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
- Nvidia Video Card: Titan Xp
- EGPU: Akitio Node
- MacBook Pro (Retina, 13-inch, Early 2015)
- Apple Thunderbolt3 to Thunderbolt2 Adapter
- Apple Thunderbolt2 Cable
- 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
- ShutDown your system, power it up again with pressing (⌘ and R) keys until you see , this will let you in Recovery Mode.
- From the Menu Bar click Utilities > Terminal and write ‘csrutil disable; reboot’ press enter to execute this command.
- 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
- 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.
- When your Mac restarted, Open up Terminal and execute this command: sudo ~/Desktop/automate-eGPU/./automate-eGPU.sh -a
- Plug your eGPU to your mac via TH2.
- Restart your Mac.
- Got to About this Mac / Sytem Report / Graphics/Displays and you should see the Nvidia Card with the correct model.
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
- Download and installing CUDA 8.0 CUDA Toolkit 8.0 GA2 (Feb 2017)
- Install it and follow the instructions
- 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/
- Create envirenment and install tensorflow
conda create -n egpu python=3
source activate egpu
pip install tensorflow-gpu==1.0.0
- 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))
- 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
- Log after importing keras in Jupyter Notebook:
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
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.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:874] OS X does not support NUMA - returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: TITAN Xp
major: 6 minor: 1 memoryClockRate (GHz) 1.582
pciBusID 0000:c3:00.0
Total memory: 12.00GiB
Free memory: 11.79GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN Xp, pci bus id: 0000:c3:00.0)
You are correct.
Differendes are:
i.e. I need to plugin the egpu before I can run the script a first time.
Then the output is:
After rebooting I usually see:
when the GPU is plugged in and even though only the generic model is recognized this window does not show up.
As outlined by you the NVIDIA WebDriver is already selected:
here a screenshot of the GPU
and the thunderbolt device
