Configure a Triton Inference Server model repository

Steps

1. Create the model repository directory structure: <model_repo_root>/<model_name>/<version>/<model_file> — for example models/resnet50/1/model.onnx
2. Write a config.pbtxt file in the model name directory specifying at minimum: name, backend (e.g., 'onnxruntime', 'tensorrt', 'python'), max_batch_size, and input/output tensor definitions with name, data_type, and dims
3. Start the Triton server pointing at the repository: docker run --gpus all -v /local/model_repo:/models nvcr.io/nvidia/tritonserver:<version>-py3 tritonserver --model-repository=/models
4. Verify model readiness by querying the health endpoint: curl localhost:8000/v2/models/<model_name>/ready — a 200 response confirms the model is loaded
5. Send inference requests using the V2 HTTP inference protocol: POST localhost:8000/v2/models/<model_name>/infer with a JSON body specifying inputs as arrays
6. Inspect auto-generated configuration for a model without config.pbtxt by querying: curl localhost:8000/v2/models/<model_name>/config