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

BART is a denoising autoencoder for pretraining sequence-to-sequence models. According to the paper, the model uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).

BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.

Other publicly available implementations of BART include:

Hugging Face
Fairseq

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.4 to 2.1x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

BART uses a standard sequence-to-sequence Transformer architecture with GeLU activations. The base model consists of 6 layers in encoder and decoder, whereas large consists of 12. The architecture has roughly 10% more parameters than BERT.

BART is trained by corrupting documents and then optimizing the reconstruction loss. The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.

Default configuration

BART model is similar to BERT with the following differences:\nDecoder layers additionally perform cross-attention over final hidden encoder layer\nBART removes the additional feed-forward network before word prediction that BERT uses

Inference is done by default with beam search 4 for CNN-DM dataset and 6 for XSum Dataset.

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	BART
PyTorch AMP	Yes
PyTorch DDP	Yes
LAMB	Yes
Multi-node	Yes
LDDL	Yes
Pre-LN	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:

$\"NVLAMB\"$

In this PyTorch BART example, we used global batch size of 64000 and 30720 on sequence lengths 128 and 512 respectively, compared to a batch size of 8000 and sequence lengths 512 on RoBERTa which Facebook used for BART. We only trained with 44% total number of tokens compared to Facebook's BART. It can get 2.7x training speedup and achieve similar accuracy.

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

Pre-LN is an transformer architecture, which layer normalization is put inside the residual blocks. In our experiments, For Pre-LN transformer, the loss decays faster and it makes training more stable without gradient exploding or vanishing .

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training previously required two steps:

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

For information about:

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

Enabling mixed precision

In this repository, mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP)\nautocast torch.cuda.amp.autocast which casts variables\nto half-precision upon retrieval, while storing variables in single-precision format.\nFurthermore, to preserve small gradient magnitudes in backpropagation,\na gradient scaling\nstep must be included.

For an in-depth walk through on AMP, check out sample usage\nhere.

TF32

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

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

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

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

Glossary

Fine-tuning\nTraining an already pretrained 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.

