# Deploy Model Endpoint for YOLOv8

YOLOv8 is the latest model in the YOLO (You Only Look Once) series — a highly efficient family of models used for **object detection** and **classification** in computer vision tasks.

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## Overview

This tutorial will cover the following:

* [A step-by-step guide on Model Endpoint creation and video object detection using YOLOv8](/docs/tir/Inference/Tutorials/yolov8_inference#a-guide-on-model-endpoint-creation-and-video-processing-using-yolo)
* [Creating a Model Endpoint with custom model weights](/docs/tir/Inference/Tutorials/yolov8_inference#creating-model-endpoint-with-custom-model-weights)

We’ll use the pre-built **YOLOv8** container for this tutorial. You can also build a custom container using [Custom Inference](/docs/tir/Inference/Tutorials/custom_inference) if needed.

Using the pre-built container eliminates the need to create a custom API handler — platform automatically generates one for you.

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## A Guide on Model Endpoint Creation and Video Processing Using YOLOv8

### Step 1: Create a Model Endpoint for YOLOv8

1. Go to the [AI Platform](https://tir.e2enetworks.com).
2. Choose your **project**.
3. Open the **Model Endpoints** section.
4. Click **Create Endpoint**.
5. Select **YOLOv8** in the framework list.
6. Choose an appropriate **GPU** or **CPU** plan.
7. Optionally, set **environment variables**.
8. In **Model Details**, skip the custom model selection to use default weights.
9. Click **Create** to deploy the endpoint.

If you wish to use your own trained weights, refer to [Creating a Model Endpoint with custom weights](#creating-model-endpoint-with-custom-model-weights).

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### Step 2: Generate Your API Token

Each model endpoint requires a valid **Auth Token** for API access.

1. Navigate to **API Tokens** in your project.
2. [Create a new API Token](/docs/tir/GettingStarted/Security/API_Tokens) or use an existing one.
3. Copy the **Auth Token** value for later use.

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### Step 3: Perform Object Detection on a Video

#### Prerequisites

1. Create a **bucket** in [E2E Object Storage](https://docs.e2enetworks.com/storage/objectstore/object_storage.html).
2. Upload your input video file to that bucket.
3. Ensure your model endpoint is in **Ready** state.

Then, get the **Sample API Request** from the endpoint page.

Below is a sample Python script for processing a video:

<details>
<summary>Click to expand code</summary>

```python
import requests  

# Enter your auth token here
auth_token = "your-auth-token"

url = "https://jupyterlabs.e2enetworks.net/project/p-<project-id>/endpoint/is-<inference-id>/predict"

payload = {
    "input": "$INPUT_VIDEO_FILE_NAME",  # Path to input video in the bucket
    "bucket_name": "$BUCKET_NAME",      # Bucket name
    "access_key": "$ACCESS_KEY",        # Access key of the bucket
    "secret_key": "$SECRET_KEY"         # Secret key of the bucket
}

headers = {
    'Content-Type': 'application/json',
    'Authorization': f'Bearer {auth_token}'
}

response = requests.post(url, json=payload, headers=headers)
print(response.content)  # Returns JOB_ID for tracking video processing
```

</details>

You’ll receive a **JOB_ID** in the response. Use it to track the video processing status.

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### Step 4: Track Video Processing Status

Use the following script to check job status:

<details>
<summary>Click to expand code</summary>

```python
import requests  

auth_token = "your-auth-token"
url = "https://jupyterlabs.e2enetworks.net/project/p-<project-id>/endpoint/is-<inference-id>/status/$JOB_ID"

headers = {
    'Content-Type': 'application/json',
    'Authorization': f'Bearer {auth_token}'
}

response = requests.get(url, headers=headers)
print(response.content)
```

</details>

Ensure the following before running:

* Correct **Auth Token** is provided.
* Correct **bucket name**, **access key**, **secret key**, and **job ID** are specified.

:::info Note
Video output files are in `.avi` format. Only `.avi` outputs are supported currently.
:::

Once complete, your processed video will appear in the bucket under `yolov8/outputs/`.

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## Creating Model Endpoint with Custom Model Weights

You can deploy your own fine-tuned YOLOv8 weights by following these steps:

### Step 1: Define a Model in Dashboard

1. Go to [AI Platform](https://tir.e2enetworks.com).
2. Choose a **project**.
3. Open the **Model** section.
4. Click **Create Model**.
5. Name your model (e.g., `yolov8-custom`).
6. Select **Model Type** as **Custom**.
7. Click **CREATE**.
8. Copy the **Setup Host** command from the **Setup MinIO CLI** tab.

:::info Note
If you forget to copy the **Setup Host** command, you can revisit the model’s detail page to retrieve it.
:::

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### Step 2: Upload the Model to EOS (E2E Object Storage)

Use the **MinIO CLI** to upload your model file.

```bash
# Go to Dashboard → Models → Select your model → Setup MinIO CLI tab.

# Example copy command:
mc cp -r <MODEL_NAME>.pt yolov8/yolov8-custom-854588
```

Ensure the uploaded file is in `.pt` (PyTorch) format — other formats are not supported.

To list directories before uploading:

```bash
$HOME/.cache/huggingface/hub
```

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### Step 3: Create an Endpoint for Custom Model

Follow the same steps from [Model Endpoint creation](#a-guide-on-model-endpoint-creation-and-video-processing-using-yolov8)
, but select your uploaded custom model under **Model Details**.

If the model is not in the root bucket directory, specify its path in the **Model Path** field during endpoint creation.

Ensure the `.pt` extension is correct and the file path is valid.

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Your YOLOv8 model endpoint is now deployed and ready for inference using either pre-trained or custom-trained weights!


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