<86>Apr 7 07:50:36 userdel[93227]: delete user 'rooter' <86>Apr 7 07:50:36 groupadd[93232]: group added to /etc/group: name=rooter, GID=621 <86>Apr 7 07:50:36 groupadd[93232]: group added to /etc/gshadow: name=rooter <86>Apr 7 07:50:36 groupadd[93232]: new group: name=rooter, GID=621 <86>Apr 7 07:50:36 useradd[93236]: new user: name=rooter, UID=621, GID=621, home=/root, shell=/bin/bash <86>Apr 7 07:50:36 userdel[93243]: delete user 'builder' <86>Apr 7 07:50:36 userdel[93243]: removed group 'builder' owned by 'builder' <86>Apr 7 07:50:36 userdel[93243]: removed shadow group 'builder' owned by 'builder' <86>Apr 7 07:50:36 groupadd[93249]: group added to /etc/group: name=builder, GID=622 <86>Apr 7 07:50:36 groupadd[93249]: group added to /etc/gshadow: name=builder <86>Apr 7 07:50:36 groupadd[93249]: new group: name=builder, GID=622 <86>Apr 7 07:50:36 useradd[93254]: new user: name=builder, UID=622, GID=622, home=/usr/src, shell=/bin/bash <13>Apr 7 07:50:39 rpmi: libruby-2.5.1-alt0.M80P.1 1525659728 installed <13>Apr 7 07:50:39 rpmi: libyaml2-0.1.6-alt1 1397147705 installed <13>Apr 7 07:50:39 rpmi: libverto-0.2.6-alt1_6 1455633232 installed <13>Apr 7 07:50:39 rpmi: libkeyutils-1.5.10-alt0.M80P.2 p8+216694.100.6.1 1547827915 installed <13>Apr 7 07:50:39 rpmi: libgdbm-1.8.3-alt10 1454943334 installed <13>Apr 7 07:50:39 rpmi: libcom_err-1.42.13-alt2 1449075923 installed <13>Apr 7 07:50:39 rpmi: ca-certificates-2016.02.25-alt1 1462368370 installed <13>Apr 7 07:50:39 rpmi: libcrypto10-1.0.2n-alt0.M80P.1 1512766170 installed <13>Apr 7 07:50:39 rpmi: ruby-2.5.1-alt0.M80P.1 1525659728 installed <13>Apr 7 07:50:39 rpmi: libssl10-1.0.2n-alt0.M80P.1 1512766170 installed <86>Apr 7 07:50:40 groupadd[106816]: group added to /etc/group: name=_keytab, GID=499 <86>Apr 7 07:50:40 groupadd[106816]: new group: name=_keytab, GID=499 <13>Apr 7 07:50:40 rpmi: libkrb5-1.14.6-alt1.M80P.1 1525355764 installed <13>Apr 7 07:50:40 rpmi: ruby-stdlibs-2.5.1-alt0.M80P.1 1525659728 installed Installing auto-nng-1.7-alt2_3.1.src.rpm Building target platforms: i586 Building for target i586 Executing(%prep): /bin/sh -e /usr/src/tmp/rpm-tmp.94406 + umask 022 + /bin/mkdir -p /usr/src/RPM/BUILD + cd /usr/src/RPM/BUILD + cd /usr/src/RPM/BUILD + rm -rf auto-nng.v1.7 + echo 'Source #0 (auto-nng.v1.7.tar.gz):' Source #0 (auto-nng.v1.7.tar.gz): + /bin/gzip -dc /usr/src/RPM/SOURCES/auto-nng.v1.7.tar.gz + /bin/tar -xf - + cd auto-nng.v1.7 + /bin/chmod -c -Rf u+rwX,go-w . + echo 'Patch #0 (auto-nng-cflags.patch):' Patch #0 (auto-nng-cflags.patch): + /usr/bin/patch -p1 -b --suffix .cflags patching file Makefile + exit 0 Executing(%build): /bin/sh -e /usr/src/tmp/rpm-tmp.91426 + umask 022 + /bin/mkdir -p /usr/src/RPM/BUILD + cd /usr/src/RPM/BUILD + cd auto-nng.v1.7 + make 'CFLAGS=-pipe -Wall -g -O2 -march=i586 -mtune=generic' make: Entering directory `/usr/src/RPM/BUILD/auto-nng.v1.7' cc -pipe -Wall -g -O2 -march=i586 -mtune=generic -o auto-nng auto-nng.c -lm auto-nng.c: In function 'main_generate': auto-nng.c:681:54: warning: 'continuous_input_stddevs' may be used uninitialized in this function [-Wmaybe-uninitialized] for (i = 0; i < continuous_input_count; i++) printf("%f, %f\n", continuous_input_averages[i], continuous_input_stddevs[i]); ^ auto-nng.c:681:54: warning: 'continuous_input_averages' may be used uninitialized in this function [-Wmaybe-uninitialized] auto-nng.c:686:11: warning: 'continuous_output_stddevs' may be used