Author: techexpert@lab4devops

This category of services provides monitoring, logging and auditing of services which are running on AWS.

AWS CLOUDWATCH

AWS cloudwatch is a monitoring service for AWS resources and applications running on AWS it’s a performance monitoring service and at same time CloudTrail is for auditing.

Used to collect and track metrics collect logs and monitor them and accordingly set alarms. Cloudwatch mostly monitors below resources.

• EC2 Instances
• Dynamo DB
• RDS DB instances
• Custom metrics generated by applications and services
• Log files generated by applications deployed on AWS. Monitors application performance, resource utilization, operational health.

CloudWatch is accessed via API, command-line interface, AWS SDKs, and the AWS Management Console. CloudWatch integrates with IAM.`

CloudWatch retains metric data as follows:

• Data points with a period of less than 60 seconds are available for 3 hours. These data points are high-resolution custom metrics.
• Data points with a period of 60 seconds (1 minute) are available for 15 days.
• Data points with a period of 300 seconds (5 minute) are available for 63 days.
• Data points with a period of 3600 seconds (1 hour) are available for 455 days (15 months).

CLOUD TRAIL

Cloudtrail provides visibility into user activity by recording actions taken on your account. API history enables security analysis, resource change tracking, and compliance auditing.
Logs API calls made via:

• AWS Management Console.
• AWS SDKs.
• Command line tools.
• Higher-level AWS services (such as CloudFormation).

CloudTrail records account activity and service events from most AWS services and logs the following records:

• The identity of the API caller.
• The time of the API call.
• The source IP address of the API caller.
• The request parameters.
• The response elements returned by the AWS service.

CloudTrail is enabled by default, it’s per AWS account. We can consolidate logs from multiple accounts using an S3 bucket:

1. Turn on CloudTrail in the paying account.
2. Create a bucket policy that allows cross-account access.
3. Turn on CloudTrail in the other accounts and use the bucket in the paying account.

You can integrate CloudTrail with CloudWatch Logs to deliver data events captured by CloudTrail to a CloudWatch Logs log stream.

CloudTrail log file integrity validation feature allows you to determine whether a CloudTrail log file was unchanged, deleted, or modified since CloudTrail delivered it to the specified Amazon S3 bucket.

software stack

A software stack, as we know, is a collection of tools used to build a software application. Similarly, developing a GENAI application also requires a specific software stack. To know more about software stacks, you can follow this link.

To better understand software stacks, you can follow the provided link. To develop our GENAI application, we should first know which software stacks we need to use.

MODELS AND API

We have different categories of GENAI models broadly it can be defined under four categories.

• Closed LLMs
  • PaLM 2 by google
  • LLaMA by Meta
• Open LLMs
  • HuggingChat
  • Dolly by Databricks
  • StableLM by stability AI
  • OpenLLaMA by UC Berkley
• Image Models
  • Midjourney
  • DALLE-3 2 by OpenAI
  • StableDiffusion by stability.ai
  • Runway
• Music Models
  • MusicLM by google
  • MusicGen by Meta

Vector Dtabase

A vector database is a specialized type of database designed to store and manage high-dimensional vector data. Unlike traditional databases that store scalar values (like numbers or text), vector databases handle complex data points represented as vectors1. These vectors can be thought of as arrows in a multi-dimensional space, capturing various characteristics or qualities of the data.

Vector databases are particularly useful for applications involving similarity searches. For example, they can be used to find images similar to a given image, recommend products based on user preferences, or even search for text that matches a particular context2.

How Vector Databases Work

Vector databases use Approximate Nearest Neighbor (ANN) search algorithms to quickly find the closest matching vectors. This is crucial for handling large datasets efficiently2. By clustering vectors based on similarity, these databases can perform low-latency queries, making them ideal for AI applications.

