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

The Neural Collaborative Filtering (NCF) model is a neural network that provides collaborative filtering based on\nimplicit feedback. Specifically, it provides product recommendations based on user and item interactions. The training\ndata for this model should contain a sequence of (user ID, item ID) pairs indicating that the specified user has\ninteracted with an item, for example, by giving a rating or clicking. NCF was first described by\nXiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua in the Neural Collaborative Filtering\npaper.

The implementation in this repository focuses on the NeuMF instantiation of the NCF architecture. We modified it to use\nDropout in the fully connected layers. This reduces overfitting and increases the final accuracy. Training the other two\ninstantiations of NCF (GMF and MLP) is not supported.

The original paper evaluates the model on the ml-1m dataset.\nConversely, we evaluate on the ml-20m dataset, which provides a more practical production scenario.\nHowever, using the ml-1m dataset is also supported.

This model takes advantage of the mixed precision Tensor Cores found on Volta, Turing, and the NVIDIA Ampere GPU architectures\ndemonstrating the reduction in\ntraining time possible by leveraging Tensor Cores. In the single GPU configuration, training times can be improved close\nto 1.6x through the usage of Tensor Cores.

This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

NCF-TF takes in a sequence of (user ID, item ID) pairs as inputs, then feeds them separately into a matrix\nfactorization step (where the embeddings are multiplied) and into a multilayer perceptron (MLP) network.

The outputs of the matrix factorization and the MLP network are then combined and fed into a single dense layer which\npredicts whether the input user is likely to interact with the input item. The architecture of the MLP network is shown\nbelow.

\n \n
\n Figure 1. The architecture of a Neural Collaborative Filtering model. Taken from the Neural Collaborative Filtering paper.\n

Default Configuration

This implementation has the following features:

model-parallel multi-gpu training with Horovod
mixed precision training with TF-AMP (TensorFlow-Automatic Mixed Precision), which enables mixed precision training\nwithout any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an\nenvironmental variable
fast negative sample generation and data preprocessing with CuPy\n
- Before each training epoch, the training data is augmented with randomly generated negatives samples. A \"shortcut\" is\nenabled by default where the script does not verify that the randomly generated samples are actually negative samples.\nWe have found that this shortcut has a low impact on model accuracy while considerably improving the speed and memory\nfootprint of the data augmentation stage of training.
- Note: The negative samples generated for the test set are always verified regardless of the shortcut being enabled or\nnot.
\n

Feature support matrix

\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	NCF-TF
Horovod	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.

Automatic Mixed Precision (AMP)

Computation graphs can be modified by TensorFlow on runtime to support mixed precision training. Detailed explanation of mixed precision can be found in the next section.

Mixed precision training

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

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

This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full mixed precision methodology in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.

In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.

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

Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.

To enable mixed precision, you can simply add the values to the environmental variables inside your training script:

Enable TF-AMP graph rewrite:

os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n

Enable Automated Mixed Precision:

os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n

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.

