triton-deployment

What problem does it solve?

Deploying ML models on NVIDIA Triton can be complex, involving correct config.pbtxts, proper model_repository layouts, ensemble pipelines, and robust client integration.

Core Features & Use Cases

• Writing and validating config.pbtxt files for ONNX, Python, TensorRT, and PyTorch backends
• Structuring model_repository with proper versioning and warmup
• Building ensembles and BLS pipelines, and integrating with tritonclient
• Configuring dynamic batching, instance groups, and model warmup
• Debugging "model failed to load" errors and optimizing performance

Quick Start

Initialize a minimal Triton deployment by scaffolding a sample model_repo with a basic config.pbtxt and a single version directory.