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Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [PyTorch 22.08-py3+](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) NGC container\n- Supported GPUs:\n- [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:\n- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry)\n- [Running PyTorch](https://docs.nvidia.com/deeplearning/dgx/pytorch-release-notes/running.html#running)\n\nFor those unable to use the PyTorch NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the BART model on the CNN-DM/XSum datasets. For the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n```bash\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/PyTorch/LanguageModeling/BART\n```\n\n2. Build the BART PyTorch container.\n\n```bash\nbash scripts/docker/build.sh\n```\n\n3. Start an interactive session in the container to run training/inference.\n\nAfter you build the container image, you can start an interactive CLI session as follows:\n\n```bash\nbash scripts/docker/launch.sh\n```\n\nThe `launch.sh` script, by default, mounts the current directory to `/workspace/bart`.\n\n4. Download and preprocess the dataset.\n\nUse the following script to download and preprocess CNN DM data as well as XSum dataset.\n\n```bash\nbash scripts/get_data.sh \n```\n\nUse the script to download Wikipedia, Common Crawl, and OpenWebTextCorpus for pre-training dataset\n```bash\nbash scripts/get_pretraining_data.sh \n```\nThe pretraining dataset is 200GB+ and takes 24+ hours to download.\n\nFor downloading less dataset, you can change the date period of Common Crawl archive to take less time. For example:\n```bash\ndownload_common_crawl \\\n --outdir $data_folder/common_crawl \\\n --warc-files-start-date 2016-09-01 \\\n --warc-files-end-date 2016-10-31 \\\n --start-date 2016-09-01 \\\n --end-date 2016-10-31\n```\n\nUse the script to preprocess the pre-training dataset into LDDL Parquet shards\n```bash\nbash scripts/preprocess_pretrain_data.sh \n```\n\nBy default, the path to the data folder is set to /workspace/bart/data for ease of use in all the scripts.\n\n5. Start pre-training\n\nBART is designed to pre-train language representations. The following scripts are to replicate pre-training on Wikipedia, Common Crawl, and OpenWebTextCorpus from the LAMB paper. These scripts are general and can be used for pre-training language representations on any corpus of choice.\nFrom within the container, you can use the following script to run pre-training using LAMB.\n\n```bash\nbash scripts/run_pretraining.sh \n```\n\n6. Start summarizing.\n\nPretrained BART representations can be fine tuned for a state-of-the-art summarization system. From within the container, you can use the following script to run summarization on CNN DM dataset.\n\n```bash\nbash scripts/run_summarization.sh \n```\n\nThis repository contains a number of predefined configurations to run the CNN+DM fine tuning on NVIDIA DGX-1 V100 or NVIDIA DGX A100 nodes in `scripts/params/cnn_dm_params.sh`. For example, to use the default DGX A100 8 gpu config, run:\n\n```bash\nbash scripts/run_summarization.sh $(source scripts/params/cnn_dm_params.sh && dgxa100_8gpu_bf16)\n```\n\nSimilarly, configurations for XSum dataset are available in `scripts/params/xsum_params.sh`.\n\n7. Start inference/predictions.\n\nYou can run the following script to run inference summarization using a fine-tuned checkpoint:\n\n```bash\nbash scripts/run_eval_summarization.sh \n```\n\nThis repository contains multiple predefined configurations in `scripts/params/cnn_dm_params.sh` and `scripts/params/xsum_params.sh`. For example, to run inference on CNN-DM with a checkpoint run:\n\n```bash\nbash scripts/run_eval_summarization.sh $(source scripts/params/cnn_dm_params.sh && dgxa100_8gpu_bf16_eval)\n```\n\nNow that you have your model trained and evaluated, you can choose to compare your training results with our [Training accuracy results](#training-accuracy-results). You can also choose to benchmark yours performance to [Training performance benchmark](#training-performance-results), or [Inference performance benchmark](#inference-performance-results). Following the steps in these sections will ensure that you achieve the same accuracy and performance results as stated in the [Results](#results) section.\n\n8. Run Custom Inference with the fine-tuned checkpoint\nWe can write a simple few lines of code to run custom inference with the fine-tuned checkpoint.