Voila!

This article will walk you through setting up a Jupyter Notebook environment with TensorFlow pre-installed using Docker. Docker allows you to run isolated containerized applications, providing a consistent environment regardless of your underlying operating system. Prerequisites: Docker: Ensure you have Docker installed and running on your system. You can download and install it from the official Docker website ( https://www.docker.com/ ). Steps: Start Docker: Open your Docker application (Docker Desktop for Windows/macOS or the command line if using Linux). Run the Jupyter Notebook container: For macOS/Linux: Open your terminal application and run the following command: docker run -it --rm -p 8888:8888 -v "${PWD}":/home/jovyan/work jupyter/tensorflow-notebook For Windows: Open your Command Prompt application and run the following command: docker run -it --rm -p 8888:8888 -v "%CD%":/home/jovyan/work jupyter/tensorflow-notebook Explanation of the command flags: -

Introduction In this article, I will guide you through the process of hosting a static website on Amazon Web Services (AWS) S3 bucket, mapping it to a custom domain, and enabling HTTPS. I recently went through this process and found it to be a bit complex, so I wanted to share my experience to help others. Prerequisites An AWS account A static website built and ready for deployment A registered domain name with a third-party registrar (e.g., GoDaddy) Step 1: Create an S3 Bucket and Upload Static Website Sign in to the AWS Management Console and navigate to the Amazon S3 service. Click “Create bucket” and provide a unique DNS-compliant name for your bucket. Choose a region for your bucket and click “Create”. Upload your static website files to the bucket and make the bucket public. Step 2: Enable Static Website Hosting and Set Index Document Go to the Properties tab of your bucket. Scroll down to the “Static website hosting” box and click “Edit”. Select “Enable” and e

I recently went through this process of hosting a static website on Amazon Web Services (AWS) S3 bucket , mapping it to a custom domain, and enabling HTTPS and found it to be a bit complex, so I wanted to share my experience to help others. Since, I wanted to share my website with the world but I needed a place to host it. So, I turned to  AWS S3 , a storage service that could handle the job. First, I created a bucket  (like a digital container) on S3 and uploaded my website files. Initially, I made the bucket public for easy access, but this meant I had to type the entire file name (e.g., index.html ) every time I visited. This wasn't ideal. To avoid the long URL , I enabled  static website hosting  on the S3 bucket. This allowed me to access my website without specifying the filename. However, the provided URL was long and difficult to remember. Needing a better address , I registered a domain name (e.g., abc.com ) from a third-party like GoDaddy. But how could I connect

Here, I have listed few of the best CSS themes which can be used for displaying textual HTML documents, e.g., privacy, terms & condition pages etc. You can just add the selected CSS theme's link tag in the head of the HTML code. That's it! Latex PDF document like < link rel = "stylesheet" href = "https://latex.now.sh/style.css" > Awsm Looks great < link rel = "stylesheet" href = "https://igoradamenko.com/awsm.css/v1/css/awsm.min.css" > New < link rel= "stylesheet" href= "https://fonts.xz.style/serve/inter.css" > < link rel= "stylesheet" href= "https://cdn.jsdelivr.net/npm/@exampledev/new.css@1.1.2/new.min.css" > Tacit < link rel= "stylesheet" href= "https://cdn.jsdelivr.net/gh/yegor256/tacit@gh-pages/tacit-css-1.7.1.min.css" /> Chota Looks standard < link rel= "stylesheet" href= "ht

Recently I have been asked to design a solution for monitoring system that can monitor different aspects of the system including time-series data, configuration data, alerts data, hardware failures, and SNMP traps. The system needs to be scalable, highly available and fault tolerant.  Since the system involves a storage box that requires monitoring of various statistics and components, so I'd chosen to store the time series data in a TDengine which is a highly scalable time series database and is a good choice for high cardinality data because it uses a partitioned storage model and columnar encoding to efficiently store and query large volume of data with a wide range of unique values. It can also handle data-ingestion, storage and retrieval efficiently in a number of ways. To handle configuration data, I'd chosen to use Puppet as a configuration management tool that allows the admin to configure all the major tools and components of other systems. Puppet can automate the deli