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Changelog

April 2020

Initial release

Known issues

There are no known issues with this model

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The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to\nmake use of both categorical and numerical inputs. It was first described in\nDeep Learning Recommendation Model for Personalization and Recommendation Systems.\nThis repository provides a reimplementation of the codebase provided originally here.\nThe scripts provided enable you to train DLRM on the Criteo Terabyte Dataset.

Using the scripts provided here, you can efficiently train models that are too large to fit into a single GPU. This is because we use a hybrid-parallel approach, which combines model parallelism for the embedding tables with data parallelism for the Top MLP. This is explained in details in next sections.

This model uses a slightly different preprocessing procedure than the one found in the original implementation. You can find a detailed description of the preprocessing steps in the Dataset guidelines section.

Using DLRM you can train a high-quality general model for providing recommendations.

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and NVIDIA Ampere GPU architectures. Therefore, researchers can get results up to 3.3x faster than training without Tensor Cores while experiencing the benefits of mixed precision training. It is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

DLRM accepts two types of features: categorical and numerical. For each categorical\nfeature, an embedding table is used to provide dense representation to each unique value. The dense features enter the model and are transformed by a\nsimple neural network referred to as \"bottom MLP\". This part of the network consists of a series\nof linear layers with ReLU activations. The output of the bottom MLP and the embedding vectors\nare then fed into the \"dot interaction\" operation. The output of \"dot interaction\" is then concatenated with the features resulting from the bottom MLP and fed into the \"top MLP\" which is also a series of dense layers with activations.\nThe model outputs a single number which can be interpreted as a likelihood of a certain user clicking an ad.

\n \n
\nFigure 1. The architecture of DLRM. \n

Default configuration

The following features were implemented in this model:

general\n
- static loss scaling for Tensor Cores (mixed precision) training
- hybrid-parallel multi-GPU training
\n
preprocessing\n
- dataset preprocessing using Spark 3 on GPUs
- dataset preprocessing using NVTabular on GPUs
\n

Feature support matrix

This model supports the following features:

\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	DLRM
Automatic mixed precision (AMP)	yes
CUDA Graphs	yes
Hybrid-parallel multi-GPU with all-2-all	yes
Preprocessing on GPU with NVTabular	yes
Preprocessing on GPU with Spark 3	yes

Features

Automatic Mixed Precision (AMP) - enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.

CUDA Graphs - This feature allows to launch multiple GPU operations through a single CPU operation. The result is a vast reduction in CPU overhead. The benefits are particularly pronounced when training with relatively small batch sizes. The CUDA Graphs feature has been available through a native PyTorch API starting from PyTorch v1.10.

Multi-GPU training with PyTorch distributed - our model uses torch.distributed to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the PyTorch Tutorial.

Preprocessing on GPU with NVTabular - Criteo dataset preprocessing can be conducted using NVTabular. For more information on the framework, see the Announcing the NVIDIA NVTabular Open Beta with Multi-GPU Support and New Data Loaders.

Preprocessing on GPU with Spark 3 - Criteo dataset preprocessing can be conducted using Apache Spark 3.0. For more information on the framework and how to leverage GPU to preprocessing, see the Accelerating Apache Spark 3.0 with GPUs and RAPIDS.

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 the half-precision floating-point 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 3.3x 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.

The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in CUDA 8 in the NVIDIA Deep Learning SDK.

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

Mixed precision training is turned off by default. To turn it on issue the --amp flag to the main.py script.

Enabling TF32

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

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models that 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.

Hybrid-parallel multi-GPU with all-2-all communication

Many recommendation models contain very large embedding tables. As a result, the model is often too large to fit onto a single device. This could be easily solved by training in a model-parallel way, using either the CPU or other GPUs as \"memory donors\". However, this approach is suboptimal as the \"memory donor\" devices' compute is not utilized. In this repository, we use the model-parallel approach for the bottom part of the model (Embedding Tables + Bottom MLP) while using a usual data parallel approach for the top part of the model (Dot Interaction + Top MLP). This way we can train models much larger than what would normally fit into a single GPU while at the same time making the training faster by using multiple GPUs. We call this approach hybrid-parallel.

The transition from model-parallel to data-parallel in the middle of the neural net needs a specific multi-GPU communication pattern called all-2-all which is available in our PyTorch 21.04-py3 NGC docker container. In the original DLRM whitepaper this has been also referred to as \"butterfly shuffle\".

\n \n
\n

In the example shown in this repository we train models of three sizes: \"small\" (~15 GB), \"large\" (~82 GB), and \"xlarge\" (~142 GB). We use the hybrid-parallel approach for the \"large\" and \"xlarge\" models, as they do not fit in a single GPU.

Embedding table placement and load balancing

We use the following heuristic for dividing the work between the GPUs:

The Bottom MLP is placed on GPU-0 and no embedding tables are placed on this device.
The tables are sorted from the largest to the smallest
Set max_tables_per_gpu := ceil(number_of_embedding_tables / number_of_available_gpus)
Repeat until all embedding tables have an assigned device:\n
- Out of all the available GPUs find the one with the largest amount of unallocated memory
- Place the largest unassigned embedding table on this GPU. Raise an exception if it does not fit.
- If the number of embedding tables on this GPU is now equal to max_tables_per_gpu remove this GPU from the list of available GPUs so that no more embedding tables will be placed on this GPU. This ensures the all2all communication is well balanced between all devices.
\n

Preprocessing on GPU

Please refer to the \"Preprocessing\" section for a detailed description of the Apache Spark 3.0 and NVTabular GPU functionality

BYO dataset functionality overview

This section describes how you can train the DeepLearningExamples RecSys models on your own datasets without changing\nthe model or data loader and with similar performance to the one published in each repository.\nThis can be achieved thanks to Dataset Feature Specification, which describes how the dataset, data loader and model\ninteract with each other during training, inference and evaluation.\nDataset Feature Specification has a consistent format across all recommendation models in NVIDIA's DeepLearningExamples\nrepository, regardless of dataset file type and the data loader,\ngiving you the flexibility to train RecSys models on your own datasets.

Glossary

The Dataset Feature Specification consists of three mandatory and one optional section:

feature_spec provides a base of features that may be referenced in other sections, along with their metadata.\nFormat: dictionary (feature name) => (metadata name => metadata value)

source_spec provides information necessary to extract features from the files that store them.\nFormat: dictionary (mapping name) => (list of chunks)

Mappings are used to represent different versions of the dataset (think: train/validation/test, k-fold splits). A mapping is a list of chunks.
Chunks are subsets of features that are grouped together for saving. For example, some formats may constrain data saved in one file to a single data type. In that case, each data type would correspond to at least one chunk. Another example where this might be used is to reduce file size and enable more parallel loading. Chunk description is a dictionary of three keys:
\n
- type provides information about the format in which the data is stored. Not all formats are supported by all models.
- features is a list of features that are saved in a given chunk. Order of this list may matter: for some formats, it is crucial for assigning read data to the proper feature.
- files is a list of paths to files where the data is saved. For Feature Specification in yaml format, these paths are assumed to be relative to the yaml file's directory (basename). Order of this list matters: It is assumed that rows 1 to i appear in the first file, rows i+1 to j in the next one, etc.
\n

channel_spec determines how features are used. It is a mapping (channel name) => (list of feature names).

Channels are model specific magic constants. In general, data within a channel is processed using the same logic. Example channels: model output (labels), categorical ids, numerical inputs, user data, and item data.

metadata is a catch-all, wildcard section: If there is some information about the saved dataset that does not fit into the other sections, you can store it here.

Dataset feature specification

Data flow can be described abstractly:\nInput data consists of a list of rows. Each row has the same number of columns; each column represents a feature.\nThe columns are retrieved from the input files, loaded, aggregated into channels and supplied to the model/training script.

