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Jupyter Notebook Use Case

{% embed url="https://www.youtube.com/watch?embeds_referring_euri=https://cdn.iframe.ly/&source_ve_path=Mjg2NjQsOTY3MTQ&v=IenVwz7iqfs" %}

  1. Create a GPU Container using Jupyter Notebook template

  2. Pulling YOLOv8 model using terminal in Jupyter Notebook

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

{% hint style="info" %} 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 {% endhint %}

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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