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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

BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations that obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding paper. NVIDIA's implementation of BERT is an optimized version of the Hugging Face implementation, leveraging mixed precision arithmetic and Tensor Cores on NVIDIA Volta V100 and NVIDIA Ampere A100 GPUs for faster training times while maintaining target accuracy.

This repository contains scripts to interactively launch data download, training, benchmarking, and inference routines in a Docker container for both pre-training and fine-tuning tasks such as question answering. The major differences between the original implementation of the paper and this version of BERT are as follows:

Scripts to download the Wikipedia dataset
Scripts to preprocess downloaded data into inputs and targets for pre-training in a modular fashion
Fused LAMB optimizer to support training with larger batches
Fused Adam optimizer for fine-tuning tasks
Fused CUDA kernels for better performance LayerNorm
Automatic mixed precision (AMP) training support
Scripts to launch on multiple number of nodes

Other publicly available implementations of BERT include:

This model trains with mixed precision Tensor Cores on NVIDIA Volta and provides a push-button solution to pre-training on a corpus of choice. As a result, researchers can get results 4x faster than training without Tensor Cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

The BERT model uses the same architecture as the encoder of the Transformer. Input sequences are projected into an embedding space before being fed into the encoder structure. Additionally, positional and segment encodings are added to the embeddings to preserve positional information. The encoder structure is simply a stack of Transformer blocks, which consist of a multi-head attention layer followed by successive stages of feed-forward networks and layer normalization. The multi-head attention layer accomplishes self-attention on multiple input representations.

An illustration of the architecture taken from the Transformer paper is shown below.

$\"BERT\"$

Default configuration

The architecture of the BERT model is almost identical to the Transformer model that was first introduced in the Attention Is All You Need paper. The main innovation of BERT lies in the pre-training step, where the model is trained on two unsupervised prediction tasks using a large text corpus. Training on these unsupervised tasks produces a generic language model, which can then be quickly fine-tuned to achieve state-of-the-art performance on language processing tasks such as question answering.

The BERT paper reports the results for two configurations of BERT, each corresponding to a unique model size. This implementation provides the same configurations by default, which are described in the table below.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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\">

Model	Hidden layers	Hidden unit size	Attention heads	Feedforward filter size	Max sequence length	Parameters
BERTBASE	12 encoder	768	12	4 x 768	512	110M
BERTLARGE	24 encoder	1024	16	4 x 1024	512	330M

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

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

Feature	BERT
PyTorch AMP	Yes
PyTorch DDP	Yes
LAMB	Yes
Multi-node	Yes
LDDL	Yes
NVFuser	Yes

Features

APEX is a PyTorch extension with NVIDIA-maintained utilities to streamline mixed precision and distributed training, whereas AMP is an abbreviation used for automatic mixed precision training.

DDP stands for DistributedDataParallel and is used for multi-GPU training.

LAMB stands for Layerwise Adaptive Moments based optimizer, is a large batch optimization technique that helps accelerate training of deep neural networks using large minibatches. It allows using a global batch size of 65536 and 32768 on sequence lengths 128 and 512 respectively, compared to a batch size of 256 for Adam. The optimized implementation accumulates 1024 gradient batches in phase 1 and 4096 steps in phase 2 before updating weights once. This results in a 15% training speedup. On multi-node systems, LAMB allows scaling up to 1024 GPUs resulting in training speedups of up to 72x in comparison to Adam. Adam has limitations on the learning rate that can be used since it is applied globally on all parameters whereas LAMB follows a layerwise learning rate strategy.

NVLAMB adds the necessary tweaks to LAMB version 1, to ensure correct convergence. The algorithm is as follows:

$\"NVLAMB\"$

LDDL is a library that enables scalable data preprocessing and loading. LDDL is used by this PyTorch BERT example.

NVFuser is NVIDIA's fusion backend for PyTorch.

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 the NVIDIA Volta, and following with both the NVIDIA Turing and NVIDIA Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:

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

For information about:

How to train using mixed precision, refer to the Mixed Precision Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, refer to the Mixed-Precision Training of Deep Neural Networks blog.
APEX tools for mixed precision training, refer to the NVIDIA APEX: Tools for Easy Mixed-Precision Training in PyTorch.

Enabling mixed precision

In this repository, mixed precision training is enabled by NVIDIA's APEX library. The APEX library has an automatic mixed precision module that allows mixed precision to be enabled with minimal code changes.

Automatic mixed precision can be enabled with the following code changes:

, loss_scale="dynamic")\n \nif fp16:\n with amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n\">

from apex import amp\nif fp16:\n    # Wrap optimizer and model\n    model, optimizer = amp.initialize(model, optimizer, opt_level=<opt_level>, loss_scale=\"dynamic\")\n \nif fp16:\n    with amp.scale_loss(loss, optimizer) as scaled_loss:\n        scaled_loss.backward()\n

Where <opt_level> is the optimization level. In the pre-training, O2 is set as the optimization level. Mixed precision training can be turned on by passing the fp16 argument to the run_pretraining.py and run_squad.py. All shell scripts have a positional argument available to enable mixed precision training.

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 NVIDIA 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 a high dynamic range for weights or activations.

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

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

Glossary

Fine-tuning
\nTraining an already pre-trained model further using a task-specific dataset for subject-specific refinements by adding task-specific layers on top if required.

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

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

Transformer
\nThe paper Attention Is All You Need introduces a novel architecture called Transformer that uses an attention mechanism and transforms one sequence into another.

Phase 1
\nPre-training on samples of sequence length 128 and 20 masked predictions per sequence.

Phase 2
\nPre-training on samples of sequence length 512 and 80 masked predictions per sequence.

