Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
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Updated
Jun 4, 2024 - Python
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
An Alternative for Triton Inference Server. Boosting DL Service Throughput 1.5-4x by Ensemble Pipeline Serving with Concurrent CUDA Streams for PyTorch/LibTorch Frontend and TensorRT/CVCUDA, etc., Backends
ClearML - Model-Serving Orchestration and Repository Solution
Deploy stable diffusion model with onnx/tenorrt + tritonserver
The Triton backend for the ONNX Runtime.
NVIDIA-accelerated DNN model inference ROS 2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU
OpenAI compatible API for TensorRT LLM triton backend
Deploy DL/ ML inference pipelines with minimal extra code.
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.
Compare multiple optimization methods on triton to imporve model service performance
Tiny configuration for Triton Inference Server
Build Recommender System with PyTorch + Redis + Elasticsearch + Feast + Triton + Flask. Vector Recall, DeepFM Ranking and Web Application.
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
Diffusion Model for Voice Conversion
Provides an ensemble model to deploy a YoloV8 ONNX model to Triton
Serving Example of CodeGen-350M-Mono-GPTJ on Triton Inference Server with Docker and Kubernetes
Magface Triton Inferece Server Using Tensorrt
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