The Synergy of Vector Databases and Gen-AI

Combining vector databases with generative AI can unlock powerful capabilities:

Efficient Data Handling: Generative AI models often require large amounts of data for training. Vector databases can manage and organize this data effectively, enabling faster and more efficient model training1.

Enhanced Search and Retrieval: Vector databases can efficiently retrieve relevant data points for generative AI models, improving the accuracy and relevance of generated content1.

Personalization: By leveraging vector databases, generative AI can provide highly personalized recommendations and content, tailored to individual user preferences. Some examples of vector databases are given below :

1. Pinecone
2. Chroma
3. Qdrant

LLM Frameworks

LLM frameworks allows developer to build complex software using LLMs that can execute a sequence of tasks and access external APIs to perform more complex tasks. Frameworks LLMs to interact with other data sources and APIs and allow them to interact with the software environment. In some of the situation LLMs require to interact with external APIS such as WolframAlpha specifically to solve mathematical problems. Some examples of LLM frameworks are

• Langchain
• LlamaIndex
• Anarchy

ai Deployment

Deployment infrastructure is a combination of services provided by vendors, which allow GENAI applications to be deployed on them. Microsoft provides GENAI deployment services under Azure OpenAI Services. Other examples of deployment infrastructure are Vertex AI by Google and Hugging Face Inference Endpoints.

Understanding the Software Stack: A Comprehensive Guide

In the ever-evolving world of technology, understanding the concept of a software stack is crucial for developers, businesses, and tech enthusiasts alike. A software stack refers to a set of tools and technologies used together to build and run applications and systems. It’s essentially the backbone of any software project, ensuring that various components work seamlessly together. In this blog, we’ll dive into the different layers of a software stack, common examples, and why it’s important to choose the right stack for your project.

The Layers of a Software Stack

A typical software stack consists of several layers, each serving a specific purpose. Here’s a breakdown of the main layers:

1. Operating System (OS):
   • The foundation of any software stack. The OS manages hardware resources and provides essential services for other software layers. Common operating systems include Linux, Windows, and macOS.
2. Web Server:
   • Responsible for handling HTTP requests from clients (e.g., web browsers). Popular web servers include Apache, Nginx, and Microsoft IIS.
3. Database:
   • Stores and manages data used by applications. Databases can be relational (like MySQL and PostgreSQL) or NoSQL (like MongoDB and Cassandra).
4. Server-Side Programming Language:
   • The backend code that powers the application’s logic. Common languages include Python, Java, Ruby, PHP, and Node.js (JavaScript).
5. Frontend Technologies:
   • Everything that users interact with directly in their browsers. This includes HTML, CSS, and JavaScript, along with frameworks like React, Angular, and Vue.js.
6. APIs (Application Programming Interfaces):
   • Enable communication between different software components. RESTful APIs and GraphQL are widely used to connect the frontend with the backend.

Common Software Stacks

There are several well-known software stacks, each with its own strengths and use cases. Here are a few popular ones:

1. LAMP Stack:
   • Linux (OS)
   • Apache (Web Server)
   • MySQL (Database)
   • PHP/Perl/Python (Server-Side Programming Language)
   • Ideal for: Web development projects, especially open-source ones.
2. MEAN Stack:
   • MongoDB (Database)
   • Express.js (Backend Framework)
   • Angular (Frontend Framework)
   • Node.js (Server-Side Programming Language)
   • Ideal for: Single-page applications and real-time web apps.
3. MERN Stack:
   • Similar to the MEAN stack but uses React instead of Angular.
   • MongoDB (Database)
   • Express.js (Backend Framework)
   • React (Frontend Framework)
   • Node.js (Server-Side Programming Language)
   • Ideal for: Dynamic web applications with complex user interfaces.
4. Django Stack:
   • Django (Backend Framework, using Python)
   • PostgreSQL/MySQL (Database)
   • JavaScript (Frontend)
   • Ideal for: Scalable web applications with robust backend needs.

Why Choose the Right Software Stack?