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Aside from these dependencies, ensure you have the following components:\n \n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [PyTorch 21.11-py3 NGC container or later](https://ngc.nvidia.com/registry/nvidia-pytorch)\n- Supported GPUs:\n- [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n- [NVIDIA Turing architecture](https://www.nvidia.com/en-us/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, refer to 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/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 environment or create your own container, refer to the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html).\n \nFor multi-node, the sample provided in this repository requires [Enroot](https://github.com/NVIDIA/enroot) and [Pyxis](https://github.com/NVIDIA/pyxis) set up on a [SLURM](https://slurm.schedmd.com) cluster.\n \nMore information on how to set up and launch can be found in the [Multi-node Documentation](https://docs.nvidia.com/ngc/multi-node-bert-user-guide).","hasSetup","quickStartGuide","To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the BERT model. Training configurations to run on 8 x A100 80G, 8 x V100 16G, 16 x V100 32G cards and examples of usage are provided at the end of this section. For the specifics concerning training and inference, refer to the [Advanced](#advanced) section.\n \n1. Clone the repository.\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples.git\ncd DeepLearningExamples/PyTorch/LanguageModeling/BERT\n```\n \n2. Download the NVIDIA pre-trained checkpoint.\n \nIf you want to use a pre-trained checkpoint, visit [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/models/bert_pyt_ckpt_large_pretraining_amp_lamb/files?version=20.03.0). This pre-trained checkpoint is used to fine-tune on SQuAD. Ensure you unzip the downloaded file and place the checkpoint in the `checkpoints/` folder. For a checkpoint already fine-tuned for QA on SQuAD v1.1 visit [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/models/bert_pyt_ckpt_large_qa_squad11_amp/files).\n\nFind all trained and available checkpoints in the table below:\n\n| Model | Description |\n|------------------------|---------------------------------------------------------------------------|\n| [bert-large-uncased-qa](https://catalog.ngc.nvidia.com/orgs/nvidia/models/bert_pyt_ckpt_large_qa_squad11_amp/files) | Large model fine-tuned on SQuAD v1.1 |\n| [bert-large-uncased-sst2](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/models/bert_pyt_ckpt_large_ft_sst2_amp) |Large model fine-tuned on GLUE SST-2 |\n| [bert-large-uncased-pretrained](https://catalog.ngc.nvidia.com/orgs/nvidia/models/bert_pyt_ckpt_large_pretraining_amp_lamb/files?version=20.03.0) | Large model pretrained checkpoint on Generic corpora like Wikipedia|\n| [bert-base-uncased-qa](https://catalog.ngc.nvidia.com/orgs/nvidia/models/bert_pyt_ckpt_base_qa_squad11_amp/files) | Base model fine-tuned on SQuAD v1.1 |\n| [bert-base-uncased-sst2](https://catalog.ngc.nvidia.com/orgs/nvidia/models/bert_pyt_ckpt_base_ft_sst2_amp_128/files) | Base model fine-tuned on GLUE SST-2 |\n| [bert-base-uncased-pretrained](https://catalog.ngc.nvidia.com/orgs/nvidia/models/bert_pyt_ckpt_base_pretraining_amp_lamb/files) | Base model pretrained checkpoint on Generic corpora like Wikipedia. |\n| [bert-dist-4L-288D-uncased-qa](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/models/bert_pyt_ckpt_distilled_4l_288d_qa_squad11_amp/files) | 4 layer distilled model fine-tuned on SQuAD v1.1 |\n| [bert-dist-4L-288D-uncased-sst2](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/models/bert_pyt_ckpt_distilled_4l_288d_ft_sst2_amp/files) | 4 layer distilled model fine-tuned on GLUE SST-2 |\n| [bert-dist-4L-288D-uncased-pretrained](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/models/bert_pyt_ckpt_distilled_4l_288d_pretraining_amp/files) | 4 layer distilled model pretrained checkpoint on Generic corpora like Wikipedia. |\n| [bert-dist-6L-768D-uncased-qa](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/models/bert_pyt_ckpt_distilled_6l_768d_qa_squad11_amp/files) | 6 layer distilled model fine-tuned on SQuAD v1.1 |\n| [bert-dist-6L-768D-uncased-sst2](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/models/bert_pyt_ckpt_distilled_6l_768d_ft_sst2_amp/files) | 6 layer distilled model fine-tuned on GLUE SST-2 |\n| [bert-dist-6L-768D-uncased-pretrained](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/models/bert_pyt_ckpt_distilled_6l_768d_pretraining_amp/files) | 6 layer distilled model pretrained checkpoint on Generic corpora like Wikipedia. |\n\n3. Build BERT on top of the NGC container.\n```\nbash scripts/docker/build.sh\n```\n \n4. Start an interactive session in the NGC container to run training/inference.\n```\nbash scripts/docker/launch.sh\n```\n \nResultant logs and checkpoints of pre-training and fine-tuning routines are stored in the `results/` folder.\n \n`data` and `vocab.txt` are downloaded in the `data/` directory by default. Refer to the [Getting the data](#getting-the-data) section for more details on how to process a custom corpus as required for BERT pre-training. \n\n5. Download the dataset.\n\nThis repository provides scripts to download, verify, and extract the following datasets:\n \n- [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) (fine-tuning for question answering)\n- [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398) (fine-tuning for paraphrase detection)\n- [SST-2](https://nlp.stanford.edu/sentiment/index.html) (fine-tuning for sentiment analysis)\n- Wikipedia (pre-training)\n \nTo download, verify, extract the datasets, run: \n```\n/workspace/bert/data/create_datasets_from_start.sh\n```\n\nNote: For fine-tuning only, downloading the Wikipedia dataset can be skipped by commenting it out.\n\nNote: Ensure a complete Wikipedia download. But if the download failed in LDDL,\nremove the output directory `data/wikipedia/` and start over again. \n\n6. Start pre-training.\n \nTo run on a single node 8 x V100 32G cards, from within the container, you can use the following script to run pre-training. \n```\nbash scripts/run_pretraining.sh\n```\n \nThe default hyperparameters are set to run on 8x V100 32G cards.\n \nTo run on multiple nodes, refer to the [Multi-node](#multi-node) section. \n \n7. Start fine-tuning with the SQuAD dataset.\n \nThe above pre-trained BERT representations can be fine-tuned with just one additional output layer for a state-of-the-art question answering system. Running the following script launches fine-tuning for question answering with the SQuAD dataset.\n```\nbash scripts/run_squad.sh /workspace/bert/checkpoints/\n```\n \n8. Start fine-tuning with the GLUE tasks.\n \nThe above pre-trained BERT representations can be fine-tuned with just one additional output layer for GLUE tasks. Running the following scripts launch fine-tuning for paraphrase detection with the MRPC dataset:\n```\nbash scripts/run_glue.sh /workspace/bert/checkpoints/\n```\n\n9. Run Knowledge Distillation (Optional).\n \nTo get setup to run distillation on BERT, follow steps provided [here](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/LanguageModeling/BERT//./distillation/README.md).\n\n10. Start validation/evaluation.