uninitialized in this function [-Wmaybe-uninitialized] printf(DOUBLE_FORMAT ", " DOUBLE_FORMAT "\n", continuous_output_averages[i], continuous_output_stddevs[i]); ^ auto-nng.c:686:11: warning: 'continuous_output_averages' may be used uninitialized in this function [-Wmaybe-uninitialized] auto-nng.c: In function 'main_run': auto-nng.c:771:11: warning: 'continuous_input_stats' may be used uninitialized in this function [-Wmaybe-uninitialized] double *continuous_input_stats; ^ auto-nng.c:910:140: warning: 'continuous_output_stats' may be used uninitialized in this function [-Wmaybe-uninitialized] printf(DOUBLE_FORMAT, read_indicators(low_indicator, high_indicator) * continuous_output_stats[2*j+1] + continuous_output_stats[2*j]); ^ auto-nng.c:904:37: warning: 'continuous_output_cols' may be used uninitialized in this function [-Wmaybe-uninitialized] if (continuous_output_cols[j] == i) { ^ auto-nng.c:897:33: warning: 'binary_output_cols' may be used uninitialized in this function [-Wmaybe-uninitialized] if (binary_output_cols[j] == i) { ^ auto-nng.c:890:36: warning: 'continuous_input_cols' may be used uninitialized in this function [-Wmaybe-uninitialized] if (continuous_input_cols[j] == i) { ^ auto-nng.c:883:32: warning: 'binary_input_cols' may be used uninitialized in this function [-Wmaybe-uninitialized] if (binary_input_cols[j] == i) { ^ make: Leaving directory `/usr/src/RPM/BUILD/auto-nng.v1.7' + exit 0 Executing(%install): /bin/sh -e /usr/src/tmp/rpm-tmp.91426 + umask 022 + /bin/mkdir -p /usr/src/RPM/BUILD + cd /usr/src/RPM/BUILD + /bin/chmod -Rf u+rwX -- /usr/src/tmp/auto-nng-buildroot + : + /bin/rm -rf -- /usr/src/tmp/auto-nng-buildroot + cd auto-nng.v1.7 + mkdir -p /usr/src/tmp/auto-nng-buildroot//usr/bin/ + install auto-nng /usr/src/tmp/auto-nng-buildroot//usr/bin/ + /usr/lib/rpm/brp-alt Cleaning files in /usr/src/tmp/auto-nng-buildroot (auto) Verifying and fixing files in /usr/src/tmp/auto-nng-buildroot (binconfig,pkgconfig,libtool,desktop) Compressing files in /usr/src/tmp/auto-nng-buildroot (auto) Verifying ELF objects in /usr/src/tmp/auto-nng-buildroot (arch=normal,fhs=normal,lfs=relaxed,lint=relaxed,rpath=normal,stack=normal,textrel=normal,unresolved=normal) verify-elf: WARNING: ./usr/bin/auto-nng: uses non-LFS functions: fopen Hardlinking identical .pyc and .pyo files Executing(%check): /bin/sh -e /usr/src/tmp/rpm-tmp.37159 + umask 022 + /bin/mkdir -p /usr/src/RPM/BUILD + cd /usr/src/RPM/BUILD + cd auto-nng.v1.7 + make test make: Entering directory `/usr/src/RPM/BUILD/auto-nng.v1.7' ruby auto-nng-test.rb ================================================== auto-nng v1.7 Copyright (c) 2011 Public Software Group e. V. This software is EXPERIMENTAL and comes with ABSOLUTELY NO WARRANTY. web: http://www.public-software-group.org/ ================================================== Training error: 0.980751 / Test error: 0.987635 / Layer sizes: 10 3 1 Training error: 0.977075 / Test error: 0.986420 / Layer sizes: 10 3 1 Training error: 0.965318 / Test error: 0.980062 / Layer sizes: 10 3 1 Training error: 0.958030 / Test error: 0.976397 / Layer sizes: 10 3 1 Training error: 0.948465 / Test error: 0.974031 / Layer sizes: 10 3 1 Training error: 0.943003 / Test error: 0.971088 / Layer sizes: 10 3 1 Training error: 0.929027 / Test error: 0.970977 / Layer sizes: 10 3 1 Training error: 0.929144 / Test error: 0.965220 / Layer sizes: 10 7 1 Training error: 0.868726 / Test error: 