我已经使用paddlepaddle中的.deb文件安装了https://github.com/baidu/Paddle/releases/download/V0.8.0b1/paddle-gpu-0.8.0b1-Linux.deb
我在装有4 GTX 1080的计算机上安装了带有cudnn v5.1且没有NVIDIA Accelerated Graphics Driver的CUDA 8.0：
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Sun_Sep__4_22:14:01_CDT_2016
Cuda compilation tools, release 8.0, V8.0.44
我已经设置了shell变量：
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda
所有的
cuda都工作正常，因为我已经运行了所有的NVIDIA_CUDA-8.0_Samples并且它们“通过”了所有测试。quick_start中的Paddle/demo/quick_start演示代码也可以平稳运行，并且不会引发错误。但是，当我尝试从Paddle github存储库中运行
image_classification演示程序时，出现了invalid device function错误。有什么办法可以解决这个问题？hl_gpu_matrix_kernel.cuh:181] Check failed: cudaSuccess == err (0 vs. 8) [hl_gpu_apply_unary_op failed] CUDA error: invalid device function
完整的回溯：
~/Paddle/demo/image_classification$ bash train.sh
I1005 14:34:51.929863 10461 Util.cpp:151] commandline: /home/ltan/Paddle/binary/bin/../opt/paddle/bin/paddle_trainer --config=vgg_16_cifar.py --dot_period=10 --log_period=100 --test_all_data_in_one_period=1 --use_gpu=1 --trainer_count=1 --num_passes=200 --save_dir=./cifar_vgg_model
I1005 14:34:56.705898 10461 Util.cpp:126] Calling runInitFunctions
I1005 14:34:56.706171 10461 Util.cpp:139] Call runInitFunctions done.
[INFO 2016-10-05 14:34:56,918 layers.py:1620] channels=3 size=3072
[INFO 2016-10-05 14:34:56,919 layers.py:1620] output size for __conv_0__ is 32
[INFO 2016-10-05 14:34:56,920 layers.py:1620] channels=64 size=65536
[INFO 2016-10-05 14:34:56,920 layers.py:1620] output size for __conv_1__ is 32
[INFO 2016-10-05 14:34:56,922 layers.py:1681] output size for __pool_0__ is 16*16
[INFO 2016-10-05 14:34:56,923 layers.py:1620] channels=64 size=16384
[INFO 2016-10-05 14:34:56,923 layers.py:1620] output size for __conv_2__ is 16
[INFO 2016-10-05 14:34:56,924 layers.py:1620] channels=128 size=32768
[INFO 2016-10-05 14:34:56,925 layers.py:1620] output size for __conv_3__ is 16
[INFO 2016-10-05 14:34:56,926 layers.py:1681] output size for __pool_1__ is 8*8
[INFO 2016-10-05 14:34:56,927 layers.py:1620] channels=128 size=8192
[INFO 2016-10-05 14:34:56,927 layers.py:1620] output size for __conv_4__ is 8
[INFO 2016-10-05 14:34:56,928 layers.py:1620] channels=256 size=16384
[INFO 2016-10-05 14:34:56,929 layers.py:1620] output size for __conv_5__ is 8
[INFO 2016-10-05 14:34:56,930 layers.py:1620] channels=256 size=16384
[INFO 2016-10-05 14:34:56,930 layers.py:1620] output size for __conv_6__ is 8
[INFO 2016-10-05 14:34:56,932 layers.py:1681] output size for __pool_2__ is 4*4
[INFO 2016-10-05 14:34:56,932 layers.py:1620] channels=256 size=4096
[INFO 2016-10-05 14:34:56,933 layers.py:1620] output size for __conv_7__ is 4
[INFO 2016-10-05 14:34:56,934 layers.py:1620] channels=512 size=8192
[INFO 2016-10-05 14:34:56,934 layers.py:1620] output size for __conv_8__ is 4
[INFO 2016-10-05 14:34:56,936 layers.py:1620] channels=512 size=8192
[INFO 2016-10-05 14:34:56,936 layers.py:1620] output size for __conv_9__ is 4
[INFO 2016-10-05 14:34:56,938 layers.py:1681] output size for __pool_3__ is 2*2
[INFO 2016-10-05 14:34:56,938 layers.py:1681] output size for __pool_4__ is 1*1
[INFO 2016-10-05 14:34:56,941 networks.py:1125] The input order is [image, label]
[INFO 2016-10-05 14:34:56,941 networks.py:1132] The output order is [__cost_0__]
I1005 14:34:56.948256 10461 Trainer.cpp:170] trainer mode: Normal
F1005 14:34:56.949136 10461 hl_gpu_matrix_kernel.cuh:181] Check failed: cudaSuccess == err (0 vs. 8) [hl_gpu_apply_unary_op failed] CUDA error: invalid device function
*** Check failure stack trace: ***
@ 0x7fa557316daa (unknown)
@ 0x7fa557316ce4 (unknown)
@ 0x7fa5573166e6 (unknown)
@ 0x7fa557319687 (unknown)
@ 0x78a939 hl_gpu_apply_unary_op<>()
@ 0x7536bf paddle::BaseMatrixT<>::applyUnary<>()
@ 0x7532a9 paddle::BaseMatrixT<>::applyUnary<>()
@ 0x73d82f paddle::BaseMatrixT<>::zero()
@ 0x66d2ae paddle::Parameter::enableType()
@ 0x669acc paddle::parameterInitNN()
@ 0x66bd13 paddle::NeuralNetwork::init()
@ 0x679ed3 paddle::GradientMachine::create()
@ 0x6a6355 paddle::TrainerInternal::init()
@ 0x6a2697 paddle::Trainer::init()
@ 0x53a1f5 main
@ 0x7fa556522f45 (unknown)
@ 0x545ae5 (unknown)
@ (nil) (unknown)
/home/xxx/Paddle/binary/bin/paddle: line 81: 10461 Aborted (core dumped) ${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_trainer ${@:2}
No data to plot. Exiting!
根据git repo的问题158，应在＃170中解决此问题，并支持带有CUDA 8.0的GTX 1080，但在访问GPU函数时仍会引发错误。 （对不起，声望低的链接不能超过2个）
有谁知道如何解决此问题并安装它以便
image_classification可以运行？我还尝试过从源代码进行编译和安装，而
quick_start演示运行平稳时会引发相同的错误。 最佳答案
问题是由于在CUDA 8.0的Paddle/cmake/flags.cmake中为体系结构设置了标志。
通过添加compute_52，sm_52和compute_60和sm_60在https://github.com/baidu/Paddle/pull/165/files中已解决此问题
关于c++ - 如何解决Paddle v0.8.0b上的“cudaSuccess = err(0 vs. 8)”错误？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/39850309/