\n \nFor both SQuAD and GLUE, validation can be performed with the `bash scripts/run_squad.sh /workspace/bert/checkpoints/` or `bash scripts/run_glue.sh /workspace/bert/checkpoints/`, setting `mode` to `eval` in `scripts/run_squad.sh` or `scripts/run_glue.sh` as follows:\n\n```\nmode=${11:-\"eval\"}\n```\n \n11. Start inference/predictions.\n \nInference can be performed with the `bash scripts/run_squad.sh /workspace/bert/checkpoints/`, setting `mode` to `prediction` in `scripts/run_squad.sh` or `scripts/run_glue.sh` as follows:\n\n```\nmode=${11:-\"prediction\"}\n```\n\nInference predictions are saved to `/predictions.json`, set in `scripts/run_squad.sh` or `scripts/run_glue.sh` as follows:\n\n```\nOUT_DIR=${10:-\"/workspace/bert/results/SQuAD\"} # For SQuAD.\n# Or…\nout_dir=${5:-\"/workspace/bert/results/MRPC\"} # For MRPC.\n# Or... \nout_dir=${5:-\"/workspace/bert/results/SST-2\"} # For SST-2.\n```\n\nThis repository contains a number of predefined configurations to run the SQuAD, GLUE and pre-training on NVIDIA DGX-1, NVIDIA DGX-2H or NVIDIA DGX A100 nodes in `scripts/configs/squad_config.sh`, `scripts/configs/glue_config.sh` and `scripts/configs/pretrain_config.sh`. For example, to use the default DGX A100 8 gpu config, run:\n\n```\nbash scripts/run_squad.sh $(source scripts/configs/squad_config.sh && dgxa100-80g_8gpu_fp16) # For the SQuAD v1.1 dataset.\nbash scripts/run_glue.sh $(source scripts/configs/glue_config.sh && mrpc_dgxa100-80g_8gpu_fp16) # For the MRPC dataset.\nbash scripts/run_glue.sh $(source scripts/configs/glue_config.sh && sst-2_dgxa100-80g_8gpu_fp16) # For the SST-2 dataset.\nbash scripts/run_pretraining.sh $(source scripts/configs/pretrain_config.sh && dgxa100-80g_8gpu_fp16) # For pre-training\n```","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n \n### Scripts and sample code\n \nDescriptions of the key scripts and folders are provided below.\n \n- `data/` - Contains scripts for downloading and preparing individual datasets and will contain downloaded and processed datasets.\n- `scripts/` - Contains shell scripts to launch data download, pre-training, and fine-tuning.\n- `run_squad.sh` - Interface for launching question answering fine-tuning with `run_squad.py`.\n- `run_glue.sh` - Interface for launching paraphrase detection and sentiment analysis fine-tuning with `run_glue.py`.\n- `run_pretraining.sh` - Interface for launching BERT pre-training with `run_pretraining.py`.\n- `create_pretraining_data.py` - Creates `.hdf5` files from shared text files in the final step of dataset creation.\n- `model.py` - Implements the BERT pre-training and fine-tuning model architectures with PyTorch.\n- `optimization.py` - Implements the LAMB optimizer with PyTorch.\n- `run_squad.py` - Implements fine-tuning training and evaluation for question answering on the [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset.\n- `run_glue.py` - Implements fine-tuning training and evaluation for [GLUE](https://gluebenchmark.com/) tasks.\n- `run_pretraining.py` - Implements BERT pre-training.\n \n### Parameters\n \n#### Pre-training parameters\n \nBERT is designed to pre-train deep bidirectional networks for language representations. The following scripts replicate pre-training on Wikipedia from this [paper](https://arxiv.org/pdf/1810.04805.pdf). These scripts are general and can be used for pre-training language representations on any corpus of choice.\n \nThe complete list of the available parameters for the `run_pretraining.py` script is :\n \n```\n --input_dir INPUT_DIR - The input data directory.\n Should contain .hdf5 files for the task.\n \n --config_file CONFIG_FILE - Path to a json file describing the BERT model\n configuration. This file configures the model\n architecture, such as the number of transformer\n blocks, number of attention heads, etc.\n \n --bert_model BERT_MODEL - Specifies the type of BERT model to use;\n should be one of the following:\n bert-base-uncased\n bert-large-uncased\n bert-base-cased\n bert-base-multilingual\n bert-base-chinese\n \n --output_dir OUTPUT_DIR - Path to the output directory where the model\n checkpoints will be written.\n \n --init_checkpoint - Initial checkpoint to start pre-training from (Usually a BERT pre-trained checkpoint)\n \n --max_seq_length MAX_SEQ_LENGTH\n - The maximum total input sequence length after\n WordPiece tokenization. Sequences longer than\n this will be truncated, and sequences shorter\n than this will be padded.\n \n --max_predictions_per_seq MAX_PREDICTIONS_PER_SEQ\n - The maximum total of masked tokens per input\n sequence for Masked LM.\n \n --train_batch_size TRAIN_BATCH_SIZE\n - Batch size per GPU for training.\n \n --learning_rate LEARNING_RATE\n - The initial learning rate for the LAMB optimizer.\n \n --max_steps MAX_STEPS - Total number of training steps to perform.\n \n --warmup_proportion WARMUP_PROPORTION\n - Proportion of training to perform linear learning\n rate warmup for. For example, 0.1 = 10% of training.\n \n --seed SEED - Sets the seed to use for random number generation.\n \n --gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS\n - Number of update steps to accumulate before\n performing a backward/update pass.\n \n --allreduce_post_accumulation - If set to true, performs allreduce only after the defined number of gradient accumulation steps.\n \n --allreduce_post_accumulation_fp16 - If set to true, performs allreduce after gradient accumulation steps in FP16.\n \n --amp or --fp16 - If set, performs computations using\n automatic mixed precision.\n \n --loss_scale LOSS_SCALE - Sets the loss scaling value to use when\n mixed precision is used. The default value (0)\n tells the script to use dynamic loss scaling\n instead of fixed loss scaling.\n \n --log_freq LOG_FREQ - If set, the script outputs the training\n loss every LOG_FREQ step.\n \n --resume_from_checkpoint - If set, training resumes from a checkpoint\n that currently exists in OUTPUT_DIR.\n \n --num_steps_per_checkpoint NUM_STEPS_PER_CHECKPOINT\n - Number of update steps until a model checkpoint\n is saved to disk.\n --phase2 - Specified if training on phase 2 only. If not specified, default pre-training is on phase 1.\n \n --phase1_end_step - The number of steps phase 1 was trained for. In order to \n resume phase 2 the correct way; phase1_end_step should correspond to the --max_steps phase 1 was trained for.\n \n```\n\n#### Fine tuning parameters\n\n* SQuAD \n \nDefault arguments are listed below in the order `scripts/run_squad.sh` expects:\n \n- Initial checkpoint - The default is `/workspace/checkpoints/bert_uncased.pt`.\n- Number of training Epochs - The default is `2`.\n- Batch size - The default is `3`.\n- Learning rate - The default is `3e-5`.\n- Precision (either `fp16`, `tf32` or `fp32`) - The default is `fp16`.\n- Number of GPUs - The default is `8`.\n- Seed - The default is `1`.\n- SQuAD directory - The default is `/workspace/bert/data/v1.1`.\n- Vocabulary file (token to ID mapping) - The default is `/workspace/bert/vocab/vocab`.\n- Output directory for results - The default is `/results/SQuAD`.\n- Mode (`train`, `eval`, `train eval`, `predict`) - The default is `train`.\n- Config file for the BERT model (It should be the same as the pre-trained model) - The default is `/workspace/bert/bert_config.json`.\n \nThe script saves the final checkpoint to the `/results/SQuAD/pytorch_model.bin` file.\n\n* GLUE\n\nDefault arguments are listed below in the order `scripts/run_glue.sh` expects:\n \n- Initial checkpoint - The default is `/workspace/bert/checkpoints/bert_uncased.pt`.