Recommendation systems drive engagement on many of the most popular online platforms. As the volume of data available to power these systems grows exponentially, data scientists are increasingly turning from more traditional machine learning methods to highly expressive deep learning models to improve the quality of their recommendations. Google's Wide & Deep Learning for Recommender Systems has emerged as a popular model for these problems both for its robustness to signal sparsity as well as its user-friendly implementation in TensorFlow.

The differences between this Wide & Deep Recommender Model and the model from the paper is the size of the Deep part of the model. Originally, in Google's paper, the fully connected part was three layers of 1024, 512, and 256 neurons. Our model consists of 5 layers each of 1024 neurons.

The model enables you to train a recommender model that combines the memorization of the Wide part and generalization of the Deep part of the network.

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.49 times 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

Wide & Deep refers to a class of networks that use the output of two parts working in parallel - wide model and deep model - to make predictions of recommenders. The wide model is a generalized linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons each beginning with a dense embedding of features. The architecture is presented in Figure 1.

\n \n
\nFigure 1. The architecture of the Wide & Deep model.\n

Applications and dataset

As a reference dataset, we used a subset of the features engineered by the 19th place finisher in the Kaggle Outbrain Click Prediction Challenge. This competition challenged competitors to predict the likelihood with which a particular ad on a website's display would be clicked on. Competitors were given information about the user, display, document, and ad in order to train their models. More information can be found here.

Default configuration

For reference, and to give context to the acceleration numbers described below, some important properties of our features and model are as follows:

\n
Features
\n
- Request Level\n
  - 16 scalar numeric features (shape=(1,))
  - 12 one-hot categorical features (all int dtype)\n
    - 5 indicator embeddings with sizes 2, 2, 3, 3, 6
    - 7 trainable embeddings\n
      - all except two have an embedding size of 64 (remaining two have 128), though it's important to note for all categorical features that we do not leverage that information to short-circuit the lookups by treating them as a single multi-hot lookup. Our API is fully general to any combination of embedding sizes.
      - all use hash bucketing with num_buckets= 300k, 100k, 4k, 2.5k, 2k, 1k, and 300 respectively
      \n
    \n
  - 3 multi-hot categorical features (all int dtype)\n
    - all trainable embeddings
    - all with embedding size 64
    - all use hash bucketing with num_buckets= 10k, 350, and 100 respectively
    \n
  \n
- Item Level\n
  - 16 scalar numeric features
  - 4 one hot categorical features (all int dtype)\n
    - embedding sizes of 128, 64, 64, 64 respectively
    - hash bucketing with num_buckets= 250k, 4k, 2.5k, and 1k respectively
    \n
  - 3 multi-hot categorical features (all int dtype)\n
    - all with embedding size 64
    - hash bucketing with num_buckets= 10k, 350, and 100 respectively
    \n
  \n
- All features are used in both wide and deep branches of the network
\n
\n
Model
\n
- Total embedding dimension is 1328
- 5 hidden layers each with size 1024
- Output dimension is 1 (probability of click)
\n

Feature support matrix

The following features are supported by this model:

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

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

Feature	Wide & Deep
Horovod Multi-GPU	Yes
Automatic mixed precision (AMP)	Yes

Features

Horovod

Horovod is a distributed training framework for TensorFlow, Keras, PyTorch and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the Horovod: Official repository.

Multi-GPU training with Horovod

Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the TensorFlow tutorial.

Mixed precision

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

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

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.
How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.

Enabling mixed precision

To enable Wide & Deep training to use mixed precision you don't need to perform input quantization, only an additional flag --amp to the training script is needed (see Quick Start Guide).

Impact of mixed precision on training accuracy

The accuracy of training, measured with MAP@12 metric was not impacted by enabling mixed precision. The obtained results were statistically similar (i.e. similar run-to-run variance was observed, with standard deviation of the level of 0.002).

Impact of mixed precision on inference accuracy

For our reference model, the average absolute error on the probability of interaction induced by reduced precision inference is 0.0002, producing a near-perfect fit between predictions produced by full and mixed precision models. Moreover, this error is uncorrelated with the magnitude of the predicted value, which means for most predictions of interest (i.e. greater than 0.01 or 0.1 likelihood of interaction), the relative magnitude of the error is approaching the noise floor of the problem.

Enabling TF32

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

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

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

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

Glossary

Request level features: Features that describe the person or object to which we wish to make recommendations.

