sentence-transformers_all-MiniLM-L6-v2/recursive_chunks/_accelerate.txt_chunk_1.txt

model.to(device)

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

train_dataloader, eval_dataloader, model, optimizer
)

num_epochs = 3
  num_training_steps = num_epochs * len(train_dataloader)
  lr_scheduler = get_scheduler(
      "linear",
      optimizer=optimizer,
      num_warmup_steps=0,
      num_training_steps=num_training_steps
  )
progress_bar = tqdm(range(num_training_steps))
model.train()
  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)

Train
Once you've added the relevant lines of code, launch your training in a script or a notebook like Colaboratory.
Train with a script
If you are running your training from a script, run the following command to create and save a configuration file:

accelerate config
Then launch your training with:

accelerate launch train.py
Train with a notebook
🤗 Accelerate can also run in a notebook if you're planning on using Colaboratory's TPUs. Wrap all the code responsible for training in a function, and pass it to [~accelerate.notebook_launcher]:

from accelerate import notebook_launcher
notebook_launcher(training_function)

For more information about 🤗 Accelerate and its rich features, refer to the documentation.