TensorFlow not loading cuDNN - tensorflow

Question

I have finally managed to get CUDA to work on a Microsoft Azure server with a Kesla T80. Now I need to get cuDNN to work, but TensorFlow won't load it.
This is the message from TensorFlow:
>>> 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>
So I see there is no cuDNN libraries being loaded.
I have the proper files in /cuda-8.0/include/ and /cuda-8.0/lib64/
$ ls /usr/local/cuda-8.0/include/ | grep "cudnn"
cudnn.h
$ ls /usr/local/cuda-8.0/lib64/ | grep "cudnn"
libcudnn.so
libcudnn.so.5
libcudnn.so.5.1.10
libcudnn_static.a
My ~/.bashrc file has the proper paths
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}
EDIT
Changed the .bashrcto:
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}
Still no luck.
Output from nvidia-smi:
+-----------------------------------------------------------------------------+
| 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 |
+-----------------------------------------------------------------------------+
I'm using tensorflow version 1.1.0, Ubuntu 16.04 and CUDA 8.0.
EDIT
So I just tried to delete the cudnn files and load tensorflow, which gave me an error. Something along couldn't finde libcuddn.so.5. So I think it loads it, but I was of the impression that TensorFlow will write something along with "libcuddn.so loaded successfully" if it used cuDNN.