Windows10下TensorFlow-GPU导入报错求助：找不到cudnn64_6.dll

Hey there! Let's work through this issue together. First off, let's clear up the hardware concern right away—your AMD Ryzen 1700 CPU and GTX 1050 Ti GPU are fully compatible with TensorFlow-GPU. The GTX 1050 Ti has a CUDA compute capability of 6.1, which is supported by all TensorFlow versions that work with CUDA 8.0/9.0, so hardware isn't the problem here.

The Root Cause: Version Mismatch #

Your error message clearly states TensorFlow is looking for cudnn64_6.dll—this means the version of TensorFlow-GPU you installed requires cuDNN 6.x, but you currently have cuDNN v5.1 installed. Different TensorFlow versions have strict dependencies on CUDA and cuDNN versions:

• TensorFlow 1.2.x: Requires CUDA 8.0 + cuDNN 5.1
• TensorFlow 1.3.x to 1.5.x: Requires CUDA 8.0 + cuDNN 6.0
• TensorFlow 1.6.x and above: Requires CUDA 9.0 + cuDNN 7.0

Since you have CUDA 8.0 set up, you just need to align your cuDNN or TensorFlow version to match.

Step-by-Step Solutions #

Option 1: Install the Matching cuDNN Version (Recommended)

1. Check your installed TensorFlow-GPU version first. Run this command in your Anaconda environment:

   pip show tensorflow-gpu
   

2. Download the corresponding cuDNN version that pairs with your TensorFlow release and CUDA 8.0.
3. Extract the cuDNN zip file—you'll find three folders: bin, include, lib.
4. Copy these three folders directly into your CUDA 8.0 installation directory (default path: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0). This merges the cuDNN files with your existing CUDA setup.
5. Double-check your environment variables:
   • Ensure CUDA_HOME is set to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0
   • Confirm your Path variable includes C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin (this should already be there from your CUDA installation, but it's good to verify)
6. Restart your Anaconda prompt (or your entire computer) to apply the changes, then test again with import tensorflow as tf.

Option 2: Downgrade TensorFlow-GPU to Match Your Existing cuDNN 5.1

If you don't want to reinstall cuDNN, you can downgrade TensorFlow-GPU to version 1.2.x, which works seamlessly with cuDNN 5.1 and CUDA 8.0. Run this command in your virtual environment:

pip install tensorflow-gpu==1.2.0

Once installed, restart your prompt and test the import again.

Verify the Fix #

After making changes, run this code to confirm TensorFlow is using your GPU:

import tensorflow as tf
print(tf.test.is_gpu_available())

If it returns True, you're all set!

内容的提问来源于stack exchange，提问作者HyeongGyu Froilan Choi