def main()

in src/sagemaker_tensorflow_container/training.py [0:0]

• 11 lines of code
• 3 McCabe index (conditional complexity)

def main():
    """Training entry point"""
    hyperparameters = environment.read_hyperparameters()
    env = environment.Environment(hyperparameters=hyperparameters)

    user_hyperparameters = env.hyperparameters

    # If the training job is part of the multiple training jobs for tuning, we need to append the training job name to
    # model_dir in case they read from/write to the same object
    if "_tuning_objective_metric" in hyperparameters:
        model_dir = _model_dir_with_training_job(hyperparameters.get("model_dir"), env.job_name)
        logger.info("Appending the training job name to model_dir: {}".format(model_dir))
        user_hyperparameters["model_dir"] = model_dir

    s3_utils.configure(user_hyperparameters.get("model_dir"), os.environ.get("SAGEMAKER_REGION"))
    train(env, mapping.to_cmd_args(user_hyperparameters))
    _log_model_missing_warning(MODEL_DIR)