\n\n```python\nfrom bart.modeling.modeling_bart import BartForConditionalGeneration\nfrom bart.tokenization.tokenization_bart import BartTokenizer\nfrom bart.configuration.configuration_bart import BartConfig\nimport json\nconfig = BartConfig(**json.load(open('configs/config.json', \"r\")))\nconfig.dtype = None\nconfig.pre_ln = True\nmodel_path = 'results/_epoch1_step2000.ckpt' # The fine-tuned checkpoint path\nmodel = BartForConditionalGeneration.from_pretrained(model_path, config=config)\ntokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')\nARTICLE_TO_SUMMARIZE = \"NVIDIA Geforce Won't Run or Uninstall\"\ninputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, truncation=True, return_tensors='pt')\nsummary_ids = model.generate(inputs['input_ids'],\n num_beams=4,\n max_length=50,\n num_beam_groups=1,\n output_scores=False,\n return_dict_in_generate=False,\n encoder_no_repeat_ngram_size=0,\n diversity_penalty=0.0,\n early_stopping=True)\nprint([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])\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\nIn the root directory, the most important files are:\n* `pretrain.py` - Serves as entry point for pre-training\n* `finetune.py` - Serves as entry point for fine-tuning\n* `run_eval.py` - Serves as entry point for inference\n* `Dockerfile` - Container with the basic set of dependencies to run BART\n\nThe `scripts/` folder encapsulates all the one-click scripts required for running various functionalities supported such as:\n* `run_summarization.sh` - Runs summarization finetuning followed by inference using the `finetune.py` and `run_eval.py` files.\n* `run_summarization_eval.sh` - Runs inference on fine tuned checkpoint using the `run_eval.py` file.\n* `get_data.sh` - Preprocesses CNN-DM dataset as well as downloads and preprocesses XSum dataset.\n* `get_pretraining_data.sh` - Downloads pre-train dataset.\n* `preprocess_pretrain_data.sh` - Preprocesses pre-train dataset.\n\nOther folders included in the root directory are:\n* `data/` - Necessary folder to download datasets required for fine tuning of BART.\n* `src/` - Modeling, tokenization and configuration functionality files for implementing the BART model.\n* `utils/` - Necessary utility files for BART model.\n\n### Parameters\nAside from the options to set hyperparameters, the relevant options to control the behaviour of the `pretrain.py` script are:\n\n```\n--config_path: The configuration file corresponding to BART Model\n--warmup_steps: Number of WARMUP_STEPS\n--max_steps: Number of MAX_STEPS\n--data_dir: Location to DATA_DIR\n--learning_rate: Learning Rate\n--n_val: Number of validation examples to test for early stopping\n--train_batch_size: Train batch size\n--gradient_accumulation_steps: Number of accumulation steps\n--max_source_length: Maximum source length\n--max_target_length: Maximum target length\n--val_max_target_length: Maximum length of validation tokens\n--eval_max_gen_length: Maximum length while generating validation tokens\n--weight_decay: weight decay\n--dropout: drop out\n--lamb: Whether to use LAMB optimizer\n--pre_ln: Whether to use Pre-LN architecture\n--allreduce_post_accumulation_half_precision: Whether to do fp16/bf16 allreduce post accumulation\n```\n\nAside from the options to set hyperparameters, the relevant options to control the behaviour of the `finetune.py` script are:\n\n```\n--config_path: The configuration file corresponding to BART Model\n--warmup_steps: Number of WARMUP_STEPS\n--max_steps: Number of MAX_STEPS\n--data_dir: Location to DATA_DIR\n--learning_rate: Learning Rate\n--n_val: Number of validation examples to test for early stopping\n--train_batch_size: Train batch size\n--gradient_accumulation_steps: Number of accumulation steps\n--max_source_length: Maximum source length\n--max_target_length: Maximum target length\n--val_max_target_length: Maximum length of validation tokens\n--eval_max_gen_length: Maximum length while generating validation tokens\n--weight_decay: weight decay\n--dropout: drop out\n--pre_ln: Whether to use Pre-LN architecture\n--allreduce_post_accumulation_half_precision: Whether to do fp16/bf16 allreduce post accumulation\n```\n\n### Command-line options\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command-line option with the Python file, for example:\n\n```bash\npython pretrain.py --help\npython finetune.py --help\npython run_eval.py --help\n```\n### Getting the data\n\nFor pre-training BART, we use the concatenation of Wikipedia, Common Crawl, and OpenWebTextCorpus.