Скачать или смотреть cuda version for python 3 10

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Sure, I can provide you with a tutorial on how to set up CUDA for Python 3.10 using the PyTorch library as an example. Make sure you have a compatible NVIDIA GPU and the latest CUDA toolkit installed on your system.
Make sure you have Python 3.10 installed on your system. You can download it from the official Python website (https://www.python.org/downloads/).
You can install PyTorch using the following command:
This command installs PyTorch version 1.10 with CUDA 11.3 support. You can check the official PyTorch website (https://pytorch.org/get-started/locally/) for the latest version compatible with your CUDA toolkit.
You can verify if CUDA is correctly installed by running the following Python code:
This code checks if CUDA is available and prints information about the number of GPUs and their names.
Now, let's run a simple PyTorch code using CUDA to accelerate tensor operations:
This code creates a random tensor on the CPU, moves it to the GPU, performs a simple operation, and then moves the result back to the CPU for printing.
By following these steps, you should have successfully set up CUDA for Python 3.10 with PyTorch on your system. Make sure to adapt the PyTorch installation command based on the latest versions available at the time of your installation.
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