Tensorflow not running on GPU in jupyter notebook

Question

Installed Cuda and cudnn sucessfully for the GTX 1080 ti on Ubuntu, running a simple TF program in the jupyter notebook the speed does not increase in a conda environment running tensorflow-gpu==1.0 vs tensorflow==1.0.

When I run nvidia-smi :

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.66 Driver Version: 375.66 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 0000:01:00.0 On | N/A |
| 24% 45C P0 62W / 250W | 537MiB / 11171MiB | 0% Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1101 G /usr/lib/xorg/Xorg 310MiB |
| 0 1877 G compiz 219MiB |
| 0 3184 G /usr/lib/firefox/firefox 5MiB |
+-----------------------------------------------------------------------------+

I have tried putting the "with tf.device("/gpu:0"):" in front of the matrix multiplications but it just gives me an error:

"InvalidArgumentError (see above for traceback): Cannot assign a device to node 'MatMul': Could not satisfy explicit device specification '/device:GPU:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:0 [[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/device:GPU:0"](Reshape, softmax/Variable/read)]]"

I know cudnn is installed correctly because I get this message when running it in the terminal.

import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally

I think this has to be something with the Jupiter notebook, is there a compatibility issue? When I run the TF session I get this output:

W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
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.
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.
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.
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.
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.
Device mapping: no known devices.

"""

Answer 1

I solved the issue. Apparently I had jupyter and regular tensorflow installed outside of my environment. Yet I had tensorflow-gpu installed within my environment. So when I ran jupyter it was calling the tensorflow outside of the environment not the tensorflow-gpu installed within the environment.