\n\nCommon Crawl is an archieve of news articles from small and major publishers world wide, which is provided from commoncrawl.org.\n\nOpenWebTextCorpus is an open source effort to reproduce OpenAI's WebText dataset. The distribution was created by Aaron Gokaslan and Vanya Cohen of Brown University.\n\nFor fine-tuning BART, we have tested fine tuning the BART model on summarization benchmarks such as CNN-DM and XSum.\n\nCNN-DM is a concatenation of CNN Stories as well as Daily Mail Stories. CNN consists of approximately 90k documents whereas Daily Mail consists of 197k documents.\n\nThese documents are preprocessed to have two features:\n* Article: text of news article, used as the document to be summarized\n* Highlights: joined text of highlights with and around each highlight, which is the target summary\n\nXSum, on the other hand, is also a single-document summarization task dataset but one that favors abstractive modeling. It consists of BBC articles and single sentence summaries. It consists of approximately 230k articles.\n\n#### Dataset guidelines\n\nThe repository contains scripts to preprocess and download data. It can be run as:\n\n```bash\nbash scripts/get_data.sh \n```\n\nThe script downloads CNN and DM raw data from [here](https://cs.nyu.edu/~kcho/DMQA/). The raw data is preprocessed using scripts from [repository](https://github.com/abisee/cnn-dailymail). The stories are first tokenized, written to serialized binary files and split into train, test and validation sets.\n\nThe script also downloads the XSum dataset from the [HuggingFace storage](https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz).\n\n```bash\nbash scripts/get_pretraining_data.sh \n```\nThe script uses the LDDL downloader to download Wikipedia, Common Crawl, and OpenWebTextCorpus dataset. The Common Crawl is downloaded by [news-please](https://github.com/fhamborg/news-please). And OpenWebTextCorpus is downloaded from [here](https://skylion007.github.io/OpenWebTextCorpus/)\n\nFor downloading less dataset, you can change the date period of Common Crawl archive in the script to take less time. For example:\n```bash\ndownload_common_crawl \\\n --outdir $data_folder/common_crawl \\\n --warc-files-start-date 2016-09-01 \\\n --warc-files-end-date 2016-10-31 \\\n --start-date 2016-09-01 \\\n --end-date 2016-10-31\n```\n\n```bash\nbash scripts/preprocess_pretrain_data.sh \n```\nThe script uses the LDDL preprocessor and load balancer to preprocess the pre-training dataset into Parquet shards which are then streamed during the pre-training by the LDDL data loader.\n\nThe script by default stores the data into the `/workspace/bart/data` folder.\n### Training process\n\nThe training process consists of two steps: pre-training and fine-tuning.\n\n#### Pre-training\nPre-training BART is done using `scripts/run_pretraining.sh` script that, in turn, uses the `pretrain.py` file to perform training.\n\nFor example, it can be invoked by calling:\n\n```bash\nbash scripts/run_pretraining.sh \n```\n\nWhere:\n* train_batch_size_phase* - per-GPU batch size used for training in the respective phase\n* learning_rate_phase* - Learning rate in the respective phase\n* precision - fp16/bf16/fp32/tf32 precision for training\n* use_preln - Whether to use Pre-LN architecture\n* num_gpus - number of GPUs to run training with\n* warmup_steps_phase* - Number of warmup steps for learning rate scheduler in the respective phase\n* train_steps_phase* - Number of training steps in the respective phase\n* save_checkpoint_steps - Number of steps for saving checkpoint\n* num_accumulation_phase* - Number of accumulation steps for an effective larger training batch size in the respective phase\n* config_path - path to configuration file of BART Model\n\nBy default, the training script stores results to `results/bart_pyt_pretraining` and runs with:\n\n```bash\nbash scripts/run_pretraining.sh 200 32 5e-3 4e-3 bf16 true 8 2166 200 95040 7560 100 40 120 configs/config.json\n```\n\n#### Fine-tuning\nTraining BART for summarization is done using `scripts/run_summarization.sh` script that, in turn, uses the `finetune.py` file to perform training.\n\nFor example, it can be invoked by calling:\n\n```bash\nbash scripts/run_summarization.sh \n```\n\nWhere:\n* DATA_DIR - path to data directory with train/test/val files.