Item level features: Features that describe those objects which we are considering recommending.

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Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [20.10-tf1-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) NGC container\n- Supported GPUs:\n\t- [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n\t- [NVIDIA Turing architecture](https://www.nvidia.com/en-us/geforce/turing/)\n\t- [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:\n- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry)\n- [Running TensorFlow](https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/running.html#running)\n\nFor those unable to use the TensorFlow NGC container, to set up the required environment or create your own container, see the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the Wide & Deep model on the Outbrain dataset. For the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n\n```\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/TensorFlow/Recommendation/WideAndDeep\n```\n\n2. Download the Outbrain dataset.\n\nThe Outbrain dataset can be downloaded from [Kaggle](https://www.kaggle.com/c/outbrain-click-prediction/data) (requires Kaggle account).\nUnzip the downloaded archive e.g. to `/raid/outbrain/orig` and set the `HOST_OUTBRAIN_PATH` variable to the parent directory:\n\n```bash\nHOST_OUTBRAIN_PATH=/raid/outbrain\n```\n\n3. Build the Wide & Deep Tensorflow NGC container.\n\n```bash\ndocker build . -t wide_deep\n```\n\n4. Start an interactive session in the NGC container to run preprocessing/training/inference.\n\n```bash\ndocker run --runtime=nvidia --privileged --rm -ti -v ${HOST_OUTBRAIN_PATH}:/outbrain wide_deep /bin/bash\n```\n5. Start preprocessing.\n\n```bash\nbash scripts/preproc.sh 4096\n```\nThe result of preprocessing scripts are prebatched TFRecords. The argument to the script is the prebatch\nsize (4096 is the default).\n\n6. Start training.\n\nSingle GPU:\n```bash\npython -m trainer.task --gpu\n```\n8 GPU:\n```bash\nmpiexec --allow-run-as-root --bind-to socket -np 8 python -m trainer.task --gpu --hvd\n```\n\nIf you want to run validation or inference, you can either use the checkpoint obtained from the training \ncommands above, or download the pretrained checkpoint from NGC. \n\nIn order to download the checkpoint from NGC, visit [ngc.nvidia.com](https://ngc.nvidia.com) website and\nbrowse the available models.\nDownload the checkpoint files and unzip them to some path, e.g. to `/raid/outbrain/checkpoints/`\n(which is the default path for storing the checkpoints during training).\n\n7. Start validation/evaluation.\n\nIn order to validate the checkpoint on the evaluation set, run the `task.py` script with `--evaluate` flag:\n\n```bash\npython -m trainer.task --gpu --evaluate --model_dir /outbrain/checkpoints\n```\n\n8. Start inference/predictions.\n\nIn order to run inference and predict the results, run the `task.py`\nscript with `--predict` flag:\n\n```bash\npython -m trainer.task --gpu --predict --model_dir /outbrain/checkpoints\n```","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training, and the training results.\n\n### Scripts and sample code\n\nThese are the important scripts in this repository:\n* `trainer/task.py` - Python script for training the Wide & Deep recommender model\n* `trainer/features.py` - Python file describing the request and item level features\n\n### Parameters\n\nThese are the important parameters in the `trainer/task.py` script:\n\n```\n--model_dir: Path to model checkpoint directory\n--deep_hidden_units: [DEEP_LAYER1 DEEP_LAYER2 ...] hidden units per layer, separated by spaces\n--prebatch_size: Number of samples in each pre-batch in tfrecords\n--global_batch_size: Training batch size (per all GPUs, must be a multiplicity of prebatch_size)\n--eval_batch_size: Evaluation batch size (must be a multiplicity of prebatch_size)\n--num_epochs: Number of epochs to train\n--linear_learning_rate: Learning rate for the wide part of the model\n--linear_l1_regularization: L1 regularization for the wide