This repository contains a docker image that I use to develop my artificial intelligence applications in an uncomplicated fashion. Python, TensorFlow, PyTorch, ONNX, Keras, OpenCV, TensorRT, Numpy, Jupyter notebook... 🐋🔥

Examples for inference models with ONNXRuntime and CUDA

Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.

TensorRT In Docker

Use KataGo TensorRT with Docker, or with a remote Docker using SSH, or with Slurm using SSH, Pyxis, Enroot.

jupyter/scipy-notebook with CUDA Toolkit, cuDNN, NCCL, and TensorRT

Docker image with CUDA, TensorRT, cuDNN, Python3, OpenCV 3.4.6

Docker container for high-speed YOLOv8 inference using TensorRT, integrated with ROS Noetic. Supports GPU/CPU fallback, automatic model optimization, and deployment on Jetson and GPU servers.

End-to-End Perception Pipeline for Self-Driving using Multi-Task Learning

A guide and template for building a Docker image with a specific version of Ubuntu, CUDA, TensorRT, and OpenCV, plus instructions on how to open a Dev Container from it using VS Code. Ideal for computer vision and AI development in a consistent, reproducible environment.

High performance computing Images with pycuda and tensorrt preinstalled

DOCKER CUDA VNC 5901