pyspark/bigdl/nn/layer.py
def predict_distributed(self, data_rdd, batch_size = -1):
        """
        Model inference base on the given data.
        You need to invoke collect() to trigger those action \
        as the returning result is an RDD.

        :param data_rdd: the data to be predict.
        :param batch_size: total batch size of prediction.
        :return: An RDD represent the predict result.
        """
        result = callBigDlFunc(self.bigdl_type,
                               "modelPredictRDD", self.value, data_rdd, batch_size)
        return result.map(lambda data: data.to_ndarray())
def predict_distributed(self, data_rdd, batch_size = -1):
        """
        Model inference base on the given data.
        You need to invoke collect() to trigger those action \
        as the returning result is an RDD.

        :param data_rdd: the data to be predict.
        :param batch_size: total batch size of prediction.
        :return: An RDD represent the predict result.
        """
        result = callBigDlFunc(self.bigdl_type,
                               "modelPredictRDD", self.value, data_rdd, batch_size)
        return result.map(lambda data: data.to_ndarray())
Model inference base on the given data.
        You need to invoke collect() to trigger those action \
        as the returning result is an RDD.

        :param data_rdd: the data to be predict.
        :param batch_size: total batch size of prediction.
        :return: An RDD represent the predict result.