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Ensure that you have python 2.7
Run: "sudo apt-get install python-pip python-dev python-virtualenv"
Run: "pip install --upgrade tensorflow-gpu"
Afterwards, if you run python on terminal, you should be able to import tensorflow with an error.
Add this line to .bashrc: "export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64".
Download cudnn version 5 for linux from the official website and copy the files into /usr/local/cuda/include and /usr/local/cuda/lib64 according to how the files are placed in cudnn.

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Simply install Theano with: 'sudo pip install Theano'
Run the Python scripts as: 'THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python script_name.py'

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Download directly the Git repo of opencv and opencv_contrib;
Type: "cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_CUDA=ON -D ENABLE_FAST_MATH=1 -D CUDA_FAST_MATH=1 -D WITH_CUBLAS=1  -D INSTALL_PYTHON_EXAMPLES=ON  -D WITH_IPP=OFF -D BUILD_EXAMPLES=ON -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules ..
"make -j 4"
"sudo make install"
"sudo ldconfig"
Even if it runs on 4 cores, building OpenCV requires 4/4.5 h, so equip yourself with patience.

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Go to "Device" --> "Install Guest Additions"
As root, run the program VBoxLinuxAdditions.run
Go to "Device" --> "Shared folders" and select the folders of interest
sudo mkdir /media/windows-share
sudo mount -t vboxsf folder-name /media/windows-share

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ml python2
ml virtualenv
source ~/mypio/bin/activate
(mypio) pip install jupyter
(mypio) python -m ipykernel install --user --name=mypio
(mypio) jupyter notebook --no-browser --NotebookApp.token='' --NotebookApp.ip='0.0.0.0'

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srun --partition gpu -I --pty /bin/bash -l
ml python2
ml virtualenv
ml CUDA/8.0.44
ml cuDNN/5.1-CUDA-8.0.44
ml OpenCV/2.4.11-goolf-1.7.20
ml TensorFlow/1.1.0-goolf-1.7.20-CUDA-8.0.44-Python-2.7.11

# Check whether tensorflow-gpu works fine:
python

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virtualenv ~/mypio
source ~/mypio/bin/activate
pip install scipy scikit-image scikit-learn SimpleITK h5py
deactivate

sbatch shellwrapper.sh

Inside the shellwrapper:
#!/bin/bash
#SBATCH --partition gpu

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# To mount the keep
mkdir keep
arv-mount keep/

# To unmount the keep:
arv-mount --unmount keep/
rm -r keep

# To run a workflow
arvados-cwl-runner --project-uuid <PROJECT_UUID> --submit --no-wait file.cwl file.yml

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cp -as absolute_path_to_source/ absolute_path_to_target/

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Change disk:
cd /d C:\

Go back:
cd..