Training a model with Hugging Face’s Accelerate

Hugging Face’s Accelerate is a library that provides a high-level API over different PyTorch distributed frameworks, aiming to simplify the process of distributed and mixed-precision training. It is designed to keep changes to your training loop to a minimum and allow the same functions to work for any distributed setup. Let’s see what Accelerate can bring to the table.

Applying Hugging Face’s Accelerate

Let’s apply Accelerate to our simple but working model. Accelerate is designed to be used together with PyTorch, so we don’t need to change too much code. Here are the steps to use Accelerate to train a model:

1. Generate the default configuration file:

   from accelerate import utils

   utils.write_basic_config()

2. Initialize an Accelerate instance, and send the model instance and data to the device managed by Accelerate:

   from accelerate import Accelerator

   accelerator = Accelerator()

   device...