FeatureSpec contains metadata to configure this process and can be divided into three parts:

\n
Specification of how data is organized on disk (source_spec). It describes which feature (from feature_spec) is stored in which file and how files are organized on disk.
\n
\n
Specification of features (feature_spec). Describes a dictionary of features, where key is feature name and values are features' characteristics such as dtype and other metadata (for example, cardinalities for categorical features)
\n
\n
Specification of model's inputs and outputs (channel_spec). Describes a dictionary of model's inputs where keys specify model channel's names and values specify lists of features to be loaded into that channel. Model's channels are groups of data streams to which common model logic is applied, for example categorical/continuous data, user/item ids. Required/available channels depend on the model
\n

The FeatureSpec is a common form of description regardless of underlying dataset format, dataset data loader form and model.

Data flow in NVIDIA Deep Learning Examples recommendation models

The typical data flow is as follows:

S.0. Original dataset is downloaded to a specific folder.
S.1. Original dataset is preprocessed into Intermediary Format. For each model, the preprocessing is done differently, using different tools. The Intermediary Format also varies (for example, for DLRM PyTorch, the Intermediary Format is a custom binary one.)
S.2. The Preprocessing Step outputs Intermediary Format with dataset split into training and validation/testing parts along with the Dataset Feature Specification yaml file. Metadata in the preprocessing step is automatically calculated.
S.3. Intermediary Format data together with Dataset Feature Specification are fed into training/evaluation scripts. Data loader reads Intermediary Format and feeds the data into the model according to the description in the Dataset Feature Specification.
S.4. The model is trained and evaluated

\n \n
\n

Fig.1. Data flow in Recommender models in NVIDIA Deep Learning Examples repository. Channels of the model are drawn in green.

Example of dataset feature specification

As an example, let's consider a Dataset Feature Specification for a small CSV dataset for some abstract model.

feature_spec:\n  user_gender:\n    dtype: torch.int8\n    cardinality: 3 #M,F,Other\n  user_age: #treated as numeric value\n    dtype: torch.int8\n  user_id:\n    dtype: torch.int32\n    cardinality: 2655\n  item_id:\n    dtype: torch.int32\n    cardinality: 856\n  label:\n    dtype: torch.float32\n\nsource_spec:\n  train:\n    - type: csv\n      features:\n        - user_gender\n        - user_age\n      files:\n        - train_data_0_0.csv\n        - train_data_0_1.csv\n    - type: csv\n      features:\n        - user_id\n        - item_id\n        - label\n      files:\n        - train_data_1.csv\n  test:\n    - type: csv\n      features:\n        - user_id\n        - item_id\n        - label\n        - user_gender\n        - user_age\n        \n      files:\n        - test_data.csv\n\nchannel_spec:\n  numeric_inputs: \n    - user_age\n  categorical_user_inputs: \n    - user_gender\n    - user_id\n  categorical_item_inputs: \n    - item_id\n  label_ch: \n    - label\n

The data contains five features: (user_gender, user_age, user_id, item_id, label). Their data types and necessary metadata are described in the feature specification section.

In the source mapping section, two mappings are provided: one describes the layout of the training data, the other of the testing data. The layout for training data has been chosen arbitrarily to showcase the flexibility.\nThe train mapping consists of two chunks. The first one contains user_gender and user_age, saved as a CSV, and is further broken down into two files. For specifics of the layout, refer to the following example and consult the glossary. The second chunk contains the remaining columns and is saved in a single file. Notice that the order of columns is different in the second chunk - this is alright, as long as the order matches the order in that file (that is, columns in the .csv are also switched)

Let's break down the train source mapping. The table contains example data color-paired to the files containing it.

\n\n

The channel spec describes how the data will be consumed. Four streams will be produced and available to the script/model.\nThe feature specification does not specify what happens further: names of these streams are only lookup constants defined by the model/script.\nBased on this example, we can speculate that the model has three input channels: numeric_inputs, categorical_user_inputs,\ncategorical_item_inputs, and one output channel: label.\nFeature names are internal to the FeatureSpec and can be freely modified.

BYO dataset functionality

In order to train any Recommendation model in NVIDIA Deep Learning Examples one can follow one of three possible ways:

\n
One delivers already preprocessed dataset in the Intermediary Format supported by data loader used by the training script\n(different models use different data loaders) together with FeatureSpec yaml file describing at least specification of dataset, features and model channels
\n
\n
One uses a transcoding script
\n
\n
One delivers dataset in non-preprocessed form and uses preprocessing scripts that are a part of the model repository.\nIn order to use already existing preprocessing scripts, the format of the dataset needs to match the one of the original datasets.\nThis way, the FeatureSpec file will be generated automatically, but the user will have the same preprocessing as in the original model repository.
\n

