Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

enable_thread_pinning.py

1 # file: enable_thread_pinning.py
2 #===============================================================================
3 # Copyright 2014-2020 Intel Corporation
4 #
5 # Licensed under the Apache License, Version 2.0 (the "License");
6 # you may not use this file except in compliance with the License.
7 # You may obtain a copy of the License at
8 #
9 # http://www.apache.org/licenses/LICENSE-2.0
10 #
11 # Unless required by applicable law or agreed to in writing, software
12 # distributed under the License is distributed on an "AS IS" BASIS,
13 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 # See the License for the specific language governing permissions and
15 # limitations under the License.
16 #===============================================================================
17 
18 
19 
20 
21 import os
22 import sys
23 
24 import daal.algorithms.kmeans as kmeans
25 import daal.algorithms.kmeans.init as init
26 from daal.data_management import FileDataSource, DataSourceIface
27 from daal.services import Environment
28 
29 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
30 if utils_folder not in sys.path:
31  sys.path.insert(0, utils_folder)
32 from utils import printNumericTable
33 # Input data set parameters
34 datasetFileName = os.path.join('..', 'data', 'batch', 'kmeans_dense.csv')
35 
36 # K-Means algorithm parameters
37 nClusters = 20
38 nIterations = 5
39 nThreads = 2
40 nThreadsInit = None
41 nThreadsNew = None
42 
43 if __name__ == "__main__":
44 
45  # Initialize FileDataSource to retrieve the input data from a .csv file
46  dataSource = FileDataSource(
47  datasetFileName, DataSourceIface.doAllocateNumericTable,
48  DataSourceIface.doDictionaryFromContext
49  )
50 
51  # Retrieve the data from the input file
52  dataSource.loadDataBlock()
53 
54  # Get initial clusters for the K-Means algorithm
55  initAlg = kmeans.init.Batch(nClusters)
56 
57  initAlg.input.set(kmeans.init.data, dataSource.getNumericTable())
58 
59  # Enables thread pinning for next algorithm runs
60  Environment.getInstance().enableThreadPinning(True)
61 
62  res = initAlg.compute()
63 
64  # Disables thread pinning for next algorithm runs
65  Environment.getInstance().enableThreadPinning(False)
66 
67  centroids = res.get(kmeans.init.centroids)
68 
69  # Create an algorithm object for the K-Means algorithm
70  algorithm = kmeans.Batch(nClusters, nIterations)
71 
72  algorithm.input.set(kmeans.data, dataSource.getNumericTable())
73  algorithm.input.set(kmeans.inputCentroids, centroids)
74 
75  # Run computations
76  unused_result = algorithm.compute()
77 
78  printNumericTable(unused_result.get(kmeans.assignments), "First 10 cluster assignments:", 10);
79  printNumericTable(unused_result.get(kmeans.centroids), "First 10 dimensions of centroids:", 20, 10);
80  printNumericTable(unused_result.get(kmeans.objectiveFunction), "Objective function value:");

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