def run_algorithm_mode()

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

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

def run_algorithm_mode():
    """Run training in algorithm mode, which does not require a user entry point.

    This parses the following environ elements for training:

        'SM_INPUT_TRAINING_CONFIG_FILE'
        'SM_INPUT_DATA_CONFIG_FILE'
        'SM_CHANNEL_TRAIN'
        'SM_CHANNEL_VALIDATION'
        'SM_HOSTS'
        'SM_CURRENT_HOST'
        'SM_MODEL_DIR'
        'SM_CHECKPOINT_CONFIG_FILE'

    """
    # TODO: replace with CSDK constants in sagemaker_containers._env
    with open(os.getenv(sm_env_constants.SM_INPUT_TRAINING_CONFIG_FILE), "r") as f:
        train_config = json.load(f)
    with open(os.getenv(sm_env_constants.SM_INPUT_DATA_CONFIG_FILE), "r") as f:
        data_config = json.load(f)

    checkpoint_config_file = os.getenv(sm_env_constants.SM_CHECKPOINT_CONFIG_FILE)
    if os.path.exists(checkpoint_config_file):
        with open(checkpoint_config_file, "r") as f:
            checkpoint_config = json.load(f)
    else:
        checkpoint_config = {}

    train_path = os.environ[sm_env_constants.SM_CHANNEL_TRAIN]
    val_path = os.environ.get(sm_env_constants.SM_CHANNEL_VALIDATION)
    sm_hosts = json.loads(os.environ[sm_env_constants.SM_HOSTS])
    sm_current_host = os.environ[sm_env_constants.SM_CURRENT_HOST]

    model_dir = os.getenv(sm_env_constants.SM_MODEL_DIR)

    sagemaker_train(
        train_config=train_config,
        data_config=data_config,
        train_path=train_path,
        val_path=val_path,
        model_dir=model_dir,
        sm_hosts=sm_hosts,
        sm_current_host=sm_current_host,
        checkpoint_config=checkpoint_config,
    )