This repository provides a script and recipe to train the BERT model for TensorFlow to achieve state-of-the-art accuracy, and is tested and maintained by NVIDIA.
BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) paper. NVIDIA's BERT is an optimized version of [Google's official implementation](https://github.com/google-research/bert), leveraging mixed precision arithmetic and Tensor Cores on V100 GPUs for faster training times while maintaining target accuracy.
Other publicly available implementations of BERT include:
This model is trained with mixed precision using Tensor Cores on NVIDIA Volta and Turing GPUs. Therefore, researchers can get results upto 4x 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.
BERT's model architecture is a multi-layer bidirectional Transformer encoder. Based on the model size, we have the following two default configurations of BERT:
BERT training consists of two steps, pre-training the language model in an unsupervised fashion on vast amounts of unannotated datasets, and then using this pre-trained model for fine-tuning for various NLP tasks, such as question and answer, sentence classification, or sentiment analysis. Fine-tuning typically adds an extra layer or two for the specific task and further trains the model using a task-specific annotated dataset, starting from the pre-trained backbone weights. The end-to-end process in depicted in the following image:
This repository contains scripts to interactively launch data download, training, benchmarking and inference routines in a Docker container for both pre-training and fine tuning for Question Answering. The major differences between the official implementation of the paper and our version of BERT are as follows:
- Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.
The following performance optimizations were implemented in this model:
- [XLA](https://www.tensorflow.org/xla) support (experimental).
These techniques and optimizations improve model performance and reduce training time, allowing you to perform various NLP tasks with no additional effort.
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](https://github.com/horovod/horovod/#usage)
[LAMB](https://arxiv.org/pdf/1904.00962.pdf) stands for Layerwise Adaptive Moments based optimizer, is a large batch optimization technique that helps accelerate training of deep neural networks using large minibatches. It allows using a global batch size of 65536 and 32768 on sequence lengths 128 and 512 respectively, compared to a batch size of 256 for Adam. The optimized implementation accumulates 1024 gradients batches in phase 1 and 4096 steps in phase 2 before updating weights once. This results in 27% training speedup on a single DGX2 node. On multi-node systems, LAMB allows scaling up to 1024 GPUs resulting in training speedups of up to 17x in comparison to [Adam](https://arxiv.org/pdf/1412.6980.pdf). Adam has limitations on the learning rate that can be used since it is applied globally on all parameters whereas LAMB follows a layerwise learning rate strategy.
Mixed precision is the combined use of different numerical precision in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in the Volta and Turing architecture, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:
2. Adding loss scaling to preserve small gradient values.
The ability to train deep learning networks with lower precision was introduced in the Pascal architecture and first supported in [CUDA 8](https://devblogs.nvidia.com/parallelforall/tag/fp16/) in the NVIDIA Deep Learning SDK.
For information about:
- 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.
- 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.
- 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.
Automatic Mixed Precision (AMP) for TensorFlow enables 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 and Turing GPUs 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 maximizes 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.
Training an already pretrained model further using a task specific dataset for subject-specific refinements, by adding task-specific layers on top if required.
The 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.
This repository contains `Dockerfile` which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:
For 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:
- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)
For 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).
For multi-node, the sample provided in this repository requires [Enroot](https://github.com/NVIDIA/enroot) and [Pyxis](https://github.com/NVIDIA/pyxis) set up on a [SLURM](https://slurm.schedmd.com) cluster.
More information on how to set up and launch can be found in the [Multi-node Documentation](https://docs.nvidia.com/ngc/multi-node-bert-user-guide).
To pretrain or fine tune your model for Question Answering using mixed precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the BERT model.
This repository provides scripts to download, verify and extract the SQuAD dataset, GLUE dataset and pretrained weights for fine tuning as well as Wikipedia and BookCorpus dataset for pre-training.
Note: The dataset is 170GB+ and takes 15+ hours to download. The BookCorpus server could sometimes get overloaded and also contain broken links resulting in HTTP 403 and 503 errors. You can either skip the missing files or retry downloading at a later time. Expired dataset links are ignored during data download.
