Installing TensorFlow with GPU Support for Windows in order to use CARE = Prerequisites = # Get latest [[https://www.geforce.com/drivers|NVIDIA drivers]] # Get [[https://developer.nvidia.com/cuda-10.0-download-archive|CUDA 10.0 Toolkit]] # Get [[https://developer.nvidia.com/rdp/cudnn-download|cuDNN for CUDA 10.0]] (Needs you to login) # Get [[https://www.python.org/downloads/|Python 3.7]] After installing python, install pip and virtualenv. Start `cmd` as an administrator and use `python -m pip install --upgrade pip` `pip install virtualenv` = Setup = 1. Create the virtual environement wherever you want, for example `D:\CARE-TF-GPU` `virtualenv -p python D:\CARE-TF-GPU\` 2. Activate the virtual environement ```d: cd CARE-TF-GPU Scripts\activate ``` 2. Install `tensorflow-gpu`, `csbdeep` and `jupyter notebook` `pip install tensorflow-gpu csbdeep jupyter notebook` D:\care-oli>jupyter --paths

Installing TensorFlow with GPU Support for Windows in order to use CARE = Prerequisites = # Get latest [[https://www.geforce.com/drivers|NVIDIA drivers]] # Get [[https://developer.nvidia.com/cuda-10.0-download-archive|CUDA 10.0 Toolkit]] # Get [[https://developer.nvidia.com/rdp/cudnn-download|cuDNN for CUDA 10.0]] (Needs you to login) # Get [[https://www.python.org/downloads/|Python 3.7]] After installing python, install pip and virtualenv. Start `cmd` as an administrator and use `python -m pip install --upgrade pip` `pip install virtualenv` = Setup = == 1. Create the virtual environement wherever you want, for example `D:\CARE-TF-GPU` == `virtualenv -p python D:\CARE-TF-GPU\` ==2. Activate the virtual environement == ```d: cd CARE-TF-GPU Scripts\activate ``` == 3. Install `tensorflow-gpu`, `csbdeep` and `jupyter notebook` == `pip install tensorflow-gpu csbdeep jupyter notebook` == 4. Check that the `jupyter notebook` is loading the right kernel with == `jupyter kernelspec list` And your output should look like this ``` Available kernels: python3 d:\care-tf-gpu\share\jupyter\kernels\python3 ``` = Test = Launch Jupyter Notebook with `jupyter notebook` Make a new Notebook and run a new cell with ```lang=python 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)) ```

Installing TensorFlow with GPU Support for Windows in order to use CARE = Prerequisites = # Get latest [[https://www.geforce.com/drivers|NVIDIA drivers]] # Get [[https://developer.nvidia.com/cuda-10.0-download-archive|CUDA 10.0 Toolkit]] # Get [[https://developer.nvidia.com/rdp/cudnn-download|cuDNN for CUDA 10.0]] (Needs you to login) # Get [[https://www.python.org/downloads/|Python 3.7]] After installing python, install pip and virtualenv. Start `cmd` as an administrator and use `python -m pip install --upgrade pip` `pip install virtualenv` = Setup = == 1. Create the virtual environement wherever you want, for example `D:\CARE-TF-GPU` == `virtualenv -p python D:\CARE-TF-GPU\` ==2. Activate the virtual environement == ```d: cd CARE-TF-GPU Scripts\activate ``` 2== 3. Install `tensorflow-gpu`, `csbdeep` and `jupyter notebook` == `pip install tensorflow-gpu csbdeep jupyter notebook` == 4. Check that the `jupyter notebook` is loading the right kernel with == `jupyter kernelspec list` And your output should look like this ``` Available kernels: python3 d:\care-tf-gpu\share\jupyter\kernels\python3 ``` = Test = Launch Jupyter Notebook with `jupyter notebook` Make a new Notebook and run a new cell with ```lang=python 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)) D:\care-oli>jupyter --paths```