BAAI_bge-large-en-v1.5/fixed_chunks/_accelerate.txt_chunk_1.txt

from accelerate import Accelerator
accelerator = Accelerator()

Prepare to accelerate
The next step is to pass all the relevant training objects to the [~accelerate.Accelerator.prepare] method. This includes your training and evaluation DataLoaders, a model and an optimizer:

train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
     train_dataloader, eval_dataloader, model, optimizer
 )

Backward
The last addition is to replace the typical loss.backward() in your training loop with 🤗 Accelerate's [~accelerate.Accelerator.backward]method:

for epoch in range(num_epochs):
     for batch in train_dataloader:
         outputs = model(**batch)
         loss = outputs.loss
         accelerator.backward(loss)

         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()
         progress_bar.update(1)

As you can see in the following code, you only need to add four additional lines of code to your training loop to enable distributed training!