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Launching a Jupyter Notebook with TensorFlow using Docker

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: -
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Hosting a Static Website on AWS S3 with Custom Domain and HTTPS

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

Story of Hosting a Static Website on AWS

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

Best CSS for textual HTML docs

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

How to design a solution for storage monitoring system?

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

QoT: A Powerful Command-Line Tool for Querying Data from CSV Files

Are you looking for a way to query and manipulate data in CSV files without using complex software or coding skills? If so, you might be interested in QoT, a powerful command-line tool that allows you to query data from tables or sheets with ease. QoT  stands for  Query over Table , and it is a tool that lets you filter, sort, and display data from CSV files using simple and intuitive commands. You can use QoT to quickly extract insights from large datasets, perform data analysis, or integrate QoT into your workflow. With QoT, you can: Select the columns you want to display using the  --select  option Specify the file name of the table or sheet using the  --from  option Apply a condition to filter the query result using the  --where  option Limit the number of rows in the query result using the  --limit  option Sort the query result by a certain column using the  --orderby  option and choose either  --asc  or  --desc  to order the result in ascending or descending order Print

The ultimate tool for finding and replacing patterns in files

Are you looking for a simple and powerful tool that can help you find and replace patterns in files? If so, you might want to check out  FindReplace , a command line tool that allows you to search and replace text or data structures in any file using JSON, YAML,  CSON , or plain text formats. FindReplace  is a Rust-based tool that is easy to install and use. You can download it from  crates.io  or  GitHub , or build it from source using Cargo. Once installed, you can use it by running the command findreplace followed by the pattern, the substitute, and the file path. For example, if you want to replace all occurrences of “foo” with “bar” in a file named file.txt, you can run the command: findreplace 'foo' 'bar' file.txt FindReplace also supports using JSON, YAML, or CSON files as substitutes. This means you can use these formats to define complex data structures that can be used to replace multiple patterns at once. For example, if you have a file named file.txt t