Code terminates without training while using accelerate
Hi everyone. I’m using accelerate and I don’t know why my code terminates right away. There’s probably something wrong as I could easily train models using accelerate before. I’m not getting any errors–it’s just that the code terminates as soon as I execute the notebook launcher cell. Here’s my code:def dtr(): 
    from transformers import Trainer, TrainingArguments
    from accelerate import Accelerator
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained(model_checkpoint)
    #model.to(device)

    model_name = model_checkpoint.split("/")[-1]
    training_args = TrainingArguments(
        f"models/S/100-10%",
        evaluation_strategy = "epoch",
        learning_rate=2e-5,
        weight_decay=0.01,
        push_to_hub=False,
        num_train_epochs=1,
        per_device_train_batch_size=4, 
        per_device_eval_batch_size=4, 
        save_strategy="steps", 
        save_steps = 242, 
    )

    accelerator = Accelerator()
    trainer = accelerator.prepare(Trainer(
        model=model,
        args=training_args,
        train_dataset=lm_datasets["train"],
        eval_dataset=lm_datasets["validation"],
    )) 
    trainer.train() 

from accelerate import notebook_launcher
args = ()
notebook_launcher(dtr, num_processes=4)
2024-04-13T21:28:57Z
jaydeepb:accelerator = Accelerator()
    trainer = accelerator.prepare(Trainer(
        model=model,
        args=training_args,
        train_dataset=lm_datasets["train"],
        eval_dataset=lm_datasets["validation"],
    )) 
    trainer.train()First thing I see, don’t do this. Trainer uses Accelerate so no need to do any of this
2024-04-13T21:32:04Z
I see, thanks! So would it automatically run the model on multiple GPUs?Also, I was able to figure out the issue. Turns out it was memory error
2024-04-13T22:12:44Z
jaydeepb:So would it automatically run the model on multiple GPUs?Correct!There’s nothing special needed to do for DDP etc since accelerate is fully integrated (code-wise)