How do I reinstate the GPU version of tensorflow that was running 2 days ago on my host?

Question

Just two days ago, after much work on my part downloading and installing the latest stable GPU version of tensorflow, my tensorflow installation was behaving correctly as I wanted, and it reported this:

$ source activate tensorflowgpu
(tensorflowgpu) ga@ga-HP-Z820:~$ python
Python 3.5.4 |Anaconda, Inc.| (default, Nov 20 2017, 18:44:38) 
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant("Hello tensorflow")
>>> sess = tf.Session()
2018-01-22 12:37:32.119300: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX
2018-01-22 12:37:33.339324: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.797
pciBusID: 0000:41:00.0
totalMemory: 7.92GiB freeMemory: 7.80GiB
2018-01-22 12:37:33.339414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:41:00.0, compute capability: 6.1)
>>> print(sess.run(hello))
b'Hello tensorflow'

Sadly however, to my complete surprise today my tensorflow installation is misbehaving because it is reporting the following, that is, it's using the CPU version. What in the world happened to it, and how do I make it behave correctly again, that is, reinstate the GPU version of tensorflow? This is my privately owned workstation of which I am the sole user.

ga@ga-HP-Z820:~$ source activate tensorflowgpu
(tensorflowgpu) ga@ga-HP-Z820:~$ ipython
Python 3.5.4 |Anaconda custom (64-bit)| (default, Nov 20 2017, 18:44:38) 
Type 'copyright', 'credits' or 'license' for more information
IPython 6.2.1 -- An enhanced Interactive Python. Type '?' for help.
...   
In [2]: import tensorflow as tf
   ...: hello = tf.constant("Hello tensorflow")
   ...: sess = tf.Session()
   ...: 
2018-01-24 12:34:56.792676: 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.
2018-01-24 12:34:56.792719: 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.
2018-01-24 12:34:56.792729: 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.

Now looking closer, todauy my python (not ipython) does the following, which is good behavior again. That's strange -- the 2 pythons load different tensorflows. So how do I compel ipython and python to both use the GPU version of tensorflow? I really use ipython more, esp. for jupyter notebooks.

(tensorflowgpu) ga@ga-HP-Z820:~$ python
Python 3.5.4 |Anaconda, Inc.| (default, Nov 20 2017, 18:44:38) 
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant("Hello tensorflow")
>>> sess = tf.Session()
2018-01-24 12:50:35.846985: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX
2018-01-24 12:50:37.222662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.797
pciBusID: 0000:41:00.0
totalMemory: 7.92GiB freeMemory: 7.80GiB
2018-01-24 12:50:37.222721: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:41:00.0, compute capability: 6.1)

I had followed the installation instructions for tensorflow GPU on linux ubuntu at tensorflow website. It did not say to remove the CPU version first.

Edit -- As follows, I explicitly queried the tf versions. It confirms that the two different versions that are installed are being loaded by python vs ipython. I would want to cause both pythons to use only the newer version of tf, somehow.

(tensorflowgpu) ga@ga-HP-Z820:~$ python
Python 3.5.4 |Anaconda, Inc.| (default, Nov 20 2017, 18:44:38) 
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'1.4.1'
>>> 
(tensorflowgpu) ga@ga-HP-Z820:~$ ipython
Python 3.5.4 |Anaconda custom (64-bit)| (default, Nov 20 2017, 18:44:38) 
Type 'copyright', 'credits' or 'license' for more information
IPython 6.2.1 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import tensorflow as tf

In [2]: tf.__version__
Out[2]: '1.3.0'

Answer 1

You'll need to install the kernel in ipython, so that it knows about your environment. Currently, ipython is picking up the default python on your system, not the environment one. You can install the kernel by (make sure your environment is active)

pip install ipykernel
python -m ipykernel install --user --name tensorflowgpu

Now, just select this kernel when running ipython