I'm following an HuggingFace course where it is presented the following code.
from accelerate import Accelerator
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
accelerator = Accelerator()
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
optimizer = AdamW(model.parameters(), lr=3e-5)
train_dl, eval_dl, model, optimizer = accelerator.prepare(
train_dataloader, eval_dataloader, model, optimizer
)
num_epochs = 3
num_training_steps = num_epochs * len(train_dl)
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_dl:
outputs = model(**batch)
loss = outputs.loss
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
To take advantage of the accelerate library, can I simply execute this script as a typical Python program (python train.py
), or is it mandatory to use the accelerate CLI commands (accelerate launch train.py
) to enable the distributed training?
maybe you can try "notebook_launcher" mode
tips: accelerate >= 0.23.0