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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 21.10-py3](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch/tags) NGC container\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/design-visualization/technologies/turing-architecture/)\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/frameworks/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/frameworks/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\nthe default parameters of DLRM on the Criteo Terabyte dataset. For the specifics concerning training and inference,\nsee the [Advanced](#advanced) section.\n\n1. Clone the repository.\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/PyTorch/Recommendation/DLRM\n```\n\n2. Download the dataset.\n\nYou can download the data by following the instructions at: http://labs.criteo.com/2013/12/download-terabyte-click-logs/.\nWhen you have successfully downloaded it and unpacked it, set the `CRITEO_DATASET_PARENT_DIRECTORY` to its parent directory:\n```\nCRITEO_DATASET_PARENT_DIRECTORY=/raid/dlrm\n``` \nWe recommend to choose the fastest possible file system, otherwise it may lead to an IO bottleneck.\n\n3. Build DLRM Docker containers\n```bash\ndocker build -t nvidia_dlrm_pyt .\ndocker build -t nvidia_dlrm_preprocessing -f Dockerfile_preprocessing . --build-arg DGX_VERSION=[DGX-2|DGX-A100]\n```\n\n3. Start an interactive session in the NGC container to run preprocessing.\nThe DLRM PyTorch container can be launched with:\n```bash\ndocker run --runtime=nvidia -it --rm --ipc=host -v ${CRITEO_DATASET_PARENT_DIRECTORY}:/data/dlrm nvidia_dlrm_preprocessing bash\n```\n\n4. Preprocess the dataset.\n\nHere are a few examples of different preprocessing commands. Out of the box, we support preprocessing on DGX-2 and DGX A100 systems. For the details on how those scripts work and detailed description of dataset types (small FL=15, large FL=3, xlarge FL=2), system requirements, setup instructions for different systems and all the parameters consult the [preprocessing section](#preprocessing).\nFor an explanation of the `FL` parameter, see the [Dataset Guidelines](#dataset-guidelines) and [Preprocessing](#preprocessing) sections. \n\nDepending on dataset type (small FL=15, large FL=3, xlarge FL=2) run one of following command:\n\n4.1. Preprocess to small dataset (FL=15) with Spark GPU:\n```bash\ncd /workspace/dlrm/preproc\n./prepare_dataset.sh 15 GPU Spark\n```\n\n4.2. Preprocess to large dataset (FL=3) with Spark GPU:\n```bash\ncd /workspace/dlrm/preproc\n./prepare_dataset.sh 3 GPU Spark\n```\n\n4.3. Preprocess to xlarge dataset (FL=2) with Spark GPU:\n```bash\ncd /workspace/dlrm/preproc\n./prepare_dataset.sh 2 GPU Spark\n```\n\n5. Start training.\n\n- First start the docker container (adding `--security-opt seccomp=unconfined` option is needed to take the full advantage of processor affinity in multi-GPU training):\n```bash\ndocker run --security-opt seccomp=unconfined --runtime=nvidia -it --rm --ipc=host -v ${PWD}/data:/data nvidia_dlrm_pyt bash\n```\n\n- single-GPU:\n```bash\npython -m dlrm.scripts.main --mode train --dataset /data/dlrm/binary_dataset/ --amp --cuda_graphs\n```\n\n- multi-GPU for DGX A100:\n```bash\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \\\n bash -c './bind.sh --cpu=dgxa100_ccx.sh --mem=dgxa100_ccx.sh python -m dlrm.scripts.main \\\n --dataset /data/dlrm/binary_dataset/ --seed 0 --epochs 1 --amp --cuda_graphs'\n```\n\n- multi-GPU for DGX-1 and DGX-2:\n```bash\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \\\n bash -c './bind.sh --cpu=exclusive -- python -m dlrm.scripts.main \\\n --dataset /data/dlrm/binary_dataset/ --seed 0 --epochs 1 --amp --cuda_graphs'\n```\n\n6. Start validation/evaluation. If you want to run validation or evaluation, you can either:\n - use the checkpoint obtained from the training commands above, or\n - download the pretrained checkpoint from NGC.\n\nIn order to download the checkpoint from NGC, visit ngc.nvidia.com website and browse the available models. Download the checkpoint files and unzip them to some path, for example, to `$CRITEO_DATASET_PARENT_DIRECTORY/checkpoints/`. The checkpoint requires around 15GB of disk space.\n\nCommands:\n- single-GPU:\n```bash\npython -m dlrm.scripts.main --mode test --dataset /data/dlrm/binary_dataset/ --load_checkpoint_path `$CRITEO_DATASET_PARENT_DIRECTORY/checkpoints/checkpoint`\n```\n\n- multi-GPU for DGX A100:\n```bash\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \\\n bash -c './bind.sh --cpu=dgxa100_ccx.sh --mem=dgxa100_ccx.sh python -m dlrm.scripts.main \\\n --dataset /data/dlrm/binary_dataset/ --seed 0 --epochs 1 --amp --load_checkpoint_path `$CRITEO_DATASET_PARENT_DIRECTORY/checkpoints/checkpoint`'\n```\n\n- multi-GPU for DGX-1 and DGX-2:\n```bash\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \\\n bash -c './bind.sh --cpu=exclusive -- python -m dlrm.scripts.main \\\n --dataset /data/dlrm/binary_dataset/ --seed 0 --epochs 1 --amp --load_checkpoint_path `$CRITEO_DATASET_PARENT_DIRECTORY/checkpoints/checkpoint`'\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\nThe `dlrm/scripts/main.py` script provides an entry point to most of the functionality. Using different command-line flags allows you to run training, validation, and benchmark both training and inference on real or synthetic data.\n\nUtilities related to loading the data reside in the `data` directory.\n\n### Command-line options\n\nThe `dlrm/scripts/main.py` script supports a number of command-line flags. You can get the descriptions of those by running `python -m dlrm.scripts.main --help`.\n\nThe following example output is printed when running the model:\n```\nEpoch:[0/1] [200/128028] eta: 1:28:44 loss: 0.1782 step_time: 0.041657 lr: 0.8794\nEpoch:[0/1] [400/128028] eta: 1:25:15 loss: 0.1403 step_time: 0.038504 lr: 1.7544\nEpoch:[0/1] [600/128028] eta: 1:23:56 loss: 0.1384 step_time: 0.038422 lr: 2.6294\nEpoch:[0/1] [800/128028] eta: 1:23:13 loss: 0.1370 step_time: 0.038421 lr: 3.5044\nEpoch:[0/1] [1000/128028] eta: 1:22:45 loss: 0.1362 step_time: 0.038464 lr: 4.3794\nEpoch:[0/1] [1200/128028] eta: 1:22:24 loss: 0.1346 step_time: 0.038455 lr: 5.2544\nEpoch:[0/1] [1400/128028] eta: 1:22:07 loss: 0.1339 step_time: 0.038459 lr: 6.1294\nEpoch:[0/1] [1600/128028] eta: 1:21:52 loss: 0.1320 step_time: 0.038481 lr: 7.0044\nEpoch:[0/1] [1800/128028] eta: 1:21:39 loss: 0.1315 step_time: 0.038482 lr: 7.8794\nEpoch:[0/1] [2000/128028] eta: 1:21:27 loss: 0.1304 step_time: 0.038466 lr: 8.7544\nEpoch:[0/1] [2200/128028] eta: 1:21:15 loss: 0.1305 step_time: 0.038430 lr: 9.6294\n```\n\n### Getting the data\n\nThis example uses the [Criteo Terabyte Dataset](https://labs.criteo.com/2013/12/download-terabyte-click-logs/).\nThe first 23 days are used as the training set. The last day is split in half. The first part, referred to as \"test\", is used for validating training results. The second one, referred to as \"validation\", is unused.\n\n#### Dataset guidelines\n\nThe preprocessing steps applied to the raw data include:\n- Replacing the missing values with `0`\n- Replacing the categorical values that exist fewer than `FL` times with a special value (FL value is called a frequency threshold or a frequency limit)\n- Converting the hash values to consecutive integers\n- Adding 3 to all the numerical features so that all of them are greater or equal to 1\n- Taking a natural logarithm of all numerical features\n\n#### BYO dataset \n\nThis implementation supports using other datasets thanks to BYO dataset functionality. \nThe BYO dataset functionality allows users to plug in their dataset in a common fashion for all Recommender models \nthat support this functionality. Using BYO dataset functionality, the user does not have to modify the source code of \nthe model thanks to the Feature Specification file. For general information on how BYO dataset works, refer to the \n[BYO dataset overview section](#byo-dataset-functionality-overview).\n\nThere are three ways to plug in user's dataset:\n

1. Provide an unprocessed dataset in a format matching the one used by Criteo 1TB, then use Criteo 1TB's preprocessing. Feature Specification file is then generated automatically.

\nThe required format of the user's dataset is:\n\nThe data should be split into text files. Each line of those text files should contain a single training example. \nAn example should consist of multiple fields separated by tabulators:\n\n* The first field is the label – 1 for a positive example and 0 for negative.\n* The next N tokens should contain the numerical features separated by tabs.\n* The next M tokens should contain the hashed categorical features separated by tabs.\n\nThe correct dataset files together with the Feature Specification yaml file will be generated automatically by preprocessing script.\n\nFor an example of using this process, refer to the [Quick Start Guide](#quick-start-guide)\n\n

\n\n

2. Provide a CSV containing preprocessed data and a simplified Feature Specification yaml file, then transcode the data with `transcode.py` script

\nThis option should be used if the user has their own CSV file with a preprocessed dataset they want to train on.\n\nThe required format of the user's dataset is:\n* CSV files containing the data, already split into train and test sets. \n* Feature Specification yaml file describing the layout of the CSV data\n\nFor an example of a feature specification file, refer to the `tests/transcoding` folder.\n\nThe CSV containing the data:\n* should be already split into train and test\n* should contain no header\n* should contain one column per feature, in the order specified by the list of features for that chunk \n in the source_spec section of the feature specification file\n* categorical features should be non-negative integers in the range [0,cardinality-1] if cardinality is specified\n\nThe Feature Specification yaml file:\n* needs to describe the layout of data in CSV files\n* may contain information about cardinalities. However, if set to `auto`, they will be inferred from the data by the transcoding script.\n\nRefer to `tests/transcoding/small_csv.yaml` for an example of the yaml Feature Specification.\n\nThe following example shows how to use this way of plugging user's dataset:\n\nPrepare your data and save the path:\n```bash\nDATASET_PARENT_DIRECTORY=/raid/dlrm\n```\n\nBuild the DLRM image with:\n```bash\ndocker build -t nvidia_dlrm_pyt .\n```\nLaunch the container with:\n```bash\ndocker run --runtime=nvidia -it --rm --ipc=host -v ${DATASET_PARENT_DIRECTORY}:/data nvidia_dlrm_preprocessing bash\n```\n\nIf you are just testing the process, you can create synthetic csv data:\n```bash\npython -m dlrm.scripts.gen_csv --feature_spec_in tests/transcoding/small_csv.yaml\n```\n\nConvert the data:\n```bash\nmkdir /data/conversion_output\npython -m dlrm.scripts.transcode --input /data --output /data/converted\n```\nYou may need to tune the --chunk_size parameter. Higher values speed up the conversion but require more RAM.\n\nThis will convert the data from `/data` and save the output in `/data/converted`.\nA feature specification file describing the new data will be automatically generated.\n\nTo run the training on 1 GPU:\n```bash\npython -m dlrm.scripts.main --mode train --dataset /data/converted --amp --cuda_graphs\n```\n\n- multi-GPU for DGX A100:\n```bash\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \\\n bash -c './bind.sh --cpu=dgxa100_ccx.sh --mem=dgxa100_ccx.sh python -m dlrm.scripts.main \\\n --dataset /data/converted --seed 0 --epochs 1 --amp --cuda_graphs'\n```\n\n- multi-GPU for DGX-1 and DGX-2:\n```bash\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \\\n bash -c './bind.sh --cpu=exclusive -- python -m dlrm.scripts.main \\\n --dataset /data/converted --seed 0 --epochs 1 --amp --cuda_graphs'\n```\n

