Keras-TensorFlow-GPU-Windows-Installation (Updated: 12th Apr, 2019)
10 easy steps on the installation of TensorFlow-GPU and Keras in Windows
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Step 1: Install NVIDIA DriverSelect the appropriate version and click search
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Step 2: Install Anaconda (Python 3.7 version)Step 3: Update Anaconda
Open Anaconda Prompt to type the following command(s)
conda update conda
conda update --all
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Step 4: Install CUDA Tookit 10.0Choose your version depending on your Operating System
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Step 5: Download cuDNNChoose 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 !