\n- Data directory - The default is `/workspace/bert/data/download/glue/MRPC/`.\n- Vocabulary file (token to ID mapping) - The default is `/workspace/bert/vocab/vocab`.\n- Config file for the BERT model (It should be the same as the pre-trained model) - The default is `/workspace/bert/bert_config.json`.\n- Output directory for result - The default is `/workspace/bert/results/MRPC`.\n- The name of the GLUE task (`mrpc` or `sst-2`) - The default is `mrpc`\n- Number of GPUs - The default is `8`.\n- Batch size per GPU - The default is `16`.\n- Number of update steps to accumulate before performing a backward/update pass (this option effectively normalizes the GPU memory footprint down by the same factor) - The default is `1`.\n- Learning rate - The default is `2.4e-5`.\n- The proportion of training samples used to warm up the learning rate - The default is `0.1`.\n- Number of training Epochs - The default is `3`.\n- Total number of training steps to perform - The default is `-1.0`, which means it is determined by the number of epochs.\n- Precision (either `fp16`, `tf32` or `fp32`) - The default is `fp16`.\n- Seed - The default is `2`.\n- Mode (`train`, `eval`, `prediction`, `train eval`, `train prediction`, `eval prediction`, `train eval prediction`) - The default is `train eval`.\n\n#### Multi-node\n \nMulti-node runs can be launched on a pyxis/enroot Slurm cluster (refer to [Requirements](#requirements)) with the `run.sub` script with the following command for a 4-node DGX-1 example for both phase 1 and phase 2:\n \n```\nBATCHSIZE=2048 LR=6e-3 GRADIENT_STEPS=128 PHASE=1 sbatch -N4 --ntasks-per-node=8 run.sub\nBATCHSIZE=1024 LR=4e-3 GRADIENT_STEPS=256 PHASE=2 sbatch -N4 --ntasks-per-node=8 run.sub\n```\n \nCheckpoints after phase 1 will be saved in `checkpointdir` specified in `run.sub`. The checkpoint will be automatically picked up to resume training on phase 2. Note that phase 2 should be run after phase 1.\n \nVariables to re-run the [Training performance results](#training-performance-results) are available in the `configurations.yml` file. \n \nThe batch variables `BATCHSIZE`, `LR`, `GRADIENT_STEPS`,`PHASE` refer to the Python arguments `train_batch_size`, `learning_rate`, `gradient_accumulation_steps`, `phase2` respectively.\n \nNote that the `run.sub` script is a starting point that has to be adapted depending on the environment. In particular, variables such as `datadir` handle the location of the files for each phase. \n \nRefer to the file's contents to find the full list of variables to adjust for your system.\n \n### Command-line options\n \nTo view the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:\n \n`python run_pretraining.py --help`\n \n`python run_squad.py --help`\n\n`python run_glue.py --help`\n \nDetailed descriptions of command-line options can be found in the [Parameters](#parameters) section.\n \n### Getting the data\n \nFor pre-training BERT, we use the Wikipedia (2500M words) dataset. We extract \nonly the text passages and ignore headers, lists, and tables. BERT requires that\ndatasets are structured as a document level corpus rather than a shuffled \nsentence-level corpus because it is critical to extract long contiguous \nsentences. `data/create_datasets_from_start.sh` uses the LDDL downloader to \ndownload the Wikipedia dataset, and `scripts/run_pretraining.sh` uses the LDDL \npreprocessor and load balancer to preprocess the Wikipedia dataset into Parquet\nshards which are then streamed during the pre-training by the LDDL data loader.\nRefer to [LDDL's README](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/LanguageModeling/BERT//../../../Tools/lddl/README.md) for more \ninformation on how to use LDDL. Depending on the speed of your internet \nconnection, downloading and extracting the Wikipedia dataset takes a few hours,\nand running the LDDL preprocessor and load balancer takes half an hour on a \nsingle DGXA100 node.\n\nFor fine-tuning a pre-trained BERT model for specific tasks, by default, this repository prepares the following dataset:\n \n- [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/): for question answering\n- [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398): for paraphrase detection.\n- [SST-2](https://nlp.stanford.edu/sentiment/index.html): for sentiment analysis.\n \n#### Dataset guidelines\n \nThe procedure to prepare a text corpus for pre-training is described in the above section. This section provides additional insight into how exactly raw text is processed so that it is ready for pre-training.\n \nFirst, raw text is tokenized using [WordPiece tokenization](https://arxiv.org/pdf/1609.08144.pdf). A [CLS] token is inserted at the start of every sequence, and the two sentences in the sequence are separated by a [SEP] token.\n \nNote: BERT pre-training looks at pairs of sentences at a time. A sentence embedding token [A] is added to the first sentence and token [B] to the next.\n \nBERT pre-training optimizes for two unsupervised classification tasks. The first is Masked Language Modeling (Masked LM). One training instance of Masked LM is a single modified sentence. Each token in the sentence has a 15% chance of being replaced by a [MASK] token. The chosen token is replaced with [MASK] 80% of the time, 10% with a random token and the remaining 10% the token is retained. The task is then to predict the original token.\n \nThe second task is next sentence prediction. One training instance of BERT pre-training is two sentences (a sentence pair). A sentence pair may be constructed by simply taking two adjacent sentences from a single document or by pairing up two random sentences with equal probability. The goal of this task is to predict whether or not the second sentence followed the first in the original document.\n\n### Training process\n \nThe training process consists of two steps: pre-training and fine-tuning.\n \n#### Pre-training\n \nPre-training is performed using the `run_pretraining.py` script along with parameters defined in the `scripts/run_pretraining.sh`.\n \nThe `run_pretraining.sh` script runs a job on a single node that trains the BERT-large model from scratch using the Wikipedia dataset as training data using the LAMB optimizer. By default, the training script runs two phases of training with a hyperparameter recipe specific to 8x V100 32G cards:\n \nPhase 1: (Maximum sequence length of 128)\n- Runs on 8 GPUs with a training batch size of 64 per GPU\n- Uses a learning rate of 6e-3\n- Has FP16 precision enabled\n- Runs for 7038 steps, where the first 28.43% (2000) are warm-up steps\n- Saves a checkpoint every 200 iterations (keeps only the latest three checkpoints) and at the end of training. All checkpoints and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).\n- Creates a log file containing all the output\n \nPhase 2: (Maximum sequence length of 512)\n- Runs on 8 GPUs with a training batch size of 8 per GPU\n- Uses a learning rate of 4e-3\n- Has FP16 precision enabled\n- Runs for 1563 steps, where the first 12.8% are warm-up steps\n- Saves a checkpoint every 200 iterations (keeps only the latest three checkpoints) and at the end of training. All checkpoints and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).\n- Creates a log file containing all the output\n \nThese parameters will train on the Wikipedia dataset to state-of-the-art accuracy on a DGX-1 with 32GB V100 cards.\n \n`bash run_pretraining.sh

