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As .NET 6.0 approaches its end-of-support date, it’s time for developers and businesses to consider upgrading to stay on the cutting edge and ensure that applications remain secure, efficient, and compliant. With the upcoming .NET 8 offering new features and performance boosts, an upgrade promises more than just continued support—it’s an opportunity to leverage a new generation of improvements. In this guide, we’ll walk you through the upgrade process, highlighting the critical changes you’ll need to make to ensure a smooth transition from .NET 6.0. Why Does upgrade is needed from .Net6.0? End of Support and Security Risks .NET 6.0, as a Long-Term Support (LTS) version, provided stability and support, but its support period is ending. Staying on an unsupported version exposes applications to security vulnerabilities and compliance risks, making an upgrade essential for data protection and system integrity. Access to Enhanced Features and Improvements Newer .NET versions bring valuable features like better performance optimizations, productivity tools, and cloud-native support. Upgrading unlocks these advancements, helping applications run faster and utilize fewer resources. Improved Developer Productivity .NET 7 and .NET 8 include new language features, such as enhanced pattern matching and streamlined lambda expressions in C#, which simplify code and reduce boilerplate. These additions help developers write code more quickly and with fewer errors. Reduced Operational Costs Performance improvements in .NET 7 and .NET 8 mean that applications can often perform better with fewer resources. This reduction in memory and CPU usage can translate to lower costs, especially for cloud-hosted applications. Compatibility with Modern Tools and Libraries Many popular third-party libraries and tools adopt newer .NET versions quickly, ensuring compatibility and stability. Staying up-to-date with .NET versions helps maintain compatibility with these tools, avoiding potential issues with outdated dependencies. Preparing for Future Technology As Microsoft continues to evolve .NET, each version brings it closer to the needs of modern workloads, such as machine learning, cloud computing, and IoT. Upgrading ensures that applications remain ready for future tech innovations and integrate easily with emerging solutions. These improvements make upgrading to .NET 8 essential for any business looking to stay competitive and secure in today’s fast-paced digital world.   Action Plan for .NET 6 Users: Steps to Upgrade to .NET 8 Upgrading from .NET 6 to .NET 8 requires a structured approach to ensure application stability and compatibility. Follow this action plan to make your migration process smooth and efficient. 1. Prioritize Upgrading to .NET 8 Since .NET 8 is the newest LTS release, it’s vital to prioritize upgrading business-critical applications first. Applications managing sensitive data, high user traffic, or core operations will benefit most from .NET 8’s security patches and performance improvements. 2. Evaluate Your Applications for Compatibility Conduct a thorough audit of your applications to identify any potential compatibility issues. Here’s what to focus on: Dependency Audit: Ensure that third-party libraries, plugins, and tools are compatible with .NET 8. Update or replace any that are not supported. Database and ORM Compatibility: If you’re using ORMs like Entity Framework, verify their compatibility with .NET 8 and prepare for any required schema migrations. Code Review for Deprecated APIs: Review your code for deprecated APIs and replace or refactor any incompatible code. Microsoft’s .NET 8 upgrade documentation highlights specific API changes and deprecations to consider. 3. Use the .NET Upgrade Assistant Tool Microsoft’s .NET Upgrade Assistant is designed to streamline the migration process, especially for complex or large projects. It helps automate and identify critical changes needed for a successful upgrade: Flags deprecated APIs and unsupported code. Automates refactoring for .NET 8 compatibility. Provides guidance on manual changes necessary to complete the transition. This tool saves significant time, particularly for enterprise applications with complex dependencies and workflows. 4. Thoroughly Test Your Upgraded Application Testing is essential to ensure that your application performs correctly after the upgrade. Here’s a recommended testing approach: Automated Unit Testing: Run unit tests on your upgraded code to verify core functionality. Add or update tests to cover modified areas. Integration Testing: Validate that services or components work correctly together in .NET 8. Load and Performance Testing: Test under real-world conditions to validate .NET 8’s performance benefits and catch any potential bottlenecks. User Acceptance Testing (UAT): Conduct UAT to confirm that your end-users experience expected functionality, especially if significant code or API changes have been made.   