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

The Temporal Fusion Transformer TFT model is a state-of-the-art architecture for interpretable, multi-horizon time-series prediction. The model was first developed and implemented by Google with the collaboration with the University of Oxford.\nThis implementation differs from the reference implementation by addressing the issue of missing data, which is common in production datasets, by either masking their values in attention matrices or embedding them as a special value in the latent space.\nThis model enables the prediction of confidence intervals for future values of time series for multiple future timesteps.

This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.45x 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

The TFT model is a hybrid architecture joining LSTM encoding of time series and interpretability of transformer attention layers. Prediction is based on three types of variables: static (constant for a given time series), known (known in advance for whole history and future), observed (known only for historical data). All these variables come in two flavors: categorical, and continuous. In addition to historical data, we feed the model with historical values of time series. All variables are embedded in high-dimensional space by learning an embedding vector. Categorical variables embeddings are learned in the classical sense of embedding discrete values. The model learns a single vector for each continuous variable, which is then scaled by this variable's value for further processing. The next step is to filter variables through the Variable Selection Network (VSN), which assigns weights to the inputs in accordance with their relevance to the prediction. Static variables are used as a context for variable selection of other variables and as an initial state of LSTM encoders.\nAfter encoding, variables are passed to multi-head attention layers (decoder), which produce the final prediction. Whole architecture is interwoven with residual connections with gating mechanisms that allow the architecture to adapt to various problems by skipping some parts of it.\nFor the sake of explainability, heads of self-attention layers share value matrices. This allows interpreting self-attention as an ensemble of models predicting different temporal patterns over the same feature set. The other feature that helps us understand the model is VSN activations, which tells us how relevant the given feature is to the prediction.\n $\"\"$ \nimage source: https://arxiv.org/abs/1912.09363

Default configuration

The specific configuration of the TFT model depends on the dataset used. Not only is the volume of the model subject to change but so are the data sampling and preprocessing strategies. During preprocessing, data is normalized per feature. For a part of the datasets, we apply scaling per-time-series, which takes into account shifts in distribution between entities (i.e., a factory consumes more electricity than an average house). The model is trained with the quantile loss: $\\Large\\sum_{i=1}^N\\sum_{q\\in\\mathcal{Q}}\\sum_{t=1}^{t_{max}}\\frac{QL(y_it,\\hat{y}_i(q,t),q)}{Nt_{max}}\"$ \nFor quantiles in [0.1, 0.5, 0.9]. The default configurations are tuned for distributed training on DGX-1-32G with mixed precision. We use dynamic loss scaling. Specific values are provided in the table below.

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

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Dataset	Training samples	Validation samples	Test samples	History length	Forecast horizon	Dropout	Hidden size	#Heads	BS	LR	Gradient clipping
Electricity	450k	50k	53.5k	168	24	0.1	128	4	8x1024	1e-3	0.0
Traffic	450k	50k	139.6k	168	24	0.3	128	4	8x1024	1e-3	0.0

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

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Feature	Yes column
Distributed data parallel	Yes
PyTorch AMP	Yes

Features

Automatic Mixed Precision\nprovides an easy way to leverage Tensor Cores' performance. It allows the execution of parts of a network in lower precision. Refer to Mixed precision training for more information.

PyTorch\nDistributedDataParallel - a module\nwrapper that enables easy multiprocess distributed data-parallel\ntraining.

Mixed precision training

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

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

The ability to train deep learning networks with lower precision was introduced\nin the Pascal architecture and first supported in CUDA\n8 in the NVIDIA Deep\nLearning SDK.

For information about:

How to train using mixed precision, refer to the Mixed Precision\nTraining paper and Training With Mixed\nPrecision\ndocumentation.
Techniques used for mixed precision training, refer to the Mixed-Precision\nTraining of Deep Neural\nNetworks\nblog.

Enabling mixed precision

Mixed precision is enabled in PyTorch by using the Automatic Mixed Precision torch.cuda.amp module, 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 PyTorch, loss scaling can be applied automatically by the GradScaler class. All the necessary steps to implement AMP are verbosely described here.

To enable mixed precision for TFT, simply add the --use_amp option to the training script.

Enabling TF32

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

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

Multi horizon prediction
\nProcess of estimating values of a time series for multiple future time steps.

Quantiles
\nCut points dividing the range of a probability distribution intervals with equal probabilities.

