265.23| 2.000|\n| 512| 2| 395.61| 2.129| 428.54| 2.219| 431.58| 2.224| 433.86| 2.227|\n| 512| 5| 627.21| 2.767| 691.72| 2.878| 696.01| 2.895| 702.13| 2.887|\n\n##### Inference latency: NVIDIA T4\nOur results were obtained by running the `translate.py` script in the\npytorch-20.06-py3 NGC Docker container with NVIDIA T4.\nFull command to launch the inference latency benchmark was provided in the\n[Inference performance benchmark](#inference-performance-benchmark) section.\n\n**FP16**\n\n|**Batch Size**|**Beam Size**|**Avg (ms)**|**Speedup**|**90% (ms)**|**Speedup**|**95% (ms)**|**Speedup**|**99% (ms)**|**Speedup**|\n|-------------:|------------:|-----------:|----------:|-----------:|----------:|-----------:|----------:|-----------:|----------:|\n| 1| 1| 51.08| 1.261| 84.82| 1.254| 97.45| 1.251| 114.6| 1.257|\n| 1| 2| 72.05| 1.168| 117.41| 1.165| 132.33| 1.170| 155.8| 1.174|\n| 1| 5| 74.20| 1.345| 119.45| 1.352| 135.07| 1.354| 160.3| 1.354|\n| 2| 1| 66.31| 1.232| 100.90| 1.232| 108.52| 1.235| 126.9| 1.238|\n| 2| 2| 88.35| 1.185| 131.47| 1.188| 141.46| 1.185| 164.7| 1.191|\n| 2| 5| 92.12| 1.305| 136.30| 1.310| 148.66| 1.309| 174.8| 1.320|\n| 4| 1| 78.54| 1.260| 108.53| 1.256| 117.19| 1.259| 133.7| 1.259|\n| 4| 2| 105.54| 1.315| 142.74| 1.317| 154.36| 1.307| 178.7| 1.303|\n| 4| 5| 110.43| 1.351| 150.62| 1.388| 161.61| 1.397| 191.2| 1.427|\n| 8| 1| 91.65| 1.418| 117.92| 1.421| 126.60| 1.405| 144.0| 1.411|\n| 8| 2| 123.39| 1.315| 156.00| 1.337| 167.34| 1.347| 193.4| 1.340|\n| 8| 5| 129.69| 1.666| 165.01| 1.705| 178.18| 1.723| 200.3| 1.765|\n| 32| 1| 126.53| 1.641| 153.23| 1.689| 159.58| 1.692| 167.0| 1.700|\n| 32| 2| 174.37| 1.822| 209.04| 1.899| 219.59| 1.877| 228.6| 1.878|\n| 32| 5| 226.15| 2.598| 277.38| 2.636| 290.27| 2.648| 299.4| 2.664|\n| 128| 1| 218.29| 2.755| 238.94| 2.826| 243.18| 2.843| 267.1| 2.828|\n| 128| 2| 354.83| 2.796| 396.63| 2.832| 410.53| 2.803| 433.2| 2.866|\n| 128| 5| 628.32| 3.311| 699.57| 3.353| 723.98| 3.323| 771.0| 3.337|\n| 512| 1| 663.07| 3.330| 748.62| 3.388| 753.20| 3.388| 758.0| 3.378|\n| 512| 2| 1134.04| 3.295| 1297.85| 3.283| 1302.25| 3.304| 1306.9| 3.308|\n| 512| 5| 2428.82| 3.428| 2771.72| 3.415| 2801.32| 3.427| 2817.6| 3.422|\n\nTo achieve these same results, follow the [Quick Start Guide](#quick-start-guide)\noutlined above.","hasPerformance","policies",[],{"_75":197,"_77":198,"_168":-5,"_174":30,"_176":30,"_192":196,"_179":85},{"_75":21,"_77":21,"_168":-5,"_174":30,"_176":30,"_192":196,"_179":85},[],"-","Latest (20.06.0)","nvidia/dle/gnmt_pyt","key","nvidia/dle/resources/gnmt_pyt","resourceType","resources","resourceId","gnmt_pyt","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-pyt)\n\nThe GNMT v2 model is similar to the one discussed in the [Google's Neural\nMachine Translation 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\ntime step.\n\nThe same attention mechanism is also implemented in the default GNMT-like\nmodels from [TensorFlow Neural Machine Translation\nTutorial](https://github.com/tensorflow/nmt) and [NVIDIA OpenSeq2Seq\nToolkit](https://github.com/NVIDIA/OpenSeq2Seq).\n\n### Model architecture\n![ModelArchitecture](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/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)\n training\n * trained with label smoothing loss (smoothing factor 0.1)\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 * uses standard PyTorch nn.LSTM 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* decoder:\n * 4-layer unidirectional LSTM with hidden size 1024 and fully-connected\n classifier\n * with residual connections starting from 3rd layer\n * uses standard PyTorch nn.LSTM 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 * weights and bias of fully-connected classifier is initialized with\n uniform(-0.1,0.1) distribution\n* attention:\n * normalized Bahdanau attention\n * output from first LSTM layer of decoder goes into attention, then\n re-weighted context is concatenated with the input to all subsequent LSTM\n layers of the decoder at the current timestep\n * linear transform of keys and queries is initialized with uniform(-0.1,\n 0.1), normalization scalar is initialized with 1.0/sqrt(1024),\n normalization bias is initialized with zero\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\n [SacreBLEU](https://github.com/mjpost/sacrebleu)\n * [motivation](https://github.com/mjpost/sacrebleu#motivation) for choosing\n 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 [WMT16\nEnglish-German](http://www.statmt.org/wmt16/translation-task.html) dataset. The\nnumber of LSTM layers is controlled by the `--num-layers` parameter in the\n`train.py` training script.\n\n### Feature support matrix\nThe following features are supported by this model.\n\n| **Feature** | **GNMT v2** |\n|:------------|------------:|\n|[Apex AMP](https://nvidia.github.io/apex/amp.html) | Yes |\n|[Apex DistributedDataParallel](https://nvidia.github.io/apex/parallel.html#apex.parallel.DistributedDataParallel) | Yes |\n\n#### Features\n[Apex AMP](https://nvidia.github.io/apex/amp.html) - a tool that enables Tensor\nCore-accelerated training. Refer to the [Enabling mixed\nprecision](#enabling-mixed-precision) section for more details.\n\n[Apex\nDistributedDataParallel](https://nvidia.github.io/apex/parallel.html#apex.parallel.DistributedDataParallel) -\na module wrapper that enables easy multiprocess distributed data parallel\ntraining, similar to\n[torch.nn.parallel.DistributedDataParallel](https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel).