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 x64]] # Get [[https://nodejs.org/en/|NodeJS ]] Install everything with defaults except Python. Mae sure to add Python to the PATH Everything installs automatically except for cuDNN. For cuDNN, copy paste the contents of the zip file into `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0` 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)) ``` = Magic BAT file for installation = ``` @echo off set installPath=D:\CARE-TF-GPU2 echo %installPath% python -m pip install --upgrade pip pip install virtualenv virtualenv -p python %installPath% cmd /k " cd /D %installPath%\Scripts\ & activate & pip install tensorflow-gpu csbdeep jupyter notebook & jupyter kernelspec list" PAUSE ```

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 x64]] # Get [[https://nodejs.org/en/|NodeJS ]] Install everything with defaults except Python. Mae sure to add Python to the PATH Everything installs automatically except for cuDNN. For cuDNN, copy paste the contents of the zip file into `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0` 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)) ``` = Magic BAT file for installation = ``` @echo off set installPath=D:\CARE-TF-GPU-N2V echo %installPath% python -m pip install --upgrade pip pip install virtualenv virtualenv -p python %installPath% cmd /k "cd /D %installPath%\Scripts\ & activate & pip install tensorflow-gpu nodejs csbdeep tensorflow-gpu jupyterlab jupyter_tensorboard ipywidgets widgetsnbextension scikit-image & jupyter labextension install @jupyter-widgets/jupyterlab-manager &jupyter labextension install jupyterlab_tensorboard & jupyter kernelspec list PAUSE ```

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 x64]] # Get [[https://nodejs.org/en/|NodeJS ]] Install everything with defaults except Python. Mae sure to add Python to the PATH Everything installs automatically except for cuDNN. For cuDNN, copy paste the contents of the zip file into `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0` 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)) ``` = Magic BAT file for installation = ``` @echo off set installPath=D:\CARE-TF-GPU2-N2V echo %installPath% python -m pip install --upgrade pip pip install virtualenv virtualenv -p python %installPath% cmd /k " cd /D %installPath%\Scripts\ & activate & pip install tensorflow-gpu csbdeep jupyter notebook & jupyter kernelspec list" nodejs csbdeep tensorflow-gpu jupyterlab jupyter_tensorboard ipywidgets widgetsnbextension scikit-image & jupyter labextension install @jupyter-widgets/jupyterlab-manager &jupyter labextension install jupyterlab_tensorboard & jupyter kernelspec list PAUSE ```