\n* CONFIG_PATH - path to configuration file of BART Model\n* NUM_GPU - number of GPUs to run training with\n* LR - Learning rate for training process\n* BS - Training batch size\n* ACCUM - Number of accumulation steps for an effective larger training batch size\n* PREC - fp16/fp32/tf32 precision for training and inference\n* TRAIN_STEPS - Maximum number of training steps\n* WARMUP_STEPS - Number of warmup steps for learning rate scheduler\n* MAX_SOURCE_LEN - Maximum source length of articles\n* MAX_TARGET_LEN - Maximum target length of summaries\n* EVAL_BEAMS - Number of beams to run during inference\n* EVAL_BS - Batch size for inference during validation\n* PRED_BS - Batch size for inference on test data\n* PRELN - Whether to use Pre-LN architecture\n\nBy default, the training script stores results to `results/bart_pyt_${DATESTAMP}` and runs with:\n\n```bash\nbash scripts/run_summarization.sh data/cnn_dm/ data/nvidia_pretrained/bart_large/ configs/config.json 8 1.25e-4 40 1 bf16 2000 50 1024 142 4 128 true\n```\n\nThese parameters train CNN-DM with reasonable rouge scores on a LUNA with 80GB A100 cards. Other tested configurations are available under `scripts/params/cnn_dm_params.sh` for CNN-DM and `scripts/params/xsum_params.sh` for XSum datasets.\n### Inference process\n\nEvaluating BART for summarization is done using `scripts/run_eval_summarization.sh` script that, in turn, uses the `run_eval.py` file to perform inference.\n\nFor example, it can be invoked by calling:\n\n```bash\nbash scripts/run_eval_summarization.sh \n```\n\nWhere:\n* `INIT_CKPT` - Model name or path to initialize BART Model weights with.\n* `PRED_BS` - Batch size for inference\n* `NUM_GPU` - number of GPUs to run training with\n* `PRECISION` - FP16/FP32/TF32 precision for training and inference\n* `EVAL_BEAMS` - Number of beams to run during inference\n* `MAX_SOURCE_LEN` - Maximum source length of articles\n* `MAX_TARGET_LEN` - Maximum target length of summaries\n* `DATA_DIR` - path to data directory with train/test/val files.\n* `CONFIG_PATH` - path to configuration file of BART Model\n* `PRELN` - Whether to use Pre-LN architecture\n\nBy default, the training script stores results to `results/bart_pyt_inference_${DATESTAMP}` and runs with:\n\n```bash\nbash scripts/run_eval_summarization.sh data/nvidia-pretrained/model.ckpt128 8 fp16 4 1024 142 data/cnn_dm/ configs/config.json\n```\n\nThese parameters run inference on CNN-DM on a DGX A100 with 80GB A100 cards. For XSum, try `EVAL_BEAMS`=6, `MAX_SOURCE_LEN`=1024 and `MAX_TARGET_LEN`=60. For other GPUS/precisions, reduce PRED_BS as indicated in `scripts/params`.","hasAdvanced","performance","### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nTo benchmark the training performance on a specific batch size, source length, target length and dataset for one epoch, run:\n\n```bash\nbash scripts/run_training_benchmark.sh \n```\n\nThe resulting `NUM_GPU` and PRECISION vs Throughput is stored in `results/bart_pyt_training_benchmark_${DATESTAMP}/inference_benchmark.log`\n#### Inference performance benchmark\n\nTo benchmark the inference performance on a specific batch size, source length, target length and dataset, run:\n\n```bash\nbash scripts/run_inference_benchmark.sh \n```\nThe resulting `NUM_GPU` and PRECISION vs Throughput is stored in `results/bart_pyt_inference_benchmark_${DATESTAMP}/inference_benchmark.log`\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##### Pre-training accuracy: NVIDIA DGX A100 (320x A100 80GB)\nOur results were obtained by running the `run_pretraining.sh` training script in the PyTorch 22.08-py3 NGC container on 40 nodes NVIDIA DGX A100 (320x A100 80GB) GPUs.\n\n| Nodes | Sequence Length | Batch size/GPU (BF16) | Accumulation Steps | Final loss - BF16 | Time to train (hrs) - BF16 |\n|-------|-------|---------------------------------------|------------------------------------|----------------------------------|-----------------------------------|\n| 40 | 128 | 200 | 1 | 0.5095 | 17.38 |\n| 40 | 512 | 32 | 3 | 0.6085 | 3.28 |\n##### Fine-tuning accuracy: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results for XSUM dataset were obtained by running the `run_summarization.sh` training script in the PyTorch 22.08-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Rogue1, rogue2 and rogueLSum scores list as accuracy.