Fixing OpenCV DNN CUDA Error: (-215:Assertion failed) python Solved by Onkar Chaudhari - March 18, 2025March 18, 20250 Introduction When using OpenCV’s Deep Neural Network (DNN) module with Compute Unified Device Architecture CUDA, you may encounter the following error: (-215:Assertion failed) preferableBackend != DNN_BACKEND_CUDA || IS_DNN_CUDA_TARGET(preferableTarget) This error occurs when OpenCV’s DNN module fails to properly configure CUDA as the preferred backend for inference. In this article, we will explore why this happens, how we fixed it, and why we had to rebuild OpenCV. Understanding the Error This assertion failure happens in OpenCV when: CUDA is selected as the backend (DNN_BACKEND_CUDA), but The target (DNN_TARGET_CUDA) is not properly recognized as a CUDA-compatible target. The primary causes of this issue include: Missing cuDNN: OpenCV’s DNN module requires cuDNN to run inference on GPUs. Incorrect OpenCV Build: OpenCV must be built with CUDA and cuDNN support. Incompatible CUDA or cuDNN Versions:
Building OpenCV with CUDA Support: A Step-by-Step Guide python by Onkar Chaudhari - February 19, 2025February 19, 20250 Introduction OpenCV is a powerful library for computer vision, but to achieve real-time performance, we need GPU acceleration using CUDA. This guide will walk you through building OpenCV with CUDA support, solving common errors, and ensuring OpenCV uses the GPU. ✅ You Will Learn: How to build OpenCV with CUDA on Linux Common errors and their fixes How to verify GPU acceleration in OpenCV 🔹 Prerequisites Before starting, ensure you have: Ubuntu 22.04 or later NVIDIA GPU (GTX 1050 or higher) CUDA 11.8+ and cuDNN installed CMake 3.16+ 🔹 Step 1: Install Dependencies 1️⃣ Update Your System sudo apt update && sudo apt upgrade -y 2️⃣ Install Required Libraries sudo apt install -y build-essential cmake git unzip pkg-config \ libgtk-3-dev libcanberra-gtk3-module \ libavcodec-dev libavformat-dev libswscale-dev \ libv4l-dev
APScheduler Jobs How-To Guide: Mastering Model Edit, Delete, and Update Events python by komal swami - January 4, 2025January 4, 20250 As developers, we often encounter use cases where tasks need to run periodically—be it sending email notifications, cleaning up stale data, or monitoring devices. This is where a robust task scheduler like APScheduler shines. In this blog, we'll dive into integrating APScheduler with Django REST Framework (DRF), debugging common issues, and demonstrate how to create scheduled jobs to automate tasks in short APScheduler Jobs How-To Guide: Mastering Model Edit, Delete, and Update Events What is APScheduler? APScheduler (Advanced Python Scheduler) is a lightweight Python library for scheduling jobs. It supports multiple backends, such as in-memory, database, or external queues. Its flexible API makes it an excellent choice for Django-based projects. Why Use APScheduler in DRF? Automate repetitive tasks (e.g., device monitoring, report generation). Replace