Time series
\nSeries of data points indexed and equally spaced in time.

Transformer
\nThe paper Attention Is All You Need introduces a novel architecture called Transformer that uses an attention mechanism and transforms one sequence into another.

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For the most up-to-date performance measurements, go to https://developer.nvidia.com/deep-learning-performance-training-inference.\n\n### Changelog\n\nMarch 2023\n- 23.01 Container Update\n- Switch from NVIDIA Apex AMP and NVIDIA Apex FusedLayerNorm to Native PyTorch AMP and Native PyTorch LayerNorm\n- Acceleration using NvFuser\n\nFebruary 2022\n- 21.12 Container Update\n- Triton Inference Performance Numbers\n\nNovember 2021\n- Initial release\n\n### Known issues\nThere are no known issues with this model.","hasReleaseNotes","setup","The following section lists the requirements that you need to meet in order to start training the TFT model.\n\n### Requirements\n\nThis repository contains Dockerfile, which extends the PyTorch 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- [PyTorch 22.11 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch)\n- Supported GPUs:\n- [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n- [NVIDIA Turing architecture](https://www.nvidia.com/en-us/design-visualization/technologies/turing-architecture/)\n- [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, refer to the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:\n- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry)\n- Running [PyTorch](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/running.html#running)\n\nFor those unable to use the PyTorch NGC container to set up the required environment or create your own container, refer to 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, perform the following steps using the default parameters of the TFT model on any of the benchmark datasets. For the specifics concerning training and inference, refer to the [Advanced](#advanced) section.\n\n1. Clone the repository.\n```bash\ngit clone https://github.com/NVIDIA/DeepLearningExamples\ncd DeepLearningExamples/PyTorch/Forecasting/TFT\n```\n\n2. Build the TFT PyTorch NGC container.\n```bash\ndocker build --network=host -t tft .\n```\n\n3. Start an interactive session in the NGC container to run training/inference.\n```bash\ndocker run -it --rm --ipc=host --network=host --gpus all -v /path/to/your/data:/data/ tft\n```\n\nNote: Ensure to mount your dataset using the -v flag to make it available for training inside the NVIDIA Docker container.\n\n4. Download and preprocess datasets.\n```bash\nbash scripts/get_data.sh\n```\n\n5. Start training. Choose one of the scripts provided in the `scripts/` directory. Results are stored in the `/results` directory.\nThese scripts are tuned for DGX1-32G. If you have a different system, use NGPU and BATCH_SIZE variables to adjust the parameters for your system.\n```bash\nbash scripts/run_electricity.sh\nbash scripts/run_traffic.sh\n```\n\n6. Start validation/evaluation. The metric we use for evaluation is q-risk. We can compare it per-quantile in the Pareto sense or jointly as one number indicating accuracy.\n```bash\npython inference.py \\\n--checkpoint \\\n--data /data/processed//test.csv \\\n--cat_encodings /data/processed//cat_encodings.bin \\\n--tgt_scalers /data/processed//tgt_scalers.bin\n```\n\n7. Start inference/predictions. Visualize and save predictions by running the following command.\n```bash\npython inference.py \\\n--checkpoint \\\n--data /data/processed//test.csv \\\n--cat_encodings /data/processed//cat_encodings.bin \\\n--tgt_scalers /data/processed//tgt_scalers.bin \\\n--visualize \\\n--save_predictions\n```\n\nNow that you have your model trained and evaluated, you can choose to compare your training results with our [Training accuracy results](#training-accuracy-results). You can also choose to benchmark your performance to [Training performance benchmark](#training-performance-results). Following the steps in these sections will ensure that you achieve the same accuracy and performance results as stated in the [Results](#results) section.","hasQuickStartGuide","advanced","The following sections provide more details about the dataset, running training and inference, and the training results.\n\n### Scripts and sample code\n\nIn the root directory, the most important files are:\n\n`train.py`: Entry point for training\n`data_utils.py`: File containing the dataset implementation and preprocessing functions\n`modeling.py`: Definition of the model\n`configuration.py`: Contains configuration classes for various experiments\n`test.py`: Entry point testing trained model.