To install the GPU version of TensorFlow on Windows, you need to follow a series of steps to ensure a successful installation. Before proceeding, it is important to note that TensorFlow GPU support requires a compatible NVIDIA GPU and the corresponding CUDA toolkit installed on your system.
Here is a detailed guide on how to install the GPU version of TensorFlow on Windows:
Step 1: Verify GPU Compatibility
Firstly, you need to ensure that your GPU is compatible with TensorFlow. TensorFlow requires a CUDA-enabled GPU, which means it must support compute capability 3.5 or higher. You can check the compute capability of your GPU by referring to the NVIDIA documentation or by using the following command in the command prompt:
nvidia-smi
This command will display information about your GPU, including its compute capability.
Step 2: Install CUDA Toolkit
Next, you need to install the CUDA toolkit, which is a prerequisite for TensorFlow GPU support. Visit the NVIDIA website and download the CUDA toolkit version that is compatible with your GPU. Make sure to select the version that includes the GPU drivers as well. During the installation, choose the appropriate options based on your system requirements.
Step 3: Set Environment Variables
After installing the CUDA toolkit, you need to set the environment variables to allow TensorFlow to locate the CUDA libraries. Open the System Properties window by right-clicking on the Computer icon, selecting Properties, and then clicking on Advanced system settings. In the System Properties window, click on the Environment Variables button.
In the Environment Variables window, under the System variables section, click on the New button to add a new variable. Set the Variable name to `CUDA_HOME` and the Variable value to the installation path of CUDA. For example, if CUDA is installed in `C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.0`, then set the Variable value to `C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.0`.
Next, locate the `Path` variable under the System variables section, select it, and click on the Edit button. In the Edit Environment Variable window, click on the New button and add the following paths:
%CUDA_HOME%bin %CUDA_HOME%libnvvp
Click OK to save the changes and close all the windows.
Step 4: Install cuDNN
cuDNN (CUDA Deep Neural Network library) is another prerequisite for TensorFlow GPU support. Visit the NVIDIA Developer website and download the cuDNN library that is compatible with your CUDA version. Extract the downloaded file and copy the contents of the `bin`, `include`, and `lib` folders to the corresponding directories inside the CUDA installation directory.
Step 5: Install TensorFlow GPU
Now that you have set up the necessary dependencies, you can proceed with installing the GPU version of TensorFlow. Open the command prompt and execute the following command to install TensorFlow using pip:
pip install tensorflow-gpu
This command will download and install the latest version of TensorFlow with GPU support.
Step 6: Verify the Installation
To verify that TensorFlow is installed correctly and is utilizing the GPU, you can run a simple script that prints the list of available GPUs. Open Python in the command prompt by executing the following command:
python
In the Python interpreter, enter the following code:
python import tensorflow as tf print(tf.config.list_physical_devices('GPU'))
If TensorFlow is correctly installed and configured to use the GPU, it will display information about the available GPUs on your system.
By following these steps, you should be able to successfully install the GPU version of TensorFlow on Windows. Remember to ensure compatibility with your GPU and follow the installation instructions carefully to avoid any issues.
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