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Since we could not find hyper-parameters that could hit the\ntarget accuracy for batch size of over 1 million samples, we elected to use a\nstrong scaling strategy which generally has worse scaling efficiency compared\nto a more common weak scaling strategy. Additionally, we believe that the small\ndataset size does not facilitate great scaling. However, the training scripts\nallow the use of custom datasets provided they are in the correct format.\n\n#### Scaling beyond 8 GPUs\n\nNeural Collaborative Filtering (NCF) is a relatively lightweight model that\ntrains quickly with this relatively smaller dataset, ml-20m. Because of the\nsmaller dataset, the high ratio of communication to computation makes it\ndifficult to efficiently use more than 8 GPUs. Typically, this is not an issue\nbecause when using 8 GPUs with FP16 precision the training is sufficiently\nfast. However, if you would like to scale the training to 16 GPUs and beyond,\nyou might try modifying the model so that the communication to computation\nratio facilitates better scaling. This could be done, for example, by finding\nhyper-parameters that enable using a larger global batch size.\n\n### Preprocessing Out-of-Memory with 16GB GPUs\n\nWhen running on GPUs with 16GB of memory, ensure the `--no-neg-trick` flag is\n**not** set. Otherwise, the data augmentation stage of training will consume\ntoo much GPU memory, causing TensorFlow to raise an out-of-memory error.\n\nThis flag, when it is not set, reduces memory consumption in the negative\nsamples generation phase of training by telling the script not to verify that\nthe randomly generated samples are actually negative samples (verification\nstill occurs for negative samples generated for the test set). Therefore, there\nis no need to keep the data structures used to verify negative samples in\nmemory during training.","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to start training NCF-TF.\n\n### Requirements\n\nThis repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- TensorFlow 20.07-py3+ 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/geforce/turing/)\n - [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, see the 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\nFor those unable to use the [framework name] 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\nparameters of the NCF model on the ml-20m dataset.\n\n1. Clone the repository.\n\n ```bash\n git clone https://github.com/NVIDIA/DeepLearningExamples\n cd DeepLearningExamples/TensorFlow/Recommendation/NCF\n ```\n\n2. Build the NCF TensorFlow NGC container.\n\n After Docker is correctly set up, you can build the NCF image with:\n\n ```bash\n docker build . -t nvidia_ncf\n ```\n\n3. Launch the NCF TensorFlow Docker container.\n\n ```bash\n mkdir data\n docker run --runtime=nvidia -it --rm --ipc=host -v ${PWD}/data:/data nvidia_ncf bash\n ```\n\n This will launch the container and mount the `./data` directory as a volume to the `/data` directory inside the container.\n Any datasets and experiment results (logs, checkpoints etc.) saved to `/data` will be accessible in the `./data` directory\n on the host.\n\n4. Download and preprocess the dataset.\n\n **ml-20m**\n\n Preprocessing consists of downloading the data, filtering out users that have less than 20 ratings (by default), sorting\n the data and dropping the duplicates. No data augmentation techniques are used in the preprocessing stage.\n\n To download and preprocess the ml-20m dataset, run:\n\n ```bash\n ./prepare_dataset.sh\n ```\n\n **ml-1m**\n\n To download and preprocess the ml-1m dataset, run:\n\n ```bash\n ./prepare_dataset.sh ml-1m\n ```\n\n This will store the preprocessed training and evaluation data in the `/data` directory, so that it can be later used to\n train the model (by passing the appropriate `--data` argument to the `ncf.py` script).\n\n5. Start the training.\n\n After the Docker container is launched, the training with the default hyper-parameters can be started with:\n\n ```bash\n mpirun -np 4 --allow-run-as-root python ncf.py --amp --data /data/cache/ml-20m --checkpoint-dir /data/checkpoints/\n ```\n\n After the training is complete, the model parameters that provide the best evaluation accuracy are saved to the\n directory passed to the `--checkpoint-dir` argument. By default, this will be in the `/data/checkpoints/` directory.\n\n6. Perform a validation/evaluation.