We have uploaded checkpoints for both fine tuning and pre-training for various configurations on the NGC Model Registry. You can download them directly from the [NGC model catalog](https://ngc.nvidia.com/catalog/models). Download them to the `results/models/` to easily access them in your scripts.
BERT is designed to pre-train deep bidirectional representations for language representations. The following scripts are to replicate pre-training on Wikipedia and BookCorpus from the [LAMB paper](https://arxiv.org/pdf/1904.00962.pdf). These scripts are general and can be used for pre-training language representations on any corpus of choice.
The above pretrained BERT representations can be fine tuned with just one additional output layer for a state-of-the-art Question Answering system. From within the container, you can use the following script to run fine-training for SQuAD.
The `run_squad_inference.sh` script runs inference on a checkpoint fine tuned for SQuAD and evaluates the validity of predictions on the basis of exact match and F1 score.
*`run_glue.sh` - Runs GLUE training and inference using the `run_classifier.py` file
*`run_pretraining_adam.sh` - Runs pre-training with Adam optimizer using the `run_pretraining.py` file
*`run_pretraining_lamb.sh` - Runs pre-training with LAMB optimizer using the `run_pretraining.py` file in two phases. Phase 1 does 90% of training with sequence length = 128. In phase 2, the remaining 10% of the training is done with sequence length = 512.
*`data_download.sh` - Downloads datasets using files in the `data/` folder
--[no]do_train: Whether to run training. (default: 'false')
--learning_rate: The initial learning rate for Adam.(default: '5e-06')(a number)
--max_answer_length: The maximum length of an answer that can be generated. This is needed because the start and end predictions are not conditioned on one another.(default: '30')(an integer)
--max_query_length: The maximum number of tokens for the question. Questions longer than this will be truncated to this length.(default: '64')(an integer)
--max_seq_length: The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded.(default: '384')(an integer)
--predict_batch_size: Total batch size for predictions.(default: '8')(an integer)
--train_batch_size: Total batch size for training.(default: '8')(an integer)
Aside from the options to set hyperparameters, some relevant options to control the behaviour of the `run_classifier.py` script are:
```
--bert_config_file: The config json file corresponding to the pre-trained BERT model. This specifies the model architecture.
--data_dir: The input data dir. Should contain the .tsv files (or other data files) for the task.
--[no]do_eval: Whether to run eval on the dev set.
(default: 'false')
--[no]do_predict: Whether to run the model in inference mode on the test set.(default: 'false')
--[no]do_train: Whether to run training.(default: 'false')
--[no]horovod: Whether to use Horovod for multi-gpu runs(default: 'false')
--init_checkpoint: Initial checkpoint (usually from a pre-trained BERT model).
--max_seq_length: The maximum total input sequence length after WordPiece tokenization. Sequences longer than this will be truncated, and sequences shorter than this will be padded.(default: '128')(an integer)
--num_train_epochs: Total number of training epochs to perform.(default: '3.0')(a number)
--output_dir: The output directory where the model checkpoints will be written.
--task_name: The name of the task to train.
--train_batch_size: Total batch size for training.(default: '32')(an integer)
--[no]use_fp16: Whether to use fp32 or fp16 arithmetic on GPU.
(default: 'false')
--[no]use_xla: Whether to enable XLA JIT compilation.
(default: 'false')
--vocab_file: The vocabulary file that the BERT model was trained on.
--warmup_proportion: Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% of training.(default: '0.1')(a number)
Note: When initializing from a checkpoint using `--init_checkpoint` and a corpus of your choice, keep in mind that `bert_config_file` and `vocab_file` should remain unchanged.
For pre-training BERT, we use the concatenation of Wikipedia (2500M words) as well as BookCorpus (800M words). For Wikipedia, we extract only the text passages from [here](ftp://ftpmirror.your.org/pub/wikimedia/dumps/enwiki/latest/enwiki-latest-pages-articles-multistream.xml.bz2) and ignore headers list and tables. It is structured as a document level corpus rather than a shuffled sentence level corpus because it is critical to extract long contiguous sentences.