Common Troubleshooting for .NET 8 Migration Here are some common issues developers encounter during migration and how to resolve them. 1. Continuous Integration Challenges with Azure DevOps Upgrading applications within CI/CD environments like Azure DevOps can present challenges. Here’s how to address some common issues: Pipeline Configuration: Update Azure DevOps pipelines to reference .NET 8 SDKs. Modify YAML files or pipeline configurations to install .NET 8 before the build, ensuring dependencies are correctly aligned. NuGet Feed Compatibility: Verify that NuGet feeds are compatible with .NET 8 packages. Update outdated or incompatible packages to avoid build errors. Validate CI Tests: Re-run automated tests in Azure DevOps pipelines to catch compatibility issues before deploying to production. 2. Problem Management on Azure App Service Deploying upgraded applications on Azure App Service may lead to configuration or compatibility issues. Here’s how to manage these effectively: Set Runtime Stack to .NET 8: In the Azure Portal, navigate to App Service Settings > Configuration > Stack Settings and select .NET 8 as the runtime stack. Diagnostic Log Analysis: Check the App Service diagnostic logs for code incompatibility, runtime errors, or dependency conflicts. Enable detailed error messages to troubleshoot effectively. Leverage Azure Application Insights: Use Application Insights to monitor performance, track errors, and get real-time insights into application health, which helps identify optimization areas post-upgrade.   Need Help with Your Upgrade? Connect with Magnusminds If you need assistance with your migration or encounter complex issues, Magnusminds offers expert support for .NET upgrades. out team provides tailored solutions for migration, implementation of new features, and troubleshooting specific to your application needs.  

Custom Vision Azure AI Custom Vision is an image recognition service that lets you build, deploy, and improve your own image identifier models. An image identifier applies labels to images, according to their visual characteristics. Each label represents a classification or object. Custom Vision allows you to specify your own labels and train custom models to detect them. How Custom Vision Works The Custom Vision service leverages a machine learning algorithm to analyze images for unique features. Here’s a step-by-step overview: Image Submission: Upload sets of images with and without the desired visual characteristics. Labeling: Tag the images with custom labels during submission. Training: The algorithm trains on this data and tests its accuracy using the same images. Deployment: Once trained, the model can be tested, retrained, and deployed in your app to classify images or detect objects. Offline Use: You can also export the model for offline applications.   Getting Started with Custom Vision Step 1: Create a New Project Visit the Custom Vision portal. Click on New Project. Fill in the required fields. Note: Charges apply as per the pricing model.   Step 2: Upload Training Images Navigate to the Training Images section. Click on the Browse button to select images. Add tags to the selected images. Ensure each tag is specific and accurate.   Browse the photos to start training and upload photos.   Add Tags to uploading images.   Uplaod Arround 50+ Photos for seamless training for each Tags. Step 4: Evaluate Model Performance Once training is complete, the model’s performance metrics will be displayed: Precision: Indicates the likelihood of a correct tag prediction. Recall: Shows the percentage of correct predictions out of all possible correct tags. Average Precision (AP): Summarizes precision and recall across different thresholds. To start training: Click on Train Button on the top,   So GO to Performance page, And Click on  Select the quick training for faster results and quick outputs.   Wait Until the Training teration is completed.   Wait until the models are Developed and  Once Training complets then it will show the Charts as per below.   Step 5: Test the Trained Model Click on Quick Test. Select an image to test. Review the precision percentages to understand tag accuracy   Supported Browsers The Custom Vision portal supports the following browsers: Microsoft Edge (latest version) Google Chrome (latest version) Conclusion Azure AI Custom Vision empowers you to create tailored image recognition models with ease. By following the steps outlined in this guide, you can harness the power of AI to enhance your applications.  

