pip install cuda gpu

Download this code from https://codegive.com Sure, let's create an informative tutorial on installing CUDA and cuDNN for GPU support with TensorFlow using the pip package manager. This assumes you have a compatible NVIDIA GPU. Before starting, make sure that your NVIDIA GPU is compatible with CUDA. You can check the CUDA-enabled GPUs on the official NVIDIA website. The CUDA Toolkit is a prerequisite for GPU acceleration. Follow these steps to install it: cuDNN is a GPU-accelerated library for deep neural networks. Follow these steps to install it: Now, you can install TensorFlow with GPU support using pip. Ensure that you have a virtual environment activated if you are using one. This command installs the TensorFlow package optimized for GPU usage. It automatically takes advantage of CUDA and cuDNN if they are properly installed. After the installation, you can verify that TensorFlow is using the GPU: If the installation is successful, you should see information about the available GPU(s). Congratulations! You have successfully installed CUDA, cuDNN, and TensorFlow with GPU support. Now, you can train and run deep learning models more efficiently on your GPU. Note: Always check the official documentation for the latest versions and updates. Remember that proper version compatibility is crucial, so make sure to use versions of TensorFlow, CUDA Toolkit, and cuDNN that are compatible with each other. ChatGPT