Скачать или смотреть How to Run TensorFlow and PyTorch in One Virtual Environment on Windows 11

Discover how to successfully configure your virtual environment to run both TensorFlow and PyTorch with CUDA support on Windows 11, ensuring compatibility and performance.
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How to Run TensorFlow and PyTorch in One Virtual Environment on Windows 11

If you’re diving into deep learning, you might face the challenge of using multiple libraries, like TensorFlow and PyTorch, in the same virtual environment. This can often lead to compatibility issues with CUDA and cuDNN, especially when you’re using libraries that rely on specific versions.

In this guide, we will explore a foolproof method to set up TensorFlow and PyTorch in one virtual environment on Windows 11, using an NVIDIA RTX 2070S GPU, and ensuring that everything runs smoothly.

The Problem

Many users, including those with newer GPUs, encounter difficulties when trying to run both TensorFlow and PyTorch simultaneously due to mismatched CUDA and cuDNN versions. You might experience kernel crashes or issues during installations, making your deep learning journey frustrating.

In this guide, we'll directly discuss the installation steps for TensorFlow 2.10.1 and PyTorch 1.13.0, ensuring they can coexist peacefully within the same environment.

Step-by-Step Solution

1. Download CUDA and cuDNN

Install CUDA 11.2: This version is suitable for TensorFlow 2.10.1.

Install cuDNN 8.6.0: Make sure it is compatible with CUDA 11.X.

2. Set the Environment Variables

After you’ve installed CUDA and cuDNN, it’s essential to set the environment variables for the system to recognize their paths:

Set CUDA_PATH and CUDA_PATH_V11_2 correctly.

Include the following in your System PATH:

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin

3. Install Additional CUDA and cuDNN Versions

Install CUDA 11.6: This is needed for PyTorch 1.13.0.

Install cuDNN 8.6.0 for CUDA 11.X again and ensure that the paths for libnvvp and bin are also set.

4. Check Visibility of Both CUDA Versions

Both installed CUDA versions should now be visible within Windows. Note that CUDA_PATH will point to version 11.6, which is perfectly fine.

5. Install TensorFlow and PyTorch

Now, it’s time to install the libraries:

For TensorFlow:

[[See Video to Reveal this Text or Code Snippet]]

For PyTorch:

[[See Video to Reveal this Text or Code Snippet]]

6. Verify Installation

To ensure that both TensorFlow and PyTorch can access the GPU and were compiled correctly, check the following within the Python interpreter of your virtual environment:

Check TensorFlow

[[See Video to Reveal this Text or Code Snippet]]

Check PyTorch

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following the steps outlined above, you should be able to successfully configure your virtual environment to run both TensorFlow and PyTorch without any hitches. This setup not only ensures compatibility but also harnesses the full power of your NVIDIA GPU, making your deep learning tasks faster and more efficient.

Happy coding, and may your experiments lead to groundbreaking discoveries in the realm of artificial intelligence!