Transformers documentation

Performance and Scalability

You are viewing v4.33.3 version. A newer version v4.37.2 is available.
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

Performance and Scalability

Training 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.

Training

Training 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.

Inference

Efficient 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.

Training and inference

Here you’ll find techniques, tips and tricks that apply whether you are training a model, or running inference with it.

Contribute

This 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).