Performance and Scalability
Source: transformers

<!---Copyright 2021 The HuggingFace Team. All rights reserved.Licensed under the Apache License, Version 2.0 (the "License");you may not use this file except in compliance with the License.You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an "AS IS" BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not berendered properly in your Markdown viewer.--># Performance and ScalabilityTraining large transformer models and deploying them to production present various challenges.  During training, the model may require more GPU memory than available or exhibit slow training speed. In the deployment phase, the model can struggle to handle the required throughput in a production environment.This documentation aims to assist you in overcoming these challenges and finding the optimal setting for your use-case. The guides are divided into training and inference sections, as each comes with different challenges and solutions. Within each section you'll find separate guides for different hardware configurations, such as single GPU vs. multi-GPU for training or CPU vs. GPU for inference.Use this document as your starting point to navigate further to the methods that match your scenario.## TrainingTraining large transformer models efficiently requires an accelerator such as a GPU or TPU. The most common case is where you have a single GPU. The methods that you can apply to improve training efficiency on a single GPU extend to other setups such as multiple GPU. However, there are also techniques that are specific to multi-GPU or CPU training. We cover them in separate sections.* [Methods and tools for efficient training on a single GPU](perf_train_gpu_one): start here to learn common approaches that can help optimize GPU memory utilization, speed up the training, or both. * [Multi-GPU training section](perf_train_gpu_many): explore this section to learn about further optimization methods that apply to a multi-GPU settings, such as data, tensor, and pipeline parallelism.* [CPU training section](perf_train_cpu): learn about mixed precision training on CPU.* [Efficient Training on Multiple CPUs](perf_train_cpu_many): learn about distributed CPU training.* [Training on TPU with TensorFlow](perf_train_tpu_tf): if you are new to TPUs, refer to this section for an opinionated introduction to training on TPUs and using XLA. * [Custom hardware for training](perf_hardware): find tips and tricks when building your own deep learning rig.* [Hyperparameter Search using Trainer API](hpo_train)## InferenceEfficient inference with large models in a production environment can be as challenging as training them. In the following sections we go through the steps to run inference on CPU and single/multi-GPU setups.* [Inference on a single CPU](perf_infer_cpu)* [Inference on a single GPU](perf_infer_gpu_one)* [Multi-GPU inference](perf_infer_gpu_one)* [XLA Integration for TensorFlow Models](tf_xla)## Training and inferenceHere you'll find techniques, tips and tricks that apply whether you are training a model, or running inference with it.* [Instantiating a big model](big_models)* [Troubleshooting performance issues](debugging)## ContributeThis document is far from being complete and a lot more needs to be added, so if you have additions or corrections to make please don't hesitate to open a PR or if you aren't sure start an Issue and we can discuss the details there.When making contributions that A is better than B, please try to include a reproducible benchmark and/or a link to the source of that information (unless it comes directly from you).