Intel DAAL AI加速——支持从数据预处理到模型预测，数据源必须使用DAAL的底层封装库

数据源加速见官方文档（必须使用DAAL自己的库）：

Data Management

• Numeric Tables
• Tensors
• Data Sources
• Data Dictionaries
• Data Serialization andDeserialization
• Data Compression
• Data Model

可以看到支持的数据源：同数据类型的table（matrix），不同类型的table，以及从DB文件取数据、数据序列化、压缩等。

在这些定制的数据源上，Intel DAAL使用自己底层的CPU进行硬件加速！下面摘自其官方：

Intel DAAL addresses all stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling, validation, and decision-making.

Intel DAAL is developed by the same team as the Intel? Math Kernel Library (Intel? MKL)—the leading math library in the world. This team works closely with Intel? processor architects to squeeze performance from Intel processor-based systems.

Specs at a Glance

统计特征的计算加速例子：

# file: low_order_moms_dense_batch.py
#===============================================================================
# Copyright 2014-2018 Intel Corporation.
#
# This software and the related documents are Intel copyrighted  materials,  and
# your use of  them is  governed by the  express license  under which  they were
# provided to you (License).  Unless the License provides otherwise, you may not
# use, modify, copy, publish, distribute,  disclose or transmit this software or
# the related documents without Intel‘s prior written permission.
#
# This software and the related documents  are provided as  is,  with no express
# or implied  warranties,  other  than those  that are  expressly stated  in the
# License.
#===============================================================================

## <a name="DAAL-EXAMPLE-PY-LOW_ORDER_MOMENTS_DENSE_BATCH"></a>
## \example low_order_moms_dense_batch.py

import os
import sys

from daal.algorithms import low_order_moments
from daal.data_management import FileDataSource, DataSourceIface

utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printNumericTable

DAAL_PREFIX = os.path.join(‘..‘, ‘data‘)

# Input data set parameters
dataFileName = os.path.join(DAAL_PREFIX, ‘batch‘, ‘covcormoments_dense.csv‘)

def printResults(res):
    printNumericTable(res.get(low_order_moments.minimum),              "Minimum:")
    printNumericTable(res.get(low_order_moments.maximum),              "Maximum:")
    printNumericTable(res.get(low_order_moments.sum),                  "Sum:")
    printNumericTable(res.get(low_order_moments.sumSquares),           "Sum of squares:")
    printNumericTable(res.get(low_order_moments.sumSquaresCentered),   "Sum of squared difference from the means:")
    printNumericTable(res.get(low_order_moments.mean),                 "Mean:")
    printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
    printNumericTable(res.get(low_order_moments.variance),             "Variance:")
    printNumericTable(res.get(low_order_moments.standardDeviation),    "Standard deviation:")
    printNumericTable(res.get(low_order_moments.variation),            "Variation:")

if __name__ == "__main__":

    # Initialize FileDataSource to retrieve input data from .csv file
    dataSource = FileDataSource(
        dataFileName,
        DataSourceIface.doAllocateNumericTable,
        DataSourceIface.doDictionaryFromContext
    )

    # Retrieve the data from input file
    dataSource.loadDataBlock()

    # Create algorithm for computing low order moments in batch processing mode
    algorithm = low_order_moments.Batch()

    # Set input arguments of the algorithm
    algorithm.input.set(low_order_moments.data, dataSource.getNumericTable())

    # Get computed low order moments
    res = algorithm.compute()

    printResults(res)　　

原文地址：https://www.cnblogs.com/bonelee/p/9702982.html