Which TensorFlow and CUDA version combinations are compatible?

When discussing the compatibility between TensorFlow and CUDA versions, it is indeed a critical consideration, as the correct version combination can maximize TensorFlow performance and avoid unnecessary runtime errors. The TensorFlow official website provides specific compatibility guidelines, which include the following common combinations of TensorFlow with CUDA and the corresponding cuDNN versions:

1. TensorFlow 2.8

   • CUDA 11.2
   • cuDNN 8.1

2. TensorFlow 2.7

   • CUDA 11.2
   • cuDNN 8.1

3. TensorFlow 2.6

   • CUDA 11.2
   • cuDNN 8.1

4. TensorFlow 2.5

   • CUDA 11.2
   • cuDNN 8.1

5. TensorFlow 2.4

   • CUDA 11.0
   • cuDNN 8.0

6. TensorFlow 2.3

   • CUDA 10.1
   • cuDNN 7.6

7. TensorFlow 2.2

   • CUDA 10.1
   • cuDNN 7.6

8. TensorFlow 2.1

   • CUDA 10.1
   • cuDNN 7.6

9. TensorFlow 2.0

   • CUDA 10.0
   • cuDNN 7.4

For instance, when configuring an environment to run TensorFlow 2.4, based on the above information, we need to install CUDA 11.0 and cuDNN 8.0. Ensuring the compatibility of these specific versions is key to avoiding runtime errors. Additionally, when installing, ensure that the corresponding NVIDIA driver supports the installed CUDA version.

In practical work, understanding and adhering to these compatibility guidelines ensures seamless collaboration between software libraries, making the development and training of deep learning models more efficient and stable. If a new version of TensorFlow is released, the relevant compatibility information is typically updated on the TensorFlow official website, so it is important to regularly check this information.