python tensorflow check cuda

TensorFlow is a popular open-source machine learning library that allows developers to build and train deep learning models. To accelerate computations, TensorFlow can leverage CUDA, a parallel computing platform and application programming interface model created by NVIDIA. In this tutorial, we will guide you through the process of checking whether your TensorFlow installation is configured to use CUDA.
Install TensorFlow: Make sure you have TensorFlow installed. You can install it using the following command:
Install CUDA Toolkit and cuDNN: If you haven't installed CUDA Toolkit and cuDNN, you can follow the official NVIDIA installation guide based on your operating system. Visit the NVIDIA CUDA Toolkit Installation Guide for more information.
Now, let's verify if your TensorFlow installation is configured to use CUDA. We'll create a simple Python script for this purpose.
Checking GPU Availability: The list_physical_devices('GPU') function is used to list all available GPUs. If you have a compatible GPU, the script will print the number of GPUs available.
Checking TensorFlow with CUDA: The is_built_with_cuda() function checks if TensorFlow is built with CUDA support. It returns True if CUDA is supported and False otherwise.
Displaying CUDA Version: The gpu_device_name() function displays the name of the GPU device, indicating that TensorFlow recognizes and is using a GPU.
Displaying cuDNN Version: The is_built_with_cudnn() function checks if TensorFlow is built with cuDNN support and prints the cuDNN version.
This tutorial helps you confirm whether your TensorFlow installation is set up to use CUDA for GPU acceleration. If you encounter any issues, refer to the TensorFlow and CUDA documentation for troubleshooting and further information.
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