3. Provide a fully preprocessed dataset, saved in split binary files, and a Feature Specification yaml file

\nThis is the option to choose if you want full control over preprocessing and/or want to preprocess data directly to the target format.\n\nYour final output will need to contain a Feature Specification yaml describing data and file layout. \nFor an example feature specification file, refer to `tests/feature_specs/criteo_f15.yaml`\n\nFor details, refer to the [BYO dataset overview section](#byo-dataset-functionality-overview).\n

\n\n##### Channel definitions and requirements\n\nThis model defines three channels:\n\n- categorical, accepting an arbitrary number of features\n- numerical, accepting an arbitrary number of features\n- label, accepting a single feature\n\nThe training script expects two mappings:\n\n- train\n- test\n\nFor performance reasons:\n* The only supported dataset type is split binary\n* Splitting chunks into multiple files is not supported.\n* Each categorical feature has to be provided in a separate chunk\n* All numerical features have to be provided in a single chunk\n* All numerical features have to appear in the same order in channel_spec and source_spec\n* Only integer types are supported for categorical features\n* Only float16 is supported for numerical features\n\n##### BYO dataset constraints for the model\n\nThere are the following constraints of BYO dataset functionality for this model:\n1. The performance of the model depends on the dataset size. Generally, the model should scale better for datasets containing more data points. For a smaller dataset, you might experience slower performance than the one reported for Criteo\n2. Using other datasets might require tuning some hyperparameters (for example, learning rate, beta1 and beta2) to reach desired accuracy.\n3. The optimized cuda interaction kernels for FP16 and TF32 assume that the number of categorical variables is smaller than WARP_SIZE=32 and embedding size is <=128\n#### Preprocessing \n\nThe preprocessing scripts provided in this repository support running both on CPU and GPU using [NVtabular](https://developer.nvidia.com/blog/announcing-the-nvtabular-open-beta-with-multi-gpu-support-and-new-data-loaders/) (GPU only) and [Apache Spark 3.0](https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/apache-spark-3/).\n\nPlease note that the preprocessing will require about 4TB of disk storage. \n\nThe syntax for the preprocessing script is as follows:\n```bash\ncd /workspace/dlrm/preproc\n./prepare_dataset.sh \n```\n\nFor the Criteo Terabyte dataset, we recommend a frequency threshold of `FL=3`(when using A100 40GB or V100 32 GB) or `FL=2`(when using A100 80GB) if you intend to run the hybrid-parallel mode\non multiple GPUs. If you want to make the model fit into a single NVIDIA Tesla V100-32GB, you can set `FL=15`. \n\nThe first argument means the frequency threshold to apply to the categorical variables. For a frequency threshold `FL`, the categorical values that occur less \noften than `FL` will be replaced with one special value for each category. Thus, a larger value of `FL` will require smaller embedding tables \nand will substantially reduce the overall size of the model.\n\nThe second argument is the hardware to use (either GPU or CPU). \n\nThe third arguments is a framework to use (either NVTabular or Spark). In case of choosing a CPU preprocessing this argument is omitted as it only Apache Spark is supported on CPU.\n\nThe preprocessing scripts make use of the following environment variables to configure the data directory paths:\n- `download_dir` – this directory should contain the original Criteo Terabyte CSV files\n- `spark_output_path` – directory to which the parquet data will be written\n- `conversion_intermediate_dir` – directory used for storing intermediate data used to convert from parquet to train-ready format\n- `final_output_dir` – directory to store the final results of the preprocessing which can then be used to train DLRM \n\nIn the `final_output_dir` will be three subdirectories created: `train`, `test`, `validation`, and one json file – `model_size.json` – containing a maximal index of each category. \nThe `train` is the train dataset transformed from day_0 to day_22. \nThe `test` is the test dataset transformed from the prior half of day_23. \nThe `validation` is the dataset transformed from the latter half of day_23.\n\nThe model is tested on 3 datasets resulting from Criteo dataset preprocessing: small (Freqency threshold = 15), large (Freqency threshold = 3) and xlarge (Freqency threshold = 2). Each dataset occupies approx 370GB of disk space. Table below presents information on the supercomputer and GPU count that are needed to train model on particular dataset.\n\n| Dataset | GPU VRAM consumption\\* | Model checkpoint size\\* | FL setting | DGX A100 40GB, 1GPU | DGX A100 40GB, 8GPU | DGX A100 80GB, 1GPU | DGX A100 80GB, 8GPU | DGX-1** or DGX-2, 1 GPU | DGX-1** or DGX-2, 8GPU | DGX-2, 16GPU |\n| ------- | ---------------------- | ----------------------- | ---------- | -------------------- | -------------------- | -------------------- | -------------------- | ---------------------- | --------------------- | ------------ |\n| small (FL=15) | 20.5 | 15.0 | 15 | Yes | Yes | Yes | Yes | Yes | Yes | Yes |\n| large (FL=3) | 132.3 | 81.9 | 3 | NA | Yes | NA | Yes | NA | Yes | Yes |\n| xlarge (FL=2) | 198.8 | 141.3 | 2 | NA | NA | NA | Yes | NA | NA | NA |\n\n\\*with default embedding dimension setting\n\\**DGX-1 V100 32GB\n\n##### NVTabular\n\nNVTabular preprocessing is calibrated to run on [DGX A100](https://www.nvidia.com/en-us/data-center/dgx-a100/) and [DGX-2](https://www.nvidia.com/en-us/data-center/dgx-2/) AI systems. However, it should be possible to change the values of `ALL_DS_MEM_FRAC`, `TRAIN_DS_MEM_FRAC`, `TEST_DS_MEM_FRAC`, `VALID_DS_MEM_FRAC` in `preproc/preproc_NVTabular.py`, so that they'll work on also on other hardware platforms such as DGX-1 or a custom one. \n\n##### Spark\n\nThe script `spark_data_utils.py` is a PySpark application, which is used to preprocess the Criteo Terabyte Dataset. In the Docker image, we have installed Spark 3.0.1, which will start a standalone cluster of Spark. The scripts `run_spark_cpu.sh` and `run_spark_gpu.sh` start Spark, then run several PySpark jobs with `spark_data_utils.py`. \n\nNote that the Spark job requires about 3TB disk space used for data shuffling.\n\nSpark preprocessing is calibrated to run on [DGX A100](https://www.nvidia.com/en-us/data-center/dgx-a100/) and [DGX-2](https://www.nvidia.com/en-us/data-center/dgx-2/) AI systems. However, it should be possible to change the values in `preproc/DGX-2_config.sh` or `preproc/DGX-A100_config.sh`\nso that they'll work on also on other hardware platforms such as DGX-1 or a custom one. \n\n### Training process\n\nThe main training script resides in `dlrm/scripts/main.py`. Once the training is completed, it stores the checkpoint\nin the path specified by `--save_checkpoint_path` and a JSON training log in `--log_path`. The quality of the predictions \ngenerated by the model is measured by the [ROC AUC metric](https://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics).\nThe speed of training and inference is measured by throughput i.e., the number \nof samples processed per second. We use mixed precision training with static loss scaling for the bottom and top MLPs while embedding tables are stored in FP32 format.\n\n### Inference process\n\nThis section describes inference with PyTorch in Python. If you're interested in inference using the Triton Inference Server, refer to [https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM/triton/README.md](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM/triton/README.md) file.\n\nTwo modes for inference are currently supported by the `dlrm/scripts/main.py` script:\n\n1. Inference benchmark – this mode will measure and print out throughput and latency numbers for multiple batch sizes. You can activate it by passing the `--mode inference_benchmark` command line flag. The batch sizes to be tested can be set with the `--inference_benchmark_batch_sizes` command-line argument.\n2. Test-only – this mode can be used to run a full validation on a checkpoint to measure ROC AUC. You can enable it by passing `--mode test`.\n\n### Deploying DLRM Using NVIDIA Triton Inference Server\nThe 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 [https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM/triton/README.md](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM/triton/README.md).","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA's latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nTo benchmark the training performance on a specific batch size, please follow the instructions\nin the [Quick Start Guide](#quick-start-guide). You can also add the `--max_steps 1000 --benchmark_warmup_steps 500`\nif you want to get a reliable throughput measurement without running the entire training. \n\nYou can create a synthetic dataset by running `python -m dlrm.scripts.prepare_synthetic_dataset --synthetic_dataset_dir /tmp/dlrm_synthetic_data` if you haven't yet downloaded the dataset.\n\n#### Inference performance benchmark\n\nTo benchmark the inference performance on a specific batch size, run:\n\n```\npython -m dlrm.scripts.main --mode inference_benchmark --dataset /data\n```\n\nYou can also create a synthetic dataset by running `python -m dlrm.scripts.prepare_synthetic_dataset --synthetic_dataset_dir /tmp/dlrm_synthetic_data` if you haven't yet downloaded the dataset.\n\n### Results \n\nThe following sections provide details on how we achieved our performance and accuracy in training and inference. \n\nWe used three model size variants to show memory scalability in a multi-GPU setup:\n\n| Model variant | Frequency threshold | Model size\n|---:|---|---|\n|small | 15 | 15 GB |\n|large | 3 | 82 GB |\n|xlarge| 2 | 142 GB|\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running `dlrm/scripts/main.py` script as described in the Quick Start Guide in the DLRM Docker container using NVIDIA A100 80GB GPUs.\n\n| GPUs | Model size | Batch size / GPU | Accuracy (AUC) - TF32 | Accuracy (AUC) - mixed precision | Time to train - TF32] | Time to train - mixed precision | Time to train speedup (TF32 to mixed precision) |\n|-------:|:-------------|:-------------------|------------------------:|-----------------------------------:|:------------------------|:----------------------------------|--------------------------------------------------:|\n| 8 | large | 8k | 0.802509 | 0.802528 | 0:06:27 | 0:04:36 | 1.40217 |\n| 1 | small | 64k | 0.802537 | 0.802521 | 0:24:26 | 0:17:47 | 1.37395 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 32GB)\n\nOur results were obtained by running `dlrm/scripts/main.py` script as described in the Quick Start Guide in the DLRM Docker container using NVIDIA V100 32GB GPUs.\n\n| GPUs | Model size | Batch size / GPU | Accuracy (AUC) - FP32 | Accuracy (AUC) - mixed precision | Time to train - FP32] | Time to train - mixed precision | Time to train speedup (FP32 to mixed precision) |\n|-------:|:-------------|:-------------------|------------------------:|-----------------------------------:|:------------------------|:----------------------------------|--------------------------------------------------:|\n| 8 | large | 8k | 0.802568 | 0.802562 | 0:28:24 | 0:11:45 | 2.41702 |\n| 1 | small | 64k | 0.802784 | 0.802723 | 1:58:10 | 0:38:17 | 3.08663 |\n\n##### Training accuracy plots\n\nModels trained with FP32, TF32, and Automatic Mixed Precision (AMP) achieve similar accuracy.\n\nThe plot represents ROC AUC metric as a function of steps (step is single batch) during training for default precision (FP32 for Volta architecture (DGX-1) and TF32 for Ampere GPU architecture (DGX-A100)), and AMP for all three datasets. \nAll other parameters of training are default.\n\n

