Installing TensorFlow with GPU Support for Windows in order to use CARE. NOTE: This page has been updated to install JupyterLab and includes TensorBoard in the launcher (WARNING) Protocol was made by Olivier Burri, BIOP = 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 the required dependencies == `pip install tensorflow-gpu nodejs csbdeep tensorflow-gpu jupyterlab jupyter_tensorboard ipywidgets widgetsnbextension scikit-image` == 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 ``` == 5. Install the JupyterLab extensions == ` jupyter labextension install @jupyter-widgets/jupyterlab-manager & jupyter labextension install jupyterlab_tensorboard ` = Test = Launch Jupyter Notebook with `jupyter lab` 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)) ``` If that works, then tensorflow is running for you. = Magic BAT file for installation = ``` @echo off set installPath=D:\CARE-TF-GPU 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 ``` = Magic BAT file for running (add as shortcut in desktop, for instance) = ``` @echo off cmd /k "cd /d D:\CARE-TF-GPU & Scripts\activate & jupyter lab" ```

Installing TensorFlow with GPU Support for Windows in order to use CARE. NOTE: This page has been updated to install JupyterLab and includes TensorBoard in the launcher (WARNING) Protocol was made by Olivier Burri, BIOP = 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 the required dependencies ==` We use ipywidgets and scikit-image in the group, this is why they are here. You can ignore them ``` pip install tensorflow-gpu nodejs csbdeep tensorflow-gpu jupyterlab jupyter_tensorboard ipywidgets widgetsnbextension scikit-image ``` == 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 ``` == 5. Install the JupyterLab extensions == ` jupyter labextension install @jupyter-widgets/jupyterlab-manager & jupyter labextension install jupyterlab_tensorboard ` = Test = Launch Jupyter Notebook with `jupyter lab` 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)) ``` If that works, then tensorflow is running for you. = Magic BAT file for installation = ``` @echo off set installPath=D:\CARE-TF-GPU 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 ``` = Magic BAT file for running (add as shortcut in desktop, for instance) = ``` @echo off cmd /k "cd /d D:\CARE-TF-GPU & Scripts\activate & jupyter lab" ```

Installing TensorFlow with GPU Support for Windows in order to use CARE. NOTE: This page has been updated to install JupyterLab and includes TensorBoard in the launcher (WARNING) Protocol was made by Olivier Burri, BIOP = 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 the required dependencies ==` We use ipywidgets and scikit-image in the group, this is why they are here. You can ignore them ``` pip install tensorflow-gpu nodejs csbdeep tensorflow-gpu jupyterlab jupyter_tensorboard ipywidgets widgetsnbextension scikit-image `pip install tensorflow-gpu nodejs csbdeep tensorflow-gpu jupyterlab jupyter_tensorboard ipywidgets widgetsnbextension scikit-image`` == 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 ``` == 5. Install the JupyterLab extensions == ` jupyter labextension install @jupyter-widgets/jupyterlab-manager & jupyter labextension install jupyterlab_tensorboard ` = Test = Launch Jupyter Notebook with `jupyter lab` 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)) ``` If that works, then tensorflow is running for you. = Magic BAT file for installation = ``` @echo off set installPath=D:\CARE-TF-GPU 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 ``` = Magic BAT file for running (add as shortcut in desktop, for instance) = ``` @echo off cmd /k "cd /d D:\CARE-TF-GPU & Scripts\activate & jupyter lab" ```