The next step is to run `create_pretraining_data.py` with the document level corpus as input, which generates input data and labels for the masked language modeling and next sentence prediction tasks. Pre-training can also be performed on any corpus of your choice. The collection of data generation scripts are intended to be modular to allow modifications for additional preprocessing steps or to use additional data. They can hence easily be modified for an arbitrary corpus.
The preparation of an individual pre-training dataset is described in the `create_datasets_from_start.sh` script found in the `data/` folder. The component steps to prepare the datasets are as follows:
1. Data download and extract - the dataset is downloaded and extracted.
2. Clean and format - document tags, etc. are removed from the dataset. The end result of this step is a `{dataset_name_one_article_per_line}.txt` file that contains the entire corpus. Each line in the text file contains an entire document from the corpus. One file per dataset is created in the `formatted_one_article_per_line` folder.
3. Sharding - the sentence segmented corpus file is split into a number of smaller text documents. The sharding is configured so that a document will not be split between two shards. Sentence segmentation is performed at this time using NLTK.
4. TFRecord file creation - each text file shard is processed by the `create_pretraining_data.py` script to produce a corresponding TFRecord file. The script generates input data and labels for masked language modeling and sentence prediction tasks for the input text shard.
For fine tuning BERT for the task of Question Answering, we use SQuAD and GLUE. SQuAD v1.1 has 100,000+ question-answer pairs on 500+ articles. SQuAD v2.0 combines v1.1 with an additional 50,000 new unanswerable questions and must not only answer questions but also determine when that is not possible. GLUE consists of single-sentence tasks, similarity and paraphrase tasks and inference tasks. We support one of each: CoLA, MNLI and MRPC.
The procedure to prepare a text corpus for pre-training is described in the previous section. This section provides additional insight into how exactly raw text is processed so that it is ready for pre-training.
First, raw text is tokenized using [WordPiece tokenization](https://arxiv.org/pdf/1609.08144.pdf). A [CLS] token is inserted at the start of every sequence, and the two sentences in the sequence are separated by a [SEP] token.
Note: BERT pre-training looks at pairs of sentences at a time. A sentence embedding token [A] is added to the first sentence and token [B] to the next.
BERT pre-training optimizes for two unsupervised classification tasks. The first is Masked Language Modelling (Masked LM). One training instance of Masked LM is a single modified sentence. Each token in the sentence has a 15% chance of being replaced by a [MASK] token. The chosen token is replaced with [MASK] 80% of the time, 10% with another random token and the remaining 10% with the same token. The task is then to predict the original token.
The second task is next sentence prediction. One training instance of BERT pre-training is two sentences (a sentence pair). A sentence pair may be constructed by simply taking two adjacent sentences from a single document, or by pairing up two random sentences with equal probability. The goal of this task is to predict whether or not the second sentence followed the first in the original document.
The `create_pretraining_data.py` script takes in raw text and creates training instances for both pre-training tasks.
We are able to combine multiple datasets into a single dataset for pre-training on a diverse text corpus. Once TFRecords have been created for each component dataset, you can create a combined dataset by adding the directory to `SOURCES` in `run_pretraining_*.sh`. This will feed all matching files to the input pipeline in `run_pretraining.py`. However, in the training process, only one TFRecord file is consumed at a time, therefore, the training instances of any given training batch will all belong to the same source dataset.
The `run_pretraining_lamb.sh` script runs a job on a single node that trains the BERT-large model from scratch using the Wikipedia and BookCorpus datasets as training data. By default, the training script:
- Saves a checkpoint every 100 iterations (keeps only the latest checkpoint) and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).
-`<training_batch_size_phase*>` is per-GPU batch size used for training in the respective phase. Batch size varies with precision, larger batch sizes run more efficiently, but require more memory.
-`<eval_batch_size>` is per-GPU batch size used for evaluation after training.
- Saves a checkpoint every 1000 iterations (keeps only the latest checkpoint) and at the end of training. All checkpoints, evaluation results and training logs are saved to the `/results` directory (in the container which can be mounted to a local directory).
