Tutorial 19: Triton Inference Server - High-Performance Model Serving

Table of Contents

Introduction

NVIDIA Triton Inference Server is an open-source inference serving software that enables teams to deploy AI models from any framework on any GPU or CPU-based infrastructure. It supports multiple model formats simultaneously — TensorFlow, PyTorch, ONNX Runtime, TensorRT, and custom Python backends — all from a single server instance.

Triton addresses the hard problems of production model serving: dynamic batching to maximize GPU utilization, model ensembles for multi-stage pipelines, concurrent model execution, model versioning, health monitoring, and metrics export. This tutorial covers everything from initial setup to production-grade Kubernetes deployment.

Prerequisites

• Docker installed with NVIDIA Container Toolkit
• NVIDIA GPU with CUDA 12.0+ (for GPU serving)
• Python 3.9+ with tritonclient library
• Basic understanding of model serving concepts
• kubectl and helm (for Kubernetes section)

# Install Triton client libraries
pip install tritonclient[all] grpcio numpy

import tritonclient.grpc as grpcclient
import tritonclient.http as httpclient
import numpy as np

Setting Up Triton Inference Server

Pulling and Running Triton

# Pull the Triton Inference Server container
docker pull nvcr.io/nvidia/tritonserver:24.01-py3

Create model repository directory
mkdir -p /opt/triton/modelrepository


Run Triton with GPU support
docker run --gpus all \
    --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 \
    -v /opt/triton/modelrepository:/models \
    nvcr.io/nvidia/tritonserver:24.01-py3 \
    tritonserver --model-repository=/models \
                 --log-verbose=1

Ports:
8000 - HTTP/REST endpoint
8001 - gRPC endpoint
8002 - Metrics endpoint (Prometheus format)

Verifying the Server

import tritonclient.http as httpclient

Check server health
client = httpclient.InferenceServerClient(url="localhost:8000")

print(f"Server live: {client.isserverlive()}")
print(f"Server ready: {client.isserverready()}")

List loaded models
repositoryindex = client.getmodelrepositoryindex()
for model in repositoryindex:

    print(f"Model: {model['name']}, Version: {model.get('version', 'N/A')}, "
          f"State: {model.get('state', 'N/A')}")

modelrepository/
├── imageclassifier/

│   ├── config.pbtxt
│   ├── 1/
│   │   └── model.onnx
│   └── 2/
│       └── model.onnx
├── textencoder/
│   ├── config.pbtxt
│   └── 1/
│       └── model.pt
├── featureextractor/

│   ├── config.pbtxt
│   └── 1/
│       └── model.savedmodel/
│           ├── savedmodel.pb
│           └── variables/
└── preprocessor/
    ├── config.pbtxt
    └── 1/
        └── model.py

Model Configuration (config.pbtxt)

# config.pbtxt for an ONNX image classifier
name: "imageclassifier"

platform: "onnxruntimeonnx"
maxbatchsize: 32

input [
  {
    name: "input"
    datatype: TYPEFP32
    dims: [ 3, 224, 224 ]
  }
]

output [
  {