Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.
Note: To find daal4py examples, refer to daal4py documentation or browse github repository.
23 from daal
import step1Local, step2Master, step3Local
24 from daal.algorithms
import svd
25 from daal.data_management
import FileDataSource, DataSourceIface
27 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
28 if utils_folder
not in sys.path:
29 sys.path.insert(0, utils_folder)
30 from utils
import printNumericTable
32 DAAL_PREFIX = os.path.join(
'..',
'data')
38 os.path.join(DAAL_PREFIX,
'distributed',
'svd_1.csv'),
39 os.path.join(DAAL_PREFIX,
'distributed',
'svd_2.csv'),
40 os.path.join(DAAL_PREFIX,
'distributed',
'svd_3.csv'),
41 os.path.join(DAAL_PREFIX,
'distributed',
'svd_4.csv')
44 dataFromStep1ForStep2 = [0] * nBlocks
45 dataFromStep1ForStep3 = [0] * nBlocks
46 dataFromStep2ForStep3 = [0] * nBlocks
52 def computestep1Local(block):
53 global dataFromStep1ForStep2, dataFromStep1ForStep3
56 dataSource = FileDataSource(
57 datasetFileNames[block],
58 DataSourceIface.doAllocateNumericTable,
59 DataSourceIface.doDictionaryFromContext
63 dataSource.loadDataBlock()
66 algorithm = svd.Distributed(step1Local,fptype=np.float64)
68 algorithm.input.set(svd.data, dataSource.getNumericTable())
71 pres = algorithm.compute()
73 dataFromStep1ForStep2[block] = pres.get(svd.outputOfStep1ForStep2)
74 dataFromStep1ForStep3[block] = pres.get(svd.outputOfStep1ForStep3)
77 def computeOnMasterNode():
78 global Sigma, V, dataFromStep2ForStep3
81 algorithm = svd.Distributed(step2Master,fptype=np.float64)
83 for i
in range(nBlocks):
84 algorithm.input.add(svd.inputOfStep2FromStep1, i, dataFromStep1ForStep2[i])
87 pres = algorithm.compute()
89 for i
in range(nBlocks):
90 dataFromStep2ForStep3[i] = pres.getCollection(svd.outputOfStep2ForStep3, i)
92 res = algorithm.finalizeCompute()
94 Sigma = res.get(svd.singularValues)
95 V = res.get(svd.rightSingularMatrix)
98 def finalizeComputestep1Local(block):
102 algorithm = svd.Distributed(step3Local,fptype=np.float64)
104 algorithm.input.set(svd.inputOfStep3FromStep1, dataFromStep1ForStep3[block])
105 algorithm.input.set(svd.inputOfStep3FromStep2, dataFromStep2ForStep3[block])
109 res = algorithm.finalizeCompute()
111 Ui[block] = res.get(svd.leftSingularMatrix)
113 if __name__ ==
"__main__":
115 for i
in range(nBlocks):
118 computeOnMasterNode()
120 for i
in range(nBlocks):
121 finalizeComputestep1Local(i)
124 printNumericTable(Sigma,
"Singular values:")
125 printNumericTable(V,
"Right orthogonal matrix V:")
126 printNumericTable(Ui[0],
"Part of left orthogonal matrix U from 1st node:", 10)