\n`DistributedDataParallel` is optimized for use with\n[NCCL](https://github.com/NVIDIA/nccl). It achieves high performance by\noverlapping communication with computation during `backward()` and bucketing\nsmaller gradient transfers to reduce the total number of transfers required.\n\n### Mixed precision training\nMixed precision is the combined use of different numerical precisions in a\ncomputational method.\n[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant\ncomputational speedup by performing operations in half-precision format, while\nstoring minimal information in single-precision to retain as much information\nas possible in critical parts of the network. Since the introduction of [Tensor\nCores](https://developer.nvidia.com/tensor-cores) in Volta, and following with\nboth the Turing and Ampere architectures, significant training speedups are\nexperienced by switching to mixed precision -- up to 3x overall speedup on the\nmost arithmetically intense model architectures. Using mixed precision training\npreviously required two steps:\n\n1. Porting the model to use the FP16 data type where appropriate.\n2. Manually adding loss scaling to preserve small gradient values.\n\nThe ability to train deep learning networks with lower precision was introduced\nin the Pascal architecture and first supported in [CUDA\n8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep\nLearning SDK.\n\nFor information about:\n* How to train using mixed precision, see the [Mixed Precision\n Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed\n Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)\n documentation.\n* Techniques used for mixed precision training, see the [Mixed-Precision\n Training of Deep Neural\n Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)\n blog.\n* APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy\n Mixed-Precision Training in\n PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/)\n .\n\n#### Enabling mixed precision\nMixed precision is enabled in PyTorch by using the Automatic Mixed Precision\n(AMP), library from [APEX](https://github.com/NVIDIA/apex) that casts variables\nto half-precision upon retrieval, while storing variables in single-precision\nformat. Furthermore, to preserve small gradient magnitudes in backpropagation,\na [loss\nscaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling)\nstep must be included when applying gradients. In PyTorch, loss scaling can be\neasily applied by using `scale_loss()` method provided by AMP. The scaling\nvalue to be used can be\n[dynamic](https://nvidia.github.io/apex/amp.html#apex.amp.initialize) or fixed.\n\nFor an in-depth walk through on AMP, check out sample usage\n[here](https://nvidia.github.io/apex/amp.html#).\n[APEX](https://github.com/NVIDIA/apex) is a PyTorch extension that contains\nutility libraries, such as AMP, which require minimal network code changes to\nleverage Tensor Cores performance.\n\nThe following steps were needed to enable mixed precision training in GNMT:\n\n* Import AMP from APEX (file: `seq2seq/train/trainer.py`):\n\n```\nfrom apex import amp\n```\n\n* Initialize AMP and wrap the model and the optimizer (file:\n `seq2seq/train/trainer.py`, class: `Seq2SeqTrainer`):\n\n```\nself.model, self.optimizer = amp.initialize(\n self.model,\n self.optimizer,\n cast_model_outputs=torch.float16,\n keep_batchnorm_fp32=False,\n opt_level='O2')\n```\n\n* Apply `scale_loss` context manager (file: `seq2seq/train/fp_optimizers.py`,\n class: `AMPOptimizer`):\n\n```\nwith amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n```\n\n* Apply gradient clipping on single precision master weights (file:\n `seq2seq/train/fp_optimizers.py`, class: `AMPOptimizer`):\n\n```\nif self.grad_clip != float('inf'):\n clip_grad_norm_(amp.master_params(optimizer), self.grad_clip)\n```\n\n#### Enabling TF32\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA\nA100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the\nmatrix math also called tensor operations. TF32 running on Tensor Cores in A100\nGPUs can provide up to 10x speedups compared to single-precision floating-point\nmath (FP32) on Volta GPUs.\n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of\naccuracy. It is more robust than FP16 for models which require high dynamic\nrange for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates\nAI Training, HPC up to\n20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/)\nblog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by\ndefault.","displayName","GNMT v2 for PyTorch",["D",1680631525404],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/gnmt_pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[241],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":242,"_243":244,"_245":246,"_247":248},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_249":250,"_243":244},"category","label","field","application",{"_106":1428,"_108":1429},"ngcApps",[1381],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1362":1363,"_1364":1365,"_1366":1367,"_1368":1369,"_1370":262,"_137":286,"_1371":1372,"_1373":55,"_1374":85,"_1375":85,"_1376":1377,"_1378":85,"_1379":1380},"amplitude",{"_1359":1360,"_960":-7,"_1361":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1353":1354,"_1355":1356,"_1357":1358},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1235":368,"_1236":1237,"_1238":1239,"_1240":1241},"announcements",[],"consentState",{"_325":1225,"_1226":30,"_1227":30,"_1228":85,"_1229":1230},"consentConfig",{"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326},"showContactAdmin",1783880552932,[],[],[300,301,302,303,304,305],{"_44":59,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":306,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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