After using cudnn ,the training doesn't work

[lvxiao123]

I meet the error:
I0426 10:18:05.214186 8129 net.cpp:408] conv1_1 -> conv1_1
*** Aborted at 1493173085 (unix time) try "date -d @1493173085" if you are using GNU date ***
PC: @ 0x7f8ea2df52b3 (unknown)
*** SIGSEGV (@0x58) received by PID 8129 (TID 0x7f8ed70469c0) from PID 88; stack trace: ***
@ 0x7f8ed49f3cb0 (unknown)
@ 0x7f8ea2df52b3 (unknown)
@ 0x7f8ea33ee3ad (unknown)
@ 0x7f8ea2df8854 (unknown)
@ 0x7f8ea2df9f1a (unknown)
@ 0x7f8ea2df084c (unknown)
@ 0x7f8eaf296562 (unknown)
@ 0x7f8eaf67f5e2 (unknown)
@ 0x7f8eaf680163 (unknown)
@ 0x7f8eaf5d858e (unknown)
@ 0x7f8eaf5d8870 (unknown)
@ 0x7f8ec565bb9d (unknown)
@ 0x7f8ec56515c0 (unknown)
@ 0x7f8ec565ee16 (unknown)
@ 0x7f8ec5662421 (unknown)
@ 0x7f8ec5655f8c (unknown)
@ 0x7f8ec5641782 (unknown)
@ 0x7f8ec5677cdf (unknown)
@ 0x7f8ec51b73c4 (unknown)
@ 0x7f8ed68ac422 caffe::CuDNNConvolutionLayer<>::LayerSetUp()
@ 0x7f8ed69a11ac caffe::Net<>::Init()
@ 0x7f8ed69a2045 caffe::Net<>::Net()
@ 0x7f8ed6a1c97a caffe::Solver<>::InitTrainNet()
@ 0x7f8ed6a1d8fc caffe::Solver<>::Init()
@ 0x7f8ed6a1dc2a caffe::Solver<>::Solver()
@ 0x7f8ed698d2b3 caffe::Creator_SGDSolver<>()
@ 0x410ac8 caffe::SolverRegistry<>::CreateSolver()
@ 0x409ec2 train()
@ 0x40744c main
@ 0x7f8ed49def45 (unknown)
@ 0x407d53 (unknown)
@ 0x0 (unknown)
Segmentation fault (core dumped)

when I don't use cudnn,the training works well.Once using cudnn,the training process aborted here.I can pass all the test with cudnn.In addition,the training file is wrote in python .
Thanks a lot for your reply,the error confuse me for a long time.
Operating system: ubuntu 14.04
Compiler:
CUDA version (if applicable):cuda 8.0
CUDNN version (if applicable):cudnn v5.0
BLAS:
Python or MATLAB version (for pycaffe and matcaffe respectively):python
Graphics:GTX1060

[williford]

Can you make the problem reproducible for others?

[lvxiao123]

@williford ，thank you for your reply.I'm sorry for that I don't know how to reproduce them.My makefile.config is as follows:

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#	You should not set this flag if you will be reading LMDBs with any
#	possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1

# Uncomment if you're using OpenCV 3
 OPENCV_VERSION := 3

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the lines after *_35 for compatibility.
CUDA_ARCH := #-gencode arch=compute_20,code=sm_20 \
             #-gencode arch=compute_20,code=sm_21 \
             -gencode arch=compute_30,code=sm_30 \
             -gencode arch=compute_35,code=sm_35 \
             -gencode arch=compute_50,code=sm_50 \
             -gencode arch=compute_52,code=sm_52 \
             -gencode arch=compute_61,code=sm_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
# BLAS := atlas
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
		/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda2
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
		$(ANACONDA_HOME)/include/python2.7 \
		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
#                 /usr/lib/python3.5/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
 WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1

# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

[lvxiao123]