\n\n| GPUs | Batch size (TF32, BF16) | R1 - TF32 | R2 - TF32 | RL - TF32 | R1 - BF16 | R2 - BF16 | RL - BF16 | Time to train (hrs) - TF32 | Time to train (hrs) - BF16 | Time to train (hrs) speedup (TF32 to BF16) |\n|------|------------------|-----|-----|-----|-----|-----|-----|----------------------|---------------------------------|-------------------------------------------------|\n| 1 | 24, 40 | 45.22 | 22.03 | 36.95 | 44.91 | 21.85 | 36.78 | 2.41 | 1.69 | 1.43 |\n| 8 | 192, 320 | 45.04 | 21.92 | 36.82 | 45.01 | 21.86 | 36.81 | 0.64 | 0.39 | 1.64 |\n\nIn addition,results for CNN-DM dataset are:\n\n| GPUs | Batch size (TF32, BF16) | R1 - TF32 | R2 - TF32 | RL - TF32 | R1 - BF16 | R2 - BF16 | RL - BF16 | Time to train (hrs) - TF32 | Time to train (hrs) - BF16 | Time to train (hrs) speedup (TF32 to BF16) |\n|------|------------------|-----|-----|-----|-----|-----|-----|----------------------|---------------------------------|-------------------------------------------------|\n| 1 | 24, 40 | 43.76 | 20.79 | 40.51 | 43.58 | 20.63 | 40.32 | 3.87 | 2.42 | 1.60 |\n| 8 | 192, 320 | 43.77 | 20.77 | 40.53 | 43.76 | 20.73 | 40.50 | 0.73 | 0.45 | 1.62 |\n\n##### Fine-tuning stability test\n\nOur results for XSUM dataset were obtained by running the `run_summarization.sh` training script in the PyTorch 22.08-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Accuracy column lists rogue1 scores across 5 different training runs with different seeds on DGX A100.\n\n| **FP16, 8x GPUs** | **seed 1** | **seed 2** | **seed 3** | **seed 4** | **seed 5** | **mean** | **std** |\n|:-----------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n|rogue1 | 45.08 | 44.98 | 45.10 | 44.91 | 44.95 | 45.00 |\n\n#### Training performance results\n##### Pre-training performance: Single-node on NVIDIA DGX A100 (8x A100 80GB)\nOur results were obtained by running the `run_pretraining.sh` training script in the PyTorch 22.08-py3 NGC container on single node NVIDIA DGX A100 (8x A100 80GB) GPUs.\n\n| GPUs | Sequence Length | Batch size / GPU (TF32, BF16) | Throughput - TF32 | Throughput - BF16 | Throughput speedup (TF32 - BF16) | Weak scaling - TF32 | Weak scaling - BF16 |\n|------|------|------------------|-------------------|------------------------------|---------------------------------------------|---------------------|--------------------------------|\n| 1 | 128 | 100, 200 | 202.53 | 326.53 | 1.61 | 1 | 1 |\n| 8 | 128 | 100, 200 | 1556.23 | 2572.86 | 1.65 | 7.68 | 7.88 |\n| 1 | 512 | 16, 32 | 41.35 | 69.31 | 1.68 | 1 | 1 |\n| 8 | 512 | 16, 32 | 317.85 | 549.67 | 1.73 | 7.69 | 7.93 |\n##### Pre-training performance: Multi-node on NVIDIA DGX A100 (8x A100 80GB)\nOur results were obtained by running the `run_pretraining.sh` training script in the PyTorch 22.08-py3 NGC container on multi node NVIDIA DGX A100 (8x A100 80GB) GPUs.\n\n| Nodes | Sequence Length |Batch size / GPU (TF32, BF16) | Throughput - TF32 | Throughput - BF16 | Throughput speedup (TF32 - BF16) | Weak scaling - TF32 | Weak scaling - BF16 |\n|------|------|------------------|-------------------|------------------------------|---------------------------------------------|---------------------|--------------------------------|\n| 1 | 128 | 100, 200 | 1556.23 | 2572.86 | 1.65 | 1 | 1 |\n| 20 | 128 | 100, 200 | 31067.96 | 52,459.02 | 1.69 | 19.96 | 20.39 |\n| 40 | 128 | 100, 200 | 61,538.46 | 97028.51 | 1.58 | 39.54 | 37.71 |\n| 1 | 512 | 16, 32 | 317.85 | 549.67 | 1.73 | 1 | 1 |\n| 20 | 512 | 16, 32 | 5953.49 | 10520.54 | 1.77 | 18.73 | 19.14 |\n| 40 | 512 | 16, 32 | 11,636.36 | 19948.05 | 1.71 | 36.61 | 36.29 |\n##### Fine-tuning performance: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the `run_summarization.sh` training script in the PyTorch 22.08-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers (in items per second) were averaged over an entire training epoch.\n\n| GPUs | Batch size / GPU (TF32, BF16) | Throughput - TF32 | Throughput - BF16 | Throughput speedup (TF32 - BF16) | Weak scaling - TF32 | Weak scaling - BF16 |\n|------|------------------|-------------------|------------------------------|---------------------------------------------|---------------------|--------------------------------|\n| 1 | 24, 40 | 48.61 | 74.59 | 1.53 | 1.00 | 1.00 |\n| 8 | 24, 40 | 243.03 | 390.24 | 1.61 | 3.39 | 4.08 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\nThe performance metrics used are tokens per second computed from iterating through an entire epoch of XSum dataset with source length = 1024 and target length = 60.\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)\n\nOur results were obtained by running the `run_eval_summarization.sh` inferencing benchmarking script in the PyTorch 22.08-py3 NGC container on NVIDIA DGX A100 (1x A100 80GB) GPU.