TensorFlow导入失败：缺失cudart64_100.dll及连锁错误求助

Hey there, let's work through this TensorFlow issue you're dealing with. The core problem here is tied to CUDA dependencies, and even downgrading to TensorFlow 1.15 didn't fix it because the underlying setup for GPU support is missing or misconfigured. Let's break down the solutions step by step:

1. Resolve the cudart64_100.dll Missing Error (GPU Setup) #

TensorFlow 1.15 requires CUDA Toolkit 10.0 and cuDNN 7.4+ to run on NVIDIA GPUs. That missing DLL is a core file from CUDA 10.0, so here's how to fix it:

• Uninstall any existing CUDA versions first to avoid conflicts.
• Download and install CUDA Toolkit 10.0 (match it to your Windows system). During installation, ensure the option to Add CUDA to PATH is checked—if not, manually add these paths to your system's %PATH% environment variable:
  • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
  • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\libnvvp
• Download cuDNN 7.6.5 (compatible with CUDA 10.0), extract the zip file, and copy the contents of its bin, include, and lib folders into the corresponding folders in your CUDA 10.0 installation directory (e.g., paste bin files into C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin).

2. Fix Chain Exceptions (Inspect Module, AttributeError, TypeError) #

These errors pop up because leftover files from old TensorFlow versions or incompatible dependency packages are causing conflicts. Here's how to clean up your environment:

• First, completely remove all TensorFlow installations and clear the pip cache:

  pip uninstall -y tensorflow tensorflow-gpu
  pip cache purge
  

• Reinstall the GPU-specific version of TensorFlow 1.15 (installing the regular version might still try to look for GPU dependencies):

  pip install tensorflow-gpu==1.15
  

• Check and update incompatible dependencies. TensorFlow 1.15 works best with:
  • numpy ≤ 1.18.5
  • keras ≤ 2.3.1
    Use pip list to check your current versions, and downgrade if needed:

  pip install numpy==1.18.5 keras==2.3.1
  

3. Alternative: Use TensorFlow CPU Version (No GPU Access) #

If you don't have an NVIDIA GPU or can't install CUDA due to permissions issues, switch to the CPU-only version of TensorFlow. This eliminates all GPU-related dependencies:

• Uninstall existing TensorFlow versions:

  pip uninstall -y tensorflow tensorflow-gpu
  

• Install the CPU-specific package:

  pip install tensorflow-cpu==1.15
  

Give these steps a shot—start with the GPU setup if you have access to an NVIDIA card, or switch to the CPU version if you don't. This should resolve both the missing DLL error and the subsequent chain of exceptions.

内容的提问来源于stack exchange，提问作者Revolucion for Monica