Jupyter Notebook Use Case

This guide walks you through running an object detection model using YOLOv8 on Jupyter Notebook, from setup to inference

Create a GPU Container using Jupyter Notebook template
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

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()

PreviousUse cases NextvLLM Use Case

Last updated 9 days ago