A problem with slow first startup recognition using Gpus about ultralytics HOT 2 OPEN

👋 Hello @qinsehm1128, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

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@qinsehm1128 hello,

Thank you for reaching out and providing a detailed description of your issue along with the code and command outputs. The initial delay you are experiencing is a common occurrence and can be attributed to several factors, especially when using GPUs.

Explanation

1. Model Loading and Initialization: The first time you load a model and perform inference, there are several initialization steps that take place, such as loading the model weights into memory, initializing CUDA kernels, and setting up the computational graph. These steps can be time-consuming but are necessary for the model to function correctly.

2. CUDA Context Initialization: When you use model.to("cuda"), the CUDA context is initialized, which can take a significant amount of time. This is a one-time cost, and subsequent inferences are much faster because the context is already set up.

Recommendations

1. Warm-Up Inference: To mitigate the initial delay, you can perform a "warm-up" inference right after loading the model. This will initialize all necessary components, and subsequent inferences will be faster. Here's how you can modify your code:

   from ultralytics import YOLO
   import time
   
   if __name__ == '__main__':
       t = time.time()
       model = YOLO(r'C:\Users\Administrator\PycharmProjects\pythonProject\runs\detect\train4\weights\best.pt').to("cuda")
       print((time.time() - t) * 1000, "ms")
   
       # Warm-up inference
       model.predict(source="0.png")
   
       for i in range(10):
           t = time.time()
           metrics = model.predict(source="0.png",
                                   project='runs/detect',
                                   name='exp',
                                   save=True)
           print((time.time() - t) * 1000, "ms")
           for m in metrics:
               box = m.boxes
               for cls_idx, xyxy in zip(box.cls.tolist(), box.xyxy.tolist()):
                   class_name = model.names[int(cls_idx)]
                   x1, y1, x2, y2 = xyxy
                   print(f"{class_name}: {x1:.2f} {y1:.2f} {x2:.2f} {y2:.2f}")

2. Ensure Latest Versions: Make sure you are using the latest versions of torch and ultralytics. You can upgrade them using the following commands:

   pip install --upgrade torch ultralytics

Additional Resources

For more detailed information on optimizing performance and handling similar issues, you can refer to our FAQ section.

I hope this helps! If you have any further questions or need additional assistance, feel free to ask. 😊

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