mpharsha26/keras-tensorflow-windows-installation

10 easy steps to install Tensorflow-GPU and Keras in Windows

Keras-TensorFlow-GPU-Windows-Installation (Updated: 12th Apr, 2019)

10 easy steps on the installation of TensorFlow-GPU and Keras in Windows

Step 1: Install NVIDIA Driver Download

Select the appropriate version and click search

Step 2: Install Anaconda (Python 3.7 version) Download

Step 3: Update Anaconda

Open Anaconda Prompt to type the following command(s)

conda update conda
conda update --all

Step 4: Install CUDA Tookit 10.0 Download

Choose your version depending on your Operating System

Step 5: Download cuDNN Download

Choose your version depending on your Operating System. Membership registration is required.

Put your unzipped folder in C drive as follows:

D:\cudnn-10.1-windows10-x64-v7.5.0.56

Step 6: Add cuDNN into Environment PATH

Add the following path in your Environment. Subjected to changes in your installation path.

D:\cudnn-8.0-windows10-x64-v5.1\cuda\bin

You can either follow this Tutorial here or the following steps (for Windows 10).

Step 6.1: open the Start Search, type in “env”

Step 6.2: choose “Edit environment variables for your account”:

Step 6.3: under the “Users' Variables” section (the upper half), find the row with “Path” in the first column, and click edit.

Step 6.4: the “Edit environment variable” user interface will appear. click New.

Now you can add a new path to the environment varible

Turn off all the prompts. Open a new Anaconda Prompt to type the following command(s)

echo %PATH%

You shall see that the new Environment PATH is there.

Step 7: Create an Anaconda environment with Python=3.6

Open Anaconda Prompt to type the following command(s)

conda create -n keras-gpu python=3.6 numpy scipy keras-gpu

Step 8: Activate the environment

Open Anaconda Prompt to type the following command(s)

activate keras-gpu

Step 9: Testing

Let's try running mnist_mlp.py in your prompt.

Open Anaconda Prompt to type the following command(s)

activate keras-gpu
python mnist_mlp.py

Congratulations ! You have successfully run Keras (with Tensorflow backend) over GPU on Windows !

Step 10: Done !