运行导入tensorflow后的非法指令(核心转储)
运行导入tensorflow后的非法指令(核心转储)
提问于 2018-03-04 10:54:06
我创建了一个新的虚拟环境：virtualenv -p python2 test_venv/并安装了tensorflow：pip install --upgrade --no-cache-dir tensorflow
import tensorflow给了我Illegal instruction (core dumped)
请帮助我了解发生了什么事，以及我怎样才能解决它。谢谢。
CPU信息：
-cpu
description: CPU
product: Intel(R) Core(TM) i3 CPU M 330 @ 2.13GHz
bus info: cpu@0
version: CPU Version
capabilities: x86-64 fpu fpu_exception wp vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf pni dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm sse4_1 sse4_2 popcnt lahf_lm tpr_shadow vnmi flexpriority ept vpid dtherm arat cpufreq在gdb中获得的Stacktrace：
#0 0x00007fffe5793880 in std::pair<std::__detail::_Node_iterator<std::pair<tensorflow::StringPiece const, std::function<bool (tensorflow::Variant*)> >, false, true>, bool> std::_Hashtable<tensorflow::StringPiece, std::pair<tensorflow::StringPiece const, std::function<bool (tensorflow::Variant*)> >, std::allocator<std::pair<tensorflow::StringPiece const, std::function<bool (tensorflow::Variant*)> > >, std::__detail::_Select1st, std::equal_to<tensorflow::StringPiece>, tensorflow::StringPieceHasher, std::__detail::_Mod_range_hashing, std::__detail::_Default_ranged_hash, std::__detail::_Prime_rehash_policy, std::__detail::_Hashtable_traits<true, false, true> >::_M_emplace<std::pair<tensorflow::StringPiece, std::function<bool (tensorflow::Variant*)> > >(std::integral_constant<bool, true>, std::pair<tensorflow::StringPiece, std::function<bool (tensorflow::Variant*)> >&&) ()
from /media/gerry/hdd_1/ws_hdd/test_venv/local/lib/python2.7/site-packages/tensorflow/python/../libtensorflow_framework.so
#1 0x00007fffe5795735 in tensorflow::UnaryVariantOpRegistry::RegisterDecodeFn(std::string const&, std::function<bool (tensorflow::Variant*)> const&) () from /media/gerry/hdd_1/ws_hdd/test_venv/local/lib/python2.7/site-packages/tensorflow/python/../libtensorflow_framework.so
#2 0x00007fffe5770a7c in tensorflow::variant_op_registry_fn_registration::UnaryVariantDecodeRegistration<tensorflow::Tensor>::UnaryVariantDecodeRegistration(std::string const&) ()
from /media/gerry/hdd_1/ws_hdd/test_venv/local/lib/python2.7/site-packages/tensorflow/python/../libtensorflow_framework.so
#3 0x00007fffe56ea165 in _GLOBAL__sub_I_tensor.cc ()
from /media/gerry/hdd_1/ws_hdd/test_venv/local/lib/python2.7/site-packages/tensorflow/python/../libtensorflow_framework.so
#4 0x00007ffff7de76ba in call_init (l=<optimized out>, argc=argc@entry=2, argv=argv@entry=0x7fffffffd5c8, env=env@entry=0xa7b4d0)
at dl-init.c:72
#5 0x00007ffff7de77cb in call_init (env=0xa7b4d0, argv=0x7fffffffd5c8, argc=2, l=<optimized out>) at dl-init.c:30
#6 _dl_init (main_map=main_map@entry=0xa11920, argc=2, argv=0x7fffffffd5c8, env=0xa7b4d0) at dl-init.c:120