Choosing the right software stack is vital for the success of your project. Here’s why:

1. Performance:
   • Different stacks offer varying levels of performance. Choosing the right stack ensures your application runs efficiently and can handle the expected load.
2. Scalability:
   • As your application grows, so do the demands on your software stack. Selecting a stack that supports scalability ensures your project can expand without major overhauls.
3. Development Speed:
   • Some stacks come with powerful frameworks and tools that speed up development. This can significantly reduce time-to-market for your application.
4. Community Support:
   • Popular stacks often have large communities and extensive documentation, making it easier to find solutions to common problems and stay updated with best practices.
5. Cost:
   • The cost of development and maintenance varies between stacks. Open-source stacks like LAMP can be more cost-effective compared to proprietary solutions.

In conclusion, a software stack is an integral part of any software development project. Understanding the different layers and common examples helps you make informed decisions that align with your project’s goals and requirements. Whether you’re building a simple website or a complex application, choosing the right stack sets the foundation for success.

BENEFIT OF INTEGRATION OF github COPILOT WITH GITHUB

Integrating Copilot with GitHub brings a range of exciting features that enhance your coding experience:

Key Features of Copilot and GitHub Integration

Adaptation to Repositories: Copilot can adapt to the coding standards and practices of your specific repositories.

Autocompletion and Suggestions:

Contextual Code Suggestions: Copilot can understand your code context and provide intelligent code suggestions as you type.

Autocomplete Code: It can finish lines or entire blocks of code based on your initial input.

Pull Requests and Issues:

Assisted Pull Requests: Copilot can help you draft pull requests by suggesting code snippets and descriptions.

Issue Resolution: It can suggest fixes for issues reported in your repository, making maintenance more efficient.

Documentation and Comments:

Generate Documentation: Copilot can help generate comments and documentation for your code, improving readability and maintainability.

Code Explanation: It can explain code snippets, making it easier to understand complex logic.

Code Review and Quality:

Review Assistance: Copilot can assist in reviewing code by suggesting improvements and catching potential errors.

Test Generation: It can help generate unit tests, ensuring your code is well-tested and robust.

Learning and Adaptation:

Personalized Learning: As you use Copilot, it learns from your coding style and preferences, offering more relevant suggestions over time.

requirement

• Subscription to Copilot. To use GitHub Copilot in Visual Studio Code, you must have an active GitHub Copilot subscription. For information about how to get access to Copilot, see “What is GitHub Copilot?.”
• Visual Studio Code. To use GitHub Copilot in Visual Studio Code, you must have Visual Studio Code installed. For more information, see the Visual Studio Code download page.
• Copilot extension for Visual Studio Code. To use GitHub Copilot in Visual Studio Code, you must install the GitHub Copilot extension. For more information, see “Set up GitHub Copilot in Visual Studio Code” in the Visual Studio Code documentation.

steps / instructions

INSTALLING GITHUB COPILOT EXTENTION

• Within VSCode open the extension view
  
• Search for the extension ‘GitHub Copilot‘ and then click on ‘Install‘.
  Search for GitHub Copilot
• You may then be prompted to enter your GitHub credentials. Click on Sign in to GitHub, then click on Allow and enter your GitHub credentials.
• Install the GitHub Copilot Extension
• Click the Install button next to the GitHub Copilot extension. VSCode will download and install the extension.
  Sign In to GitHub
• Once the extension is installed, you need to sign in to your GitHub account. You will see a prompt asking you to sign in. Click the Sign in with GitHub button. This will open a browser window where you can log in to your GitHub account and authorize GitHub Copilot.
  
• It might prompt for authorization request as shown so authorize the same.
  