[----------] Global test environment tear-down
[==========] 2361 tests from 309 test cases ran. (411727 ms total)
[ PASSED ] 2361 tests.
@williford ,As above,I pass all the test.Is the error raised for using python?And the whole error message is as follows:
I0427 00:05:54.126808 5304 layer_factory.hpp:77] Creating layer data
I0427 00:05:54.127187 5304 net.cpp:100] Creating Layer data
I0427 00:05:54.127197 5304 net.cpp:408] data -> data
I0427 00:05:54.127254 5304 net.cpp:408] data -> label
I0427 00:05:54.127836 5312 db_lmdb.cpp:35] Opened lmdb examples/VOC0712/VOC0712_trainval_lmdb
I0427 00:05:56.077105 5304 annotated_data_layer.cpp:62] output data size: 16,3,300,300
I0427 00:05:56.130715 5304 net.cpp:150] Setting up data
I0427 00:05:56.130758 5304 net.cpp:157] Top shape: 16 3 300 300 (4320000)
I0427 00:05:56.130776 5304 net.cpp:157] Top shape: 1 1 1 8 (8)
I0427 00:05:56.130779 5304 net.cpp:165] Memory required for data: 17280032
I0427 00:05:56.130820 5304 layer_factory.hpp:77] Creating layer data_data_0_split
I0427 00:05:56.130847 5304 net.cpp:100] Creating Layer data_data_0_split
I0427 00:05:56.130861 5304 net.cpp:434] data_data_0_split <- data
I0427 00:05:56.130880 5304 net.cpp:408] data_data_0_split -> data_data_0_split_0
I0427 00:05:56.130892 5304 net.cpp:408] data_data_0_split -> data_data_0_split_1
I0427 00:05:56.130904 5304 net.cpp:408] data_data_0_split -> data_data_0_split_2
I0427 00:05:56.130911 5304 net.cpp:408] data_data_0_split -> data_data_0_split_3
I0427 00:05:56.130915 5304 net.cpp:408] data_data_0_split -> data_data_0_split_4
I0427 00:05:56.130919 5304 net.cpp:408] data_data_0_split -> data_data_0_split_5
I0427 00:05:56.130923 5304 net.cpp:408] data_data_0_split -> data_data_0_split_6
I0427 00:05:56.131019 5304 net.cpp:150] Setting up data_data_0_split
I0427 00:05:56.131038 5304 net.cpp:157] Top shape: 16 3 300 300 (4320000)
I0427 00:05:56.131042 5304 net.cpp:157] Top shape: 16 3 300 300 (4320000)
I0427 00:05:56.131057 5304 net.cpp:157] Top shape: 16 3 300 300 (4320000)
I0427 00:05:56.131060 5304 net.cpp:157] Top shape: 16 3 300 300 (4320000)
I0427 00:05:56.131062 5304 net.cpp:157] Top shape: 16 3 300 300 (4320000)
I0427 00:05:56.131079 5304 net.cpp:157] Top shape: 16 3 300 300 (4320000)
I0427 00:05:56.131083 5304 net.cpp:157] Top shape: 16 3 300 300 (4320000)
I0427 00:05:56.131085 5304 net.cpp:165] Memory required for data: 138240032
I0427 00:05:56.131088 5304 layer_factory.hpp:77] Creating layer conv1_1
I0427 00:05:56.131114 5304 net.cpp:100] Creating Layer conv1_1
I0427 00:05:56.131117 5304 net.cpp:434] conv1_1 <- data_data_0_split_0
I0427 00:05:56.131121 5304 net.cpp:408] conv1_1 -> conv1_1
*** Aborted at 1493222756 (unix time) try "date -d @1493222756" if you are using GNU date ***
PC: @ 0x7fe4d73f0628 (unknown)
*** SIGSEGV (@0x34) received by PID 5304 (TID 0x7fe4d6536700) from PID 52; stack trace: ***
@ 0x7fe508fd5cb0 (unknown)
@ 0x7fe4d73f0628 (unknown)
@ 0x7fe4d6e4941f (unknown)
@ 0x7fe4d6e49e10 (unknown)
@ 0x7fe4d6df5165 (unknown)
@ 0x7fe4d73ee3ad (unknown)
@ 0x7fe4d6df8854 (unknown)
@ 0x7fe4d6df9f1a (unknown)
@ 0x7fe4d6df084c (unknown)
@ 0x7fe4e3878562 (unknown)
@ 0x7fe4d54ba392 (unknown)
@ 0x7fe4d54baf13 (unknown)
@ 0x7fe4d54bb5e3 (unknown)
@ 0x7fe4d53b57c7 (unknown)
@ 0x7fe4d53b606d (unknown)
@ 0x7fe4d53b4fed (unknown)
@ 0x7fe500b1ac79 (anonymous namespace)::opencl_fn6<>::switch_fn()
@ 0x7fe500b6c896 cv::ocl::Context::getDefault()
@ 0x7fe500b6c9db cv::ocl::Device::getDefault()
@ 0x7fe500b6ca3d cv::ocl::useOpenCL()
@ 0x7fe4fee29d06 cv::cvtColor()
@ 0x7fe50afcc63a caffe::AdjustSaturation()
@ 0x7fe50afd182a caffe::RandomSaturation()
@ 0x7fe50afd1f97 caffe::ApplyDistort()
@ 0x7fe50adf6bce caffe::DataTransformer<>::DistortImage()
@ 0x7fe50aeff204 caffe::AnnotatedDataLayer<>::load_batch()
@ 0x7fe50af15549 caffe::BasePrefetchingDataLayer<>::InternalThreadEntry()
@ 0x7fe50af95c80 caffe::InternalThread::entry()
@ 0x7fe4fe412585 thread_proxy
@ 0x7fe4f7a3e184 start_thread
@ 0x7fe50909937d (unknown)
@ 0x0 (unknown)
Segmentation fault (core dumped)

[xiaoybboy]

I got the same problem as you ?Did you solve it?