`\n \nWhere:\n \n- `` is per-GPU batch size used for training. Larger batch sizes run more efficiently but require more memory.\n- `` is the base learning rate for training\n- `` is the type of math in your model, which can be either `fp32` or `fp16`. The options mean:\n - FP32: 32-bit IEEE single precision floats.\n - FP16: Mixed precision 16 and 32-bit floats.\n- `` is the number of GPUs to use for training. Must be equal to or smaller than the number of GPUs attached to your node.\n- `` is the percentage of training steps used for warm-up at the start of training.\n- `` is the total number of training steps.\n- `` controls how often checkpoints are saved.\n- `` if set to `true`, training should resume from the latest model in `/results/checkpoints`. Default is `false`.\n- `` a flag indicating if output should be written to a log file or not (acceptable values are `true` or 'false`. `true` indicates output should be saved to a log file.)\n- `` a flag indicating whether a larger batch should be simulated with gradient accumulation.\n- `` an integer indicating the number of steps to accumulate gradients over. Effective batch size = `training_batch_size` / `gradient_accumulation_steps`.\n- `` random seed for the run.\n- `` - If set to `true`, performs `allreduce` only after the defined number of gradient accumulation steps.\n- `` - If set to `true`, performs `allreduce` after gradient accumulation steps in FP16.\n \n Note: The above two options need to be set to false when running either TF32 or FP32. \n \n- `` is per-GPU batch size used for training in phase 2. Larger batch sizes run more efficiently but require more memory.\n- `` is the base learning rate for training phase 2.\n- `` is the percentage of training steps used for warm-up at the start of training.\n- `` is the total number of training steps for phase 2, to be continued in addition to phase 1.\n- `` an integer indicating the number of steps to accumulate gradients over in phase 2. Effective batch size = `training_batch_size_phase2` / `gradient_accumulation_steps_phase2`.\n- `` A checkpoint to start the pre-training routine on (Usually a BERT pre-trained checkpoint).\n \nFor example:\n \n`bash scripts/run_pretraining.sh`\n \nTrains BERT-large from scratch on a DGX-1 32G using FP16 arithmetic. 90% of the training steps are done with sequence length 128 (phase 1 of training), and 10% of the training steps are done with sequence length 512 (phase 2 of training).\n \nTo train on a DGX-1 16G, set `gradient_accumulation_steps` to `512` and `gradient_accumulation_steps_phase2` to `1024` in `scripts/run_pretraining.sh`.\n \nTo train on a DGX-2 32G, set `train_batch_size` to `4096`, `train_batch_size_phase2` to `2048`, `num_gpus` to `16`, `gradient_accumulation_steps` to `64` and `gradient_accumulation_steps_phase2` to `256` in `scripts/run_pretraining.sh`\n \nIn order to run a pre-training routine on an initial checkpoint, perform the following in `scripts/run_pretraining.sh`:\n- point the `init_checkpoint` variable to the location of the checkpoint\n- set `resume_training` to `true`\n- Note: The parameter value assigned to `BERT_CONFIG` during training should remain unchanged. Also, to resume pre-training on your corpus of choice, the training dataset should be created using the same vocabulary file used in `data/create_datasets_from_start.sh`.\n \n#### Fine-tuning\n \nFine-tuning is provided for a variety of tasks. The following tasks are included with this repository through the following scripts:\n \n- Question Answering (`scripts/run_squad.sh`)\n- Paraphrase Detection and Sentiment Analysis (`script/run_glue.sh`)\n \nBy default, each Python script implements fine-tuning a pre-trained BERT model for a specified number of training epochs as well as evaluation of the fine-tuned model. Each shell script invokes the associated Python script with the following default parameters:\n \n- Uses 8 GPUs\n- Has FP16 precision enabled\n- Saves a checkpoint at the end of training to the `results/` folder\n \nFine-tuning Python scripts implement support for mixed precision and multi-GPU training through NVIDIA's [APEX](https://github.com/NVIDIA/apex) library. For a full list of parameters and associated explanations, refer to the [Parameters](#parameters) section.\n \nThe fine-tuning shell scripts have positional arguments outlined below:\n \n```\n# For SQuAD.\nbash scripts/run_squad.sh \n# For GLUE\nbash scripts/run_glue.sh \n```\n \nBy default, the mode positional argument is set to train eval. Refer to the [Quick Start Guide](#quick-start-guide) for explanations of each positional argument.\n \nNote: The first positional argument (the path to the checkpoint to load) is required.\n \nEach fine-tuning script assumes that the corresponding dataset files exist in the `data/` directory or separate path can be a command-line input to `run_squad.sh`.\n \n### Inference process\n\nFine-tuning inference can be run in order to obtain predictions on fine-tuning tasks, for example, Q&A on SQuAD.\n \n#### Fine-tuning inference\n \nEvaluation fine-tuning is enabled by the same scripts as training:\n \n- Question Answering (`scripts/run_squad.sh`)\n- Paraphrase Detection and Sentiment Analysis (`scripts/run_glue.sh`)\n \nThe mode positional argument of the shell script is used to run in evaluation mode. The fine-tuned BERT model will be run on the evaluation dataset, and the evaluation loss and accuracy will be displayed.\n \nEach inference shell script expects dataset files to exist in the same locations as the corresponding training scripts. The inference scripts can be run with default settings. By setting the `mode` variable in the script to either `eval` or `prediction` flag, you can choose between running predictions and evaluating them on a given dataset or just obtain the model predictions.\n \n`bash scripts/run_squad.sh `\n`bash scripts/run_glue.sh `\n\nFor SQuAD, to run inference interactively on question-context pairs, use the script `inference.py` as follows:\n \n`python inference.py --bert_model \"bert-large-uncased\" --init_checkpoint= --config_file=\"bert_config.json\" --vocab_file= --question=\"What food does Harry like?\" --context=\"My name is Harry and I grew up in Canada. I love apples.\"`\n\n### Deploying BERT using NVIDIA Triton Inference Server\n \nThe [NVIDIA Triton Inference Server](https://github.com/NVIDIA/triton-inference-server) provides a cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. More information on how to perform inference using NVIDIA Triton Inference Server can be found in [triton/README.md](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/LanguageModeling/BERT//./triton/README.md).","hasAdvanced","performance","### Benchmarking\n \nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n \n#### Training performance benchmark\n \nTraining performance benchmarks for pre-training can be obtained by running `scripts/run_pretraining.sh`, and for fine-tuning can be obtained by running `scripts/run_squad.sh` or `scripts/run_glue.sh` for SQuAD or GLUE, respectively. The required parameters can be passed through the command-line as described in [Training process](#training-process).\n \nAs an example, to benchmark the training performance on a specific batch size for SQuAD, run:\n`bash scripts/run_squad.sh train `\n \nAn example call used to generate throughput numbers:\n`bash scripts/run_squad.sh /workspace/bert/bert_large_uncased.pt 2.0 4 3e-5 fp16 8 42 /workspace/bert/squad_data /workspace/bert/scripts/vocab/vocab /results/SQuAD train /workspace/bert/bert_config.json -1`\n \n#### Inference performance benchmark\n \nInference performance benchmarks for both fine-tuning can be obtained by running `scripts/run_squad.sh` and `scripts/run_glue.sh` respectively. The required parameters can be passed through the command-line as described in [Inference process](#inference-process).\n \nAs an example, to benchmark the inference performance on a specific batch size for SQuAD, run:\n`bash scripts/run_squad.sh eval `\n \nAn example call used to generate throughput numbers:\n`bash scripts/run_squad.sh /workspace/bert/bert_large_uncased.pt 2.0 4 3e-5 fp16 8 42 /workspace/bert/squad_data /workspace/bert/scripts/vocab/vocab /results/SQuAD eval /workspace/bert/bert_config.json -1`\n \n### Results\n \nThe following sections provide details on how we achieved our performance and accuracy in training and inference. \n \n#### Training accuracy results\n \nOur results were obtained by running the `scripts/run_squad.sh` and `scripts/run_pretraining.sh` training scripts in the pytorch:21.11-py3 NGC container unless otherwise specified.