TensorFlow-GPU 1.6.0致Python崩溃求助：Coursera课程作业运行异常

Hey there, let's tackle this TensorFlow-GPU 1.6.0 crash issue you ran into with your Coursera Bayesian ML assignments. I've seen this exact problem a bunch with older TF GPU versions, so here's what's going on and how to fix it:

1. Critical Version Mismatches (Most Likely Culprit) #

TensorFlow-GPU 1.6.0 has super strict dependency requirements that are easy to miss:

• It only works with CUDA Toolkit 9.0 (newer versions like 10.x/11.x will cause hard crashes)
• You need CuDNN 7.0.x (exact version—no newer or older)
• Your NVIDIA GPU driver must be compatible with CUDA 9.0

If you installed the latest GPU drivers or a newer CUDA stack, that's almost certainly why Python was crashing. Older TensorFlow GPU builds don't play nice with modern CUDA environments.

2. Unclean GPU Version Uninstallation #

Even if you ran pip uninstall tensorflow-gpu, leftover files or cached environment variables can cause hidden conflicts. To ensure a clean slate:

• Run pip uninstall -y tensorflow-gpu to fully remove the package
• Navigate to your Python site-packages folder and delete any remaining tensorflow or tensorflow_gpu directories
• Restart your terminal/IDE to reset environment variables before reinstalling anything

3. GPU Hardware Incompatibility #

TensorFlow-GPU 1.6.0 requires a GPU with Compute Capability 3.0 or higher. If you're using an older entry-level NVIDIA card (or a non-NVIDIA GPU entirely), it won't be supported—leading to crashes when the code tries to initialize the GPU context.

4. GPU Memory Overload #

Sometimes the assignment model tries to allocate more VRAM than your GPU has available, causing a hard crash. Test this by limiting GPU memory growth with this snippet at the start of your code:

import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)

If this stops the crash, your model was hitting a VRAM limit that the CPU (with more system RAM) doesn't encounter.

Quick Fix for Your Course Work #

Since the CPU version runs fast and error-free, sticking with it is totally reasonable—especially if troubleshooting GPU dependencies feels like extra work. If you do want to get the GPU version working later, make sure to match every exact version requirement for TensorFlow 1.6.0.

内容的提问来源于stack exchange，提问作者Alessandro Corradini