Question

Ubuntu 14.04，CUDA版本7.5.18，每晚构建张量流

在tensorflow中运行tf.nn.max_pool()操作时，出现以下错误：

E tensorflow / stream_executor / cuda / cuda_dnn.cc：286]加载的cudnn   library：5005但是source是针对4007编译的。如果使用二进制文件   安装，升级您的cudnn库以匹配。如果建设   来源，确保加载的库与您的版本匹配   在编译配置期间指定。

W tensorflow / stream_executor / stream.cc：577]尝试执行DNN   使用没有DNN支持的StreamExecutor进行操作

追踪（最近一次呼叫最后一次）：

...

如何在tensorflow的编译配置中指定我的cudnn版本？

Answer 1

进入TensorFlow源代码目录，然后执行配置文件：/.configure。

以下是TensorFlow documentation：

的示例

$ ./configure
Please specify the location of python. [Default is /usr/bin/python]:
Do you wish to build TensorFlow with GPU support? [y/N] y
GPU support will be enabled for TensorFlow

Please specify which gcc nvcc should use as the host compiler. [Default is
/usr/bin/gcc]: /usr/bin/gcc-4.9

Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave
empty to use system default]: 7.5

Please specify the location where CUDA 7.5 toolkit is installed. Refer to
README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda

Please specify the Cudnn version you want to use. [Leave empty to use system
default]: 4.0.4

Please specify the location where the cuDNN 4.0.4 library is installed. Refer to
README.md for more details. [default is: /usr/local/cuda]: /usr/local/cudnn-r4-rc/

Please specify a list of comma-separated Cuda compute capabilities you want to
build with. You can find the compute capability of your device at:
https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your
build time and binary size. [Default is: \"3.5,5.2\"]: 3.5

Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished

Answer 2

好像你已经安装了cudnn 5。您需要在运行./configure

时进行设置

Please specify the Cudnn version you want to use. [Leave empty to use system
default]: 5

Answer 3

添加我的2美分：在我的情况下（TF0.12.1，从UPDATE t SET USER_FNM = bauser.user_fnm FROM @Users t INNER JOIN bauser ON bauser.user_key = t.USER_KEY安装到anaconda，没有pip权限）安装了CuDNNv5，但不是默认值。

设置sudo解决了问题

Answer 4

我也遇到了这样一个不兼容的问题：

Loaded runtime CuDNN library: 5005 (compatibility version 5000) but source wascompiled with 5110 (compatibility version 5100).  If using a binary install, upgrade your CuDNNlibrary to match.  If building fromsources, make sure the library loaded at runtime matches a compatible versionspecified during compile configuration.

所以我下载了CuDNN 5.1（与CUDA8.0兼容）并用它替换5.0然后一切顺利。

警告：来自nvidia的CuDNN是不可用的，但您可以从其他人的共享中找到它。

censnFlow

4 个答案: