Intel® oneAPI Math Kernel Library
Ask questions and share information with other developers who use Intel® Math Kernel Library.
Announcements
This community is designed for sharing of public information. Please do not share Intel or third-party confidential information here.

numpy issue with mkl 10.1

aksharb
Beginner
119 Views
hi,
i have ict 11/069 and mkl 10.1, os is rhel 5.2 x86_64, processor is intel xeon. I have installed numpy 1.6.0 in python 2.6 using mkl.
now when i run numpy.test, it hangs here :

test_invalid_type_descr (test_defchararray.TestVecString) ... ok
test_non_existent_method (test_defchararray.TestVecString) ... ok
test_non_string_array (test_defchararray.TestVecString) ... ok
test1 (test_defchararray.TestWhitespace) ... ok
test_dtype (test_dtype.TestBuiltin) ... ok
Only test hash runs at all. ... ok
test_metadata_rejects_nondict (test_dtype.TestMetadata) ... ok
test_metadata_takes_dict (test_dtype.TestMetadata) ... ok
test_nested_metadata (test_dtype.TestMetadata) ... ok
test_no_metadata (test_dtype.TestMetadata) ... ok
test1 (test_dtype.TestMonsterType) ... ok
test_different_names (test_dtype.TestRecord) ... ok
test_different_titles (test_dtype.TestRecord) ... ok
Test whether equivalent record dtypes hash the same. ... ok
Test if an appropriate exception is raised when passing bad values to ... ok
Test whether equivalent subarray dtypes hash the same. ... ok
Test whether different subarray dtypes hash differently. ... ok
Test some data types that are equal ... ok
Test some more complicated cases that shouldn't be equal ... ok
Test some simple cases that shouldn't be equal ... ok
test_single_subarray (test_dtype.TestSubarray) ... ok
test_einsum_errors (test_einsum.TestEinSum) ... ok
test_einsum_sums_cfloat128 (test_einsum.TestEinSum) ...

in site.cfg,
mkl_libs = mkl_intel_lp64, mkl_intel_thread, mkl_core, mkl_mc, mkl_def
lapack_libs = mkl_lapack
0 Kudos
1 Reply
TimP
Black Belt
119 Views
There's no cfloat128 support in MKL, so a correctly configured build wouldn't be using MKL in that test. Perhaps you should be consulting numpy experts.
Reply