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

OpenAI compatible API for TensorRT LLM triton backend

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.

Serving Inside Pytorch

ClearML - Model-Serving Orchestration and Repository Solution

The Triton backend for the ONNX Runtime.

Deploy stable diffusion model with onnx/tenorrt + tritonserver

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

Deploy DL/ ML inference pipelines with minimal extra code.

Анализ трафика на круговом движении с использованием компьютерного зрения

Diffusion Model for Voice Conversion

Compare multiple optimization methods on triton to imporve model service performance

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

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

Advanced inference pipeline using NVIDIA Triton Inference Server for CRAFT Text detection (Pytorch), included converter from Pytorch -> ONNX -> TensorRT, Inference pipelines (TensorRT, Triton server - multi-format). Supported model format for Triton inference: TensorRT engine, Torchscript, ONNX

triton server ensemble model demo