part of the model\n--linear_l2_regularization: L2 regularization for the wide part of the model\n--deep_learning_rate: Learning rate for the deep part of the model\n--deep_dropout: Dropout probability for deep model\n--deep_warmup_epochs: Number of epochs with linear learning rate warmup\n--predict: Perform only the prediction on the validation set, do not train\n--evaluate: Perform only the evaluation on the validation set, do not train\n--gpu: Run computations on GPU\n--amp: Enable Automatic Mixed Precision\n--xla: Enable XLA\n--hvd: Use Horovod for multi-GPU training\n--eval_epoch_interval: Perform evaluation every this many epochs\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:\n```bash\npython -m trainer.task --help\n```\n\n### Getting the data\n\nThe Outbrain dataset can be downloaded from [Kaggle](https://www.kaggle.com/c/outbrain-click-prediction/data) (requires Kaggle account).\n\n#### Dataset guidelines\n\nThe dataset contains a sample of users' page views and clicks, as observed on multiple publisher sites. Viewed pages and clicked recommendations have additional semantic attributes of the documents.\nThe dataset contains sets of content recommendations served to a specific user in a specific context. Each context (i.e. a set of recommended ads) is given a `display_id`. In each such recommendation set, the user has clicked on exactly one of the ads.\n\nThe original data is stored in several separate files:\n- `page_views.csv` - log of users visiting documents (2B rows, ~100GB uncompressed)\n- `clicks_train.csv` - data showing which ad was clicked in each recommendation set (87M rows)\n- `clicks_test.csv` - used only for the submission in the original Kaggle contest\n- `events.csv` - metadata about the context of each recommendation set (23M rows)\n- `promoted_content.csv` - metadata about the ads\n- `document_meta.csv`, `document_topics.csv`, `document_entities.csv`, `document_categories.csv` - metadata about the documents\n \nDuring the preprocessing stage the data is transformed into 59M rows tabular data of 54 features and eventually saved in pre-batched TFRecord format.\n\n#### Spark preprocessing\n\nThe original dataset is preprocessed using Spark scripts from the `preproc` directory. The preprocessing consists of the following operations:\n- separating out the validation set for cross-validation\n- filling missing data with the most frequent value\n- generating the user profiles from the page views data\n- joining the tables for the ad clicks data\n- computing click-through rates (CTR) for ads grouped by different contexts\n- computing cosine similarity between the features of the clicked ads and the viewed ads\n- math transformations of the numeric features (taking logarithm, scaling, binning)\n- storing the resulting set of features in TFRecord format\n\nThe `preproc1-4.py` preprocessing scripts use PySpark. \nIn the Docker image, we have installed Spark 2.3.1 as a standalone cluster of Spark. \nThe `preproc1.py` script splits the data into a training set and a validation set. \nThe `preproc2.py` script generates the user profiles from the page views data. \nThe `preproc3.py` computes the click-through rates (CTR) and cosine similarities between the features. \nThe `preproc4.py` script performs the math transformations and generates the final TFRecord files. \nThe data in the output files is pre-batched (with the default batch size of 4096) to avoid the overhead \nof the TFRecord format, which otherwise is not suitable for the tabular data - \nit stores a separate dictionary with each feature name in plain text for every data entry.\n\nThe preprocessing includes some very resource-exhausting operations, like joining 2B+ rows tables. \nSuch operations may not fit into the RAM memory, therefore we decided to use Spark which is a suitable tool \nfor handling tabular operations on large data. \nNote that the Spark job requires about 1 TB disk space and 500 GB RAM to perform the preprocessing.\nFor more information about Spark, please refer to the\n[Spark documentation](https://spark.apache.org/docs/2.3.1/).