triton-inference-server

Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

Add bisenetv2. My implementation of BiSeNet

This repository deploys YOLOv4 as an optimized TensorRT engine to Triton Inference Server

NVIDIA DeepStream Monorepo: DeepStream SDK and reference apps for building GPU‑accelerated, real-time video and multi‑sensor analytics pipelines with GStreamer, TensorRT, and vision AI models across edge, on‑prem, and cloud deployments.

OpenAI compatible API for TensorRT LLM triton backend

The Triton backend for the ONNX Runtime.

Serving Inside Pytorch

ClearML - Model-Serving Orchestration and Repository Solution

Deep Learning Deployment Framework: Supports tf/torch/trt/trtllm/vllm and other NN frameworks. Support dynamic batching, and streaming modes. It is dual-language compatible with Python and C++, offering scalability, extensibility, and high performance. It helps users quickly deploy models and provide services through HTTP/RPC interfaces.

NVIDIA-accelerated DNN model inference ROS 2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU

Deploy stable diffusion model with onnx/tenorrt + tritonserver

Highly Scalable RecSys ML & Data system

Анализ трафика на круговом движении с использованием компьютерного зрения

Deploy DL/ ML inference pipelines with minimal extra code.

Diffusion Model for Voice Conversion

Build Recommender System with PyTorch + Redis + Elasticsearch + Feast + Triton + Flask. Vector Recall, DeepFM Ranking and Web Application.

Compare multiple optimization methods on triton to imporve model service performance

Tiny configuration for Triton Inference Server

Set up CI in DL/ cuda/ cudnn/ TensorRT/ onnx2trt/ onnxruntime/ onnxsim/ Pytorch/ Triton-Inference-Server/ Bazel/ Tesseract/ PaddleOCR/ NVIDIA-docker/ minIO/ Supervisord on AGX or PC from scratch.

Provides an ensemble model to deploy a YoloV8 ONNX model to Triton