\n`Dockerfile`: Container definition\n`log_helper.py`: Contains helper functions for setting up dllogger\n`criterions.py`: Definitions of loss functions\n\nThe `scripts` directory contains scripts for default use cases:\n`run_electricity.sh`: train default model on the electricity dataset\n`run_traffic.sh`: train default model on the traffic dataset\n\n### Command-line options\n\nTo view the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:\n`python train.py --help`.\n\nThe following example output is printed when running the model:\n```\nusage: train.py [-h] --data_path DATA_PATH --dataset {electricity,traffic} [--epochs EPOCHS] [--sample_data SAMPLE_DATA SAMPLE_DATA] [--batch_size BATCH_SIZE] [--lr LR] [--seed SEED] [--use_amp] [--clip_grad CLIP_GRAD]\n [--early_stopping EARLY_STOPPING] [--results RESULTS] [--log_file LOG_FILE] [--distributed_world_size N] [--distributed_rank DISTRIBUTED_RANK] [--local_rank LOCAL_RANK] [--overwrite_config OVERWRITE_CONFIG]\n\noptional arguments:\n -h, --help show this help message and exit\n --data_path DATA_PATH\n --dataset {electricity,traffic}\n --epochs EPOCHS\n --sample_data SAMPLE_DATA SAMPLE_DATA\n --batch_size BATCH_SIZE\n --lr LR\n --seed SEED\n --use_amp Enable automatic mixed precision\n --clip_grad CLIP_GRAD\n --early_stopping EARLY_STOPPING\n Stop training if validation loss does not improve for more than this number of epochs.\n --results RESULTS\n --log_file LOG_FILE\n --distributed_world_size N\n total number of GPUs across all nodes (default: all visible GPUs)\n --distributed_rank DISTRIBUTED_RANK\n rank of the current worker\n --local_rank LOCAL_RANK\n rank of the current worker\n --overwrite_config OVERWRITE_CONFIG\n JSON string used to overload config\n\n```\n\n### Getting the data\n \nThe TFT model was trained on the electricity and traffic benchmark datasets. This repository contains the `get_data.sh` download script, which for electricity and and traffic datasets will automatically download and preprocess the training, validation and test datasets, and produce files that contain scalers.\n#### Dataset guidelines\n\nThe `data_utils.py` file contains all functions that are used to preprocess the data. Initially the data is loaded to a `pandas.DataFrame` and parsed to the common format which contains the features we will use for training. Then standardized data is cleaned, normalized, encoded and binarized.\nThis step does the following:\nDrop all the columns that are not marked in the configuration file as used for training or preprocessing\nFlatten indices in case time series are indexed by more than one column\nSplit the data into training, validation and test splits\nFilter out all the time series shorter than minimal example length\nNormalize columns marked as continuous in the configuration file\nEncode as integers columns marked as categorical\nSave the data in csv and binary formats\n\n#### Multi-dataset\nIn order to use an alternate dataset, you have to write a function that parses your data to a common format. The format is as follows:\nThere is at least one id column\nThere is exactly one time column (that can also be used as a feature column)\nEach feature is in a separate column\nEach row represents a moment in time for only one time series\nAdditionally, you must specify a configuration of the network, including a data description. Refer to the example in `configuration.py` file.\n### Training process\n\nThe `train.py` script is an entry point for a training procedure. Refined recipes can be found in the `scripts` directory.\nThe model trains for at most `--epochs` epochs. If option `--early_stopping N` is set, then training will end if for N subsequent epochs validation loss hadn't improved.\nThe details of the architecture and the dataset configuration are encapsulated by the `--dataset` option. This option chooses one of the configurations stored in the `configuration.py` file. You can enable mixed precision training by providing the `--use_amp` option. The training script supports multi-GPU training with the APEX package. To enable distributed training prepend training command with `python -m torch.distributed.run --nproc_per_node=${NGPU}`.