python tensorflow gpu cuda смотреть онлайн

Download this code from https://codegive.com
In this tutorial, we'll walk through the process of setting up TensorFlow to utilize the power of your GPU through CUDA. TensorFlow is a popular open-source machine learning library, and using GPU acceleration with CUDA can significantly speed up training and inference for deep learning models.
NVIDIA GPU: Ensure that you have an NVIDIA GPU with CUDA support. You can check the CUDA-enabled devices on the NVIDIA website.
NVIDIA CUDA Toolkit: Download and install the NVIDIA CUDA Toolkit. Make sure to add the toolkit's bin directory to your system's PATH.
cuDNN: Download and install the cuDNN library from the NVIDIA website. This library provides highly optimized implementations of deep learning primitives.
Python and TensorFlow: Make sure you have Python installed. You can install TensorFlow-GPU using the following command:
Now, let's proceed with a simple example using TensorFlow and GPU acceleration.
This example demonstrates the following:
Remember that not all parts of your code will automatically run on the GPU; TensorFlow will automatically use the GPU for operations that support GPU acceleration. If you encounter any issues, make sure that your GPU setup is correct and that the required libraries are installed.
By following this tutorial, you should now be able to harness the power of your GPU to accelerate TensorFlow workflows using CUDA.
ChatGPT