\n \n##### Pre-training loss results: NVIDIA DGX A100 (8x A100 80GB)\n\n| DGX System | GPUs / Node | Batch size / GPU (Phase 1 and Phase 2) | Accumulated Batch size / GPU (Phase 1 and Phase 2) | Accumulation steps (Phase 1 and Phase 2) | Final Loss - TF32 | Final Loss - mixed precision | Time to train(hours) - TF32 | Time to train(hours) - mixed precision | Time to train speedup (TF32 to mixed precision) |\n|--------------------|-------------|----------------------------------------------------|------------------------------------------|-------------------|------------------------------|-----------------------------|----------------------------------------|-------------------------------------------------|-----|\n| 32 x DGX A100 80GB | 8 | 256 and 32 | 256 and 128 | 1 and 4 | --- | 1.2437 | --- | 1.2 | 1.9 |\n| 32 x DGX A100 80GB | 8 | 128 and 16 | 256 and 128 | 2 and 8 | 1.2465 | --- | 2.4 | --- | --- |\n\n##### Pre-training loss curves\n![Pre-training Loss Curves](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/LanguageModeling/BERT//images/loss_curves.png)\n\n\n##### Fine-tuning accuracy results: NVIDIA DGX A100 (8x A100 80GB)\n* SQuAD\n\n| GPUs | Batch size / GPU (TF32 and FP16) | Accuracy - TF32(% F1) | Accuracy - mixed precision(% F1) | Time to train(hours) - TF32 | Time to train(hours) - mixed precision | Time to train speedup (TF32 to mixed precision) |\n|------|----------------------------------|-----------------------|----------------------------------|-----------------------------|----------------------------------------|-------------------------------------------------|\n| 8 | 32 | 90.93 | 90.96 | 0.102 | 0.0574 | 1.78 |\n\n* MRPC\n\n| GPUs | Batch size / GPU (TF32 and FP16) | Accuracy - TF32(%) | Accuracy - mixed precision(%) | Time to train(seconds) - TF32 | Time to train(seconds) - mixed precision | Time to train speedup (TF32 to mixed precision) |\n|------|----------------------------------|--------------------|-------------------------------|-------------------------------|------------------------------------------|-------------------------------------------------|\n| 8 | 16 | 87.25 | 88.24 | 17.26 | 7.31 | 2.36 |\n\n* SST-2\n\n| GPUs | Batch size / GPU (TF32 and FP16) | Accuracy - TF32(%) | Accuracy - mixed precision(%) | Time to train(seconds) - TF32 | Time to train(seconds) - mixed precision | Time to train speedup (TF32 to mixed precision) |\n|------|----------------------------------|--------------------|-------------------------------|-------------------------------|------------------------------------------|-------------------------------------------------|\n| 8 | 128 | 91.97 | 92.78 | 119.28 | 62.59 | 1.91 |\n\n##### Training stability test\n \n###### Pre-training stability test\n \n| Accuracy Metric | Seed 0 | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Mean | Standard Deviation |\n|-----------------|--------|--------|--------|--------|--------|-------|--------------------|\n| Final Loss | 1.260 | 1.265 | 1.304 | 1.256 | 1.242 | 1.265 | 0.023 |\n \n###### Fine-tuning stability test\n* SQuAD\n \nTraining stability with 8 GPUs, FP16 computations, batch size of 4:\n \n| Accuracy Metric | Seed 0 | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Seed 6 | Seed 7 | Seed 8 | Seed 9 | Mean | Standard Deviation |\n|-----------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|-------|--------------------|\n| Exact Match % | 83.64 | 84.05 | 84.51 | 83.69 | 83.87 | 83.94 | 84.27 | 83.97 | 83.75 | 83.92 | 83.96 | 0.266 |\n| f1 % | 90.60 | 90.65 | 90.96 | 90.44 | 90.58 | 90.78 | 90.81 | 90.82 | 90.51 | 90.68 | 90.68 | 0.160 |\n \n* MRPC\n\nTraining stability with 8 A100 GPUs, FP16 computations, batch size of 16 per GPU:\n \n| Accuracy Metric | Seed 0 | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Seed 6 | Seed 7 | Seed 8 | Seed 9 | Mean | Standard Deviation |\n|-----------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|-------|--------------------|\n| Exact Match % | 85.78 | 85.54 | 84.56 | 86.27 | 84.07 | 86.76 | 87.01 | 85.29 | 88.24 | 86.52 | 86.00 | 1.225 |\n\n> Note: Since MRPC is a very small dataset where overfitting can often occur, the resulting validation accuracy can often have high variance. By repeating the above experiments for 100 seeds, the max accuracy is 88.73, and the average accuracy is 82.56 with a standard deviation of 6.01.\n\n* SST-2\n\nTraining stability with 8 A100 GPUs, FP16 computations, batch size of 128 per GPU:\n \n| Accuracy Metric | Seed 0 | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Seed 6 | Seed 7 | Seed 8 | Seed 9 | Mean | Standard Deviation |\n|-----------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|-------|--------------------|\n| Exact Match % | 91.86 | 91.28 | 91.86 | 91.74 | 91.28 | 91.86 | 91.40 | 91.97 | 91.40 | 92.78 | 91.74 | 0.449 |\n \n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the `scripts run_pretraining.sh` training script in the pytorch:21.11-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers (in items/images per second) were averaged over a few training iterations.\n\n###### Pre-training NVIDIA DGX A100 (8x A100 80GB)\n\n| GPUs | Batch size / GPU (TF32 and FP16) | Accumulated Batch size / GPU (TF32 and FP16) | Accumulation steps (TF32 and FP16) | Sequence length | Throughput - TF32(sequences/sec) | Throughput - mixed precision(sequences/sec) | Throughput speedup (TF32 - mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision |\n|------|----------------------------------|------------------------------------|-----------------|----------------------------------|---------------------------------------------|---------------------------------------------|---------------------|--------------------------------|----|\n| 1 | 128 and 256 | 8192 and 8192 | 64 and 32 | 128 | 317 | 580 | 1.83 | 1.00 | 1.00 |\n| 8 | 128 and 256 | 8192 and 8192 | 64 and 32 | 128 | 2505 | 4591 | 1.83 | 7.90 | 7.91 |\n| 1 | 16 and 32 | 4096 and 4096 | 256 and 128 | 512 | 110 | 210 | 1.90 | 1.00 | 1.00 |\n| 8 | 16 and 32 | 4096 and 4096 | 256 and 128 | 512 | 860 | 1657 | 1.92 | 7.81 | 7.89 |\n\n###### Pre-training NVIDIA DGX A100 (8x A100 80GB) Multi-node Scaling\n\n| Nodes | GPUs / node | Batch size / GPU (TF32 and FP16) | Accumulated Batch size / GPU (TF32 and FP16) | Accumulation steps (TF32 and FP16) | Sequence length | Mixed Precision Throughput | Mixed Precision Strong Scaling | TF32 Throughput | TF32 Strong Scaling | Speedup (Mixed Precision to TF32) |\n|-------|-------------|----------------------------------|------------------------------------|-----------------|----------------------------|--------------------------------|-----------------|---------------------|-----------------------------------|-----|\n| 1 | 8 | 126 and 256 | 8192 and 8192 | 64 and 32 | 128 | 4553 | 1 | 2486 | 1 | 1.83 |\n| 2 | 8 | 126 and 256 | 4096 and 4096 | 32 and 16 | 128 | 9191 | 2.02 | 4979 | 2.00 | 1.85 |\n| 4 | 8 | 126 and 256 | 2048 and 2048 | 16 and 18 | 128 | 18119 | 3.98 | 9859 | 3.97 | 1.84 |\n| 8 | 8 | 126 and 256 | 1024 and 1024 | 8 and 4 | 128 | 35774 | 7.86 | 19815 | 7.97 | 1.81 |\n| 16 | 8 | 126 and 256 | 512 and 512 | 4 and 2 | 128 | 70555 | 15.50 | 38866 | 15.63 | 1.82 |\n| 32 | 8 | 126 and 256 | 256 and 256 | 2 and 1 | 128 | 138294 | 30.37 | 75706 | 30.45 | 1.83 |\n| 1 | 8 | 16 and 32 | 4096 and 4096 | 256 and 128 | 512 | 1648 | 1 | 854 | 1 | 1.93 |\n| 2 | 8 | 16 and 32 | 2048 and 2048 | 128 and 64 | 512 | 3291 | 2.00 | 1684 | 1.97 | 1.95 |\n| 4 | 8 | 16 and 32 | 1024 and 1024 | 64 and 32 | 512 | 6464 | 3.92 | 3293 | 3.86 | 1.96 |\n| 8 | 8 | 16 and 32 | 512 and 512 | 32 and 16 | 512 | 13005 | 7.89 | 6515 | 7.63 | 2.00 |\n| 16 | 8 | 16 and 32 | 256 and 256 | 16 and 8 | 512 | 25570 | 15.51 | 12131 | 14.21 | 2.11 |\n| 32 | 8 | 16 and 32 | 128 and 128 | 8 and 4 | 512 | 49663 | 30.13 | 21298 | 24.95 | 2.33 |\n\n###### Fine-tuning NVIDIA DGX A100 (8x A100 80GB)\n\n* SQuAD\n \n| GPUs | Batch size / GPU (TF32 and FP16) | Throughput - TF32(sequences/sec) | Throughput - mixed precision(sequences/sec) | Throughput speedup (TF32 - mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision |\n|------|----------------------------------|----------------------------------|---------------------------------------------|---------------------------------------------|---------------------|--------------------------------|\n| 1 | 32 and 32 | 61.5 | 110.5 | 1.79 | 1.00 | 1.00 |\n| 8 | 32 and 32 | 469.8 | 846.7 | 1.80 | 7.63 | 7.66 |\n \n##### Training performance: NVIDIA DGX-1 (8x V100 32G)\n\nOur results were obtained by running the `scripts/run_pretraining.sh` and `scripts/run_squad.sh` training scripts in the pytorch:21.11-py3 NGC container on NVIDIA DGX-1 with (8x V100 32G) GPUs. Performance numbers (in sequences per second) were averaged over a few training iterations.