TensorFlow未加载cuDNN

我终于设法让CUDA在带有Kesla T80的Microsoft Azure服务器上工作。现在我需要让cuDNN工作，但TensorFlow不会加载它

这是来自TensorFlow的消息：

>>> import tensorflow as tf
>>> tf.Session()

2017-04-27 13:05:51.476251: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-04-27 13:05:51.476306: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-04-27 13:05:51.476338: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-04-27 13:05:51.476366: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-04-27 13:05:51.476394: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-04-27 13:05:58.164781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: 
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID ad52:00:00.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-04-27 13:05:58.164822: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
2017-04-27 13:05:58.164835: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y 
2017-04-27 13:05:58.164853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K80, pci bus id: ad52:00:00.0)
<tensorflow.python.client.session.Session object at 0x7fc3c76c0050>

我的

~/.bashrc

文件具有正确的路径

export CUDA_HOME=/usr/local/cuda8.0
export PATH=${CUDA_HOME}/bin:${PATH}
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:/usr/local/cuda/lib64:${LD_LIBRARY_PATH}

编辑 将

.bashrc

更改为：

export CUDA_HOME=/usr/local/cuda-8.0
export PATH=${CUDA_HOME}/bin:${PATH}
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:/usr/local/cuda/lib64:${LD_LIBRARY_PATH}
export PATH=${CUDA_HOME}/include:${PATH}

还是不走运

nvidia smi的输出：

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.51                 Driver Version: 375.51                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | AD52:00:00.0     Off |                    0 |
| N/A   71C    P0    61W / 149W |      0MiB / 11439MiB |     24%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

我使用的是tensorflow版本1.1.0、Ubuntu 16.04和CUDA 8.0

编辑

---

所以我只是尝试删除cudnn文件并加载tensorflow，这给了我一个错误。有些东西找不到libcuddn所以。因此，我认为它会加载它，但我的印象是，如果TensorFlow使用cuDNN，它将与“libcuddn.So loaded successfully”一起编写一些东西。

您是如何安装TensorFlow、使用pip还是从源代码中安装的。@Kristrove使用pip。您是否手动设置cuDNN路径。如果是这样的话，您可能需要添加lib并包含用户环境变量的路径。那些在.bashrc文件中？是。检查是否有此路径/usr/local/cuda-8.0/include

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.51                 Driver Version: 375.51                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | AD52:00:00.0     Off |                    0 |
| N/A   71C    P0    61W / 149W |      0MiB / 11439MiB |     24%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+