\n\nBF16\n\n| Batch size | Latency Avg | Latency 90% | Latency 95% | Latency 99% | Throughput |\n|------------|-------------|:-----------:|:-----------:|:-----------:|------------|\n| 1 | 0.28 | 0.35 | 0.38 | 0.46 | 3.54 |\n| 4 | 0.44 | 0.52 | 0.56 | 0.71 | 9.16 |\n| 8 | 0.63 | 0.75 | 0.83 | 0.98 | 12.79 |\n| 16 | 0.98 | 1.2\t | 1.29 | 1.47 | 16.3 |\n| 32 | 1.8 | 2.27 | 2.47 | 2.63 | 17.73 |\n| 64 | 3.78 | 4.85 | 5.21 | 5.4 | 16.83 |\n| 128 | 8.29 | 10.53 | 10.69 | 10.93 | 15.36 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\nThe inference performance metrics used are milliseconds per iteration. They are computed by iterating through the XSum test data with source length = 1024, target length = 60 and beam search = 6.","hasPerformance",[],{"_87":242,"_115":243,"_116":-5,"_124":35,"_123":35,"_125":241,"_223":92},{"_87":133,"_115":133,"_116":-5,"_124":35,"_123":35,"_125":241,"_223":92},{"_87":29,"_115":29,"_116":-5,"_124":35,"_123":35,"_125":240,"_223":92},[],[],"-","Latest (22.08.0)","nvidia/dle/bart_pyt","key","nvidia/dle/resources/bart_pyt","resourceId","bart_pyt","shortDescription","BART is a denoising autoencoder for pretraining sequence-to-sequence models.","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/bart-pyt)\n\n\nBART is a denoising autoencoder for pretraining sequence-to-sequence models. According to the [paper](https://arxiv.org/abs/1910.13461), the model uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).\n\nBART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.\n\nOther publicly available implementations of BART include:\n1. [Hugging Face](https://huggingface.co/transformers/model_doc/bart.html)\n2. [Fairseq](https://github.com/pytorch/fairseq/tree/master/examples/bart)\n\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.4 to 2.1x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n### Model architecture\n\nBART uses a standard sequence-to-sequence Transformer architecture with GeLU activations. The base model consists of 6 layers in encoder and decoder, whereas large consists of 12. The architecture has roughly 10% more parameters than BERT.\n\nBART is trained by corrupting documents and then optimizing the reconstruction loss. The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.\n\n### Default configuration\n\nBART model is similar to BERT with the following differences:\nDecoder layers additionally perform cross-attention over final hidden encoder layer\nBART removes the additional feed-forward network before word prediction that BERT uses\n\nInference is done by default with beam search 4 for CNN-DM dataset and 6 for XSum Dataset.\n### Feature support matrix\n\nThe following features are supported by this model:\n\n| **Feature** | **BART** |\n|:---------:|:----------:|\n| PyTorch AMP | Yes |\n| PyTorch DDP | Yes |\n| LAMB | Yes |\n| Multi-node | Yes |\n| LDDL | Yes |\n| Pre-LN | 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/BART//images/nvlamb.png)\n\n\nIn this PyTorch BART example, we used global batch size of 64000 and 30720 on sequence lengths 128 and 512 respectively, compared to a batch size of 8000 and sequence lengths 512 on [RoBERTa](https://arxiv.org/pdf/1907.11692.pdf) which Facebook used for BART. We only trained with 44% total number of tokens compared to Facebook's BART. It can get 2.7x training speedup and achieve similar accuracy.\n\n[LDDL](../lddl) is a library that enables scalable data preprocessing and loading. LDDL is used by this PyTorch BART example.\n\n[Pre-LN](https://arxiv.org/pdf/2002.04745.pdf) is an transformer architecture, which layer normalization is put inside the residual blocks. In our experiments, For Pre-LN transformer, the loss decays faster and it makes training more stable without gradient exploding or vanishing .\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Adding loss scaling to preserve small gradient values.\n\nFor information about:\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n- APEX tools for mixed precision training, see the [NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/).\n\n#### Enabling mixed precision\n\nIn this repository, mixed precision is enabled in PyTorch by using the Automatic Mixed Precision (AMP)\nautocast [torch.cuda.amp.autocast](https://pytorch.org/docs/stable/amp.html#autocasting) which casts variables\nto half-precision upon retrieval, while storing variables in single-precision format.\nFurthermore, to preserve small gradient magnitudes in backpropagation,\na [gradient scaling](https://pytorch.org/docs/stable/amp.html#gradient-scaling)\nstep must be included.\n\nFor an in-depth walk through on AMP, check out sample usage\n[here](https://pytorch.org/docs/stable/amp.html).