\n \n
\n Figure 1. Training stability for a FL3 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision. \n

\n\n

\n \n
\n Figure 2. Training stability for a FL15 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision. \n

\n\n##### Training stability test\n\nTraining of the model is stable for multiple configurations achieving a standard deviation of 10e-4. \nThe model achieves similar ROC AUC scores for A100 and V100, training precisions. \nIt was trained for one epoch (roughly 4 billion samples, 64014 batches), starting from 10 different initial random seeds for each setup.\nThe training was performed in the pytorch:21.10-py3 NGC container with and without mixed precision enabled.\nThe provided charts and numbers consider single and multi GPU training. After training, the models were evaluated on the test set. \nThe following plots compare distributions of ROC AUC on the test set.\n\n

\n \n
\n Figure 3. Training stability for a FL3 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision. \n

\n\n

\n \n
\n Figure 4. Training stability for a FL15 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision. \n

\n\n##### Impact of mixed precision on training accuracy\n\nThe accuracy of training, measured with ROC AUC on the test set after the final epoch metric was not impacted by enabling mixed precision. The obtained results were statistically similar. The similarity was measured according to the following procedure:\n\nThe model was trained 10 times for default settings (FP32 or TF32 for Volta and Ampere architecture respectively) and 10 times for AMP. After the last epoch, the accuracy score ROC AUC was calculated on the test set.\n\nDistributions for two hardware configurations (A100, V100) for 2 datasets are presented below.\n\n

\n \n
\n Figure 5. Impact of AMP on ROC AUC distribution for A100 and V100 GPUs for single- and multi-gpu training on a dataset with a frequency threshold of 3. \n

\n\n

\n \n
\n Figure 6. Impact of AMP on ROC AUC distribution for A100 and V100 GPUs for single- and multi-gpu training on a dataset with a frequency threshold of 15. \n

\n\nDistribution of AUC ROC for single precision training (TF32 for A100, FP32 for Volta) and AMP training were compared in terms of mean, variance and [Kolmogorov–Smirnov test](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test) to state statistical difference between single precision and AMP results. Refer to the expandable table below.\n\n

Full tabular data for AMP influence on AUC ROC

\n\n| Hardware | Dataset | GPUs | mean AUC ROC for full precision | std AUC ROC for full precision | mean AUC ROC for AMP | std AUC ROC for AMP | KS test value: statictics, p-value |\n|:-----------|:----------|:-------|:----------------------------------|:---------------------------------|:-----------------------|:----------------------|:-------------------------------------|\n| DGX A100 | FL3 | 8 | 0.802681 | 0.000073 | 0.802646 | 0.000063 | ('0.400', '0.418') |\n| DGX-2 | FL3 | 16 | 0.802614 | 0.000073 | 0.802623 | 0.000122 | ('0.267', '0.787') |\nSample size was set to 10 experiments for each training setup.\n\n