\n\n To run evaluation on a specific checkpoint, simply run the following command:\n\n ```bash\n python ncf.py --data /data/cache/ml-20m --mode test --load-checkpoint-path /data/checkpoints/model.ckpt\n ```\n\n Note: TensorFlow checkpoints consist of three files each with a `*.ckpt` prefix.","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Command Line Arguments\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command line option, for\nexample:\n\n```bash\npython ncf.py --help\n```\n\nAside from options to set hyperparameters, the relevant options to control the behavior of the script are:\n\n```\n--data DATA path to test and training data files\n-e EPOCHS, --epochs EPOCHS\n number of epochs to train for\n-b BATCH_SIZE, --batch-size BATCH_SIZE\n number of examples for each iteration\n--valid-users-per-batch VALID_USERS_PER_BATCH\n Number of users tested in each evaluation batch\n-n NEGATIVE_SAMPLES, --negative-samples NEGATIVE_SAMPLES\n number of negative examples per interaction\n-k TOPK, --topk TOPK rank for test examples to be considered a hit\n--amp enable half-precision computations using automatic\n mixed precision (only available in supported\n containers)\n--xla enable TensorFlow XLA (Accelerated Linear Algebra)\n--valid-negative VALID_NEGATIVE\n Number of negative samples for each positive test\n example\n--loss-scale LOSS_SCALE\n Loss scale value to use when manually enabling mixed precision training\n--checkpoint-dir CHECKPOINT_DIR\n Path to store the result checkpoint file for training, or to read from for evaluation\n--mode {train,test} Passing \"test\" will only run a single evaluation,\n otherwise full training will be performed\n--no-neg-trick do not use negative sample generation shortcut to\n speed up preprocessing (will increase GPU memory\n consumption)\n--eval-after EVAL_AFTER\n Perform evaluations only after this many epochs\n--verbose Log the performance and accuracy after every epoch\n\n```\n\n### Getting the Data\n\nFor each user, the test dataset is generated by removing one movie the user has\ninteracted with. For each removed movie, the data is augmented with a large\nnumber of movies (corresponding to the `--valid-negative option`) that the user\nhas not interacted with.\n\nThe repository contains the `prepare_dataset.sh` that will preprocess the training and test datasets.\nBy default, the data will be downloaded to the `/data` directory.\n\n#### Multi-dataset\n\nThis implementation is tuned for the ml-20m and ml-1m datasets. Using other\ndatasets might require tuning some hyperparameters (for example, learning rate,\nbeta1, beta2).\n\nIf you'd like to use your custom dataset, you can do so by adding support for\nit in the `prepare_dataset.sh` and `download_dataset.sh` scripts. The required\nformat of the data is a CSV file which should follow the pattern outlined\nbelow:\n\n```\nuserId, movieId\n1,2\n1,10\n...\n```\n\nThe CSV file may contain additional columns with extra features such as ratings\nand timestamps, but only the `userId` and `movieId` columns are required.\n\nThe performance of the model depends on the dataset size. Generally, the model\nshould scale better for datasets containing more data points. For a smaller\ndataset, you might experience slower performance as fixed cost operations that\ndo not scale with input size will have a larger impact. Furthermore, it will be\ndifficult for the model to converge.\n\n### Training Process\n\nThe training can be launched with the `ncf.py` script. This script will train the\nNCF model for a number of epochs specified by the `--epochs` argument, which has\na default value of 30.\n\nDuring training, the script will begin logging after the number of epochs\nspecified by the `--eval-after` option. After that the script will output a line like the one below:\n```\nDLL 2020-07-03 10:58:43.371321 - (26,) train_time : 9.889576196670532 eval_time : 0.674187183380127 hr@10 : 0.9526329850606168 ndcg : 0.7448715819572108\n```\n\nThe evaluation metrics are: HR (hit rate), and NDCG (normalized discounted\ncumulative gain). In the evaluation set, each user will be assigned one item\nthat they have actually interacted with, and a number (by default 99) of items\nthat they have not interacted with. For each user, the evaluation process will\nrank each of the items assigned to that user based on the user's likeliness to\ninteract with the items. The hit rate measures the percentage of users for\nwhich the item that they have interacted with is ranked within the top `k` items,\nwhere `k` is a number (by default 10) specified by the `-k` option. NDCG has a\nsimilar meaning, except the rank of the positive item is taken into account.