I'm use VMware Fusion and just downloaded "Install macOS Sierra.app" from the App Store and then made an ISO Image to install from. The ISO Image should also work in Parallels Desktop.

To create an ISO Image from the "Install macOS Sierra.app" application bundle, I used the following bash script. Note: This requires 12 GB of free space to create but only uses 6 GB when finished.

In Terminal:

touch makeSierraISO
open makeSierraISO

• Copy and paste the code below into the document, modifying the path to the InstallESD.dmg if necessary, then save and close.

Back in Terminal:

chmod u+x makeSierraISO

To run the script in Terminal: ./makeSierraISO

#!/bin/bash

hdiutil attach "/Applications/Install macOS Sierra.app/Contents/SharedSupport/InstallESD.dmg" -noverify -nobrowse -mountpoint /Volumes/esd
hdiutil create -o macOS_Sierra_10.12.0.cdr -size 6144m -layout SPUD -fs HFS+J
hdiutil attach macOS_Sierra_10.12.0.cdr.dmg -noverify -nobrowse -mountpoint /Volumes/iso
asr restore -source /Volumes/esd/BaseSystem.dmg -target /Volumes/iso -noprompt -noverify -erase
rm /Volumes/OS\ X\ Base\ System/System/Installation/Packages
cp -rp /Volumes/esd/Packages /Volumes/OS\ X\ Base\ System/System/Installation
cp -rp /Volumes/esd/BaseSystem.chunklist /Volumes/OS\ X\ Base\ System/
cp -rp /Volumes/esd/BaseSystem.dmg /Volumes/OS\ X\ Base\ System/
hdiutil detach /Volumes/esd
hdiutil detach /Volumes/OS\ X\ Base\ System
hdiutil convert macOS_Sierra_10.12.0.cdr.dmg -format UDTO -o macOS_Sierra_10.12.0.iso
rm macOS_Sierra_10.12.0.cdr.dmg
mv macOS_Sierra_10.12.0.iso.cdr macOS_Sierra_10.12.0.iso

Assuming you ran the script from your Home Folder in Terminal, then look for the file macOS_Sierra_10.12.0.iso in your Home Folder. Use Parallels to create new VM. Point the new VM wizard to that .iso file. Parallels may complain, saying it is unable to recognize the OS being installed. Proceed and choose macOS from the offered menu.

I could finally install Nvidia Titan XP + MacBook Pro + Akitio Node + Tensorflow + Keras

I wrote a gist with the procedure, hope it helps

https://gist.github.com/jganzabal/8e59e3b0f59642dd0b5f2e4de03c7687

Here is what I did:

This configuration worked for me, hope it helps

It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b

and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support

Hardware

• Nvidia Video Card: Titan Xp
• EGPU: Akitio Node
• MacBook Pro (Retina, 13-inch, Early 2015)
• Apple Thunderbolt3 to Thunderbolt2 Adapter
• Apple Thunderbolt2 Cable

Software versions

• macOS Sierra Version 10.12.6
• GPU Driver Version: 10.18.5 (378.05.05.25f01)
• CUDA Driver Version: 8.0.61
• cuDNN v5.1 (Jan 20, 2017), for CUDA 8.0: Need to register and download
• tensorflow-gpu 1.0.0
• Keras 2.0.8

Procedure:

Install GPU driver

1. ShutDown your system, power it up again with pressing (⌘ and R) keys until you see , this will let you in Recovery Mode.
2. From the Menu Bar click Utilities > Terminal and write ‘csrutil disable; reboot’ press enter to execute this command.

3. When your mac restarted, run this command in Terminal:

   cd ~/Desktop; git clone https://github.com/goalque/automate-eGPU.git
   chmod +x ~/Desktop/automate-eGPU/automate-eGPU.sh
   sudo ~/Desktop/automate-eGPU/./automate-eGPU.sh
   

4. Unplug your eGPU from your Mac, and restart. This is important if you did not unplug your eGPU you may end up with black screen after restarting.

5. When your Mac restarted, Open up Terminal and execute this command:

   sudo ~/Desktop/automate-eGPU/./automate-eGPU.sh -a
   

   6. Plug your eGPU to your mac via TH2.
   7. Restart your Mac.

Install CUDA, cuDNN, Tensorflow and Keras

At this moment, Keras 2.08 needs tensorflow 1.0.0. Tensorflow-gpu 1.0.0 needs CUDA 8.0 and cuDNN v5.1 is the one that worked for me. I tried other combinations but doesn't seem to work

1. Download and installing CUDA 8.0 CUDA Toolkit 8.0 GA2 (Feb 2017)
2. Install it and follow the instructions

3. Set env variables

   vim ~/.bash_profile
   export CUDA_HOME=/usr/local/cuda
   export DYLD_LIBRARY_PATH="$CUDA_HOME/lib:$CUDA_HOME:$CUDA_HOME/extras/CUPTI/lib"
   export LD_LIBRARY_PATH=$DYLD_LIBRARY_PATH
   

(If your bash_profile does not exist, create it. This is executed everytime you open a terminal window)

4. Downloading and installing cuDNN (cudnn-8.0-osx-x64-v5.1) Need to register before downloading it

5. Copy cuDNN files to CUDA

   cd ~/Downloads/cuda
   sudo cp include/* /usr/local/cuda/include/
   sudo cp lib/* /usr/local/cuda/lib/
   

6. Create envirenment and install tensorflow

   conda create -n egpu python=3
   source activate egpu
   pip install tensorflow-gpu==1.0.0
   

7. Verify it works

Run the following script:

import tensorflow as tf
with tf.device('/gpu:0'):
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
    c = tf.matmul(a, b)

with tf.Session() as sess:
    print (sess.run(c))

8. Install Keras in the envirenment and set tensorflow as backend:

   pip install --upgrade --no-deps keras # Need no-deps flag to prevent from installing tensorflow dependency
   KERAS_BACKEND=tensorflow python -c "from keras import backend"
   

   Output:

   Using TensorFlow backend.
   I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.8.0.dylib locally
   I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.5.dylib locally
   I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.8.0.dylib locally
   I tensorflow/stream_executor/dso_loader.cc:126] Couldn't open CUDA library libcuda.1.dylib. LD_LIBRARY_PATH: /usr/local/cuda/lib:/usr/local/cuda:/usr/local/cuda/extras/CUPTI/lib
   I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.dylib locally
   I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.8.0.dylib locally