# 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&#x20;

   <figure><img src="/files/R9ZgAvuS6Nwq2g9vygaF" alt=""><figcaption></figcaption></figure>

   <figure><img src="/files/rr9Ve4hAEEMEfLpYEX7D" alt=""><figcaption></figcaption></figure>

   <figure><img src="/files/ZKXDGN53UZYdOOEX56vO" alt=""><figcaption></figcaption></figure>

2. Pulling YOLOv8 model using terminal in Jupyter Notebook&#x20;

**Step 1:** Setup environment to run YOLO models, in this lab, we will use YOLOv8 to detect type of animals&#x20;

<pre><code>!pip install ultralytics 
<strong>!apt update &#x26;&#x26; apt install -y libglib2.0-0 libgl1 
</strong></code></pre>

<figure><img src="/files/4e9VqjwzbPhLvQPorfiU" alt=""><figcaption></figcaption></figure>

**Step 2**: Install YOLOv8&#x20;

```
from ultralytics import YOLO  
import cv2  
import matplotlib.pyplot as plt  
import torch  
model = YOLO("yolov8l.pt") 
```

<figure><img src="/files/BD0Pxv22BxDe1MAa3j8s" alt=""><figcaption></figcaption></figure>

**Step 3**: Load model into NVIDIA GPU H100 then check whether the model is using correct GPU&#x20;

```
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") 
```

<figure><img src="/files/VA87aZGsq82HI4Fqkck1" alt=""><figcaption></figcaption></figure>

**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&#x20;

{% hint style="info" %}
*<mark style="color:blue;">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</mark>*&#x20;
{% 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() 
```

<figure><img src="/files/aJzPupxz95sT8QSE093N" alt=""><figcaption></figcaption></figure>


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