def __init_

def init()

in src/sagemaker_tensorflow/pipemode.py [0:0]
22 lines of code
5 McCabe index (conditional complexity)

    def __init__(self, channel, record_format='RecordIO',
                 state_dir='/opt/ml/pipe_state', pipe_dir='/opt/ml/input/data',
                 config_dir='/opt/ml/input/config', benchmark=False, benchmark_records_interval=0,
                 max_corrupted_records_to_skip=0):
        """Create a Dataset for reading from a SageMaker PipeMode channel.

        Supports records encoded using either RecordIO, TFRecord, or new line text encoding.

        Args:
            record_format: The record format to use. One of 'RecordIO', 'TFRecord', or 'TextLine'
            channel: The name of the SageMaker channel.
            pipe_dir: The directory to read SageMaker Channels from.
            state_dir: The directory where pipe index state is persisted.
            config_dir: The path for SageMaker input data config.
            benchmark: Controls whether to emit timing and throughput metrics after closing an Iterator created from
                     this Dataset. If True, timing and throughput metrics will be emitted to stdout after an Iterator
                     created from this Dataset is destroyed.
            benchmark_records_interval: Controls whether to emit timing and throughput metrics while records are being
                    read from this Dataset. Defines the number of records per interval to emit timing and throughput
                    metrics. If zero, no metrics will be emitted while records are being read from this Dataset.
                    Metrics are emitted to stdout.
            max_corrupted_records_to_skip: the number of corrupted records encountered in sequence that it's ok to
                    skip. Only applicable for record_format='TFRecord'.
        """
        try:
            os.makedirs(state_dir)
        except OSError as e:
            if e.errno != errno.EEXIST:
                raise
        self.record_format = record_format
        self.channel = channel
        self.pipe_dir = pipe_dir
        self.state_dir = state_dir
        self.benchmark = benchmark
        self.benchmark_records_interval = benchmark_records_interval
        self.max_corrupted_records_to_skip = max_corrupted_records_to_skip
        with open(os.path.join(config_dir, 'inputdataconfig.json')) as f:
            self.input_data_config = json.load(f)
        self._validate_input_data_config()

        if self.max_corrupted_records_to_skip > 0 and record_format != 'TFRecord':
            raise PipeModeDatasetException("max_corrupted_records_to_skip can only be set for record_format='TFRecord'")

        super(PipeModeDataset, self).__init__()
def __init__()

def init()