\n\n#### TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.\n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.\n\n### Glossary\n\n**Fine-tuning**\nTraining an already pretrained 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.","displayName","BART for PyTorch",["D",1680631869194],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/bart_pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[283],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_252":284,"_285":286,"_287":288,"_289":89},"Natural Language Understanding","value","uscs_natural_language_understanding","filter",{"_290":291,"_285":286},"category","field","application",{"_158":1270,"_160":1271},"ngcApps",[1223],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1204":1205,"_1206":1207,"_1208":1209,"_1210":1211,"_1212":303,"_111":329,"_1213":1214,"_1215":60,"_1216":92,"_1217":92,"_1218":1219,"_1220":92,"_1221":1222},"amplitude",{"_1201":1202,"_842":-7,"_1203":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1195":1196,"_1197":1198,"_1199":1200},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787562040364,"1.12.0","defaultSearchSuggestions",{"_1082":410,"_1083":1084,"_1085":1086,"_1087":1088},"announcements",[],"consentState",{"_367":1072,"_1073":92,"_1074":35,"_1075":35,"_1076":1077},"consentConfig",{"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368},"showContactAdmin",1787562040366,[],[],[343,344,345,346,347,348],{"_49":64,"_51":75,"_53":153,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":153,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":69,"_53":153,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":153,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":63,"_59":60},{"_49":50,"_51":52,"_53":153,"_55":-5,"_57":-5,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623],[570,571,572,573,574],{"_408":484,"_252":484,"_217":410,"_381":594},{"_408":458,"_252":458,"_217":410,"_381":589},{"_408":584,"_252":584,"_217":410,"_381":585},{"_408":579,"_252":579,"_217":410,"_381":580},{"_408":575,"_252":575,"_217":410,"_381":576},"nvidia.partners",[577],{"_115":578,"_415":575,"_416":35,"_217":452},"edge","app.hushly.com",[581],{"_115":582,"_415":579,"_416":35,"_217":583},"*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",[586],{"_115":587,"_415":584,"_416":35,"_217":588},"_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.",[682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710],[662,663,664,665],{"_408":677,"_252":677,"_217":410,"_381":678},{"_408":672,"_252":672,"_217":410,"_381":673},{"_408":471,"_252":471,"_217":410,"_381":669},{"_408":584,"_252":584,"_217":410,"_381":666},[667],{"_115":668,"_415":584,"_416":92,"_217":588},"_gh_sess",[670],{"_115":671,"_415":471,"_416":92,"_217":478},"YSC","bizible.com",[674],{"_115":675,"_415":672,"_416":35,"_217":676},"_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",[679],{"_115":680,"_415":677,"_416":35,"_217":681},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_115":752,"_415":530,"_416":35,"_217":753},{"_115":737,"_415":751,"_416":92,"_217":738},{"_115":720,"_415":533,"_416":35,"_217":645},{"_115":720,"_415":633,"_416":35,"_217":645},{"_115":749,"_415":530,"_416":35,"_217":750},{"_115":737,"_415":536,"_416":92,"_217":738},{"_115":747,"_415":530,"_416":35,"_217":748},{"_115":737,"_415":533,"_416":92,"_217":738},{"_115":745,"_415":632,"_416":35,"_217":746},{"_115":737,"_415":654,"_416":92,"_217":738},{"_115":717,"_415":533,"_416":35,"_217":719},{"_115":743,"_415":530,"_416":92,"_217":744},{"_115":741,"_415":530,"_416":35,"_217":742},{"_115":739,"_415":530,"_416":92,"_217":740},{"_115":737,"_415":628,"_416":92,"_217":738},{"_115":735,"_415":530,"_416":35,"_217":736},{"_115":733,"_415":530,"_416":35,"_217":734},{"_115":731,"_415":530,"_416":92,"_217":732},{"_115":729,"_415":637,"_416":35,"_217":730},{"_115":717,"_415":628,"_416":35,"_217":719},{"_115":727,"_415":530,"_416":35,"_217":728},{"_115":725,"_415":530,"_416":92,"_217":726},{"_115":723,"_415":530,"_416":35,"_217":724},{"_115":721,"_415":530,"_416":35,"_217":722},{"_115":720,"_415":530,"_416":35,"_217":645},{"_115":717,"_415":718,"_416":35,"_217":719},{"_115":715,"_415":530,"_416":35,"_217":716},{"_115":713,"_415":530,"_416":92,"_217":714},{"_115":711,"_415":530,"_416":35,"_217":712},"_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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22.08.0	1	37.35 KB	January 31, 2023 UTC
21.02.0Selected	1	33.23 KB	September 22, 2022 UTC