\n \n###### Pre-training NVIDIA DGX-1 With 32G\n \n| GPUs | Batch size / GPU (FP32 and FP16) | Accumulation steps (FP32 and FP16) | Sequence length | Throughput - FP32(sequences/sec) | Throughput - mixed precision(sequences/sec) | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision |\n|------|----------------------------------|------------------------------------|-----------------|----------------------------------|---------------------------------------------|---------------------------------------------|---------------------|--------------------------------|\n| 1 | 4096 and 4096 | 128 and 64 | 128 | 50 | 224 | 4.48 | 1.00 | 1.00 |\n| 8 | 4096 and 4096 | 128 and 64 | 128 | 387 | 1746 | 4.51 | 7.79 | 7.79 |\n| 1 | 2048 and 2048 | 512 and 256 | 512 | 19 | 75 | 3.94 | 1.00 | 1.00 |\n| 8 | 2048 and 2048 | 512 and 256 | 512 | 149.6 | 586 | 3.92 | 7.87 | 7.81 |\n\n###### Fine-tuning NVIDIA DGX-1 With 32G\n\n* SQuAD \n \n| GPUs | Batch size / GPU (FP32 and FP16) | Throughput - FP32(sequences/sec) | Throughput - mixed precision(sequences/sec) | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision |\n|------|----------------------------------|----------------------------------|---------------------------------------------|---------------------------------------------|---------------------|--------------------------------|\n| 1 | 8 and 16 | 12 | 52 | 4.33 | 1.00 | 1.00 |\n| 8 | 8 and 16 | 85.5 | 382 | 4.47 | 7.12 | 7.34 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n \n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 80GB) \n \nOur results were obtained by running `scripts/run_squad.sh` in the pytorch:21.11-py3 NGC container on NVIDIA DGX A100 with (1x A100 80G) GPUs.\n \n###### Fine-tuning inference on NVIDIA DGX A100 (1x A100 80GB)\n\n* SQuAD\n \n| GPUs | Batch Size \$TF32/FP16\$ | Sequence Length | Throughput \\- TF32\$sequences/sec\$ | Throughput \\- Mixed Precision\$sequences/sec\$ |\n|------|--------------------------|-----------------|-------------------------------------|------------------------------------------------|\n| 1 | 32/32 | 384 | 216 | 312 |\n \nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n \nThe inference performance metrics used were sequences/second.","hasPerformance","policies",[],{"_77":21,"_79":199,"_170":-5,"_176":31,"_178":31,"_194":198,"_181":87},{"_77":23,"_79":23,"_170":-5,"_176":31,"_178":31,"_194":198,"_181":87},[],"Latest (21.11.0)","nvidia/dle/bert_pyt","key","nvidia/dle/resources/bert_pyt","resourceType","resources","resourceId","bert_pyt","shortDescription","BERT is a method of pre-training language representations which obtains state-of-the-art results on a wide array of NLP tasks.","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/bert-pyt)\n\n \nBERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations that obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) paper. NVIDIA's implementation of BERT is an optimized version of the [Hugging Face implementation](https://github.com/huggingface/pytorch-pretrained-BERT), leveraging mixed precision arithmetic and Tensor Cores on NVIDIA Volta V100 and NVIDIA Ampere A100 GPUs for faster training times while maintaining target accuracy.\n \nThis repository contains scripts to interactively launch data download, training, benchmarking, and inference routines in a Docker container for both pre-training and fine-tuning tasks such as question answering. The major differences between the original implementation of the paper and this version of BERT are as follows:\n \n- Scripts to download the Wikipedia dataset\n- Scripts to preprocess downloaded data into inputs and targets for pre-training in a modular fashion\n- Fused [LAMB](https://arxiv.org/pdf/1904.00962.pdf) optimizer to support training with larger batches\n- Fused Adam optimizer for fine-tuning tasks\n- Fused CUDA kernels for better performance LayerNorm\n- Automatic mixed precision (AMP) training support\n- Scripts to launch on multiple number of nodes\n \nOther publicly available implementations of BERT include:\n1. [NVIDIA TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT)\n2. [Hugging Face](https://github.com/huggingface/pytorch-pretrained-BERT)\n3. [codertimo](https://github.com/codertimo/BERT-pytorch)\n4. [gluon-nlp](https://github.com/dmlc/gluon-nlp/tree/v0.10.x/scripts/bert)\n5. [Google's implementation](https://github.com/google-research/bert)\n \nThis model trains with mixed precision Tensor Cores on NVIDIA Volta and provides a push-button solution to pre-training on a corpus of choice. As a result, researchers can get results 4x faster than training without Tensor Cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n \n### Model architecture\n \nThe BERT model uses the same architecture as the encoder of the Transformer. Input sequences are projected into an embedding space before being fed into the encoder structure. Additionally, positional and segment encodings are added to the embeddings to preserve positional information. The encoder structure is simply a stack of Transformer blocks, which consist of a multi-head attention layer followed by successive stages of feed-forward networks and layer normalization. The multi-head attention layer accomplishes self-attention on multiple input representations.\n \nAn illustration of the architecture taken from the [Transformer paper](https://arxiv.org/pdf/1706.03762.pdf) is shown below.\n \n ![BERT](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/LanguageModeling/BERT//images/model.png)\n\n \n### Default configuration\n \nThe architecture of the BERT model is almost identical to the Transformer model that was first introduced in the [Attention Is All You Need paper](https://arxiv.org/pdf/1706.03762.pdf). The main innovation of BERT lies in the pre-training step, where the model is trained on two unsupervised prediction tasks using a large text corpus. Training on these unsupervised tasks produces a generic language model, which can then be quickly fine-tuned to achieve state-of-the-art performance on language processing tasks such as question answering.\n \nThe BERT paper reports the results for two configurations of BERT, each corresponding to a unique model size. This implementation provides the same configurations by default, which are described in the table below. \n \n| **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** |\n|:---------:|:-----------------:|:--------------------:|:-------------------:|:---------------------------:|:-----------------------:|:--------------:|\n| BERTBASE | 12 encoder | 768 | 12 | 4 x 768 | 512 | 110M |\n| BERTLARGE | 24 encoder | 1024 | 16 | 4 x 1024 | 512 | 330M |\n \n### Feature support matrix\n \nThe following features are supported by this model. \n \n| **Feature** | **BERT** |\n|:-----------:|:--------:|\n| PyTorch AMP | Yes |\n| PyTorch DDP | Yes |\n| LAMB | Yes |\n| Multi-node | Yes |\n| LDDL | Yes |\n| NVFuser | Yes |\n \n#### Features\n \n[APEX](https://github.com/NVIDIA/apex) is a PyTorch extension with NVIDIA-maintained utilities to streamline mixed precision and distributed training, whereas [AMP](https://nvidia.github.io/apex/amp.html) is an abbreviation used for automatic mixed precision training.\n \n[DDP](https://nvidia.github.io/apex/parallel.html) stands for DistributedDataParallel and is used for multi-GPU training.\n \n[LAMB](https://arxiv.org/pdf/1904.00962.pdf) stands for Layerwise Adaptive Moments based optimizer, is a large batch optimization technique that helps accelerate training of deep neural networks using large minibatches. It allows using a global batch size of 65536 and 32768 on sequence lengths 128 and 512 respectively, compared to a batch size of 256 for [Adam](https://arxiv.org/pdf/1412.6980.pdf). The optimized implementation accumulates 1024 gradient batches in phase 1 and 4096 steps in phase 2 before updating weights once. This results in a 15% training speedup. On multi-node systems, LAMB allows scaling up to 1024 GPUs resulting in training speedups of up to 72x in comparison to Adam. Adam has limitations on the learning rate that can be used since it is applied globally on all parameters whereas LAMB follows a layerwise learning rate strategy.