src/sagemaker_xgboost_container/training.py
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
        )
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'

    
Run training in algorithm mode, which does not require a user entry point.
This parses the following environ elements for training.


Run training in algorithm mode which does not require a user entry point . This parses the following environ elements for training .
def run_algorithm_mode():
    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
        )
def run_algorithm_mode ( ) : 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 )
Run training in algorithm mode, which does not require a user entry point.
Run training in algorithm mode which does not require a user entry point .
"""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