The Variational Autoencoder (VAE) shown here is an optimized implementation of the architecture first described in Variational Autoencoders for Collaborative Filtering and can be used for recommendation tasks. The main differences between this model and the original one are the performance optimizations, such as using sparse matrices, mixed precision, larger mini-batches and multiple GPUs. These changes enabled us to achieve a significantly higher speed while maintaining the same accuracy. Because of our fast implementation, we've also been able to carry out an extensive hyperparameter search to slightly improve the accuracy metrics.

When using Variational Autoencoder for Collaborative Filtering (VAE-CF), you can quickly train a recommendation model for the collaborative filtering task. The required input data consists of pairs of user-item IDs for each interaction between a user and an item. With a trained model, you can run inference to predict what items is a new user most likely to interact with.

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and Ampere GPUs. Therefore, researchers can get results 1.9x 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.

This implementation has been initially developed as an educational project at the University of Warsaw by Albert Cieślak, Michał Filipiuk, Frederic Grabowski and Radosław Rowicki.

Model architecture

Figure 1. The architecture of the VAE-CF model

The Variational Autoencoder is a neural network that provides collaborative filtering based on implicit feedback. Specifically, it provides product recommendations based on user and item interactions. The training data for this model should contain a sequence of (user ID, item ID) pairs indicating that the specified user has interacted with the specified item.

The model consists of two parts: the encoder and the decoder.\nThe encoder transforms the vector, which contains the interactions for a specific user, into a n-dimensional variational distribution. We can then use this variational distribution to obtain a latent representation of a user.\nThis latent representation is then fed into the decoder. The result is a vector of item interaction probabilities for a particular user.

Default configuration

The following features were implemented in this model:

Sparse matrix support
Data-parallel multi-GPU training
Dynamic loss scaling with backoff for tensor cores (mixed precision) training

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	VAE-CF
Horovod Multi-GPU (NCCL)	Yes
Automatic mixed precision (AMP)	Yes

Features

Horovod:\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.

Multi-GPU training with Horovod:\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.

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 requires 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

To enable mixed precision in VAE-CF, run the main.py script with the --amp flag.