\n\n### Training process\n\nThe training can be started by running the `trainer/task.py` script. By default the script is in train mode. Other training related \nconfigs are also present in the `trainer/task.py` and can be seen using the command `python -m trainer.task --help`. Training happens for `--num_epochs` epochs with a DNNLinearCombinedClassifier estimator for the model. The model has a wide linear part and a deep feed forward network, and the networks are built according to the default configuration.\n\nTwo separate optimizers are used to optimize the wide and the deep part of the network:\n \n- FTLR (Follow the Regularized Leader) optimizer is used to optimize the wide part of the network.\n- Adagrad optimizer is used to optimize the deep part of the network.\n\nThe training log will contain information about:\n\n- Loss value after every 100 steps.\n- Training throughput if `--benchmark` option is selected.\n- Evaluation metrics after every `--eval_epoch_interval` epochs.\n\nCheckpoints are stored with every evaluation at the `--model_dir` location.","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 mode.\n\n#### Training performance benchmark\n\nWe provide 8 scripts to benchmark the performance of training:\n```bash\nbash scripts/DGXA100_benchmark_training_tf32_1gpu.sh\nbash scripts/DGXA100_benchmark_training_amp_1gpu.sh\nbash scripts/DGXA100_benchmark_training_tf32_8gpu.sh\nbash scripts/DGXA100_benchmark_training_amp_8gpu.sh\nbash scripts/DGX1_benchmark_training_fp32_1gpu.sh\nbash scripts/DGX1_benchmark_training_amp_1gpu.sh\nbash scripts/DGX1_benchmark_training_fp32_8gpu.sh\nbash scripts/DGX1_benchmark_training_amp_8gpu.sh\n```\n\n### Results\n\nThe following sections provide details on how we achieved our performance and\naccuracy in training.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the `trainer/task.py` training script in the TensorFlow NGC container on NVIDIA DGX A100 with (8x A100 40GB) GPUs.\n\n|**GPUs**|**Batch size / GPU**|**Accuracy - TF32 (MAP@12)**|**Accuracy - mixed precision (MAP@12)**|**Time to train - TF32 (minutes)**|**Time to train - mixed precision (minutes)**|**Time to train speedup (TF32 to mixed precision)**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 131,072 | 0.67683 | 0.67632 | 341 | 359 | [-](#known-issues) |\n| 8 | 16,384 | 0.67709 | 0.67721 | 93 | 107 | [-](#known-issues) |\n\nTo achieve the same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `trainer/task.py` training script in the TensorFlow NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.\n\n|**GPUs**|**Batch size / GPU**|**Accuracy - FP32 (MAP@12)**|**Accuracy - mixed precision (MAP@12)**|**Time to train - FP32 (minutes)**|**Time to train - mixed precision (minutes)**|**Time to train speedup (FP32 to mixed precision)**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 131,072 | 0.67648 | 0.67744 | 654 | 440 | 1.49 |\n| 8 | 16,384 | 0.67692 | 0.67725 | 190 | 185 | 1.03 |\n\nTo achieve the same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n##### Training accuracy plots\n\nModels trained with FP32, TF32 and Automatic Mixed Precision (AMP) achieve similar precision.\n\n![MAP12](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow/Recommendation/WideAndDeep/img/lc20.06.png)\n\n\n##### Training stability test\n\nThe Wide and Deep model was trained for 54,713 training steps, starting\nfrom 6 different initial random seeds for each setup. The training was performed in the 20.10-tf1-py3 NGC container on\nNVIDIA DGX A100 40GB and DGX-1 16GB machines with and without mixed precision enabled.\nAfter training, the models were evaluated on the validation set. The following\ntable summarizes the final MAP@12 score on the validation set.