\n\n#### Training performance results\n\nWe used throughput in items processed per second as the performance metric.\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the following commands:\n- for single-GPU setup:\n```\npython -m dlrm.scripts.main --dataset /data --amp --cuda_graphs\n```\n- for multi-GPU setup:\n```\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \\\n bash -c './bind.sh --cpu=dgxa100_ccx.sh --mem=dgxa100_ccx.sh python -m dlrm.scripts.main \\\n --dataset /data --amp --cuda_graphs'\n```\n\nin the DLRM Docker container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers (in records of data per second) were averaged over an entire training epoch.\n\n| GPUs | Model size | Batch size / GPU | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 to mixed precision) |\n|-------:|:-------------|:-------------------|--------------------:|-------------------------------:|-----------------------------------------------:|\n| 8 | large | 8k | 11,400,000 | 16,500,000 | 1.447 |\n| 1 | small | 64k | 2,880,000 | 4,020,000 | 1.396 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training performance: NVIDIA DGX-1 (8x V100 32GB)\n\nOur results were obtained by running the following commands:\n- for single-GPU:\n```\npython -m dlrm.scripts.main --mode train --dataset /data --amp --cuda_graphs\n```\n- for multi-GPU :\n```\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \\\n bash -c './bind.sh --cpu=exclusive -- python -m dlrm.scripts.main \\\n --dataset /data --amp --cuda_graphs'\n```\n\n in the DLRM Docker container on NVIDIA DGX-1 with (8x V100 32GB) GPUs. Performance numbers (in records of data per second) were averaged over an entire training epoch.\n\n| GPUs | Model size | Batch size / GPU | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 to mixed precision) |\n|-------:|:-------------|:-------------------|--------------------:|-------------------------------:|-----------------------------------------------:|\n| 8 | large | 8k | 2,880,000 | 6,920,000 | 2.403 |\n| 1 | small | 64k | 672,000 | 2,090,000 | 3.110 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training performance: NVIDIA DGX-2 (16x V100 32GB)\n\nOur results were obtained by running the following commands:\n- for single-GPU:\n```\npython -m dlrm.scripts.main --dataset /data --amp --cuda_graphs \n```\n- for multi-GPU:\n```\npython -m torch.distributed.launch --no_python --use_env --nproc_per_node [8/16] \\\n bash -c './bind.sh --cpu=exclusive -- python -m dlrm.scripts.main \\\n --dataset /data --amp --cuda_graphs'\n```\n in the DLRM Docker container on NVIDIA DGX-2 with (16x V100 32GB) GPUs. Performance numbers (in records of data per second) were averaged over an entire training epoch.\n\n| GPUs | Model size | Batch size / GPU | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 to mixed precision) |\n|-------:|:-------------|:-------------------|--------------------:|-------------------------------:|-----------------------------------------------:|\n| 16 | large | 4k | 4,740,000 | 10,800,000 | 2.278 |\n| 8 | large | 8k | 3,330,000 | 7,930,000 | 2.381 |\n| 1 | small | 64k | 717,000 | 2,250,000 | 3.138 |\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 A100 (1x A100 80GB)\n\nOur results were obtained by running the --inference_benchmark mode\nin the DLRM Docker container on on the NVIDIA A100 (1x A100 80GB) GPU.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

	Mixed Precision	TF32
	CUDA Graphs ON	CUDA Graphs OFF	CUDA Graphs ON	CUDA Graphs OFF
Batch size	Throughput Avg	Latency Avg	Throughput Avg	Latency Avg	Throughput Avg	Latency Avg	Throughput Avg	Latency Avg
32768	14,796,024	0.00221	14,369,047	0.00228	8,832,225	0.00371	8,637,000	0.00379
16384	14,217,340	0.00115	13,673,623	0.00120	8,540,191	0.00192	8,386,694	0.00195
8192	12,769,583	0.00064	11,336,204	0.00072	7,658,459	0.00107	7,463,740	0.00110
4096	10,556,140	0.00039	8,203,285	0.00050	6,777,965	0.00060	6,142,076	0.00067
2048	8,415,889	0.00024	4,785,479	0.00043	5,214,990	0.00039	4,365,954	0.00047
1024	5,045,754	0.00020	2,357,953	0.00043	3,854,504	0.00027	2,615,601	0.00039
512	3,168,261	0.00016	1,190,989	0.00043	2,441,310	0.00021	1,332,944	0.00038
256	1,711,749	0.00015	542,310	0.00047	1,365,320	0.00019	592,034	0.00043
128	889,777	0.00014	274,223	0.00047	790,984	0.00016	300,908	0.00043
64	459,728	0.00014	136,180	0.00047	416,463	0.00015	150,382	0.00043
32	222,386	0.00014	70,107	0.00046	174,163	0.00018	75,768	0.00042
16	117,386	0.00014	34,983	0.00046	108,992	0.00015	38,369	0.00042
8	59,200	0.00014	18,852	0.00042	55,661	0.00014	19,440	0.00041
4	29,609	0.00014	8,505	0.00047	27,957	0.00014	10,206	0.00039
2	14,066	0.00014	4,610	0.00043	13,010	0.00015	5,229	0.00038

\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 32GB)\n\nOur results were obtained by running the `--inference_benchmark` mode\nin the DLRM Docker container on NVIDIA DGX-1 with (1x V100 32GB) GPU.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

	Mixed Precision	FP32
	CUDA Graphs ON	CUDA Graphs OFF	CUDA Graphs ON	CUDA Graphs OFF
Batch size	Throughput Avg	Latency Avg	Throughput Avg	Latency Avg	Throughput Avg	Latency Avg	Throughput Avg	Latency Avg
32768	6,716,240	0.00488	6,792,739	0.00482	1,809,345	0.01811	1,802,851	0.01818
16384	6,543,544	0.00250	6,520,519	0.00251	1,754,713	0.00934	1,745,214	0.00939
8192	6,215,194	0.00132	6,074,446	0.00135	1,669,188	0.00491	1,656,393	0.00495
4096	5,230,443	0.00078	4,901,451	0.00084	1,586,666	0.00258	1,574,068	0.00260
2048	4,261,124	0.00048	3,523,239	0.00058	1,462,006	0.00140	1,416,985	0.00145
1024	3,306,724	0.00031	2,047,274	0.00050	1,277,860	0.00080	1,161,032	0.00088
512	2,049,382	0.00025	1,005,919	0.00051	1,016,186	0.00050	841,732	0.00061
256	1,149,997	0.00022	511,102	0.00050	726,349	0.00035	485,162	0.00053
128	663,048	0.00019	264,015	0.00048	493,878	0.00026	238,936	0.00054
64	359,505	0.00018	132,913	0.00048	295,273	0.00022	124,120	0.00052
32	175,465	0.00018	64,287	0.00050	157,629	0.00020	63,919	0.00050
16	99,207	0.00016	31,062	0.00052	83,019	0.00019	34,660	0.00046
8	52,532	0.00015	16,492	0.00049	43,289	0.00018	17,893	0.00045
4	27,626	0.00014	8,391	0.00048	22,692	0.00018	8,923	0.00045
2	13,791	0.00015	4,146	0.00048	11,747	0.00017	4,487	0.00045