This script outputs checkpoints to the `/results` directory, by default, inside the container. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file. The training log contains information about:
The summary after training is printed in the following format:
```bash
I0312 23:10:45.137036 140287431493376 run_squad.py:1332] 0 Total Training Time = 3007.00 Training Time W/O start up overhead = 2855.92 Sentences processed = 175176
I0312 23:10:45.137243 140287431493376 run_squad.py:1333] 0 Training Performance = 61.3378 sentences/sec
I0312 23:14:00.550846 140287431493376 run_squad.py:1396] 0 Total Inference Time = 145.46 Inference Time W/O start up overhead = 131.86 Sentences processed = 10840
Multi-node runs can be launched on a pyxis/enroot Slurm cluster (see [Requirements](#requirements)) with the `run.sub` script with the following command for a 4-node DGX1 example for both phase 1 and phase 2:
Checkpoint after phase 1 will be saved in `checkpointdir` specified in `run.sub`. The checkpoint will be automatically picked up to resume training on phase 2. Note that phase 2 should be run after phase 1.
Variables to re-run the [Training performance results](#training-performance-results) are available in the `configurations.yml` file.
The batch variables `BATCHSIZE`, `LEARNING_RATE`, `NUM_ACCUMULATION_STEPS` refer to the Python arguments `train_batch_size`, `learning_rate`, `num_accumulation_steps` respectively.
The variable `PHASE` refers to phase specific arguments available in `run.sub`.
Note that the `run.sub` script is a starting point that has to be adapted depending on the environment. In particular, variables such as `datadir` handle the location of the files for each phase.
Refer to the files contents to see the full list of variables to adjust for your system.
Inference on a fine tuned Question Answering system is performed using the `run_squad.py` script along with parameters defined in `scripts/run_squad_inference.sh`. Inference is supported on a single GPU.
This script outputs predictions file to `/results/predictions.json` and computes F1 score and exact match score using SQuAD's evaluate file. Mount point of `/results` can be changed in the `scripts/docker/launch.sh` file.
The [NVIDIA TensorRT Inference Server](https://github.com/NVIDIA/tensorrt-inference-server) provides a datacenter and 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 number of GPU or CPU models being managed by the server. More information on how to perform inference using `TensorRT Inference Server` can be found in the subfolder `./trtis/README.md`.
Many works, including [BioBERT](https://arxiv.org/pdf/1901.08746.pdf), [SciBERT](https://arxiv.org/pdf/1903.10676.pdf), [NCBI-BERT](https://arxiv.org/pdf/1906.05474.pdf), [ClinicalBERT (MIT)](https://arxiv.org/pdf/1904.03323.pdf), [ClinicalBERT (NYU, Princeton)](https://arxiv.org/pdf/1904.05342.pdf), and others at [BioNLP’19 workshop](https://aclweb.org/aclwiki/BioNLP_Workshop), show that pre-training of BERT on large biomedical text corpus such as [PubMed](https://www.ncbi.nlm.nih.gov/pubmed/) results in better performance in biomedical text-mining tasks.
More information on how to download a biomedical corpus and pre-train as well as finetune for biomedical tasks can be found in the subfolder `./biobert/README.md`.
This script runs 2 epochs by default on the SQuAD v1.1 dataset and extracts performance numbers for various batch sizes and sequence lengths in both FP16 and FP32. These numbers are saved at `/results/squad_train_benchmark_bert_<bert_model>_gpu_<num_gpu>.log`.
This script runs 1024 eval iterations by default on the SQuAD v1.1 dataset and extracts performance and latency numbers for various batch sizes and sequence lengths in both FP16 with XLA and FP32 without XLA. These numbers are saved at `/results/squad_inference_benchmark_bert_<bert_model>.log`.
The following sections provide details on how we achieved our performance and accuracy in training and inference for pre-training using LAMB optimizer as well as fine tuning for Question Answering. All results are on BERT-large model unless otherwise mentioned. All fine tuning results are on SQuAD v1.1 using a sequence length of 384 unless otherwise mentioned.
