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cudnn在TensorFlow中编译配置

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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，我收到以下错误：

E tensorflow / stream_executor / cuda / cuda_dnn.cc：286]加载的cudnn库：5005但源代码是针对4007编译的 . 如果使用二进制安装，请升级您的cudnn库以匹配 . 如果从源构建，请确保加载的库与您在编译配置期间指定的版本匹配 . W tensorflow / stream_executor / stream.cc：577]尝试使用StreamExecutor执行DNN操作而不支持DNN Traceback（最近一次调用最后一次）：...

如何在tensorflow的编译配置中指定我的cudnn版本？

tensorflow cudnn

4 回答

  • 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
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  • 0

    好像你已经安装了cudnn 5 . 你需要在运行 ./configure 时设置它

    Please specify the Cudnn version you want to use. [Leave empty to use system
default]: 5
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  • 2

    添加我的2美分：在我的情况下（TF0.12.1，从 pip 安装到anaconda，没有 sudo 权限）CuDNNv5已安装，但不是默认值 .

    设置 export LD_LIBRARY_PATH="/usr/local/lib/cuda-8.0/lib64:/usr/local/lib/cudann5/lib64/" 解决了这个问题

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  • 0

    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 .

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