TensorFlow-GPU性能骤降问题咨询及系统信息说明

Hey there, let's figure out why your Keras/TensorFlow-GPU setup has suddenly slowed down, even though it was working perfectly for weeks. First, let's recap your environment to make sure we're aligned:

• No custom code written
• Operating System: Windows 10 64-bit
• TensorFlow Install Source: Binary package
• TensorFlow Version: 1.6.0
• Python Version: 3.6.3
• CUDA/cuDNN Version: 9.0
• GPU: GeForce GTX 780 (3GB VRAM)
• Framework: Keras with TensorFlow-GPU backend

Since TensorFlow still recognizes your GPU during initialization, we can rule out a total GPU connection failure. Let's walk through some troubleshooting steps to pinpoint the issue:

1. Check real-time GPU utilization
   Open Task Manager (Ctrl+Shift+Esc) → head to the Performance tab → select your GTX 780. Keep an eye on the usage percentage while your model runs. If it's stuck at a low level (like <10%) but your CPU is maxed out, that means TensorFlow is falling back to CPU processing—even though it detected the GPU initially.

2. Confirm TensorFlow is actually using the GPU
   Add a quick check at the start of your script to verify:

   from tensorflow.python.client import device_lib
   print(device_lib.list_local_devices())
   

   Look for a clear GPU entry in the output. You can also check where your model layers are running by adding something like print(model.layers[0].input.device) for a sample layer—this should show a GPU device path if everything's working right.

3. Kill background processes hogging GPU resources
   Apps like Discord, video editors, or even leftover TensorFlow sessions that didn't close properly can eat up your 3GB VRAM. Use Task Manager or NVIDIA Control Panel to see what's using GPU memory, and shut down any unnecessary programs before running your model.

4. Check your GPU driver version
   Outdated or recently updated drivers are a common culprit for sudden performance drops. Since your setup worked before, try rolling back to the driver version you used when things ran smoothly. If you don't remember that version, install the latest stable driver compatible with your GTX 780 and CUDA 9.0.

5. Double-check CUDA/cuDNN compatibility
   TensorFlow 1.6.0 officially supports CUDA 9.0, but it requires cuDNN 7.0.x specifically. If you accidentally updated cuDNN recently, that could break things. Reinstall the correct cuDNN version if needed, making sure it's matched to your CUDA 9.0 setup.

6. Clear cached files
   Corrupted cache files from TensorFlow or Keras can cause slowdowns. Delete the .keras folder in your user directory (C:\Users\<YourUsername>\.keras) and the TensorFlow temp cache (usually in C:\Users\<YourUsername>\AppData\Local\Temp\tensorflow). Restart your script after doing this—fresh caches might fix the issue.

7. Verify batch size and dataset changes
   Even if you didn't write custom code, maybe you adjusted the batch size or started using a larger dataset? A batch size that's too big for your 3GB VRAM will force constant swapping between VRAM and system RAM, which kills performance. Try reducing the batch size to see if that speeds things up.

8. Test with a minimal model
   Run a simple Keras example to isolate whether the issue is with your environment or your specific model/dataset:

   from keras.models import Sequential
   from keras.layers import Dense
   import numpy as np
   
   # Generate dummy data
   X = np.random.rand(1000, 100)
   y = np.random.randint(0, 2, 1000)
   
   # Build a simple model
   model = Sequential()
   model.add(Dense(64, activation='relu', input_shape=(100,)))
   model.add(Dense(1, activation='sigmoid'))
   model.compile(optimizer='adam', loss='binary_crossentropy')
   
   # Train and check speed
   model.fit(X, y, epochs=10, batch_size=32)
   

   If this runs fast, the problem is likely with your dataset or model setup (even if you didn't change code, maybe the dataset grew?). If it's still slow, the issue is definitely in your environment.

内容的提问来源于stack exchange，提问作者TwoRice