What is Docker? In this article, you will learn to build Docker image from scratch, and deploy and run your application as a Docker container using Dockerfile.Docker allows developers to build, test, and deploy applications quickly and efficiently using isolated and portable containers that run anywhere.   How to Create Docker File In order to build the container image, you’ll need to use a Dockerfile. A Dockerfile is simply a text-based file with no file extension. A Dockerfile contains a script of instructions that Docker uses to create a container image.In a Dockerfile Everything on left is INSTRUCTION, and on right is an ARGUMENT to those instructions. Remember that the file name is "Dockerfile" without any extension. To create Docker file from visual studio - Open project folder in visual studio - Right click on project folder  - Go to add - Go to docker support     - There are two options to build docker file : windows and Linux - Select one of the given option and it will create the docker file     Here we have selected windows operating system, so it will create docker file for windows image but if you select Linux operating system then it will create docker file for Linux image and rest of the docker commands will be same for windows as well as Linux   How to Build Docker Image We will build our image using the Docker command. The below command will build the image using Dockerfile from the same directory. docker build -t demoimage:1.0 . - t is for tagging the image. - demoimage is the name of the image. - 1.0 is the tag name. If you don’t add any tag, it defaults to the tag named latest. - . means, we are referring to the Dockerfile location as the docker build context.   After the image build output will look like below. Now, we can list the images by using this command. docker images   Test the Docker Image Now after building the image we will run the Docker image. The command will be, docker run -d -p 4000:80 --name democontainer2 demoimage:1.0 - d flag is for running the container in detached mode. - p flag flag for the port number, the format is local-port:container-port. - --name for the container name, democontainer2 in our case.   Docker started our container in the background and printed the Container ID on the terminal. We can check the running container by using the below command. docker ps In a web browser, access http://localhost:4000 and we can see the index page which displays the content in the custom HTML page we added to the docker image. After creating Docker image we can see all the local images in Docker windows Desktop. Go to the images tab and we can see all the images. Go to the containers tab and we can see all the containers also. Here we are using docker desktop for windows,so we have selected switch to windows option,if you are using docker desktop for Linux then select switch to Linux option.   Push Docker Image to Docker Hub Docker Hub is a registry service on the cloud that allows you to download Docker images that are built by other communities. You can also upload your own Docker built images to Docker hub.To push our Docker image to the Docker hub, we need to create an account in the Docker hub. After that, execute the below command to log in from the terminal. It will ask for a username and password (if you are login for the first time). Provide the Docker hub credentials. docker login After login, we now need to tag our image with the docker username as shown below. docker tag demoimage:1.0 <username>/<image-name>:tag For example, here hiral1 is the docker hub username. docker tag demoimage:1.0 hiral1/demoimage:1.0 Run docker images command again and check the tagged image will be there. Now we can push our images to the Docker hub using the below command. docker push hiral1/demoimage:1.0 Now we can check this image will be available in our Docker Hub account.   We can inspect a container by following command. docker inspect <container-id>   We can view Docker logs in a Docker container by following command. docker logs <container-id>   And we can stop the running container by following command. docker stop <container-id>   Pull and Run Docker Image from Docker Hub To pull image from docker hub use the following command. docker pull <imagename:tag>   Docker checks if the image already exists or not, if it is then it does not download further.In our case it is already there.So we need to remove existing image. To remove the docker image use the following command. docker rmi <imagename:tag>   Now pull the docker image from docker hub and check for the list of images.   To run the pulled image use the below command docker run -d -p 4000:80 --name windowscontainer  hiral1/demoimage:1.0 - d flag is for running the container in detached mode. - p flag flag for the port number, the format is local-port:container-port. - --name for the container name, windowscontainer  in our case. In a web browser, access http://localhost:4000 and we can see the index page which displays the content in the custom HTML page we added to the docker image. Below is the example of how to pull and run docker image on Linux VM. Here is the output of the docker image on the browser. Rename/Tag Docker Image To rename the docker image tag the image as follows. docker tag <oldimagename:tag> <newimagename:tag> In our case the old image name is hiral1/demoimage , old tag is 1.0 and new image name is newimage/latest.We have not provided a new tag to newimage, so it gives tag - latest by default.   Remove Docker Image To remove the docker image use the following command. docker rmi <imagename:tag>