def checkpoint_model()

in sagemaker/22_accelerate_sagemaker_examples/src/seq2seq/run_seq2seq_no_trainer.py [0:0]

• 13 lines of code
• 2 McCabe index (conditional complexity)

def checkpoint_model(checkpoint_folder, ckpt_id, model, epoch, last_global_step, **kwargs):
    """Utility function for checkpointing model + optimizer dictionaries
    The main purpose for this is to be able to resume training from that instant again
    """
    checkpoint_state_dict = {
        "epoch": epoch,
        "last_global_step": last_global_step,
    }
    # Add extra kwargs too
    checkpoint_state_dict.update(kwargs)

    success = model.save_checkpoint(checkpoint_folder, ckpt_id, checkpoint_state_dict)
    status_msg = f"checkpointing: checkpoint_folder={checkpoint_folder}, ckpt_id={ckpt_id}"
    if success:
        logging.info(f"Success {status_msg}")
    else:
        logging.warning(f"Failure {status_msg}")
    return