\n\nExample command:\n```\npython -m torch.distributed.run --nproc_per_node=8 train.py \\\n --dataset electricity \\\n --data_path /data/processed/electricity_bin \\\n --batch_size=1024 \\\n --sample 450000 50000 \\\n --lr 1e-3 \\\n --epochs 25 \\\n --early_stopping 5 \\\n --seed 1 \\\n --use_amp \\\n --results /results/TFT_electricity_bs8x1024_lr1e-3/seed_1\n```\n\nThe model is trained by optimizing quantile loss $\\Large\\sum_{i=1}^N\\sum_{q\\in\\mathcal{Q}}\\sum_{t=1}^{t_{max}}\\frac{QL(y_{it},\\hat{y}_i(q,t),q)}{Nt_{max}}\"$ \n. After training, the checkpoint with the least validation loss is evaluated on a test split with q-risk metric $\\Large\\frac{2\\sum_{y\\in\\Omega}\\sum_{t=1}^{t_{max}}QL(y_t,\\hat{y}(q,t),q)}{\\sum_{y\\in\\Omega}\\sum_{t=1}^{t_{max}}|y_t|}\"$ .\nResults are by default stored in the `/results` directory. This can be changed by providing the `--results` option. At the end of the training, the results directory will contain the trained checkpoint which had the lowest validation loss, dllogger logs (in dictionary per line format), and TensorBoard logs.\n\n### Inference process\n\nInference can be run by launching the `inference.py` script. The script requires a trained checkpoint to run. It is crucial to prepare the data in the same way as training data prior to running the inference. Example command:\n```\npython inference.py \\\n--checkpoint /results/checkpoint.pt \\\n--data /data/processed/electricity_bin/test.csv \\\n--tgt_scalers /data/processed/electricity_bin/tgt_scalers.bin \\\n--cat_encodings /data/processed/electricity_bin/cat_encodings.bin \\\n--batch_size 2048 \\\n--visualize \\\n--save_predictions \\\n--joint_visualization \\\n--results /results\n```\n\nIn the default setting, it performs the evaluation of the model on a specified dataset and prints q-risk evaluated on this dataset. In order to save the predictions, use the `--save_predictions` option. Predictions will be stored in the directory specified by the `--results` option in the csv format. Option `--joint_visualization` allows us to plot graphs in TensorBoard format, allowing us to inspect the results and compare them to true values. Using `--visualize`, you can save plots for each example in a separate file.\n\n### Triton deployment\n\nThe [NVIDIA Triton Inference Server](https://github.com/triton-inference-server/server) provides a cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. More information on how to perform inference using NVIDIA Triton Inference Server can be found in [triton/README.md](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Forecasting/TFT/triton).","hasAdvanced","performance","### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes. Note that the first 3 steps of each epoch are not used in the throughput or latency calculation. This is due to the fact that the nvFuser performs the optimizations on the 3rd step of the first epoch causing a multi-second pause.\n\n#### Training performance benchmark\n\nIn order to run training benchmarks, use the `scripts/benchmark.sh` script.\n\n#### Inference performance benchmark\n\nTo benchmark the inference performance on a specific batch size and dataset, run the `inference.py` script.\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\nWe conducted an extensive hyperparameter search along with stability tests. The presented results are the averages from the hundreds of runs.\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the `train.sh` training script in the [PyTorch 22.11 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) on NVIDIA A100 (8x A100 80GB) GPUs.\n\n| Dataset | GPUs | Batch size / GPU | Accuracy - TF32 | Accuracy - mixed precision | Time to train - TF32 | Time to train - mixed precision | Time to train speedup (TF32 to mixed precision) \n|-------------|---|------|-----------------------|-----------------------|-------|-------|-------\n| Electricity | 8 | 1024 | 0.026 / 0.056 / 0.029 | 0.028 / 0.058 / 0.029 | 200s | 176s | 1.136x\n| Traffic | 8 | 1024 | 0.044 / 0.108 / 0.078 | 0.044 / 0.109 / 0.079 | 140s | 129s | 1.085x\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `train.sh` training script in the [PyTorch 22.11 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) on NVIDIA DGX-1 with (8x V100 16GB) GPUs.\n\n| Dataset | 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| Electricity | 8 | 1024 | 0.028 / 0.057 / 0.028 | 0.027 / 0.059 / 0.030 | 371s | 269s | 1.379x \n| Traffic | 8 | 1024 | 0.042 / 0.110 / 0.080 | 0.043 / 0.109 / 0.080 | 251s | 191s | 1.314x\n\n##### Training stability test\n\nIn order to get a greater picture of the model's accuracy, we performed a hyperparameter search along with stability tests on 100 random seeds for each configuration. Then, for each benchmark dataset, we have chosen the architecture with the least mean test q-risk. The table below summarizes the best configurations.