\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).","hasPerformance","policies",[],{"_181":206,"_182":207,"_142":-5,"_150":26,"_152":26,"_166":205,"_153":66},{"_181":204,"_182":204,"_142":-5,"_150":26,"_152":26,"_166":205,"_153":66},{"_181":202,"_182":202,"_142":-5,"_150":26,"_152":26,"_166":203,"_153":66},{"_181":200,"_182":200,"_142":-5,"_150":26,"_152":26,"_166":201,"_153":66},{"_181":198,"_182":198,"_142":-5,"_150":26,"_152":26,"_166":199,"_153":66},{"_181":196,"_182":196,"_142":-5,"_150":26,"_152":26,"_166":197,"_153":66},{"_181":194,"_182":194,"_142":-5,"_150":26,"_152":26,"_166":195,"_153":66},{"_181":192,"_182":192,"_142":-5,"_150":26,"_152":26,"_166":193,"_153":66},{"_181":190,"_182":190,"_142":-5,"_150":26,"_152":26,"_166":191,"_153":66},{"_181":188,"_182":188,"_142":-5,"_150":26,"_152":26,"_166":189,"_153":66},{"_181":186,"_182":186,"_142":-5,"_150":26,"_152":26,"_166":187,"_153":66},{"_181":184,"_182":184,"_142":-5,"_150":26,"_152":26,"_166":185,"_153":66},{"_181":14,"_182":14,"_142":-5,"_150":26,"_152":26,"_166":183,"_153":66},"id","name",[],"20.03.0",[],"20.06.0",[],"20.07.1",[],"20.07.0",[],"20.07.2",[],"20.07.3",[],"21.02.0",[],"21.04.1",[],"21.04.2",[],"21.04.3",[],"21.10.0",[],"-","Latest (21.10.0)","nvidia/dlrm_for_pytorch","key","nvidia/resources/dlrm_for_pytorch","resourceId","dlrm_for_pytorch","shortDescription","The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.","The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to \nmake use of both categorical and numerical inputs. It was first described in \n[Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091).\nThis repository provides a reimplementation of the codebase provided originally [here](https://github.com/facebookresearch/dlrm).\nThe scripts provided enable you to train DLRM on the [Criteo Terabyte Dataset](https://labs.criteo.com/2013/12/download-terabyte-click-logs/). \n\nUsing the scripts provided here, you can efficiently train models that are too large to fit into a single GPU. This is because we use a hybrid-parallel approach, which combines model parallelism for the embedding tables with data parallelism for the Top MLP. This is explained in details in [next sections](#hybrid-parallel-multigpu-with-all-2-all-communication).\n\nThis model uses a slightly different preprocessing procedure than the one found in the original implementation. You can find a detailed description of the preprocessing steps in the [Dataset guidelines](#dataset-guidelines) section.\n\nUsing DLRM you can train a high-quality general model for providing recommendations.\n\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and NVIDIA Ampere GPU architectures. Therefore, researchers can get results up to 3.3x faster than training without Tensor Cores while experiencing the benefits of mixed precision training. It is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n### Model architecture\n\nDLRM accepts two types of features: categorical and numerical. For each categorical\nfeature, an embedding table is used to provide dense representation to each unique value. The dense features enter the model and are transformed by a \nsimple neural network referred to as \"bottom MLP\". This part of the network consists of a series\nof linear layers with ReLU activations. The output of the bottom MLP and the embedding vectors\nare then fed into the \"dot interaction\" operation. The output of \"dot interaction\" is then concatenated with the features resulting from the bottom MLP and fed into the \"top MLP\" which is also a series of dense layers with activations.\nThe model outputs a single number which can be interpreted as a likelihood of a certain user clicking an ad. \n\n

\n \n
\nFigure 1. The architecture of DLRM. \n

\n\n### Default configuration\n\nThe following features were implemented in this model:\n- general\n\t- static loss scaling for Tensor Cores (mixed precision) training\n\t- hybrid-parallel multi-GPU training\n- preprocessing\n - dataset preprocessing using Spark 3 on GPUs \n - dataset preprocessing using NVTabular on GPUs \n \n### Feature support matrix\n\nThis model supports the following features:\n\n| Feature | DLRM \n|-----------------------------------------|-----\n|Automatic mixed precision (AMP) | yes\n|CUDA Graphs | yes\n|Hybrid-parallel multi-GPU with all-2-all | yes\n|Preprocessing on GPU with NVTabular | yes\n|Preprocessing on GPU with Spark 3 | yes\n\n#### Features\n\nAutomatic Mixed Precision (AMP) - enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.\n\nCUDA Graphs - This feature allows to launch multiple GPU operations through a single CPU operation. The result is a vast reduction in CPU overhead. The benefits are particularly pronounced when training with relatively small batch sizes. The CUDA Graphs feature has been available through a [native PyTorch API](https://pytorch.org/docs/master/notes/cuda.html#cuda-graphs) starting from PyTorch v1.10. \n\nMulti-GPU training with PyTorch distributed - our model uses `torch.distributed` to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the [PyTorch Tutorial](https://pytorch.org/tutorials/intermediate/dist_tuto.html).\n\nPreprocessing on GPU with NVTabular - Criteo dataset preprocessing can be conducted using [NVTabular](https://github.com/NVIDIA/NVTabular). For more information on the framework, see the [Announcing the NVIDIA NVTabular Open Beta with Multi-GPU Support and New Data Loaders](https://developer.nvidia.com/blog/announcing-the-nvtabular-open-beta-with-multi-gpu-support-and-new-data-loaders/).\n\nPreprocessing on GPU with Spark 3 - Criteo dataset preprocessing can be conducted using [Apache Spark 3.0](https://spark.apache.org/). For more information on the framework and how to leverage GPU to preprocessing, see the [Accelerating Apache Spark 3.0 with GPUs and RAPIDS](https://developer.nvidia.com/blog/accelerating-apache-spark-3-0-with-gpus-and-rapids/).\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 the half-precision floating-point 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 3.3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:\n1. Porting the model to use the FP16 data type where appropriate. \n2. Adding loss scaling to preserve small gradient values.\n\nThe ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in [CUDA 8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep Learning SDK.\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/sdk/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\nMixed precision training is turned off by default. To turn it on issue the `--amp` flag to the `main.py` script.\n\n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. \n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models that 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### Hybrid-parallel multi-GPU with all-2-all communication\n\nMany recommendation models contain very large embedding tables. As a result, the model is often too large to fit onto a single device. This could be easily solved by training in a model-parallel way, using either the CPU or other GPUs as \"memory donors\". However, this approach is suboptimal as the \"memory donor\" devices' compute is not utilized. In this repository, we use the model-parallel approach for the bottom part of the model (Embedding Tables + Bottom MLP) while using a usual data parallel approach for the top part of the model (Dot Interaction + Top MLP). This way we can train models much larger than what would normally fit into a single GPU while at the same time making the training faster by using multiple GPUs. We call this approach hybrid-parallel.\n\nThe transition from model-parallel to data-parallel in the middle of the neural net needs a specific multi-GPU communication pattern called [all-2-all](https://en.wikipedia.org/wiki/All-to-all_\$parallel_pattern\$) which is available in our [PyTorch 21.04-py3](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch/tags) NGC docker container. In the [original DLRM whitepaper](https://arxiv.org/abs/1906.00091) this has been also referred to as \"butterfly shuffle\". \n\n

