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    Object Detection using YOLOv8 on Jupyter Notebook
    Object Detection using YOLOv8 on Jupyter Notebook
    Updated on 22 May 2025
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    This guide walks you through running an object detection model using YOLOv8 on Jupyter Notebook, from setup to inference

    1. Create a new container using Jupyter Notebook follow this guide here. Alt text
    2. After creating container successfully, open the container via HTTPs
    3. Now, pulling YOLOv8 model using terminal in the Jupyter Notebook container that we have just created
    • Step 1: Setup environment to run YOLO models, in this lab, we will use YOLOv8 to detect type of animals 
    pip install ultralytics 
apt update && apt install -y libglib2.0-0 libgl1
    • Step 2: Install YOLOv8
    from ultralytics import YOLO  
import cv2  
import matplotlib.pyplot as plt  
import torch  
model = YOLO("yolov8l.pt")
    • Step 3: Load model into NVIDIA GPU H100 then check whether the model is using correct GPU 
    model.to("cuda") 
print("Model device:", model.device)  
print("GPU available:", torch.cuda.is_available())  
print("GPU name:", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "No GPU")  
print("Current device:", torch.cuda.current_device() if torch.cuda.is_available() else "None")
    • Step 4: Object detecting using YOLOv8: load an image of some animals into the current workspace, run command below to detect the type of animals in the picture
    Info Icon
    Notice: The picture "640px-MountainLion.jpg" in this demo is pushed from local, please upload your own image and replace into the img_path before running .
    img_path = "640px-MountainLion.jpg"  
results = model(img_path) 
allocated = torch.cuda.memory_allocated() / 10242 
reserved = torch.cuda.memory_reserved() / 10242 
print(f"Memory allocated: {allocated:.2f} MB")  
print(f"Memory reserved: {reserved:.2f} MB") 
results[0].show()
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