0.963787 / Layer sizes: 10 1 Training error: 0.843821 / Test error: 0.943263 / Layer sizes: 10 1 Training error: 0.922272 / Test error: 0.938408 / Layer sizes: 10 10 1 Training error: 0.905993 / Test error: 0.926892 / Layer sizes: 10 10 1 Training error: 0.811583 / Test error: 0.925524 / Layer sizes: 10 1 Training error: 0.900459 / Test error: 0.916697 / Layer sizes: 10 10 1 Training error: 0.789176 / Test error: 0.897878 / Layer sizes: 10 1 Training error: 0.761563 / Test error: 0.864356 / Layer sizes: 10 1 Training error: 0.753023 / Test error: 0.856223 / Layer sizes: 10 1 Training error: 0.751037 / Test error: 0.847227 / Layer sizes: 10 1 Training error: 0.740342 / Test error: 0.842492 / Layer sizes: 10 1 Training error: 0.729034 / Test error: 0.834951 / Layer sizes: 10 1 Training error: 0.716739 / Test error: 0.830371 / Layer sizes: 10 1 Training error: 0.696545 / Test error: 0.829410 / Layer sizes: 10 1 Training error: 0.679480 / Test error: 0.810602 / Layer sizes: 10 1 Training error: 0.670023 / Test error: 0.809933 / Layer sizes: 10 1 Training error: 0.664251 / Test error: 0.793317 / Layer sizes: 10 1 Training error: 0.660926 / Test error: 0.783322 / Layer sizes: 10 1 Training error: 0.653229 / Test error: 0.778724 / Layer sizes: 10 1 Training error: 0.627326 / Test error: 0.755401 / Layer sizes: 10 1 Training error: 0.624751 / Test error: 0.738365 / Layer sizes: 10 1 Training error: 0.608273 / Test error: 0.725625 / Layer sizes: 10 1 Training error: 0.600373 / Test error: 0.722963 / Layer sizes: 10 1 Training error: 0.565797 / Test error: 0.706517 / Layer sizes: 10 1 Training error: 0.555630 / Test error: 0.706351 / Layer sizes: 10 1 Training error: 0.551318 / Test error: 0.684151 / Layer sizes: 10 1 Training error: 0.530573 / Test error: 0.677832 / Layer sizes: 10 1 Training error: 0.517398 / Test error: 0.665262 / Layer sizes: 10 1 Training error: 0.517281 / Test error: 0.649151 / Layer sizes: 10 1 Training error: 0.494540 / Test error: 0.648153 / Layer sizes: 10 1 Training error: 0.491022 / Test error: 0.646502 / Layer sizes: 10 1 Training error: 0.484372 / Test error: 0.644143 / Layer sizes: 10 1 Training error: 0.479836 / Test error: 0.635410 / Layer sizes: 10 1 Training error: 0.474020 / Test error: 0.612837 / Layer sizes: 10 1 Training error: 0.467638 / Test error: 0.603655 / Layer sizes: 10 1 Training error: 0.463516 / Test error: 0.594765 / Layer sizes: 10 1 Training error: 0.462510 / Test error: 0.583497 / Layer sizes: 10 1 Training error: 0.462202 / Test error: 0.580341 / Layer sizes: 10 1 Training error: 0.462074 / Test error: 0.577009 / Layer sizes: 10 1 Training error: 0.458237 / Test error: 0.570171 / Layer sizes: 10 1 Training error: 0.456929 / Test error: 0.568073 / Layer sizes: 10 1 Training error: 0.455082 / Test error: 0.566630 / Layer sizes: 10 1 Training error: 0.424501 / Test error: 0.565993 / Layer sizes: 10 1 Training error: 0.424100 / Test error: 0.554037 / Layer sizes: 10 1 Training error: 0.422153 / Test error: 0.546840 / Layer sizes: 10 1 Training error: 0.419906 / Test error: 0.542816 / Layer sizes: 10 1 Training error: 0.381661 / Test error: 0.533050 / Layer sizes: 10 3 1 Training error: 0.377165 / Test error: 0.525739 / Layer sizes: 10 3 1 Training error: 0.369102 / Test error: 0.523793 / Layer sizes: 10 3 1 Training error: 0.378496 / Test error: 0.522879 / Layer sizes: 10 10 1 Training error: 0.366558 / Test error: 0.513544 / Layer sizes: 