Add bisenetv2. My implementation of BiSeNet
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Updated
Feb 5, 2023 - Python
Add bisenetv2. My implementation of BiSeNet
ClearML - Model-Serving Orchestration and Repository Solution
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.
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
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
FastAPI middleware for comparing different ML model serving approaches
Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code.
The Purpose of this repository is to create a DeepStream/Triton-Server sample application that utilizes yolov7, yolov7-qat, yolov9 models to perform inference on video files or RTSP streams.
Triton-Pytorch Custom operator tutorial
Provides an ensemble model to deploy a YOLOv8 TensorRT model to Triton
Triton backend that enables pre-processing, post-processing and other logic to be implemented in Python. In the repository, I use tech stack including YOLOv8, ONNX, EasyOCR, Triton Inference Server, CV2, Minio, Docker, and K8S. All of which we deploy on k80 and use CUDA 11.4
📸 YOLO Serving Cookbook based on Triton Inference Server 📸
An image to text model/pipeline using VIT and Transformers and deployment using Nvidia's Pytrition and Streamlit app.
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