\n\n||**Average MAP@12**|**Standard deviation**|**Minimum**|**Maximum**|\n|:-------|-----------------:|---------------------:|----------:|----------:|\n| DGX A100 TF32 | 0.67709 | 0.00094 | 0.67463 | 0.67813 | \n| DGX A100 mixed precision | 0.67721 | 0.00048 | 0.67643 | 0.67783 | \n| DGX-1 FP32 | 0.67692 | 0.00060 | 0.67587 | 0.67791 | \n| DGX-1 mixed precision | 0.67725 | 0.00064 | 0.67561 | 0.67803 | \n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 40GB)\n\nOur results were obtained by running the benchmark scripts from the `scripts` directory in the TensorFlow NGC container on NVIDIA DGX A100 with (8x A100 40GB) GPUs. Improving model scaling for multi-GPU is [under development](#known-issues).\n\n|**GPUs**|**Batch size / GPU**|**Throughput - TF32 (samples/s)**|**Throughput - mixed precision (samples/s)**|**Strong scaling - TF32**|**Strong scaling - mixed precision**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 131,072 | 349,879 | 332,529 | 1.00 | 1.00 |\n| 8 | 16,384 | 1,283,457 | 1,111,976 | 3.67 | 3.34 |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the benchmark scripts from the `scripts` directory in the TensorFlow NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs. Improving model scaling for multi-GPU is [under development](#known-issues).\n\n|**GPUs**|**Batch size / GPU**|**Throughput - FP32 (samples/s)**|**Throughput - mixed precision (samples/s)**|**Throughput speedup (FP32 to mixed precision)**|**Strong scaling - FP32**|**Strong scaling - mixed precision**|\n|-------:|-------------------:|----------------------------:|---------------------------------------:|-----------------------------------------------:|----------------------:|---------------------------------:|\n| 1 | 131,072 | 182,510 | 271,366 | 1.49 | 1.00 | 1.00 |\n| 8 | 16,384 | 626,301 | 643,334 | 1.03 | 3.43 | 2.37 |","hasPerformance",[],{"_87":294,"_122":295,"_123":-5,"_131":35,"_130":35,"_132":293,"_263":92},{"_87":169,"_122":169,"_123":-5,"_131":35,"_130":35,"_132":293,"_263":92},{"_87":29,"_122":29,"_123":-5,"_131":35,"_130":35,"_132":292,"_263":92},{"_87":162,"_122":162,"_123":-5,"_131":35,"_130":35,"_132":291,"_263":92},{"_87":158,"_122":158,"_123":-5,"_131":35,"_130":35,"_132":290,"_263":92},{"_87":154,"_122":154,"_123":-5,"_131":35,"_130":35,"_132":289,"_263":92},{"_87":148,"_122":148,"_123":-5,"_131":35,"_130":35,"_132":288,"_263":92},{"_87":143,"_122":143,"_123":-5,"_131":35,"_130":35,"_132":287,"_263":92},{"_87":121,"_122":121,"_123":-5,"_131":35,"_130":35,"_132":286,"_263":92},[],[],[],[],[],[],[],[],"-","Latest (20.10.2)","nvidia/wideanddeep_for_tensorflow","key","nvidia/resources/wideanddeep_for_tensorflow","resourceId","wideanddeep_for_tensorflow","shortDescription","Wide & Deep Recommender model.","Recommendation systems drive engagement on many of the most popular online platforms. As the volume of data available to power these systems grows exponentially, data scientists are increasingly turning from more traditional machine learning methods to highly expressive deep learning models to improve the quality of their recommendations. Google's [Wide & Deep Learning for Recommender Systems](https://arxiv.org/abs/1606.07792) has emerged as a popular model for these problems both for its robustness to signal sparsity as well as its user-friendly implementation in [TensorFlow](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNLinearCombinedClassifier).\n\nThe differences between this Wide & Deep Recommender Model and the model from the paper is the size of the Deep part of the model. Originally, in Google's paper, the fully connected part was three layers of 1024, 512, and 256 neurons. Our model consists of 5 layers each of 1024 neurons. \n\nThe model enables you to train a recommender model that combines the memorization of the Wide part and generalization of the Deep part of the network.\n\nThis model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.49 times 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\nWide & Deep refers to a class of networks that use the output of two parts working in parallel - wide model and deep model - to make predictions of recommenders. The wide model is a generalized linear model of features together with their transforms. The deep model is a series of 5 hidden MLP layers of 1024 neurons each beginning with a dense embedding of features. The architecture is presented in Figure 1.\n\n