10 3 1 Training error: 0.376347 / Test error: 0.511260 / Layer sizes: 10 10 1 Training error: 0.356498 / Test error: 0.497357 / Layer sizes: 10 3 1 Training error: 0.372888 / Test error: 0.487426 / Layer sizes: 10 10 1 Training error: 0.366674 / Test error: 0.483223 / Layer sizes: 10 10 1 Training error: 0.295657 / Test error: 0.468231 / Layer sizes: 10 10 1 Training error: 0.294359 / Test error: 0.463149 / Layer sizes: 10 10 1 Training error: 0.293992 / Test error: 0.440021 / Layer sizes: 10 10 1 Training error: 0.283774 / Test error: 0.439199 / Layer sizes: 10 10 1 Training error: 0.281175 / Test error: 0.432096 / Layer sizes: 10 10 1 Training error: 0.274074 / Test error: 0.418025 / Layer sizes: 10 10 1 Training error: 0.273313 / Test error: 0.413074 / Layer sizes: 10 10 1 Training error: 0.273106 / Test error: 0.405507 / Layer sizes: 10 10 1 Training error: 0.167690 / Test error: 0.392319 / Layer sizes: 10 10 10 1 Training error: 0.166495 / Test error: 0.376834 / Layer sizes: 10 10 10 1 Training error: 0.159987 / Test error: 0.375592 / Layer sizes: 10 10 10 1 Training error: 0.156985 / Test error: 0.361961 / Layer sizes: 10 10 10 1 Training error: 0.155655 / Test error: 0.357328 / Layer sizes: 10 10 10 1 Training error: 0.148872 / Test error: 0.348981 / Layer sizes: 10 10 10 1 Training error: 0.146103 / Test error: 0.347685 / Layer sizes: 10 10 10 1 Training error: 0.143605 / Test error: 0.344489 / Layer sizes: 10 10 10 1 Training error: 0.135492 / Test error: 0.343206 / Layer sizes: 10 10 10 1 Training error: 0.187130 / Test error: 0.336573 / Layer sizes: 10 10 1 Training error: 0.186792 / Test error: 0.333702 / Layer sizes: 10 10 1 Training error: 0.185454 / Test error: 0.329172 / Layer sizes: 10 10 1 Training error: 0.125942 / Test error: 0.327734 / Layer sizes: 10 10 10 1 Training error: 0.152929 / Test error: 0.318419 / Layer sizes: 10 10 1 Training error: 0.151350 / Test error: 0.314623 / Layer sizes: 10 10 1 Training error: 0.145686 / Test error: 0.307479 / Layer sizes: 10 10 1 Training error: 0.143011 / Test error: 0.303742 / Layer sizes: 10 10 1 Training error: 0.119571 / Test error: 0.301722 / Layer sizes: 10 10 1 Training error: 0.094893 / Test error: 0.286179 / Layer sizes: 10 10 1 Training error: 0.093784 / Test error: 0.262461 / Layer sizes: 10 10 1 Training error: 0.093451 / Test error: 0.260789 / Layer sizes: 10 10 1 Training error: 0.085789 / Test error: 0.258883 / Layer sizes: 10 10 1 Training error: 0.082901 / Test error: 0.254606 / Layer sizes: 10 10 1 Training error: 0.072432 / Test error: 0.242947 / Layer sizes: 10 10 1 Training error: 0.059602 / Test error: 0.241311 / Layer sizes: 10 9 4 1 Training error: 0.059525 / Test error: 0.235773 / Layer sizes: 10 9 4 1 Training error: 0.057886 / Test error: 0.230542 / Layer sizes: 10 9 4 1 Training error: 0.048875 / Test error: 0.225316 / Layer sizes: 10 10 1 Training error: 0.047058 / Test error: 0.217950 / Layer sizes: 10 10 1 Training error: 0.046767 / Test error: 0.205294 / Layer sizes: 10 10 1 Training error: 0.045425 / Test error: 0.204037 / Layer sizes: 10 10 1 Training error: 0.045404 / Test error: 0.201659 / Layer sizes: 10 10 1 Training error: 0.044136 / Test error: 0.193510 / Layer sizes: 10 10 1 Training error: 0.043051 / Test error: 0.191610 / Layer sizes: 10 10 1 Training error: 0.042048 / Test error: 0.189213 / Layer sizes: 10 10 1 Training error: 0.039655 / Test error: 0.184599 / Layer sizes: 