Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

set_number_of_threads.py

1 # file: set_number_of_threads.py
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40 #===============================================================================
41 
42 
43 
44 
45 import os
46 
47 import daal.algorithms.kmeans as kmeans
48 import daal.algorithms.kmeans.init as init
49 from daal.data_management import FileDataSource, DataSourceIface
50 from daal.services import Environment
51 
52 # Input data set parameters
53 datasetFileName = os.path.join('..', 'data', 'batch', 'kmeans_dense.csv')
54 
55 # K-Means algorithm parameters
56 nClusters = 20
57 nIterations = 5
58 nThreads = 2
59 nThreadsInit = None
60 nThreadsNew = None
61 
62 if __name__ == "__main__":
63 
64  # Get the number of threads that is used by the library by default
65  nThreadsInit = Environment.getInstance().getNumberOfThreads()
66 
67  # Set the maximum number of threads to be used by the library
68  Environment.getInstance().setNumberOfThreads(nThreads)
69 
70  # Get the number of threads that is used by the library after changing
71  nThreadsNew = Environment.getInstance().getNumberOfThreads()
72 
73  # Initialize FileDataSource to retrieve the input data from a .csv file
74  dataSource = FileDataSource(
75  datasetFileName, DataSourceIface.doAllocateNumericTable,
76  DataSourceIface.doDictionaryFromContext
77  )
78 
79  # Retrieve the data from the input file
80  dataSource.loadDataBlock()
81 
82  # Get initial clusters for the K-Means algorithm
83  initAlg = init.Batch(nClusters)
84 
85  initAlg.input.set(init.data, dataSource.getNumericTable())
86  res = initAlg.compute()
87  centroids = res.get(init.centroids)
88 
89  # Create an algorithm object for the K-Means algorithm
90  algorithm = kmeans.Batch(nClusters, nIterations)
91 
92  algorithm.input.set(kmeans.data, dataSource.getNumericTable())
93  algorithm.input.set(kmeans.inputCentroids, centroids)
94 
95  # Run computations
96  unused_result = algorithm.compute()
97 
98  print("Initial number of threads: {}".format(nThreadsInit))
99  print("Number of threads to set: {}".format(nThreads))
100  print("Number of threads after setting: {}".format(nThreadsNew))

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