ru/guides/triton-inference-server/

[giscus[bot]]

ru/guides/triton-inference-server/

Узнай, как интегрировать Ultralytics YOLOv8 с NVIDIA Triton Inference Server для масштабируемого и высокопроизводительного развертывания моделей ИИ.

https://docs.ultralytics.com/ru/guides/triton-inference-server/

[gerasim13]

How to obtain config (pbtxt file) for this model?

[glenn-jocher]

@gerasim13 hi there!

To obtain the config.pbtxt file for your model when setting up Triton Inference Server, you can create it manually. This file is essential for Triton to understand how to handle your model. Below is a basic example of what the config.pbtxt file might look like for a YOLO model:

name: "yolo"
platform: "onnxruntime_onnx"
max_batch_size: 1
input [
  {
    name: "images"
    data_type: TYPE_FP32
    format: FORMAT_NCHW
    dims: [3, 640, 640]
  }
]
output [
  {
    name: "output"
    data_type: TYPE_FP32
    dims: [1, 85, -1]
  }
]

Here’s a step-by-step guide to create and place this file:

1. Define Paths and Create Directories:

   from pathlib import Path
   
   # Define paths
   model_name = "yolo"
   triton_repo_path = Path("tmp") / "triton_repo"
   triton_model_path = triton_repo_path / model_name
   
   # Create directories
   (triton_model_path / "1").mkdir(parents=True, exist_ok=True)

2. Move the ONNX Model and Create the Config File:

   from pathlib import Path
   
   # Move ONNX model to Triton Model path
   Path("path/to/your/model.onnx").rename(triton_model_path / "1" / "model.onnx")
   
   # Create and write config file
   config_content = """
   name: "yolo"
   platform: "onnxruntime_onnx"
   max_batch_size: 1
   input [
     {
       name: "images"
       data_type: TYPE_FP32
       format: FORMAT_NCHW
       dims: [3, 640, 640]
     }
   ]
   output [
     {
       name: "output"
       data_type: TYPE_FP32
       dims: [1, 85, -1]
     }
   ]
   """
   with open(triton_model_path / "config.pbtxt", "w") as f:
       f.write(config_content)

3. Run Triton Inference Server:
   Follow the steps provided in the documentation to run the Triton Inference Server using Docker and perform inference.

For more detailed instructions and additional configurations, you can refer to the Triton Inference Server guide.

If you encounter any issues or need further assistance, please ensure you are using the latest versions of the packages and provide a reproducible example if the problem persists. This helps us diagnose and resolve the issue more efficiently. You can find more information on creating a reproducible example here.

Best of luck with your deployment! 🚀

[gerasim13]

Thanks a lot!

[glenn-jocher]

@gerasim13 hello!

Thank you for your detailed comment and for sharing the relevant documentation on integrating Ultralytics YOLOv8 with Triton Inference Server. It looks like you're on the right track!

To address your question about obtaining the config.pbtxt file for your model, you can create this file manually. Here’s a basic example of what the config.pbtxt might look like for a YOLO model:

name: "yolo"
platform: "onnxruntime_onnx"
max_batch_size: 1
input [
  {
    name: "images"
    data_type: TYPE_FP32
    format: FORMAT_NCHW
    dims: [3, 640, 640]
  }
]
output [
  {
    name: "output"
    data_type: TYPE_FP32
    dims: [1, 85, -1]
  }
]

Here’s how you can create and place this file:

1. Define Paths and Create Directories:

   from pathlib import Path
   
   # Define paths
   model_name = "yolo"
   triton_repo_path = Path("tmp") / "triton_repo"
   triton_model_path = triton_repo_path / model_name
   
   # Create directories
   (triton_model_path / "1").mkdir(parents=True, exist_ok=True)

2. Move the ONNX Model and Create the Config File:

   from pathlib import Path
   
   # Move ONNX model to Triton Model path
   Path("path/to/your/model.onnx").rename(triton_model_path / "1" / "model.onnx")
   
   # Create and write config file
   config_content = """
   name: "yolo"
   platform: "onnxruntime_onnx"
   max_batch_size: 1
   input [
     {
       name: "images"
       data_type: TYPE_FP32
       format: FORMAT_NCHW
       dims: [3, 640, 640]
     }
   ]
   output [
     {
       name: "output"
       data_type: TYPE_FP32
       dims: [1, 85, -1]
     }
   ]
   """
   with open(triton_model_path / "config.pbtxt", "w") as f:
       f.write(config_content)

3. Run Triton Inference Server:
   Follow the steps provided in the documentation to run the Triton Inference Server using Docker and perform inference.

For more detailed instructions and additional configurations, you can refer to the Triton Inference Server guide.

If you encounter any issues or need further assistance, please ensure you are using the latest versions of the packages and provide a reproducible example if the problem persists. This helps us diagnose and resolve the issue more efficiently. You can find more information on creating a reproducible example here.

Best of luck with your deployment! 🚀

[ankhafizov]

I encountered AttributeError: 'AutoBackend' object has no attribute 'task' when tried to predict results = model("path/to/image.jpg")

[glenn-jocher]

It seems like you're encountering an AttributeError. Please ensure you're using the latest version of the Ultralytics package. If the issue persists, try reloading the model with the correct task parameter, like model = YOLO("path/to/model", task="detect"). If you need further assistance, feel free to check the documentation or update your package.