Workflow:Triton inference server Server Quickstart Model Deployment
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, Model_Serving, Inference |
| Last Updated | 2026-02-13 17:00 GMT |
Overview
End-to-end process for deploying a pre-trained model on Triton Inference Server using Docker containers and sending inference requests via HTTP or gRPC.
Description
This workflow covers the fundamental procedure for getting a model served through Triton Inference Server. It walks through preparing a model repository with the correct directory layout, launching the server from an NGC Docker container, verifying the server is healthy and models are loaded, and finally sending inference requests using the Triton client SDK. The workflow supports multiple backends (ONNX, TensorRT, PyTorch, OpenVINO, Python) and both GPU and CPU-only deployments.
Usage
Execute this workflow when you have a trained model in a supported framework format (ONNX, TensorRT plan, PyTorch TorchScript, OpenVINO IR, or a Python backend script) and need to serve it for inference through a production-ready HTTP/REST or gRPC endpoint. This is the recommended starting point for any new Triton deployment.
Execution Steps
Step 1: Create the model repository
Organize model files into the required Triton model repository directory structure. Each model requires a named directory containing a numeric version subdirectory with the model file, and optionally a config.pbtxt configuration file. For ONNX and TensorRT backends, the configuration can be auto-generated by Triton if omitted.
Key considerations:
- Directory layout must follow: <model-name>/<version>/model-file
- Version directories use integer names (1, 2, 3, etc.)
- config.pbtxt is optional for backends that support auto-complete (ONNX, TensorRT)
- Model files must match the expected naming convention for each backend (e.g., model.onnx for ONNX)
Step 2: Configure the model (optional)
Create or edit the config.pbtxt file to specify model metadata including input and output tensor names, data types, shapes, maximum batch size, and instance group settings. For simple deployments, this step can be skipped for backends with auto-complete support.
Key considerations:
- Input and output tensor names must match the actual model graph
- Set max_batch_size to 0 if the model has a fixed batch dimension in its inputs
- Instance groups control how many copies of the model run concurrently on each device
Step 3: Launch the Triton server container
Pull and run the NGC Triton server Docker image with the model repository mounted as a volume. The server exposes HTTP (port 8000), gRPC (port 8001), and Prometheus metrics (port 8002) endpoints by default. For GPU systems, use the --gpus flag; for CPU-only systems, omit it.
Key considerations:
- Requires NVIDIA Container Toolkit for GPU deployments
- Mount the model repository directory into the container
- Server logs will show model loading status (READY or error details)
- Use --model-control-mode explicit to selectively load models
Step 4: Verify server health
Confirm the server is running and models are loaded by checking the health and ready endpoints. The HTTP health check at /v2/health/ready returns status 200 when the server is ready to accept inference requests.
Key considerations:
- All models should show READY status in the server console output
- If a model fails to load, check the server log for error details
- The health endpoint is useful for container orchestration readiness probes
Step 5: Send inference requests
Use the Triton client libraries or the SDK container to send inference requests to the deployed model. Requests can be sent via HTTP/REST (JSON or binary) or gRPC protocols. The client specifies the model name, input tensor data, and desired outputs.
Key considerations:
- Client SDK containers include pre-built example clients (image_client, simple_grpc_infer_client, etc.)
- Input data must match the tensor shapes and types declared in the model configuration
- Both synchronous and asynchronous inference modes are supported
- Classification results can be post-processed with top-k selection