Note: Time to train includes upto 16 minutes of start up time for every restart. Experiments were run on clusters with a maximum wall clock time of 8 hours.
Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G GPUs.
The following tables compare `Final Loss` scores across 5 different training runs with different seeds, for both FP16. The runs showcase consistent convergence on all 5 seeds with very little deviation.
The following tables compare `F1` scores across 5 different training runs with different seeds, for both FP16 and FP32 respectively. The runs showcase consistent convergence on all 5 seeds with very little deviation.
Our results were obtained by running the `scripts/run_pretraining_lamb.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance (in sentences per second) is the steady state throughput.
Note: The respective values for FP32 runs that use a batch size of 16, 4 in sequence lengths 128 and 512 respectively are not available due to out of memory errors that arise.
###### Pre-training training performance: multi-node on 16G
Our results were obtained by running the `run.sub` training script in the TensorFlow 19.08-py3 NGC container using multiple NVIDIA DGX-1 with 8x V100 16G GPUs. Performance (in sentences per second) is the steady state throughput.
Note: The respective values for FP32 runs that use a batch size of 16, 2 in sequence lengths 128 and 512 respectively are not available due to out of memory errors that arise.
###### Fine-tuning training performance for SQuAD on 16G
Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-1 with 8x V100 16G GPUs. Performance (in sentences per second) is the mean throughput from 2 epochs.
Note: The respective values for FP32 runs that use a batch size of 3 are not available due to out of memory errors that arise. Batch size of 3 is only available on using FP16.
To achieve these same results, follow the [Quick Start Guide](#quick-start-guide) outlined above.
Our results were obtained by running the `scripts/run_pretraining_lamb.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs. Performance (in sentences per second) is the steady state throughput.
Note: The respective values for FP32 runs that use a batch size of 48, 8 in sequence lengths 128 and 512 respectively are not available due to out of memory errors that arise.
###### Fine-tuning training performance for SQuAD on 32G
Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-1 with 8x V100 32G GPUs. Performance (in sentences per second) is the mean throughput from 2 epochs.
Note: The respective values for FP32 runs that use a batch size of 10 are not available due to out of memory errors that arise. Batch size of 10 is only available on using FP16.
Our results were obtained by running the `scripts/run_pretraining_lamb.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G GPUs. Performance (in sentences per second) is the steady state throughput.
Note: The respective values for FP32 runs that use a batch size of 48, 8 in sequence lengths 128 and 512 respectively are not available due to out of memory errors that arise.
Our results were obtained by running the `run.sub` training script in the TensorFlow 19.08-py3 NGC container using multiple NVIDIA DGX-2 with 16x V100 32G GPUs. Performance (in sentences per second) is the steady state throughput.
Our results were obtained by running the `scripts/run_squad.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-2 with 16x V100 32G GPUs. Performance (in sentences per second) is the mean throughput from 2 epochs.
Note: The respective values for FP32 runs that use a batch size of 10 are not available due to out of memory errors that arise. Batch size of 10 is only available on using FP16.
Our results were obtained by running the `scripts/run_pretraining_lamb.sh` script in the TensorFlow 19.06-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs.
Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-1 with 1x V100 16G GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining.
Our results were obtained by running the `scripts/run_pretraining_lamb.sh` script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-1 with 1x V100 32G GPUs.
Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-1 with 1x V100 32G GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining.
Our results were obtained by running the `scripts/run_pretraining_lamb.sh` script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-2 with 1x V100 32G GPUs.
Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA DGX-2 with 1x V100 32G GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining.
Our results were obtained by running the `scripts/finetune_inference_benchmark.sh` training script in the TensorFlow 19.08-py3 NGC container on NVIDIA Tesla T4 with 1x T4 16G GPUs. Performance numbers (throughput in sentences per second and latency in milliseconds) were averaged from 1024 iterations. Latency is computed as the time taken for a batch to process as they are fed in one after another in the model ie no pipelining.
- There is a known performance regression with the 19.08 release on Tesla V100 boards with 16 GB memory, smaller batch sizes may be a better choice for this model on these GPUs with the 19.08 release. 32 GB GPUs are not affected.
