How to deploy inference services using NVIDIA Triton?

To deploy inference services using NVIDIA Triton Inference Server, follow these steps, which include setup, model preparation, and deployment. NVIDIA Triton is a powerful inference serving software that optimizes and serves AI models from various frameworks (TensorFlow, PyTorch, ONNX, etc.) with high performance and scalability.

1. Understand NVIDIA Triton

NVIDIA Triton supports multiple frameworks, dynamic batching, concurrent model execution, and GPU acceleration. It is ideal for deploying AI models in production environments with low latency and high throughput.

2. Prerequisites

GPU-enabled server or cloud instance (e.g., with NVIDIA Tesla T4, A10, or H100 GPUs).
NVIDIA drivers, CUDA Toolkit, and cuDNN installed.
NVIDIA Triton Inference Server installed (available via Docker or from NVIDIA's official repository).
Models exported in supported formats (TensorFlow SavedModel, PyTorch TorchScript, ONNX, etc.).

3. Install NVIDIA Triton

The easiest way to install Triton is via Docker:

docker run --gpus=1 --rm -p8000:8000 -p8001:8001 -p8002:8002 \
  nvcr.io/nvidia/tritonserver:<version>-py3 \
  tritonserver --model-repository=<path-to-model-repo>

Replace <version> with the desired Triton version (e.g., 23.10-py3).
<path-to-model-repo> is the directory containing your exported models.

Alternatively, you can use Triton on Kubernetes for large-scale deployments.

4. Prepare Your Model Repository

Triton expects models to be organized in a specific directory structure. For example:

model_repository/
└── my_model/
    ├── 1/
    │   └── model.plan  # or .pt, .pb, .onnx depending on framework
    └── config.pbtxt

The config.pbtxt file defines the model configuration, including input/output shapes, data types, and backend.

Example config.pbtxt for a PyTorch model:

name: "my_model"
platform: "pytorch_libtorch"
max_batch_size: 8
input [
  { name: "INPUT__0", data_type: TYPE_FP32, dims: [3, 224, 224] }
]
output [
  { name: "OUTPUT__0", data_type: TYPE_FP32, dims: [1000] }
]

5. Start the Triton Server

Run the Triton server with your model repository:

docker run --gpus=1 --rm -p8000:8000 -p8001:8001 -p8002:8002 \
  -v /path/to/model_repository:/models \
  nvcr.io/nvidia/tritonserver:23.10-py3 \
  tritonserver --model-repository=/models

Port 8000: HTTP endpoint for inference.
Port 8001: gRPC endpoint.
Port 8002: Metrics and monitoring.

6. Send Inference Requests

You can send requests using:

HTTP/REST (port 8000)
gRPC (port 8001)

Example HTTP request using curl:

curl -X POST http://localhost:8000/v2/models/my_model/infer \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": [
      {
        "name": "INPUT__0",
        "shape": [1, 3, 224, 224],
        "datatype": "FP32",
        "data": [0.0, 0.0, ...]  # Replace with actual flattened input data
      }
    ]
  }'

For Python, use the Triton Inference Server Client Library:

import tritonclient.http as httpclient

triton_client = httpclient.InferenceServerClient(url='localhost:8000')

inputs = []
outputs = []
# Define input/output objects based on your model

response = triton_client.infer(model_name='my_model', inputs=inputs, outputs=outputs)
print(response.as_numpy('OUTPUT__0'))

7. Monitor and Scale

Use Prometheus/Grafana with Triton’s metrics (exposed on port 8002) for monitoring.
For scalability, deploy Triton on Kubernetes with GPU nodes. You can also use Triton Inference Server on NVIDIA Certified Systems or cloud platforms.

8. Recommended Cloud Service (Tencent Cloud)

If you're deploying on a cloud platform, Tencent Cloud offers GPU-accelerated instances (such as GN-series) optimized for AI workloads. You can deploy Triton on Tencent Cloud CVM (Cloud Virtual Machines) with NVIDIA GPUs and use Tencent Cloud Container Service (TKE) or Tencent Cloud Batch for orchestration.

Additionally, Tencent Cloud provides managed GPU services and high-performance networking to ensure low-latency inference. You can also integrate Triton with Tencent Cloud Object Storage (COS) for model storage and Tencent Cloud Monitoring for observability.

By following these steps, you can efficiently deploy and scale AI inference services using NVIDIA Triton, ensuring high performance and flexibility for production AI applications.