\n\n| Dataset | #GPU | Hidden size | #Heads | Local BS | LR | Gradient clipping | Dropout | Mean q-risk | Std q-risk | Min q-risk | Max q-risk\n|-------------|------|-------------|--------|----------|------|-------------------|---------|-------------|------------| -----------|------ \n| Electricity | 8 | 128 | 4 | 1024 | 1e-3 | 0.0 | 0.1 | 0.1129 | 0.0025 | 0.1074 | 0.1244\n| Traffic | 8 | 128 | 4 | 1024 | 1e-3 | 0.0 | 0.3 | 0.2262 | 0.0027 | 0.2207 | 0.2331\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the `train.sh` training script in the [PyTorch 22.11 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) on NVIDIA A100 (8x A100 80GB) GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch.\n\n| Dataset | GPUs | Batch size / GPU | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 - mixed precision) | Weak scaling - TF32 | Weak scaling - mixed precision \n|-------------|---|------|--------|--------|-------|-------|-----\n| Electricity | 1 | 1024 | 12435 | 17608 | 1.42x | 1 | 1\n| Electricity | 8 | 1024 | 94389 | 130769 | 1.39x | 7.59x | 7.42x\n| Traffic | 1 | 1024 | 12509 | 17591 | 1.40x | 1 | 1\n| Traffic | 8 | 1024 | 94476 | 130992 | 1.39x | 7.55x | 7.45x\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\nThe performance metrics used were items per second.\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `train.sh` training script in the [PyTorch 22.11 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) on NVIDIA DGX-1 with (8x V100 16GB) GPUs. Performance numbers (in items/images per second) were averaged over an entire training epoch.\n\n| Dataset | GPUs | Batch size / GPU | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 - mixed precision) | Weak scaling - FP32 | Weak scaling - mixed precision \n|-------------|---|------|-------|-------|-------|------|----\n| Electricity | 1 | 1024 | 5932 | 10163 | 1.71x | 1 | 1\n| Electricity | 8 | 1024 | 45566 | 75660 | 1.66x | 7.68x | 7.44x\n| Traffic | 1 | 1024 | 5971 | 10166 | 1.70x | 1 | 1\n| Traffic | 8 | 1024 | 45925 | 75640 | 1.64x | 7.69x | 7.44x\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\nThe performance metrics used were items per second.\n\n#### Inference Performance Results\n\n##### Inference Performance: NVIDIA DGX A100\n\nOur results were obtained by running the `inference.py` script in the [PyTorch 22.11 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) on NVIDIA DGX A100. Throughput is measured in items per second and latency is measured in milliseconds.\nTo benchmark the inference performance on a specific batch size and dataset, run the `inference.py` script.\n\n| Dataset | GPUs | Batch size / GPU | Throughput - mixed precision (item/s) | Average Latency (ms) | Latency p90 (ms) | Latency p95 (ms) | Latency p99 (ms)\n|-------------|--------|-----|---------------------------------|-----------------|-------------|-------------|------------\n| Electricity | 1 | 1 | 272.43 | 3.67 | 3.70 | 3.87 | 4.18\n| Electricity | 1 | 2 | 518.13 | 3.86 | 3.88 | 3.93 | 4.19\n| Electricity | 1 | 4 | 1039.31 | 3.85 | 3.89 | 3.97 | 4.15\n| Electricity | 1 | 8 | 2039.54 | 3.92 | 3.93 | 3.95 | 4.32\n| Traffic | 1 | 1 | 269.59 | 3.71 | 3.74 | 3.79 | 4.30\n| Traffic | 1 | 2 | 518.73 | 3.86 | 3.78 | 3.91 | 4.66\n| Traffic | 1 | 4 | 1021.49 | 3.92 | 3.94 | 3.95 | 4.25\n| Traffic | 1 | 8 | 2005.54 | 3.99 | 4.01 | 4.03 | 4.39\n\n##### Inference Performance: NVIDIA DGX-1 V100\n\nOur results were obtained by running the `inference.py` script in the [PyTorch 22.11 NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) on NVIDIA DGX-1 V100. Throughput is measured in items per second and latency is measured in milliseconds.\nTo benchmark the inference performance on a specific batch size and dataset, run the `inference.py` script.\n\n| Dataset | GPUs | Batch size / GPU | Throughput - mixed precision (item/s) | Average Latency (ms) | Latency p90 (ms) | Latency p95 (ms) | Latency p99 (ms)\n|-------------|--------|-----|---------------------------------|-----------------|-------------|-------------|------------\n| Electricity | 1 | 1 | 171.68 | 5.82 | 5.99 | 6.17 | 7.00 \n| Electricity | 1 | 2 | 318.92 | 6.27 | 6.43 | 6.60 | 7.51 \n| Electricity | 1 | 4 | 684.79 | 5.84 | 6.02 | 6.08 | 6.47\n| Electricity | 1 | 8 | 1275.54 | 6.27 | 7.31 | 7.36 | 7.51\n| Traffic | 1 | 1 | 183.39 | 5.45 | 5.64 | 5.86 | 6.73\n| Traffic | 1 | 2 | 340.73 | 5.87 | 6.07 | 6.77 | 7.25\n| Traffic | 1 | 4 | 647.33 | 6.18 | 6.35 | 7.99 | 8.07\n| Traffic | 1 | 8 | 1364.39 | 5.86 | 6.07 | 6.40 | 7.31","hasPerformance",[],{"_86":188,"_115":242,"_116":-5,"_124":35,"_123":35,"_125":241,"_223":91},{"_86":29,"_115":29,"_116":-5,"_124":35,"_123":35,"_125":241,"_223":91},{"_86":114,"_115":114,"_116":-5,"_124":35,"_123":35,"_125":240,"_223":91},[],[],"Latest (22.11.0)","nvidia/dle/tft_pyt","key","nvidia/dle/resources/tft_pyt","resourceId","tft_pyt","shortDescription","Temporal Fusion Transformer is a state-of-the-art architecture for interpretable, multi-horizon time-series prediction.","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/tft-pyt)\n\n\nThe Temporal Fusion Transformer [TFT](https://arxiv.org/abs/1912.09363) model is a state-of-the-art architecture for interpretable, multi-horizon time-series prediction. The model was first developed and [implemented by Google](https://github.com/google-research/google-research/tree/master/tft) with the collaboration with the University of Oxford.