10 10 1 Training error: 0.039519 / Test error: 0.181646 / Layer sizes: 10 10 1 Training error: 0.039157 / Test error: 0.177420 / Layer sizes: 10 10 1 Training error: 0.038077 / Test error: 0.175801 / Layer sizes: 10 10 1 Training error: 0.037509 / Test error: 0.172978 / Layer sizes: 10 10 1 Training error: 0.036606 / Test error: 0.169061 / Layer sizes: 10 10 1 Training error: 0.036549 / Test error: 0.167629 / Layer sizes: 10 10 1 Training error: 0.036189 / Test error: 0.165409 / Layer sizes: 10 10 1 Training error: 0.036123 / Test error: 0.164417 / Layer sizes: 10 10 1 Training error: 0.035980 / Test error: 0.156868 / Layer sizes: 10 10 1 Training error: 0.035365 / Test error: 0.155829 / Layer sizes: 10 10 1 Training error: 0.035019 / Test error: 0.147723 / Layer sizes: 10 10 1 Training error: 0.034930 / Test error: 0.145790 / Layer sizes: 10 10 1 Training error: 0.034425 / Test error: 0.143639 / Layer sizes: 10 10 1 Training error: 0.003234 / Test error: 0.141852 / Layer sizes: 10 10 1 Training error: 0.003229 / Test error: 0.137797 / Layer sizes: 10 10 1 Training error: 0.002812 / Test error: 0.127112 / Layer sizes: 10 10 1 Training error: 0.002806 / Test error: 0.124456 / Layer sizes: 10 10 1 Training error: 0.002799 / Test error: 0.109639 / Layer sizes: 10 10 1 Training error: 0.002484 / Test error: 0.100366 / Layer sizes: 10 10 1 Training error: 0.002434 / Test error: 0.099936 / Layer sizes: 10 10 1 Training error: 0.001301 / Test error: 0.098519 / Layer sizes: 10 10 1 Training error: 0.001299 / Test error: 0.092408 / Layer sizes: 10 10 1 Training error: 0.001185 / Test error: 0.091163 / Layer sizes: 10 10 1 Training error: 0.001122 / Test error: 0.090541 / Layer sizes: 10 10 1 Training error: 0.001114 / Test error: 0.086240 / Layer sizes: 10 10 1 Training error: 0.001112 / Test error: 0.085780 / Layer sizes: 10 10 1 Training error: 0.001110 / Test error: 0.081847 / Layer sizes: 10 10 1 Training error: 0.001105 / Test error: 0.079383 / Layer sizes: 10 10 1 Training error: 0.000734 / Test error: 0.077331 / Layer sizes: 10 10 1 Training error: 0.000726 / Test error: 0.077178 / Layer sizes: 10 10 1 Training error: 0.000724 / Test error: 0.077002 / Layer sizes: 10 10 1 Training error: 0.000003 / Test error: 0.041337 / Layer sizes: 10 10 1 Training error: 0.000003 / Test error: 0.041285 / Layer sizes: 10 10 1 Training error: 0.000003 / Test error: 0.041223 / Layer sizes: 10 10 1 Training error: 0.000003 / Test error: 0.041141 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.040864 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.040776 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.040679 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.040612 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.040570 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.040223 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.039959 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.039797 / Layer sizes: 10 10 1 Training error: 0.000002 / Test error: 0.039742 / Layer sizes: 10 10 1 Training error: 0.000001 / Test error: 0.035300 / Layer sizes: 10 10 1 Training error: 0.000001 / Test error: 0.010251 / Layer sizes: 10 10 1 Training error: 0.000001 / Test error: 0.000339 / Layer sizes: 10 10 1 Training error: 0.000001 / Test error: 0.000022 / Layer sizes: 10 10 1 Training error: 0.000001 / Test error: 0.000019 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000017 