The inference time suddenly became very long. I don’t know whether it is a GPU driver problem or a change in the underlying model. about ultralytics HOT 2 OPEN

Hello @7288Fzq,

Thank you for reaching out and providing detailed information about your issue. Let's work together to resolve this.

Firstly, it's great to see that you've already updated your repositories and provided the code examples. This helps a lot in diagnosing the problem. Given that the issue started after a GPU driver update, it's possible that the driver might be causing the slowdown.

Here are a few steps to help troubleshoot and potentially resolve the issue:

1. Verify GPU Utilization: Ensure that your GPU is being utilized correctly. You can use tools like nvidia-smi to monitor GPU usage during inference. This will help determine if the GPU is being properly leveraged.

2. Check Driver and CUDA Compatibility: Sometimes, new drivers may not be fully compatible with the existing CUDA version. Verify that your CUDA version is compatible with the new driver. You can find compatibility information on the NVIDIA CUDA Compatibility page.

3. Reinstall PyTorch and Ultralytics: Ensure you are using the latest versions of torch and ultralytics. You can update them using the following commands:

   pip install --upgrade torch
   pip install --upgrade ultralytics

4. Benchmark with Different Models: Try running inference with different models and datasets to see if the issue persists across all scenarios. This can help isolate whether the problem is model-specific or more general.

5. Revert Driver Update: If possible, try reverting to the previous GPU driver version to see if the inference time improves. This can help confirm if the driver update is the root cause.

6. Profile Your Code: Use profiling tools to identify bottlenecks in your code. For example, you can use PyTorch's built-in profiler:

   import torch
   from ultralytics import YOLO
   
   model = YOLO("yolov8n.pt")
   input_image = "bus.jpg"
   
   with torch.profiler.profile(
       activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
       schedule=torch.profiler.schedule(wait=1, warmup=1, active=3),
       on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')
   ) as p:
       for _ in range(5):
           model.predict(input_image)
           p.step()

If you continue to experience issues, please provide additional details such as the specific versions of your GPU driver, CUDA, PyTorch, and Ultralytics. This will help us further investigate the problem.

Feel free to reach out if you have any more questions or need further assistance. We're here to help!

from ultralytics.

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
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• Community: https://community.ultralytics.com

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Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

from ultralytics.