• Once we authorize GITHUB extension will get downloaded and will with configured with VSCode and we can see the GITHUB extension icon over bottom right corner of the vscode editor
• Also, once we click on this icon we can see the actual COPILOT menu over the VSCODE
  
  

difference between GitHub Copilot and GitHub Copilot Chat

GitHub Copilot and GitHub Copilot Chat are both powerful tools designed to assist developers, but they have different functionalities:

GitHub Copilot

• Primary Function: GitHub Copilot is an AI-powered code completion tool that helps developers write code faster and with fewer errors.
• Usage: It integrates directly into your IDE (like Visual Studio Code) and provides real-time code suggestions as you type.
• Features: It can complete entire lines or blocks of code, generate boilerplate code, and even suggest solutions based on the context of your code.
• Focus: It’s primarily focused on improving the coding process by providing smart code suggestions.

GitHub Copilot Chat

• Primary Function: GitHub Copilot Chat is designed to provide an interactive chat-based interface for developers to ask questions and receive answers related to their code.
• Usage: It allows developers to engage in a conversation with the AI to understand code snippets, get explanations, and ask for assistance with debugging and problem-solving.
• Features: It can explain code, suggest optimizations, help with debugging, and provide detailed answers to coding questions.
• Focus: It’s geared towards creating a more interactive and educational experience, helping developers understand and improve their code through conversation.

Key Differences

• Interaction Style: Copilot is more about inline code suggestions, while Copilot Chat focuses on conversational interaction.
• Use Case: Use Copilot for quick code completions and suggestions; use Copilot Chat for in-depth explanations and assistance.

Both tools complement each other and can significantly enhance your development experience.

ABOUT TERRAFORM PROVISIONERS

Provisioners are specific built-in components that allow you to execute scripts on a local or remote machine as part of the resource creation, destruction process or cleanup process before destruction.

requirement

• AWS Free tier account
• Linux server with terraform installed.

steps / instructions

• Login to the AWS linux server and create directory with name mkdir TFProvisioners
• Change to the directory TFProvisoners
• Run the git clone command
  git clone https://github.com/devops81/TFProvisoners.git
• Once clone is completed we will get below three files
  • Main.tf
  • Setup.tf
  • Readme.md
• If we examine the code under main.tf file
  • under the resource block we are creating AWS VM named webserver
  • We are passing a number of parameters for the resource, such as the AMI that the VM will be spun up as, the instance type, the private key that the instance will be using, the public IP attached to the instance, the security group applied to the instance, and the subnet ID where the VM will be spun up
    
    
• If we examine the provisioner block
  • The remote-exec keyword tells us that this is a remote provisioner, which invokes a script on a remote resource after it is created.
  • The provisioner is using the parameters configured in the embedded connection block to connect to the AWS EC2 instance being created.
  • The provisioner will then issue the commands configured in the inline block to install Apache webserver on CentOS through the yum package manager, start up the Apache server, create a single web page called My Test Website With Help From Terraform Provisioner as an index.html file, and move that file into the data directory of the webserver to be served out globally.
    
    

STEPS TO DEPLOY THE CODE AND ACCESS THE DEPLOYED WEB SERVER

• Initialize the Terraform working directory, and download the required providers:terraform init
• Validate the code to look for any errors in syntax, parameters, or attributes within Terraform resources that may prevent it from deploying correctly:terraform validate You should receive a notification that the configuration is valid.
• Review the actions that will be performed when you deploy the Terraform code:terraform plan In this case, it will create 7 resources as configured in the Terraform code.
• Deploy the code:terraform apply
• When prompted, type yes, and press Enter.
• As the code is being deployed, you will notice that the Terraform provisioner tries to connect to the EC2 instance, and once that connection is established, it will run the bootstrapping that was configured in the provisioner block against the instance.
• When complete, it will output the public IP for the Apache webserver as the Webserver-Public-IP value.
• Copy the IP address, paste it in a new browser window or tab, and press Enter.
• Verify that the web page displays as My Test Website With Help From Terraform Provisioner, validating that the provisioner within your code worked as intended. The commands configured in the provisioner code were issued and executed successfully on the EC2 instance that was created.