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000012 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000006 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000004 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000003 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000003 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000003 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000003 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000002 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000002 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Training error: 0.000000 / Test error: 0.000001 / Layer sizes: 10 10 1 Finishing... ================================================== auto-nng v1.7 Copyright (c) 2011 Public Software Group e. V. This software is EXPERIMENTAL and comes with ABSOLUTELY NO WARRANTY. web: http://www.public-software-group.org/ ================================================== Loading neuronal network from file "test.network.nn". Network loaded, processing data. 91.87 % correct. 8.13 % wrong. Limit: 18.24 %. Test passed. make: Leaving directory `/usr/src/RPM/BUILD/auto-nng.v1.7' + exit 0 Processing files: auto-nng-1.7-alt2_3.1 Executing(%doc): /bin/sh -e /usr/src/tmp/rpm-tmp.36239 + umask 022 + /bin/mkdir -p /usr/src/RPM/BUILD + cd /usr/src/RPM/BUILD + cd auto-nng.v1.7 + DOCDIR=/usr/src/tmp/auto-nng-buildroot/usr/share/doc/auto-nng-1.7 + export DOCDIR + rm -rf /usr/src/tmp/auto-nng-buildroot/usr/share/doc/auto-nng-1.7 + /bin/mkdir -p /usr/src/tmp/auto-nng-buildroot/usr/share/doc/auto-nng-1.7 + cp -prL LICENSE README /usr/src/tmp/auto-nng-buildroot/usr/share/doc/auto-nng-1.7 + chmod -R go-w /usr/src/tmp/auto-nng-buildroot/usr/share/doc/auto-nng-1.7 + chmod -R a+rX /usr/src/tmp/auto-nng-buildroot/usr/share/doc/auto-nng-1.7 + exit 0 Finding Provides (using /usr/lib/rpm/find-provides) Executing: /bin/sh -e /usr/src/tmp/rpm-tmp.t7i31W find-provides: running scripts (alternatives,debuginfo,lib,pam,perl,pkgconfig,python,shell) Finding Requires (using /usr/lib/rpm/find-requires) Executing: /bin/sh -e /usr/src/tmp/rpm-tmp.nc6R1K find-requires: running scripts (cpp,debuginfo,files,lib,pam,perl,pkgconfig,pkgconfiglib,python,rpmlib,shebang,shell,static,symlinks) Requires: /lib/ld-linux.so.2, libc.so.6(GLIBC_2.0), libc.so.6(GLIBC_2.1), libc.so.6(GLIBC_2.3.4), libc.so.6(GLIBC_2.4), libm.so.6(GLIBC_2.0), libm.so.6(GLIBC_2.1), rtld(GNU_HASH) Finding debuginfo files (using /usr/lib/rpm/find-debuginfo-files) Executing: /bin/sh -e /usr/src/tmp/rpm-tmp.YlfyXC Creating auto-nng-debuginfo package Processing files: auto-nng-debuginfo-1.7-alt2_3.1 Finding Provides (using /usr/lib/rpm/find-provides) Executing: /bin/sh -e /usr/src/tmp/rpm-tmp.01lA1z find-provides: running scripts (debuginfo) Finding Requires (using /usr/lib/rpm/find-requires) Executing: /bin/sh -e /usr/src/tmp/rpm-tmp.Bq7ZlD find-requires: running scripts (debuginfo) Requires: auto-nng = 1.7-alt2_3.1, /usr/lib/debug/lib/ld-linux.so.2.debug, debug(libc.so.6), debug(libm.so.6) Wrote: /usr/src/RPM/RPMS/i586/auto-nng-1.7-alt2_3.1.i586.rpm Wrote: /usr/src/RPM/RPMS/i586/auto-nng-debuginfo-1.7-alt2_3.1.i586.rpm 67.06user 0.33system 1:37.03elapsed 69%CPU (0avgtext+0avgdata 34672maxresident)k 0inputs+0outputs (0major+126154minor)pagefaults 0swaps 70.06user 2.34system 1:43.76elapsed 69%CPU (0avgtext+0avgdata 122300maxresident)k 0inputs+0outputs (0major+335152minor)pagefaults 0swaps