\nThis implementation differs from the reference implementation by addressing the issue of missing data, which is common in production datasets, by either masking their values in attention matrices or embedding them as a special value in the latent space.\nThis model enables the prediction of confidence intervals for future values of time series for multiple future timesteps.\n\nThis model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 1.45x 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\nThe TFT model is a hybrid architecture joining LSTM encoding of time series and interpretability of transformer attention layers. Prediction is based on three types of variables: static (constant for a given time series), known (known in advance for whole history and future), observed (known only for historical data). All these variables come in two flavors: categorical, and continuous. In addition to historical data, we feed the model with historical values of time series. All variables are embedded in high-dimensional space by learning an embedding vector. Categorical variables embeddings are learned in the classical sense of embedding discrete values. The model learns a single vector for each continuous variable, which is then scaled by this variable's value for further processing. The next step is to filter variables through the Variable Selection Network (VSN), which assigns weights to the inputs in accordance with their relevance to the prediction. Static variables are used as a context for variable selection of other variables and as an initial state of LSTM encoders.\nAfter encoding, variables are passed to multi-head attention layers (decoder), which produce the final prediction. Whole architecture is interwoven with residual connections with gating mechanisms that allow the architecture to adapt to various problems by skipping some parts of it.\nFor the sake of explainability, heads of self-attention layers share value matrices. This allows interpreting self-attention as an ensemble of models predicting different temporal patterns over the same feature set. The other feature that helps us understand the model is VSN activations, which tells us how relevant the given feature is to the prediction.\n![](TFT_architecture.PNG)\n*image source: https://arxiv.org/abs/1912.09363*\n\n### Default configuration\n\nThe specific configuration of the TFT model depends on the dataset used. Not only is the volume of the model subject to change but so are the data sampling and preprocessing strategies. During preprocessing, data is normalized per feature. For a part of the datasets, we apply scaling per-time-series, which takes into account shifts in distribution between entities (i.e., a factory consumes more electricity than an average house). The model is trained with the quantile loss: $\\Large\\sum_{i=1}^N\\sum_{q\\in\\mathcal{Q}}\\sum_{t=1}^{t_{max}}\\frac{QL(y_it,\\hat{y}_i(q,t),q)}{Nt_{max}}\"$ \nFor quantiles in [0.1, 0.5, 0.9]. The default configurations are tuned for distributed training on DGX-1-32G with mixed precision. We use dynamic loss scaling. Specific values are provided in the table below.\n\n| Dataset | Training samples | Validation samples | Test samples | History length | Forecast horizon | Dropout | Hidden size | #Heads | BS | LR | Gradient clipping |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Electricity | 450k | 50k | 53.5k | 168 | 24 | 0.1 | 128 | 4 | 8x1024 | 1e-3 | 0.0 |\n| Traffic | 450k | 50k | 139.6k | 168 | 24 | 0.3 | 128 | 4 | 8x1024 | 1e-3 | 0.0\n\n### Feature support matrix\n\nThe following features are supported by this model: \n\n| Feature | Yes column \n|----------------------------|--------------------------\n|Distributed data parallel | Yes\n|PyTorch AMP | Yes \n \n#### Features\n\n[Automatic Mixed Precision](https://pytorch.org/docs/stable/amp.html)\nprovides an easy way to leverage Tensor Cores' performance. It allows the execution of parts of a network in lower precision. Refer to [Mixed precision training](#mixed-precision-training) for more information.