\n \n
\n

\n\nIn the example shown in this repository we train models of three sizes: \"small\" (\\~15 GB), \"large\" (\\~82 GB), and \"xlarge\" (\\~142 GB). We use the hybrid-parallel approach for the \"large\" and \"xlarge\" models, as they do not fit in a single GPU.\n\n#### Embedding table placement and load balancing\n\nWe use the following heuristic for dividing the work between the GPUs:\n- The Bottom MLP is placed on GPU-0 and no embedding tables are placed on this device.\n- The tables are sorted from the largest to the smallest\n- Set `max_tables_per_gpu := ceil(number_of_embedding_tables / number_of_available_gpus)`\n- Repeat until all embedding tables have an assigned device:\n - Out of all the available GPUs find the one with the largest amount of unallocated memory\n - Place the largest unassigned embedding table on this GPU. Raise an exception if it does not fit.\n - If the number of embedding tables on this GPU is now equal to `max_tables_per_gpu` remove this GPU from the list of available GPUs so that no more embedding tables will be placed on this GPU. This ensures the all2all communication is well balanced between all devices.\n\n### Preprocessing on GPU\n\nPlease refer to [the \"Preprocessing\" section](#preprocessing) for a detailed description of the Apache Spark 3.0 and NVTabular GPU functionality \n\n### BYO dataset functionality overview\n\nThis section describes how you can train the DeepLearningExamples RecSys models on your own datasets without changing\nthe model or data loader and with similar performance to the one published in each repository.\nThis can be achieved thanks to Dataset Feature Specification, which describes how the dataset, data loader and model\ninteract with each other during training, inference and evaluation.\nDataset Feature Specification has a consistent format across all recommendation models in NVIDIA's DeepLearningExamples\nrepository, regardless of dataset file type and the data loader,\ngiving you the flexibility to train RecSys models on your own datasets.\n\n- [Glossary](#glossary)\n- [Dataset Feature Specification](#dataset-feature-specification)\n- [Data Flow in Recommendation Models in DeepLearning examples](#data-flow-in-nvidia-deep-learning-examples-recommendation-models)\n- [Example of Dataset Feature Specification](#example-of-dataset-feature-specification)\n- [BYO dataset functionality](#byo-dataset-functionality)\n\n#### Glossary\n\nThe Dataset Feature Specification consists of three mandatory and one optional section:\n\nfeature_spec provides a base of features that may be referenced in other sections, along with their metadata.\n\tFormat: dictionary (feature name) => (metadata name => metadata value)
\n\nsource_spec provides information necessary to extract features from the files that store them. \n\tFormat: dictionary (mapping name) => (list of chunks)
\n\n* Mappings are used to represent different versions of the dataset (think: train/validation/test, k-fold splits). A mapping is a list of chunks.
\n* Chunks are subsets of features that are grouped together for saving. For example, some formats may constrain data saved in one file to a single data type. In that case, each data type would correspond to at least one chunk. Another example where this might be used is to reduce file size and enable more parallel loading. Chunk description is a dictionary of three keys:
\n * type provides information about the format in which the data is stored. Not all formats are supported by all models.
\n * features is a list of features that are saved in a given chunk. Order of this list may matter: for some formats, it is crucial for assigning read data to the proper feature.
\n * files is a list of paths to files where the data is saved. For Feature Specification in yaml format, these paths are assumed to be relative to the yaml file's directory (basename). Order of this list matters: It is assumed that rows 1 to i appear in the first file, rows i+1 to j in the next one, etc.
\n\nchannel_spec determines how features are used. It is a mapping (channel name) => (list of feature names). \n\nChannels are model specific magic constants. In general, data within a channel is processed using the same logic. Example channels: model output (labels), categorical ids, numerical inputs, user data, and item data.\n\nmetadata is a catch-all, wildcard section: If there is some information about the saved dataset that does not fit into the other sections, you can store it here.\n\n#### Dataset feature specification\n\nData flow can be described abstractly:\nInput data consists of a list of rows. Each row has the same number of columns; each column represents a feature.\nThe columns are retrieved from the input files, loaded, aggregated into channels and supplied to the model/training script. \n\nFeatureSpec contains metadata to configure this process and can be divided into three parts:\n\n* Specification of how data is organized on disk (source_spec). It describes which feature (from feature_spec) is stored in which file and how files are organized on disk.\n\n* Specification of features (feature_spec). Describes a dictionary of features, where key is feature name and values are features' characteristics such as dtype and other metadata (for example, cardinalities for categorical features)\n\n* Specification of model's inputs and outputs (channel_spec). Describes a dictionary of model's inputs where keys specify model channel's names and values specify lists of features to be loaded into that channel. Model's channels are groups of data streams to which common model logic is applied, for example categorical/continuous data, user/item ids. Required/available channels depend on the model\n\nThe FeatureSpec is a common form of description regardless of underlying dataset format, dataset data loader form and model. \n\n#### Data flow in NVIDIA Deep Learning Examples recommendation models\n\nThe typical data flow is as follows:\n* S.0. Original dataset is downloaded to a specific folder.\n* S.1. Original dataset is preprocessed into Intermediary Format. For each model, the preprocessing is done differently, using different tools. The Intermediary Format also varies (for example, for DLRM PyTorch, the Intermediary Format is a custom binary one.)\n* S.2. The Preprocessing Step outputs Intermediary Format with dataset split into training and validation/testing parts along with the Dataset Feature Specification yaml file. Metadata in the preprocessing step is automatically calculated.\n* S.3. Intermediary Format data together with Dataset Feature Specification are fed into training/evaluation scripts. Data loader reads Intermediary Format and feeds the data into the model according to the description in the Dataset Feature Specification.\n* S.4. The model is trained and evaluated\n\n

\n \n
\n\nFig.1. Data flow in Recommender models in NVIDIA Deep Learning Examples repository. Channels of the model are drawn in green.\n

\n\n#### Example of dataset feature specification\n\nAs an example, let's consider a Dataset Feature Specification for a small CSV dataset for some abstract model.\n\n```yaml\nfeature_spec:\n user_gender:\n dtype: torch.int8\n cardinality: 3 #M,F,Other\n user_age: #treated as numeric value\n dtype: torch.int8\n user_id:\n dtype: torch.int32\n cardinality: 2655\n item_id:\n dtype: torch.int32\n cardinality: 856\n label:\n dtype: torch.float32\n\nsource_spec:\n train:\n - type: csv\n features:\n - user_gender\n - user_age\n files:\n - train_data_0_0.csv\n - train_data_0_1.csv\n - type: csv\n features:\n - user_id\n - item_id\n - label\n files:\n - train_data_1.csv\n test:\n - type: csv\n features:\n - user_id\n - item_id\n - label\n - user_gender\n - user_age\n \n files:\n - test_data.csv\n\nchannel_spec:\n numeric_inputs: \n - user_age\n categorical_user_inputs: \n - user_gender\n - user_id\n categorical_item_inputs: \n - item_id\n label_ch: \n - label\n```\n\nThe data contains five features: (user_gender, user_age, user_id, item_id, label). Their data types and necessary metadata are described in the feature specification section.\n\nIn the source mapping section, two mappings are provided: one describes the layout of the training data, the other of the testing data. The layout for training data has been chosen arbitrarily to showcase the flexibility.\nThe train mapping consists of two chunks. The first one contains user_gender and user_age, saved as a CSV, and is further broken down into two files. For specifics of the layout, refer to the following example and consult the glossary. The second chunk contains the remaining columns and is saved in a single file. Notice that the order of columns is different in the second chunk - this is alright, as long as the order matches the order in that file (that is, columns in the .csv are also switched)\n\nLet's break down the train source mapping. The table contains example data color-paired to the files containing it.\n\n

\n\n

\n\nThe channel spec describes how the data will be consumed. Four streams will be produced and available to the script/model.\nThe feature specification does not specify what happens further: names of these streams are only lookup constants defined by the model/script.\nBased on this example, we can speculate that the model has three input channels: numeric_inputs, categorical_user_inputs,\ncategorical_item_inputs, and one output channel: label.\nFeature names are internal to the FeatureSpec and can be freely modified.\n\n#### BYO dataset functionality\n\nIn order to train any Recommendation model in NVIDIA Deep Learning Examples one can follow one of three possible ways:\n* One delivers already preprocessed dataset in the Intermediary Format supported by data loader used by the training script\n(different models use different data loaders) together with FeatureSpec yaml file describing at least specification of dataset, features and model channels\n\n* One uses a transcoding script\n\n* One delivers dataset in non-preprocessed form and uses preprocessing scripts that are a part of the model repository.\nIn order to use already existing preprocessing scripts, the format of the dataset needs to match the one of the original datasets.\nThis way, the FeatureSpec file will be generated automatically, but the user will have the same preprocessing as in the original model repository.","displayName","DLRM for PyTorch",["D",1680632300158],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/dlrm_for_pytorch","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",1973795,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[249,250],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_216":260,"_252":261,"_254":262,"_256":257},{"_216":251,"_252":253,"_254":255,"_256":257},"Recommendation","value","uscs_recommedation","filter",{"_258":259,"_252":253},"category","label","field","application","Deep Learning Examples","pltfm_deep_learning_examples",{"_258":263,"_252":261},"platform",{"_87":1451,"_89":1452},"ngcApps",[1404],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1386":1387,"_1388":1389,"_1390":1391,"_1392":1393,"_1394":275,"_114":299,"_1395":1396,"_1384":50,"_1397":66,"_1398":66,"_1399":1400,"_1401":66,"_1402":1403},"amplitude",{"_1382":1383,"_1384":50,"_975":-7,"_1385":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1376":1377,"_1378":1379,"_1380":1381},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1250":381,"_1251":1252,"_1253":1254,"_1255":1256},"announcements",[],"consentState",{"_338":1240,"_1241":66,"_1242":26,"_1243":26,"_1244":1245},"consentConfig",{"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339},"showContactAdmin",1782692108589,[],[],[313,314,315,316,317,318],{"_39":54,"_41":63,"_43":82,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":82,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":82,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":82,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":319,"_49":50},{"_39":40,"_41":42,"_43":82,"_45":-5,"_47":-5,"_49":50},3,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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