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Super protocol Install miniconda https://conda.io/miniconda.html Install Latest nvidia driver current driver: 417.35 Install CUDA Toolkit v9.0 Get the Patch if any During installation, # Select Advanced # Select CUDA # Deselect Visual Studio Integration Install cuDNN for tghe 9.0 version of CUDA Download Zip file (You need to register) https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows Unzip it into `C:\tools\` You just need to **copy **the //contents// of the `cuda` folder into the CUDA installation which should be `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0` Add Environment variables # C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin # C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64 # C:\tools\cuda\bin That last one might not be necessary Open Anaconda Prompt conda update conda conda update python conda update --all Make a directory for all the tensorflow projects somewhere mkdir tensorflow-projects cd tensorflow-projects Create the environement conda create --name tf-gpu python=3.6 conda activate tf-gpu pip install tensorflow-gpu

Installing TensorFlow with GPU Support for Windows in order to use CARE # Get latest [[https://www.geforce.com/drivers|NVIDIA drivers]] # Install Anaconda from `\\svfas6.epfl.ch\biop\public\0 - BIOP Data\0-Install\CARE` # Download `CARE-TensorFlow-GPU.yml` locally # Open Anaconda Prompt # Navigate to where you copied `CARE-TensorFlow-GPU.yml` # Update Anaconda with `conda update -n base -c defaults conda` # Create the CARE environement with `conda env create -n care -f CARE-TensorFlow-GPU.yml` # Activate the environement with `conda activate care` # Clone the CARE repository with `git clone https://github.com/CSBDeep/CSBDeep.git` # Run `jupyter notebook` and see if the examples work

Super protocol Install miniconda https://conda.io/miniconda.html Install Latest nvidia driver current driver: 417.35 Install CUDA Toolkit v9.0 Get the Patch if any During installation, # Select Advanced # Select CUDA # Deselect Visual Studio Integration Install cuDNN for tghe 9.0 version of CUDA Download Zip file (You need to register) https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows Unzip it into `C:\tools\` You just need to **copy **the //contents// of the `cuda` folder into the CUDA installation which should be `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0`Installing TensorFlow with GPU Support for Windows in order to use CARE Add Environment variables # C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin # C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64# Get latest [[https://www.geforce.com/drivers|NVIDIA drivers]] # C:\tools\cuda\bin# Install Anaconda from `\\svfas6.epfl.ch\biop\public\0 - BIOP Data\0-Install\CARE` That last one might not be necessary Open Anaconda Prompt# Download `CARE-TensorFlow-GPU.yml` locally conda update conda# Open Anaconda Prompt conda update python# Navigate to where you copied `CARE-TensorFlow-GPU.yml` conda u# Update --all Make a directory for all the tensorflow projects somewhereAnaconda with `conda update -n base -c defaults conda` mkdir t# Create the CARE environement with `conda env create -n care -f CARE-TensorfFlow-projectsGPU.yml` cd tensorflow-projects Create the environement# Activate the environement with `conda activate care` conda create --name tf-gpu python=3.6# Clone the CARE repository with `git clone https://github.com/CSBDeep/CSBDeep.git` conda activate tf-gpu pip install tensorflow-gpu # Run `jupyter notebook` and see if the examples work