\n \n
\nFigure 1. The architecture of the Wide & Deep model.\n

\n\n### Applications and dataset\n\nAs a reference dataset, we used a subset of [the features engineered](https://github.com/gabrielspmoreira/kaggle_outbrain_click_prediction_google_cloud_ml_engine) by the 19th place finisher in the [Kaggle Outbrain Click Prediction Challenge](https://www.kaggle.com/c/outbrain-click-prediction/). This competition challenged competitors to predict the likelihood with which a particular ad on a website's display would be clicked on. Competitors were given information about the user, display, document, and ad in order to train their models. More information can be found [here](https://www.kaggle.com/c/outbrain-click-prediction/data).\n\n### Default configuration\n\nFor reference, and to give context to the acceleration numbers described below, some important properties of our features and model are as follows:\n\n- Features\n - Request Level\n - 16 scalar numeric features `(shape=(1,)`)\n - 12 one-hot categorical features (all `int` dtype)\n - 5 indicator embeddings with sizes 2, 2, 3, 3, 6\n - 7 trainable embeddings\n - all except two have an embedding size of 64 (remaining two have 128), though it's important to note for *all* categorical features that we *do not* leverage that information to short-circuit the lookups by treating them as a single multi-hot lookup. Our API is fully general to any combination of embedding sizes.\n - all use hash bucketing with `num_buckets=` 300k, 100k, 4k, 2.5k, 2k, 1k, and 300 respectively\n - 3 multi-hot categorical features (all `int` dtype)\n - all trainable embeddings\n - all with embedding size 64\n - all use hash bucketing with `num_buckets=` 10k, 350, and 100 respectively\n - Item Level\n - 16 scalar numeric features\n - 4 one hot categorical features (all `int` dtype)\n - embedding sizes of 128, 64, 64, 64 respectively\n - hash bucketing with `num_buckets=` 250k, 4k, 2.5k, and 1k respectively\n - 3 multi-hot categorical features (all `int` dtype)\n - all with embedding size 64\n - hash bucketing with `num_buckets=` 10k, 350, and 100 respectively\n - All features are used in both wide *and* deep branches of the network\n\n- Model\n - Total embedding dimension is 1328\n - 5 hidden layers each with size 1024\n - Output dimension is 1 (probability of click)\n\n### Feature support matrix\n\nThe following features are supported by this model: \n\n| Feature | Wide & Deep \n|-----------------------|--------------------------\n|Horovod Multi-GPU | Yes \n|Automatic mixed precision (AMP) | Yes \n \n#### Features\n\nHorovod\n\nHorovod is a distributed training framework for TensorFlow, Keras, PyTorch and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the [Horovod: Official repository](https://github.com/horovod/horovod).\n\nMulti-GPU training with Horovod\n\nOur model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the [TensorFlow tutorial](https://github.com/horovod/horovod/#usage).\n\n### Mixed precision\n\nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:\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- How to access and enable AMP for TensorFlow, see [Using TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) from the TensorFlow User Guide.\n\n#### Enabling mixed precision\n\nTo enable Wide & Deep training to use mixed precision you don't need to perform input quantization, only an additional flag `--amp` to the training script is needed (see [Quick Start Guide](#quick-start-guide)).\n\n##### Impact of mixed precision on training accuracy\nThe accuracy of training, measured with MAP@12 metric was not impacted by enabling mixed precision. The obtained results were statistically similar (i.e. similar run-to-run variance was observed, with standard deviation of the level of `0.002`).\n\n##### Impact of mixed precision on inference accuracy\nFor our reference model, the average absolute error on the probability of interaction induced by reduced precision inference is `0.0002`, producing a near-perfect fit between predictions produced by full and mixed precision models. Moreover, this error is uncorrelated with the magnitude of the predicted value, which means for most predictions of interest (i.e. greater than `0.01` or `0.1` likelihood of interaction), the relative magnitude of the error is approaching the noise floor of the problem.\n\n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. \n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.\n\n### Glossary\n\nRequest level features: Features that describe the person or object _to which_ we wish to make recommendations.\n\nItem level features: Features that describe those objects which we are considering recommending.","displayName","Wide & Deep for TensorFlow",["D",1680631924036],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/resources/wideanddeep_for_tensorflow","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[336,337],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_304":346,"_339":347,"_341":348,"_343":89},{"_304":338,"_339":340,"_341":342,"_343":89},"Recommendation","value","uscs_recommedation","filter",{"_344":345,"_339":340},"category","field","application","Deep Learning Examples","pltfm_deep_learning_examples",{"_344":349,"_339":347},"platform",{"_194":1485,"_196":1486},"ngcApps",[1438],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1419":1420,"_1421":1422,"_1423":1424,"_1425":1426,"_1427":361,"_111":387,"_1428":1429,"_1430":60,"_1431":92,"_1432":92,"_1433":1434,"_1435":92,"_1436":1437},"amplitude",{"_1416":1417,"_1025":-7,"_1418":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1410":1411,"_1412":1413,"_1414":1415},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1794251438454,"1.30.0","defaultSearchSuggestions",{"_1295":470,"_1296":1297,"_1298":1299,"_1300":1301},"announcements",[],"consentState",{"_426":1285,"_1286":35,"_1287":35,"_1288":92,"_1289":1290},"consentConfig",{"_408":409,"_410":411,"_412":413,"_414":415,"_416":417,"_418":419,"_420":421,"_422":423,"_424":425,"_426":427},"showContactAdmin",1794251438455,[],[],[401,402,403,404,405,406],{"_49":64,"_51":75,"_53":70,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":70,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":69,"_53":70,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":70,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":70,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":407,"_59":60},9,"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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	Files	Compressed Size
20.10.2	35	277.18 KB	February 3, 2022 UTC
20.10.1Selected	33	277.1 KB	August 4, 2021 UTC
20.10.0	33	276.74 KB	January 6, 2021 UTC
20.06.2	33	276.23 KB	December 4, 2020 UTC
20.06.1	33	276.22 KB	August 14, 2020 UTC
20.06.0	33	276.21 KB	July 31, 2020 UTC
2	29	288.29 KB	June 16, 2020 UTC
1	30	263.93 KB	April 21, 2020 UTC