\nTypically, HR is used as the primary evaluation metric.\n\nAdditionally, the model parameters that give the best accuracy in validation\nwill be stored at the directory pointed to by the `--checkpoint-dir` argument.\n\nMultiple GPUs can be used for training through Horovod. The number of GPUs can\nbe controlled by the `-np` parameter passed to `mpirun`.\n\n### Evaluation Process\n\nThe evaluation process can be run by the ncf.py script as well. By passing the\n`--mode=test argument`, the script will run evaluation once using the TensorFlow\ncheckpoint specified by the `--checkpoint-dir` file.","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\nperformance in training and inference modes.\n\n#### Performance Benchmark\n\nTo benchmark the training and inference performance, run:\n\n```\nmpirun -np 1 --allow-run-as-root python ncf.py --data /data/cache/ml-20m\n```\n\nBy default, the `ncf.py` script outputs metrics describing the following:\n\n* Training speed and throughput\n* Evaluation speed and throughput\n\n### Results\n\nThe following sections provide details on how we achieved our performance and accuracy in training and inference.\n\nAll throughput numbers are reported in millions of samples per second while time-to-train numbers are in seconds.\n\n#### Training accuracy results\nFor all the sections below, our results were obtained by running:\n ```bash\n mpirun -np --allow-run-as-root python ncf.py [--amp] --data /data/cache/ml-20m\n ````\n in the TensorFlow-1 20.07 NGC container.\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)\n\n| GPUs | Batch size / GPU | Accuracy - TF32 | Accuracy - mixed precision | Time to train - TF32 [s] | Time to train - mixed precision [s] | Time to train speedup (TF32 to mixed precision)\n|-------:|-----------------:|-------------:|-----------:|----------------:|--------------:|---------------:|\n| 1 | 1,048,576 | 0.9588 | 0.9589 | 59.4 | 53.1 | 1.12 |\n| 4 | 262,144 | 0.9588 | 0.9590 | 22.8 | 21.5 | 1.06 |\n| 8 | 131,072 | 0.9587 | 0.9589 | 19.8 | 20.2 | 0.98 |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 32GB)\n\n| GPUs | Batch size / GPU | Accuracy - FP32 | Accuracy - mixed precision | Time to train - FP32 | Time to train - mixed precision | Time to train speedup (FP32 to mixed precision)\n|-------:|-----------------:|----------------:|--------------:|-------------:|-----------:|---------------:|\n| 1 | 1,048,576 | 0.9583 | 0.9589 | 120.9 | 91.6 | 1.32 |\n| 4 | 262,144 | 0.9589 | 0.9583 | 43.7 | 31.8 | 1.37 |\n| 8 | 131,072 | 0.9590 | 0.9588 | 26.2 | 21.9 | 1.20 |\n\n### Training Performance Results\nFor all the sections below, our results were obtained by running:\n ```bash\n mpirun -np --allow-run-as-root python ncf.py [--amp] --data /data/cache/ml-20m\n ````\n in the TensorFlow-1 20.07 NGC container.\n\n##### Training performance: NVIDIA DGX A100 (8x A100 40GB)\n\n| GPUs | Batch size / GPU | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 - mixed precision) | Strong scaling - TF32 | Strong scaling - mixed precision\n|-------:|-----------------:|-------------------:|-----------------:|---------------------:|---:|---:|\n| 1 | 1,048,576 | 20.18 | 22.84 | 1.132 | 1 | 1 |\n| 4 | 262,144 | 60.34 | 62.70 | 1.039 | 2.99 | 2.75 |\n| 8 | 131,072 | 89.88 | 80.86 | 0.900 | 4.45 | 3.54 |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 32GB)\n\n| GPUs | Batch size / GPU | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 - mixed precision) | Strong scaling - FP32 | Strong scaling - mixed precision\n|-------:|-----------------:|-------------------:|-----------------:|---------------------:|---:|---:|\n| 1 | 1,048,576 | 9.73 | 15.21 | 1.563 | 1 | 1 |\n| 4 | 262,144 | 30.31 | 39.47 | 1.302 | 3.11 | 2.60 |\n| 8 | 131,072 | 50.91 | 59.13 | 1.161 | 5.23 | 3.89 |\n\n### Inference Performance Results\n\nOur results were obtained by running the `inference.py` script in the PyTorch 20.07 NGC container.\n\nThroughput is reported in millions of samples per second while latency is reported in seconds.