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]]
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
```