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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We also use the same neural network architecture. As a consequence, the ratio of communication to computation is relatively large. This means that although using multiple GPUs speeds up the training substantially, the scaling efficiency is worse from what one would expect if using a larger model and a more realistic dataset.","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to start training the VAE-CF model.\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-1 20.06+ 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- [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 VAE-CF model on the [MovieLens 20m dataset](https://grouplens.org/datasets/movielens/20m/). For the specifics concerning training and inference, see the [Advanced](#advanced) section.\n\n1. Clone the repository.\n git clone https://github.com/NVIDIA/DeepLearningExamples\n cd DeepLearningExamples/Tensorflow/Recommendation/VAE_CF\n ```\n\n2. Build the VAE TensorFlow NGC container.\n\n ```bash\n docker build . -t vae\n ```\n\n3. Launch the VAE-CF TensorFlow Docker container.\n\n ```bash\n docker run -it --rm --runtime=nvidia -v /data/vae-cf:/data vae /bin/bash\n ```\n\n4. Downloading the dataset: Here we use the [MovieLens 20m dataset](https://grouplens.org/datasets/movielens/20m/).\n\n * If you do not have the dataset downloaded: Run the commands below to download and extract the MovieLens dataset to the ```/data/ml-20m/extracted/``` folder.\n ```\n cd /data\n mkdir ml-20m\n cd ml-20m\n mkdir extracted\n cd extracted\n wget http://files.grouplens.org/datasets/movielens/ml-20m.zip\n unzip ml-20m.zip\n ```\n \n * If you already have the dataset downloaded and unzipped elsewhere: Run the below commands to first exit the current VAE-CF Docker container and then Restart the VAE-CF Docker Container (like in Step 3 above) by mounting the MovieLens dataset location \n ```\n exit\n docker run -it --rm --runtime=nvidia -v /data/vae-cf:/data -v :/data/ml-20m/extracted/ml-20m vae /bin/bash\n ```\n where, the unzipped MovieLens dataset is at ``````\n\n5. Prepare the dataset.\n\n ```bash\n python prepare_dataset.py\n ```\n\n6. Start training on 8 GPUs.\n\n ```bash\n mpirun --bind-to numa --allow-run-as-root -np 8 -H localhost:8 python main.py --train --amp --checkpoint_dir ./checkpoints\n ```\n\n7. Start validation/evaluation.\n\n The model is exported to the default `model_dir` and can be loaded and tested using:\n ```bash\n python main.py --test --amp --checkpoint_dir ./checkpoints\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 `main.py` script provides an entry point to all the provided functionalities. This includes running training, testing and inference. The behavior of the script is controlled by command-line arguments listed below in the [Parameters](#parameters) section. The `prepare_dataset.py` script can be used to preprocess the MovieLens 20m dataset.\n\nMost of the deep learning logic is implemented in the `vae/models` subdirectory. The `vae/load` subdirectory contains the code for preprocessing the dataset. The `vae/metrics` subdirectory provides functions for computing the validation metrics such as recall and [NDCG](https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG).\n\n### Parameters\n\nThe most important command-line parameters include:\n* `--data_dir` which specifies the directory inside the docker container where the data will be stored, overriding the default location ```/data```\n* `--checkpoint_dir` which controls if and where the checkpoints will be stored\n* `--amp` for enabling mixed precision training\n\nThere are also multiple parameters controlling the various hyperparameters of the training process, such as the learning rate, batch size etc.\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, for example:\n```bash\npython main.py --help\n\nusage: main.py [-h] [--train] [--test] [--inference_benchmark]\n [--amp] [--epochs EPOCHS]\n [--batch_size_train BATCH_SIZE_TRAIN]\n [--batch_size_validation BATCH_SIZE_VALIDATION]\n [--validation_step VALIDATION_STEP]\n [--warm_up_epochs WARM_UP_EPOCHS]\n [--total_anneal_steps TOTAL_ANNEAL_STEPS]\n [--anneal_cap ANNEAL_CAP] [--lam LAM] [--lr LR] [--beta1 BETA1]\n [--beta2 BETA2] [--top_results TOP_RESULTS] [--xla] [--trace]\n [--activation ACTIVATION] [--log_path LOG_PATH] [--seed SEED]\n [--data_dir DATA_DIR] [--checkpoint_dir CHECKPOINT_DIR]\n\nTrain a Variational Autoencoder for Collaborative Filtering in TensorFlow\n\noptional arguments:\n -h, --help show this help message and exit\n --train Run training of VAE\n --test Run validation of VAE\n --inference_benchmark\n Benchmark the inference throughput and latency\n --amp Enable Automatic Mixed Precision\n --epochs EPOCHS Number of epochs to train\n --batch_size_train BATCH_SIZE_TRAIN\n Global batch size for training\n --batch_size_validation BATCH_SIZE_VALIDATION\n Used both for validation and testing\n --validation_step VALIDATION_STEP\n Train epochs for one validation\n --warm_up_epochs WARM_UP_EPOCHS\n Number of epochs to omit during benchmark\n --total_anneal_steps TOTAL_ANNEAL_STEPS\n Number of annealing steps\n --anneal_cap ANNEAL_CAP\n Annealing cap\n --lam LAM Regularization parameter\n --lr LR Learning rate\n --beta1 BETA1 Adam beta1\n --beta2 BETA2 Adam beta2\n --top_results TOP_RESULTS\n Number of results to be recommended\n --xla Enable XLA\n --trace Save profiling traces\n --activation ACTIVATION\n Activation function\n --log_path LOG_PATH Path to the detailed JSON log from to be created\n --seed SEED Random seed for TensorFlow and numpy\n --data_dir DATA_DIR Directory for storing the training data\n --checkpoint_dir CHECKPOINT_DIR\n Path for saving a checkpoint after the training\n\n```\n\n### Getting the data\n\nThe VA-CF model was trained on the [MovieLens 20M dataset](https://grouplens.org/datasets/movielens/20m/). The dataset can be preprocessed simply by running: `python prepare_dataset.py` in the Docker container. By default, the dataset will be stored in the `/data` directory. If you want to store the data in a different location, you can pass the desired location to the `--data_dir` argument.