DEV Community: Muhammad Muzammil

Deploy WordPress on AWS EC2: Quick and Easy Setup!

Muhammad Muzammil — Mon, 02 Sep 2024 14:13:16 +0000

It is essential to understand the importance of writing this blog and to grasp the significance of deploying WordPress on AWS.

In 2024, over 810 million websites are using WordPress, accounting for 43.2% of all live websites, essentially 2 out of every 5 sites you visit. WordPress holds a 63.3% market share in the CMS market and powers over a third of the world’s top websites. Impressively, more than 500 new sites are built daily on the platform. WordPress’s growth has grown substantially, from just 12% of all sites in 2011 to now powering 36% of the top 1 million websites, including major names like The New York Times, Bloomberg, and Nike.

Setting up a LAMP (Linux, Apache, MySQL/ MariaDB, PHP) server on an AWS EC2 instance is essential for hosting dynamic websites. In this guide, I will explain the process of launching an EC2 instance, installing the necessary components, and configuring a WordPress site.

Launch an EC2 Instance

Here are the basic steps to launch an EC2 instance on AWS

Login to the AWS Management Console

Go to the AWS Management Console.

Navigate to EC2 Dashboard

In the AWS Management Console, search for “EC2” in the search bar and select “EC2” from the results.

This will take you to the EC2 Dashboard.

Launch Instance

Click on the “Launch Instance” button on the EC2 Dashboard.

Name of the instance

Write the name of your choice for the instance you are going to launch in the name field.

Choose an Amazon Machine Image (AMI)

Select an AMI (e.g., Amazon Linux 2, Ubuntu, etc.) based on your requirements.

AMIs are pre-configured templates that provide the operating system and software stack.

I am choosing Ubuntu for this you can choose any AMI according to your use case.

Choose an Instance Type

Select an instance type based on the CPU, memory, and networking capacity you need.

For example, t2.micro is a common choice for a small application.

Choose or Create a Key Pair

Select an existing key pair or create a new one to securely connect to your instance.

Download the private key file (.pem file) and store it securely.

Or also you can move with “Proceed without a key pair” but it is not recommended.

Configure Security Group

A Security Group acts as a virtual firewall for your instance.

Configure the security group to allow the necessary ports (e.g., SSH, HTTP, etc.).

For now, you have to do this type of configuration that you can see below

You can leave the rest of the configuration as Default.

Add Storage

Configure the storage attached to your instance. By default, an EBS (Elastic Block Store) volume is provided.

You can increase the storage size or add additional volumes if needed.

Review and Launch

Review all your configurations.

Click on “Launch instance” to start your instance.

After a few minutes, your instance will be up and running.

You can see your instance in the instances you can see below.

Access the Instance

Select the instance and click on the “Connect” button then you will reach this screen that you can see below.

Then click on the Connect button to connect to your server.

Update the Server

It’s crucial to keep your server up to date. Run the following command to update and upgrade the packages:

sudo apt update && sudo apt upgrade -y

Set Up the LAMP Server

Install Apache

Install Apache, the web server that will serve your website:

sudo apt install apache2 -y

Ensure Apache is running by checking its status:

sudo systemctl status apache2

If Apache isn’t running, verify that port 80 is open in your EC2 security group.

Start the Apache service:

sudo systemctl start apache2

Enable Apache to Start on the Boot

Ensure Apache starts automatically if the server reboots:

sudo systemctl enable apache2

Install MySql

Install MySql Server and Client

MySql will serve as your database server:

sudo apt install mysql-server mysql-client -y

Start and Check MySql Status

Start the MySql service:

sudo systemctl start mysql

To check the status of MySql:

sudo systemctl status mysql

Setup the MYSQL Server

Set up the root password and secure your database by running:

sudo mysql_secure_installation

Follow the prompts to remove anonymous users, disallow remote login, and remove the test database.

Install PHP

Install PHP and necessary extensions to work with Apache and MySql:

sudo apt install php php-mysql php-gd php-cli php-common -y

Prepare for WordPress Installation

Install required packages:

sudo apt install wget unzip -y

Download and Set Up WordPress

Download WordPress

Download the latest version of WordPress from the official site:

sudo wget https://wordpress.org/latest.zip

Unzip the Downloaded File

Unzip the WordPress package:

sudo unzip latest.zip

Copy WordPress Files

Move the WordPress files to the Apache root directory:

sudo cp -r wordpress/* /var/www/html/

Set Permissions and Clean-Up

Change Ownership of WordPress Files

Ensure that the web server owns the WordPress files:

sudo chown www-data:www-data -R /var/www/html/

Remove the Default Apache Index File

sudo rm -rf /var/www/html/index.html

Configure the Database for WordPress

Log into MySql

Log into MySql as root:

sudo mysql -u root -p

Create a WordPress Database and User

Run the following commands to create a database and user for WordPress:

CREATE DATABASE wordpress;
CREATE USER ‘wpadmin’@’%’ IDENTIFIED BY ‘wpadminpass’;
GRANT ALL PRIVILEGES ON wordpress.* TO ‘wpadmin’@’%’;
EXIT;

Complete WordPress Installation

Visit your server’s public IP address in your web browser to complete the WordPress installation. Enter the database details, site title, and admin credentials.

Once these steps are complete, you’ll be redirected to the WordPress dashboard, and your WordPress site will be live.

Building Scalable ML Models on AWS-SageMake

Muhammad Muzammil — Tue, 20 Aug 2024 20:48:28 +0000

In my "previous blog," I talked about the ML services on AWS. I mentioned that I would discuss each service in separate blogs. I have written a blog about the first ML service, "SageMaker."