\n\n[PyTorch\nDistributedDataParallel](https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel) - a module\nwrapper that enables easy multiprocess distributed data-parallel\ntraining.\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a\ncomputational method.\n[Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant\ncomputational speedup by performing operations in half-precision format while\nstoring minimal information in single-precision to retain as much information\nas possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in Volta, and following with \nboth the Turing and Ampere architectures, significant training speedups are \nexperienced by switching to\nmixed precision -- up to 3x overall speedup on the most arithmetically intense\nmodel architectures. Using mixed precision training previously required two\nsteps:\n\n1. Porting the model to use the FP16 data type where appropriate.\n2. Manually adding loss scaling to preserve small gradient values.\n\nThe ability to train deep learning networks with lower precision was introduced\nin the Pascal architecture and first supported in [CUDA\n8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep\nLearning SDK.\n\nFor information about:\n* How to train using mixed precision, refer to the [Mixed Precision\n Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed\n Precision](https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html)\n documentation.\n* Techniques used for mixed precision training, refer to the [Mixed-Precision\n Training of Deep Neural\n Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/)\n blog.\n\n#### Enabling mixed precision\n\nMixed precision is enabled in PyTorch by using the Automatic Mixed Precision torch.cuda.amp module, 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 PyTorch, loss scaling can be applied automatically by the GradScaler class. All the necessary steps to implement AMP are verbosely described [here](https://pytorch.org/docs/stable/notes/amp_examples.html#amp-examples).\n\nTo enable mixed precision for TFT, simply add the `--use_amp` option to the training script.\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\n**Multi horizon prediction** \nProcess of estimating values of a time series for multiple future time steps.\n\n**Quantiles** \nCut points dividing the range of a probability distribution intervals with equal probabilities.\n\n**Time series** \nSeries of data points indexed and equally spaced in time.\n\n**Transformer** \nThe paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762) introduces a novel architecture called Transformer that uses an attention mechanism and transforms one sequence into another.","displayName","Temporal Fusion Transformer for PyTorch",["D",1680813678713],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/tft_pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[282,283,284],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_251":296,"_286":297,"_288":298,"_290":88},{"_251":293,"_286":293,"_288":294,"_290":88},{"_251":285,"_286":287,"_288":289,"_290":88},"Time Series","value","soln_time_series","filter",{"_291":292,"_286":287},"category","field","technology","PyTorch",{"_291":295,"_286":293},"framework","Forecasting","uscs_forecasting",{"_291":299,"_286":297},"application",{"_157":1283,"_159":1284},"ngcApps",[1236],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1217":1218,"_1219":1220,"_1221":1222,"_1223":1224,"_1225":311,"_110":339,"_1226":1227,"_1228":60,"_1229":91,"_1230":91,"_1231":1232,"_1233":91,"_1234":1235},"amplitude",{"_1214":1215,"_852":-7,"_1216":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1208":1209,"_1210":1211,"_1212":1213},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1785909264680,"1.11.0","defaultSearchSuggestions",{"_1092":419,"_1093":1094,"_1095":1096,"_1097":1098},"announcements",[],"consentState",{"_376":1082,"_1083":91,"_1084":35,"_1085":35,"_1086":1087},"consentConfig",{"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377},"showContactAdmin",1785909264684,[],[],[353,354,355,356,357],{"_49":63,"_51":74,"_53":152,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":152,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":152,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":152,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":152,"_55":-5,"_57":-5,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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22.11.0Selected	1	31.12 KB	April 6, 2023 UTC
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