\n \nNVLAMB adds the necessary tweaks to [LAMB version 1](https://arxiv.org/abs/1904.00962v1), to ensure correct convergence. The algorithm is as follows:\n \n ![NVLAMB](https://raw.githubusercontent.com/NVIDIA/DeepLearningExamples/master/PyTorch/LanguageModeling/BERT//images/nvlamb.png)\n\n\n[LDDL](../../../Tools/lddl) is a library that enables scalable data preprocessing and loading. LDDL is used by this PyTorch BERT example.\n \nNVFuser is NVIDIA's fusion backend for PyTorch.\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 the NVIDIA Volta, and following with both the NVIDIA Turing and NVIDIA Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:\n \n1. Porting the model to use the FP16 data type where appropriate.\n2. Adding loss scaling to preserve small gradient values.\n \nFor information about:\n- How to train using mixed precision, refer to 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, refer to the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n- APEX tools for mixed precision training, refer to 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 \nIn this repository, mixed precision training is enabled by NVIDIA's APEX library. The APEX library has an automatic mixed precision module that allows mixed precision to be enabled with minimal code changes.\n \nAutomatic mixed precision can be enabled with the following code changes:\n \n```\nfrom apex import amp\nif fp16:\n # Wrap optimizer and model\n model, optimizer = amp.initialize(model, optimizer, opt_level=, loss_scale=\"dynamic\")\n \nif fp16:\n with amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n ```\n \nWhere `` is the optimization level. In the pre-training, `O2` is set as the optimization level. Mixed precision training can be turned on by passing the `fp16` argument to the `run_pretraining.py` and `run_squad.py`. All shell scripts have a positional argument available to enable mixed precision training.\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 NVIDIA 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 a high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.\n\n### Glossary\n \n**Fine-tuning** \nTraining an already pre-trained model further using a task-specific dataset for subject-specific refinements by adding task-specific layers on top if required.\n \n**Language Model** \nAssigns a probability distribution over a sequence of words. Given a sequence of words, it assigns a probability to the whole sequence.\n \n**Pre-training** \nTraining a model on vast amounts of data on the same (or different) task to build general understandings.\n \n**Transformer** \nThe paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762) introduces a novel architecture called Transformer that uses an attention mechanism and transforms one sequence into another.\n \n**Phase 1** \nPre-training on samples of sequence length 128 and 20 masked predictions per sequence.\n \n**Phase 2** \nPre-training on samples of sequence length 512 and 80 masked predictions per sequence.","displayName","BERT for PyTorch",["D",1680631941583],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/bert_pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":243,"_244":245,"_246":247,"_248":249},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_250":251,"_244":245},"category","label","field","application",{"_107":1231,"_109":1232},"ngcApps",[1184],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1165":1166,"_1167":1168,"_1169":1170,"_1171":1172,"_1173":263,"_138":289,"_1174":1175,"_1176":56,"_1177":87,"_1178":87,"_1179":1180,"_1181":87,"_1182":1183},"amplitude",{"_1162":1163,"_802":-7,"_1164":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1156":1157,"_1158":1159,"_1160":1161},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787433522311,"1.12.0","defaultSearchSuggestions",{"_1042":369,"_1043":1044,"_1045":1046,"_1047":1048},"announcements",[],"consentState",{"_326":1032,"_1033":87,"_1034":31,"_1035":31,"_1036":1037},"consentConfig",{"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327},"showContactAdmin",1787433522312,[],[],[303,304,305,306,307],{"_45":60,"_47":71,"_49":68,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":68,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":68,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":68,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":59,"_55":56},"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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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487],[350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366],{"_367":461,"_210":461,"_168":369,"_340":462},{"_367":457,"_210":457,"_168":369,"_340":458},{"_367":452,"_210":452,"_168":369,"_340":453},{"_367":448,"_210":448,"_168":369,"_340":449},{"_367":443,"_210":443,"_168":369,"_340":444},{"_367":438,"_210":438,"_168":369,"_340":439},{"_367":430,"_210":430,"_168":369,"_340":431},{"_367":422,"_210":422,"_168":369,"_340":423},{"_367":417,"_210":417,"_168":369,"_340":418},{"_367":412,"_210":412,"_168":369,"_340":413},{"_367":407,"_210":407,"_168":369,"_340":408},{"_367":402,"_210":402,"_168":369,"_340":403},{"_367":397,"_210":397,"_168":369,"_340":398},{"_367":390,"_210":390,"_168":369,"_340":391},{"_367":385,"_210":385,"_168":369,"_340":386},{"_367":378,"_210":378,"_168":369,"_340":379},{"_367":368,"_210":368,"_168":369,"_340":370},"hostName","amazon-adsystem.com","",[371,372],{"_79":377,"_374":368,"_375":31,"_168":376},{"_79":373,"_374":368,"_375":31,"_168":376},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[380,381],{"_79":384,"_374":378,"_375":31,"_168":383},{"_79":382,"_374":378,"_375":31,"_168":383},"IDE","This domain is owned by Doubleclick (Google). 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If you do not allow these cookies, then some or all of these services may not function properly.",[558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582],[529,530,531,532,533],{"_367":443,"_210":443,"_168":369,"_340":553},{"_367":417,"_210":417,"_168":369,"_340":548},{"_367":543,"_210":543,"_168":369,"_340":544},{"_367":538,"_210":538,"_168":369,"_340":539},{"_367":534,"_210":534,"_168":369,"_340":535},"nvidia.partners",[536],{"_79":537,"_374":534,"_375":31,"_168":411},"edge","app.hushly.com",[540],{"_79":541,"_374":538,"_375":31,"_168":542},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[545],{"_79":546,"_374":543,"_375":31,"_168":547},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",3,[642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670],[622,623,624,625],{"_367":637,"_210":637,"_168":369,"_340":638},{"_367":632,"_210":632,"_168":369,"_340":633},{"_367":430,"_210":430,"_168":369,"_340":629},{"_367":543,"_210":543,"_168":369,"_340":626},[627],{"_79":628,"_374":543,"_375":87,"_168":547},"_gh_sess",[630],{"_79":631,"_374":430,"_375":87,"_168":437},"YSC","bizible.com",[634],{"_79":635,"_374":632,"_375":31,"_168":636},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[639],{"_79":640,"_374":637,"_375":31,"_168":641},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_79":712,"_374":489,"_375":31,"_168":713},{"_79":697,"_374":711,"_375":87,"_168":698},{"_79":680,"_374":492,"_375":31,"_168":604},{"_79":680,"_374":592,"_375":31,"_168":604},{"_79":709,"_374":489,"_375":31,"_168":710},{"_79":697,"_374":495,"_375":87,"_168":698},{"_79":707,"_374":489,"_375":31,"_168":708},{"_79":697,"_374":492,"_375":87,"_168":698},{"_79":705,"_374":591,"_375":31,"_168":706},{"_79":697,"_374":613,"_375":87,"_168":698},{"_79":677,"_374":492,"_375":31,"_168":679},{"_79":703,"_374":489,"_375":87,"_168":704},{"_79":701,"_374":489,"_375":31,"_168":702},{"_79":699,"_374":489,"_375":87,"_168":700},{"_79":697,"_374":587,"_375":87,"_168":698},{"_79":695,"_374":489,"_375":31,"_168":696},{"_79":693,"_374":489,"_375":31,"_168":694},{"_79":691,"_374":489,"_375":87,"_168":692},{"_79":689,"_374":596,"_375":31,"_168":690},{"_79":677,"_374":587,"_375":31,"_168":679},{"_79":687,"_374":489,"_375":31,"_168":688},{"_79":685,"_374":489,"_375":87,"_168":686},{"_79":683,"_374":489,"_375":31,"_168":684},{"_79":681,"_374":489,"_375":31,"_168":682},{"_79":680,"_374":489,"_375":31,"_168":604},{"_79":677,"_374":678,"_375":31,"_168":679},{"_79":675,"_374":489,"_375":31,"_168":676},{"_79":673,"_374":489,"_375":87,"_168":674},{"_79":671,"_374":489,"_375":31,"_168":672},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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