#7 0x00007ffff7dec8e2 in dl_open_worker (a=a@entry=0x7fffffffb5c0) at dl-open.c:575
#8 0x00007ffff7de7564 in _dl_catch_error (objname=objname@entry=0x7fffffffb5b0, errstring=errstring@entry=0x7fffffffb5b8,
mallocedp=mallocedp@entry=0x7fffffffb5af, operate=operate@entry=0x7ffff7dec4d0 <dl_open_worker>, args=args@entry=0x7fffffffb5c0)
at dl-error.c:187
#9 0x00007ffff7debda9 in _dl_open (
file=0x7fffea7cbc34 "/media/gerry/hdd_1/ws_hdd/test_venv/local/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so", mode=-2147483646, caller_dlopen=0x51ad19 <_PyImport_GetDynLoadFunc+233>, nsid=-2, argc=<optimized out>, argv=<optimized out>, env=0xa7b4d0)
at dl-open.c:660
#10 0x00007ffff75ecf09 in dlopen_doit (a=a@entry=0x7fffffffb7f0) at dlopen.c:66
#11 0x00007ffff7de7564 in _dl_catch_error (objname=0x9b1870, errstring=0x9b1878, mallocedp=0x9b1868, operate=0x7ffff75eceb0 <dlopen_doit>,
args=0x7fffffffb7f0) at dl-error.c:187
#12 0x00007ffff75ed571 in _dlerror_run (operate=operate@entry=0x7ffff75eceb0 <dlopen_doit>, args=args@entry=0x7fffffffb7f0) at dlerror.c:163
#13 0x00007ffff75ecfa1 in __dlopen (file=<optimized out>, mode=<optimized out>) at dlopen.c:87
#14 0x000000000051ad19 in _PyImport_GetDynLoadFunc ()
#15 0x000000000051a8e4 in _PyImport_LoadDynamicModule ()
#16 0x00000000005b7b1b in ?? ()
#17 0x00000000004bc3fa in PyEval_EvalFrameEx ()
#18 0x00000000004c136f in PyEval_EvalFrameEx ()
#19 0x00000000004b9ab6 in PyEval_EvalCodeEx ()
#20 0x00000000004b97a6 in PyEval_EvalCode ()
#21 0x00000000004b96df in PyImport_ExecCodeModuleEx ()
#22 0x00000000004b2b06 in ?? ()
#23 0x00000000004a4ae1 in ?? ()回答 9
Stack Overflow用户
回答已采纳
发布于 2018-03-04 11:36:40
我会用旧版的。看起来你的CPU不支持AVX指令。
引用他们的发布页
Breaking Changes
Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
Prebuilt binaries will use AVX instructions. This may break TF on older CPUs.你至少有两个选择：
- 使用tensorflow 1.5或更高版本
- 从源头构建
关于您对差异的关注，您可能会错过新特性，但大多数基本特性和文档并没有那么不同。
Stack Overflow用户
发布于 2018-04-02 19:30:01
不幸的是，1.6给了很多人同样的错误。我在一台带有旧Core2 CPU的机器上安装了1.7之后收到了它。我已经解决了与1.5，因为我不能适应大显卡在机器与最新的处理器！
Stack Overflow用户
发布于 2018-09-11 06:15:20
正如接受的答案中所解释的，这个问题可以通过安装较早版本的TensorFlow (v1.5)或从源构建来解决。在两者之间，尽管付出了额外的努力，但可以说，从源头构建还是一条首选的途径。假定二进制文件包含TensorFlow的最新组件。
这篇文章解释了如何从源构建TensorFlow，并为旧的CPU进行优化。关键是检测CPU标志，并在配置构建时启用所有CPU标志以进行优化。
以下命令用于检测常见的CPU优化标志：
$ grep flags -m1 /proc/cpuinfo | cut -d ":" -f 2 | tr '[:upper:]' '[:lower:]' | { read FLAGS; OPT="-march=native"; for flag in $FLAGS; do case "$flag" in "sse4_1" | "sse4_2" | "ssse3" | "fma" | "cx16" | "popcnt" | "avx" | "avx2") OPT+=" -m$flag";; esac; done; MODOPT=${OPT//_/\.}; echo "$MODOPT"; }如果通过执行命令，没有显示-mavx和/或-mavx2，则可以确认AVX支持缺失，并且应该使用输出中显示的其他优化标志来完成源构建。
在相关文章中，将更详细地讨论此问题的共同根源，并作为补充参考。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/49094597
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