\n\n#### Dataset guidelines\n\nAs a Collaborative Filtering model, VAE-CF only uses information about which user interacted with which item. For the MovieLens dataset, this means that a particular user has positively reviewed a particular movie. VAE-CF can be adapted to any other collaborative filtering task. The input to the model is generally a list of all interactions between users and items. One column of the CSV should contain user IDs, while the other should contain item IDs. Preprocessing for the MovieLens 20M dataset is provided in the `vae/load/preprocessing.py` file.\n\n### Training process\n\nThe training can be started by running the `main.py` script with the `train` argument. The resulting checkpoints containing the trained model weights are then stored in the directory specified by the `--checkpoint_dir` directory (by default no checkpoints are saved).\n\nAdditionally, a command-line argument called `--results_dir` (by default `None`) specifies where to save the following statistics in a JSON format:\n1) a complete list of command-line arguments saved as `/args.json`, and\n2) a dictionary of validation metrics and performance metrics recorded during training.\n\nThe main validation metric used is [NDCG@100](https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG). Following the original VAE-CF paper we also report numbers for Recall@20 and Recall@50.\n\nMulti-GPU training uses horovod.\n\nMixed precision support is controlled by the `--amp` command-line flag. It enables TensorFlow's Automatic Mixed Precision mode.\n\n### Inference process\n\nInference on a trained model can be run by passing the `--inference_benchmark` argument to the main.py script\n\n```\npython main.py --inference_benchmark [--amp] --checkpoint_dir ./checkpoints\n```\nThis will generate a user with a collection of random items that they interacted with and run inference for that user multiple times to measure latency and throughput.","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, run:\n```\nmpirun --bind-to numa --allow-run-as-root -np 8 -H localhost:8 python main.py --train [--amp]\n```\n\n#### Inference performance benchmark\n\nTo benchmark the inference performance, run:\n```\npython main.py --inference_benchmark [--amp]\n```\n\n### Results\n\nThe following sections provide details on how we achieved our performance and accuracy in training and inference.\n\n#### Training accuracy results\n\nAll training performance results were obtained by running:\n```\nmpirun --bind-to numa --allow-run-as-root -np -H localhost:8 python main.py --train [--amp]\n```\nin the TensorFlow 20.06 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 | 24,576 | 0.430298 | 0.430398 | 112.8 | 109.4 | 1.03 |\n| 8 | 3,072 | 0.430897 | 0.430353 | 25.9 | 30.4 | 0.85 |\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 [s] | Time to train - mixed precision [s] | Time to train speedup (FP32 to mixed precision) |\n|-------:|-----------------:|-------------:|-----------:|----------------:|--------------:|---------------:|\n| 1 | 24,576 | 0.430592 | 0.430525 | 346.5 | 186.5 | 1.86 |\n| 8 | 3,072 | 0.430753 | 0.431202 | 59.1 | 42.2 | 1.40 |\n\n#### Training performance results\n\nPerformance numbers below show throughput in users processed per second. They were averaged over an entire training run.\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 | 24,576 | 354,032 | 365,474 | 1.03 | 1 | 1 |\n| 8 | 3,072 | 1,660,700 | 1,409,770 | 0.85 | 4.69 | 3.86 |\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 | 24,576 | 114,125 | 213,283 | 1.87 | 1 | 1 |\n| 8 | 3,072 | 697,628 | 1,001,210 | 1.44 | 6.11 | 4.69 |\n\n#### Inference performance results\n\nOur results were obtained by running:\n```\npython main.py --inference_benchmark [--amp]\n```\nin the TensorFlow 20.06 NGC container.\n\nWe use users processed per second as a throughput metric for measuring inference performance.\nAll latency numbers are in seconds.\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 40GB)\nTF32\n\n| Batch size | Throughput Avg | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|-------------:|-----------------:|--------------:|--------------:|--------------:|---------------:|\n| 1 | 1181 | 0.000847 | 0.000863 | 0.000871 | 0.000901 |\n\nFP16\n\n| Batch size | Throughput Avg | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|-------------:|-----------------:|--------------:|--------------:|--------------:|---------------:|\n| 1 | 1215 | 0.000823 | 0.000858 | 0.000864 | 0.000877 |\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nFP32\n\n| Batch size | Throughput Avg | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|-------------:|-----------------:|--------------:|--------------:|--------------:|---------------:|\n| 1 | 718 | 0.001392 | 0.001443 | 0.001458 | 0.001499 |\n\nFP16\n\n| Batch size | Throughput Avg | Latency Avg | Latency 90% | Latency 95% | Latency 99% |\n|-------------:|-----------------:|--------------:|--------------:|--------------:|---------------:|\n| 1 | 707 | 0.001413 | 0.001511 | 0.001543 | 0.001622 |","hasPerformance",[],{"_85":272,"_117":273,"_118":-5,"_126":35,"_125":35,"_127":271,"_245":90},{"_85":156,"_117":156,"_118":-5,"_126":35,"_125":35,"_127":271,"_245":90},{"_85":152,"_117":152,"_118":-5,"_126":35,"_125":35,"_127":270,"_245":90},{"_85":148,"_117":148,"_118":-5,"_126":35,"_125":35,"_127":269,"_245":90},{"_85":144,"_117":144,"_118":-5,"_126":35,"_125":35,"_127":268,"_245":90},{"_85":138,"_117":138,"_118":-5,"_126":35,"_125":35,"_127":267,"_245":90},{"_85":29,"_117":29,"_118":-5,"_126":35,"_125":35,"_127":266,"_245":90},[],[],[],[],[],[],"-","Latest (20.06.3)","nvidia/vae_for_tensorflow","key","nvidia/resources/vae_for_tensorflow","resourceId","vae_for_tensorflow","shortDescription","The Variational Autoencoder for collaborative filtering focuses on providing recommendations. This is an optimized implementation.","The Variational Autoencoder (VAE) shown here is an optimized implementation of the architecture first described in [Variational Autoencoders for Collaborative Filtering](https://arxiv.org/abs/1802.05814) and can be used for recommendation tasks. The main differences between this model and the original one are the performance optimizations, such as using sparse matrices, mixed precision, larger mini-batches and multiple GPUs. These changes enabled us to achieve a significantly higher speed while maintaining the same accuracy. Because of our fast implementation, we've also been able to carry out an extensive hyperparameter search to slightly improve the accuracy metrics.\n\nWhen using Variational Autoencoder for Collaborative Filtering (VAE-CF), you can quickly train a recommendation model for the collaborative filtering task. The required input data consists of pairs of user-item IDs for each interaction between a user and an item. With a trained model, you can run inference to predict what items is a new user most likely to interact with.\n\nThis model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and Ampere GPUs. Therefore, researchers can get results 1.9x 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\nThis implementation has been initially developed as an educational project at the University of Warsaw by Albert Cieślak, Michał Filipiuk, Frederic Grabowski and Radosław Rowicki.\n\n### Model architecture\n\n\n \n \n Figure 1. The architecture of the VAE-CF model \n\nThe Variational Autoencoder is a neural network that provides collaborative filtering based on implicit feedback. Specifically, it provides product recommendations based on user and item interactions. The training data for this model should contain a sequence of (user ID, item ID) pairs indicating that the specified user has interacted with the specified item.\n\nThe model consists of two parts: the encoder and the decoder.\nThe encoder transforms the vector, which contains the interactions for a specific user, into a *n*-dimensional variational distribution. We can then use this variational distribution to obtain a latent representation of a user.\nThis latent representation is then fed into the decoder. The result is a vector of item interaction probabilities for a particular user.\n\n### Default configuration\n\nThe following features were implemented in this model:\n- Sparse matrix support\n- Data-parallel multi-GPU training\n- Dynamic loss scaling with backoff for tensor cores (mixed precision) training\n\n### Feature support matrix\n\nThe following features are supported by this model:\n\n| Feature | VAE-CF\n|-----------------------|--------------------------\n|Horovod Multi-GPU (NCCL) | Yes\n|Automatic mixed precision (AMP) | Yes\n\n#### Features\n\nHorovod: \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: \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 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 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\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/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\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\nTo enable mixed precision in VAE-CF, run the `main.py` script with the `--amp` flag.\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","VAE for TensorFlow",["D",1680632503410],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/resources/vae_for_tensorflow","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[313,314],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_282":323,"_316":324,"_318":325,"_320":87},{"_282":315,"_316":317,"_318":319,"_320":87},"Recommendation","value","uscs_recommedation","filter",{"_321":322,"_316":317},"category","field","application","Deep Learning Examples","pltfm_deep_learning_examples",{"_321":326,"_316":324},"platform",{"_181":1307,"_183":1308},"ngcApps",[1260],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1241":1242,"_1243":1244,"_1245":1246,"_1247":1248,"_1249":338,"_109":364,"_1250":1251,"_1252":60,"_1253":90,"_1254":90,"_1255":1256,"_1257":90,"_1258":1259},"amplitude",{"_1238":1239,"_877":-7,"_1240":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1232":1233,"_1234":1235,"_1236":1237},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789402618207,"1.15.0","defaultSearchSuggestions",{"_1117":444,"_1118":1119,"_1120":1121,"_1122":1123},"announcements",[],"consentState",{"_401":1107,"_1108":90,"_1109":35,"_1110":35,"_1111":1112},"consentConfig",{"_383":384,"_385":386,"_387":388,"_389":390,"_391":392,"_393":394,"_395":396,"_397":398,"_399":400,"_401":402},"showContactAdmin",[],[],[377,378,379,380,381,382],{"_49":63,"_51":74,"_53":176,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":176,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":176,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":176,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":176,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":176,"_55":-5,"_57":-5,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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These cookies may store a unique ID so that our system will remember you when you return. 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	Files	Compressed Size
20.06.3	21	169.07 KB	September 21, 2021 UTC
19.11.0	22	165.19 KB	August 27, 2021 UTC
20.06.2	21	169.61 KB	August 27, 2021 UTC
20.06.1	21	169.61 KB	August 4, 2021 UTC
20.06.0	21	169.26 KB	August 14, 2020 UTC
1Selected	23	165.19 KB	December 9, 2019 UTC