How to Perform Inference on YOLOv8 Model Deployed on Triton Inference Server using TritonClient? about ultralytics HOT 4 OPEN

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

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from ultralytics.

@Manishthakur2503 hi Manish,

Thank you for reaching out! It's great to hear that you've successfully deployed your YOLOv8n model on Triton Inference Server. You're correct that for performing inference on a model hosted on Triton, you would typically use the tritonclient library to communicate with Triton's REST or GRPC APIs.

Below is a sample script that demonstrates how to perform inference using the tritonclient library. This script will help you structure the request to the Triton server and handle the response correctly.

First, ensure you have the tritonclient library installed:

pip install tritonclient[all]

Here's a Python script to perform inference:

import numpy as np
import tritonclient.http as httpclient
from PIL import Image

# Load and preprocess the image
image_path = "path/to/image.jpg"
image = Image.open(image_path).resize((640, 640))
image = np.array(image).astype(np.float32) / 255.0
image = np.transpose(image, (2, 0, 1))  # Convert to CHW format
image = np.expand_dims(image, axis=0)  # Add batch dimension

# Initialize Triton client
url = "localhost:8000"
model_name = "yolov8n"
client = httpclient.InferenceServerClient(url=url)

# Prepare the input and output
inputs = [httpclient.InferInput("images", image.shape, "FP32")]
inputs[0].set_data_from_numpy(image)

outputs = [httpclient.InferRequestedOutput("output0")]

# Perform inference
results = client.infer(model_name, inputs, outputs=outputs)

# Process the output
output_data = results.as_numpy("output0")
print("Output shape:", output_data.shape)
print("Output data:", output_data)

Explanation:

1. Image Preprocessing: The image is loaded and resized to 640x640 pixels, normalized, and converted to the required format (CHW).
2. Triton Client Initialization: The InferenceServerClient is initialized with the URL of the Triton server.
3. Input and Output Preparation: The input tensor is prepared and set with the preprocessed image data. The output tensor is also specified.
4. Inference: The infer method is called to perform inference, and the results are obtained.
5. Output Processing: The output data is extracted and printed.

This script should help you get started with performing inference using the tritonclient library. For more detailed information, you can refer to the Triton Inference Server documentation.

If you encounter any issues or have further questions, feel free to ask. Happy coding! 😊

from ultralytics.

Hi @glenn-jocher , thanks for the response. I'll try this and let you know if I encounter any issue.

Thanks
Manish Thakur

from ultralytics.

Hi @Manishthakur2503,

You're welcome! I'm glad to hear that you're giving it a try. If you run into any issues or have further questions, feel free to reach out. We're here to help! 😊

Best of luck with your implementation!

from ultralytics.