\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)\n\nTF32\n\n| Batch size | Throughput Avg | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|-------------:|-----------------:|--------------:|--------------:|--------------:|---------------:|\n| 1,024 | 1.67 | 0.0006 | 0.0006 | 0.0007 | 0.0007 |\n| 4,096 | 6.02 | 0.0007 | 0.0007 | 0.0007 | 0.0007 |\n| 16,384 | 19.01 | 0.0009 | 0.0009 | 0.0009 | 0.0009 |\n| 65,536 | 34.91 | 0.0019 | 0.0019 | 0.0019 | 0.0019 |\n| 262,144 | 44.72 | 0.0059 | 0.0063 | 0.0063 | 0.0066 |\n| 1,048,576 | 47.22 | 0.0222 | 0.0230 | 0.0232 | 0.0237 |\n\nFP16\n\n| Batch size | Throughput Avg | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|-------------:|-----------------:|--------------:|--------------:|--------------:|---------------:|\n| 1,024 | 1.34 | 0.0008 | 0.0008 | 0.0008 | 0.0008 |\n| 4,096 | 5.23 | 0.0008 | 0.0008 | 0.0008 | 0.0008 |\n| 16,384 | 17.61 | 0.0009 | 0.0009 | 0.0010 | 0.0010 |\n| 65,536 | 38.63 | 0.0017 | 0.0017 | 0.0018 | 0.0018 |\n| 262,144 | 55.36 | 0.0047 | 0.0049 | 0.0050 | 0.0051 |\n| 1,048,576 | 59.48 | 0.0176 | 0.0178 | 0.0179 | 0.0184 |\n\n##### Inference performance: NVIDIA DGX-1 (8x V100 32GB)\n\nFP32\n\n| Batch size | Throughput Avg | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|-------------:|-----------------:|--------------:|--------------:|--------------:|---------------:|\n| 1,024 | 0.79 | 0.0013 | 0.0015 | 0.0015 | 0.0016 |\n| 4,096 | 2.88 | 0.0014 | 0.0016 | 0.0016 | 0.0017 |\n| 16,384 | 8.38 | 0.0020 | 0.0021 | 0.0021 | 0.0024 |\n| 65,536 | 16.77 | 0.0039 | 0.0041 | 0.0041 | 0.0041 |\n| 262,144 | 22.53 | 0.0116 | 0.0118 | 0.0119 | 0.0122 |\n| 1,048,576 | 25.14 | 0.0417 | 0.0425 | 0.0431 | 0.0440 |\n\nFP16\n\n| Batch size | Throughput Avg | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|-----------:|-----------------:|--------------:|--------------:|-------------:|---------------:|\n| 1,024 | 0.69 | 0.0015 | 0.0017 | 0.0017 | 0.0018 |\n| 4,096 | 2.64 | 0.0016 | 0.0017 | 0.0017 | 0.0018 |\n| 16,384 | 8.84 | 0.0019 | 0.0020 | 0.0020 | 0.0021 |\n| 65,536 | 21.43 | 0.0031 | 0.0032 | 0.0032 | 0.0032 |\n| 262,144 | 33.61 | 0.0078 | 0.0080 | 0.0081 | 0.0083 |\n| 1,048,576 | 38.83 | 0.0270 | 0.0276 | 0.0277 | 0.0286 |","hasPerformance",[],{"_85":182,"_112":234,"_113":-5,"_121":34,"_120":34,"_122":233,"_217":90},{"_85":28,"_112":28,"_113":-5,"_121":34,"_120":34,"_122":233,"_217":90},[],"Latest (20.07.0)","nvidia/dle/ncf_tf1","key","nvidia/dle/resources/ncf_tf1","resourceId","ncf_tf1","shortDescription","The NCF model focuses on providing recommendations. This is a modified implementation with improved overfitting and better accuracy.","This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/ncf-tf1)\n\n\nThe Neural Collaborative Filtering (NCF) model is a neural network that provides collaborative filtering based on\nimplicit feedback. Specifically, it provides product recommendations based on user and item interactions. The training\ndata for this model should contain a sequence of (user ID, item ID) pairs indicating that the specified user has\ninteracted with an item, for example, by giving a rating or clicking. NCF was first described by\nXiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua in the [Neural Collaborative Filtering\npaper](https://arxiv.org/abs/1708.05031).\n\nThe implementation in this repository focuses on the NeuMF instantiation of the NCF architecture. We modified it to use\nDropout in the fully connected layers. This reduces overfitting and increases the final accuracy. Training the other two\ninstantiations of NCF (GMF and MLP) is not supported.\n\nThe original paper evaluates the model on the ml-1m dataset.\nConversely, we evaluate on the ml-20m dataset, which provides a more practical production scenario.\nHowever, using the ml-1m dataset is also supported.\n\nThis model takes advantage of the mixed precision Tensor Cores found on Volta, Turing, and the NVIDIA Ampere GPU architectures\n demonstrating the reduction in\ntraining time possible by leveraging Tensor Cores. In the single GPU configuration, training times can be improved close\nto 1.6x through the usage of Tensor Cores.\n\nThis model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n### Model architecture\n\nNCF-TF takes in a sequence of (user ID, item ID) pairs as inputs, then feeds them separately into a matrix\nfactorization step (where the embeddings are multiplied) and into a multilayer perceptron (MLP) network.\n\nThe outputs of the matrix factorization and the MLP network are then combined and fed into a single dense layer which\npredicts whether the input user is likely to interact with the input item. The architecture of the MLP network is shown\nbelow.\n\n

