control: tag -1 +moreinfo On Thu, Aug 09, 2018 at 08:01:10PM +0200, Adam Borowski wrote: > On Thu, Aug 09, 2018 at 10:16:17AM +0000, Lumin wrote: > > * Package name : mkl-dnn > > Version : 0.15+git20180803.3f58c16-1 > > Upstream Author : intel > > Alas, the build flags use -march=native -mtune=native which is a big no-no. > The first makes the package crash on any processor lacking an extension that > was present on the build machine and was used by the compiler; unless some > kind of runtime detection is used, packages are allowed only the baseline > ISA for the architecture. As for -mtune=native, it makes the package build > unreproducibly, differing based on where it was compiled.
My bad, I overlooked the two flags. The cmake files have been patched in master branch of packaging repo. https://salsa.debian.org/science-team/mkl-dnn/commit/6e0a9bea677d398ee23ac9c2f84c3551d100a6d4 http://debomatic-amd64.debian.net/distribution#unstable/mkl-dnn/0.15+git20180803.3f58c16-1/buildlog > The second problem is that in the testsuite, test_convolution_format_any > fails (0/5 sub-tests). This might be related to my machine being: > vendor_id : AuthenticAMD > model name : AMD Phenom(tm) II X6 1055T Processor Well, I have been waiting for intel to fix test failures for a long time. Finally the snapshot 0.15+git20180803.3f58c16 doesn't fail any test on dom-amd64 (E5 2699v?) and my I5-7440HQ, but now it failed on AMD cpu ... > Log of the FTBFS attached. Thanks for the log, I've forwarded it to upstream. https://github.com/intel/mkl-dnn/issues/291 I shouldn't let any test failure from mkl-dnn pass, so we have to wait for upstream to fix the problem. Fortunately, TensorFlow can be compiled with or without mkl-dnn. It doesn't matter if the initial upload of TensorFlow is not linked against mkl-dnn. The difference that mkl-dnn would bring to TensorFlow is computation speed-up.