\n \n
\n Figure 1. The architecture of a Neural Collaborative Filtering model. Taken from the Neural Collaborative Filtering paper.\n

\n\n### Default Configuration\n\nThis implementation has the following features:\n\n- model-parallel multi-gpu training with Horovod\n- mixed precision training with TF-AMP (TensorFlow-Automatic Mixed Precision), which enables mixed precision training\n without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an\n environmental variable\n- fast negative sample generation and data preprocessing with CuPy\n - Before each training epoch, the training data is augmented with randomly generated negatives samples. A \"shortcut\" is\n enabled by default where the script does not verify that the randomly generated samples are actually negative samples.\n We have found that this shortcut has a low impact on model accuracy while considerably improving the speed and memory\n footprint of the data augmentation stage of training.\n - Note: The negative samples generated for the test set are always verified regardless of the shortcut being enabled or\n not.\n\n### Feature support matrix\n\n| Feature | NCF-TF |\n|-----------------------|--------------------------\n|Horovod | Yes |\n|Automatic mixed precision (AMP) | Yes |\n\n#### Features\n\n*Horovod*\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\n*Multi-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*Automatic Mixed Precision (AMP)*\n\nComputation graphs can be modified by TensorFlow on runtime to support mixed precision training. Detailed explanation of mixed precision can be found in the next section.\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Adding loss scaling to preserve small gradient values.\n\nThis can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full [mixed precision methodology](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#tensorflow) in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.\n\nIn TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.\n\nFor information about:\n- How to train using mixed precision, see the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, see the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n- 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\nMixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a [loss scaling](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#lossscaling) step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.\n\nTo enable mixed precision, you can simply add the values to the environmental variables inside your training script:\n- Enable TF-AMP graph rewrite:\n ```\n os.environ[\"TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE\"] = \"1\"\n ```\n\n- Enable Automated Mixed Precision:\n ```\n os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'\n ```\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.","displayName","NCF for TensorFlow1",["D",1680631725967],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/ncf_tf1","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[274],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_243":275,"_276":277,"_278":279,"_280":87},"Recommendation","value","uscs_recommedation","filter",{"_281":282,"_276":277},"category","field","application",{"_151":1458,"_153":1459},"ngcApps",[1411],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1392":1393,"_1394":1395,"_1396":1397,"_1398":1399,"_1400":294,"_109":318,"_1401":1402,"_1403":59,"_1404":90,"_1405":90,"_1406":1407,"_1408":90,"_1409":1410},"amplitude",{"_1389":1390,"_991":-7,"_1391":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1383":1384,"_1385":1386,"_1387":1388},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1266":398,"_1267":1268,"_1269":1270,"_1271":1272},"announcements",[],"consentState",{"_355":1256,"_1257":90,"_1258":34,"_1259":34,"_1260":1261},"consentConfig",{"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356},"showContactAdmin",1783987135162,[],[],[332,333,334,335,336],{"_48":62,"_50":73,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":146,"_54":-5,"_56":-5,"_58":59},"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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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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