Amazon-SageMaker is a fully managed machine learning service that enables data scientists and developers to easily build, train, and deploy ML models. It offers a user-friendly interface for running ML workflows across various integrated development environments. SageMaker allows data to be stored and shared without managing servers and provides managed ML algorithms for efficiently processing large datasets. Additionally, it supports bring-your-own-algorithms and frameworks and allows for easy deployment of models into a secure and scalable environment from the SageMaker console.

AI has taken over the market, with ML projects like Deep Learning, Tabular Data Models, Time Series Forecasting, Reinforcement Learning, and NLP leading the way. I will show you how to set up the SageMaker environment, which allows data storage and sharing without managing servers. It provides managed ML algorithms to efficiently process large datasets, supports custom algorithms and frameworks, and allows easy model deployment into a secure, scalable environment from the SageMaker console.

Amazon-SageMaker Pricing:

Amazon-SageMaker provides flexible pricing based on the instance type, usage duration, and features used. You only pay for the time your instances run, whether for notebooks, training, or hosting models. Costs vary with computing power, storage, and additional services like SageMaker Studio or Autopilot. AWS also offers a free tier for limited use, making it easy to start experimenting with SageMaker at no cost.

Here are the steps you need to follow to successfully set up Amazon-SageMaker.

Step 1:

Step 2:

To find Amazon-SageMaker, enter "SageMaker" into the search bar and select "Amazon-SageMaker" from the options.

Step 3:

When you arrive at this page for the first time, you will be presented with two options: setting up SageMaker for either a single user or an organization, depending on your specific use case.

Single User: Ideal for individuals or small-scale projects prioritizing simplicity and quick setup.

Organization: Ideal for teams or enterprises that require collaborative features, centralized management, and scalability.

Step 4:

After reaching this page, click on the "Notebooks" in the left sidebar to deploy the Jupyter environment for working with your model.

Step 5:

Then after reaching this page click on the Create notebook instance.

Step 6:

After reaching this page, please fill in the required information provided. I have also described them below.

Notebook Instance Name: A unique name for the instance.
Instance Type: The compute resources for the instance.
Platform Identifier: The operating system and Jupyter version.
IAM Role: The role that grants access to other AWS services.
Root Access: Whether users have root access.
Encryption Key: Optional data encryption.

After clicking on "create," you will see a page like this.

Step 7:

Once the status changes to "InService," you can launch Jupyter by clicking on "Open Jupyter."

Step 8:

You have successfully arrived at the page you intended to reach.

Conclusion:

Amazon-SageMaker empowers data scientists and developers to easily build, train, and deploy machine learning models in a fully managed and scalable environment. With flexible pricing, comprehensive features, and a user-friendly interface, SageMaker is an ideal choice for both individual users and organizations looking to leverage the power of AI. By following the outlined steps, you can quickly set up your SageMaker environment, begin working with your models, and take advantage of AWS's robust infrastructure to drive your machine-learning projects to success.

Scale Your ML Projects with AWS

Muhammad Muzammil — Sun, 04 Aug 2024 19:41:07 +0000

As we all know, the world is advancing at a rapid pace. AI plays a crucial role in this quest for rapid growth and intelligence. Machine Learning (ML) stands out among the various aspects of AI.
ML teaches machines how to think and continuously strives to enable machines to mimic human behavior and decision-making.

In the graph above, you can see how market revenue has rapidly increased from $3 billion to $42 billion in just 9 years. It is projected to continue increasing year by year. This blog will enhance your knowledge about why it's important to understand the AI/ML tools and services offered by AWS.
Many tech professionals are now delving deep into this field. For any project, the server plays a critical role. It is where we deploy and teach our ML models and store their data. Therefore, expertise in developing machine learning projects does not guarantee expertise in server management.
This is where Amazon Web Services (AWS) comes in, providing tools and services for AI and ML. AWS offers information, methods of use, requirements, and cost details for all its services. Stay tuned to this blog and upcoming blogs, where I will cover all these aspects. In this blog, I share information about all services generally, their use cases, general pricing, and provide general insights into the various services available for ML projects.

AWS offers services and tools for machine learning projects, which are divided into two types: fully managed and self-managed services. Managed services don't require you to handle infrastructure and other essential tasks, while unmanaged services require you to take responsibility for certain aspects under the AWS shared responsibility model.
AWS provides a wide array of machine learning (ML) services, infrastructure, and deployment resources that enable innovation at scale. Over 100,000 customers, including large enterprises and emerging startups, have opted for AWS machine learning services to address business challenges and foster innovation.

Pricing model for AWS ML

AWS services operate on a pay-as-you-go model with no minimum fees or upfront commitments. Amazon Machine Learning (Amazon ML) charges for compute time used to calculate data statistics, train and evaluate models, as well as for the number of predictions generated. For real-time predictions, there is an hourly reserved capacity charge based on the size of your model.
Amazon Machine Learning (ML) only provides cost estimates for predictions within the Amazon ML console.

AWS ML services List

1- Amazon SageMaker
2- AWS Deep Learning AMIs
3- AWS Deep Learning Containers
4- Hugging Face on Amazon SageMaker
5- TensorFlow on AWS
6- PyTorch on AWS
7- Apache MXNet on AWS
8- Jupyter on AWS

Here is a list of the AWS ML services that we will further classify in this blog. But I will discuss each service in each blog in detail, with a guide on how to use it.

EC2 instances optimized for high performance at lower costs

1- Amazon SageMaker HyperPod
Leverage a purpose-built infrastructure for distribution at scale.

2- Amazon EC2 Trn1 Instances
Get high-performance, cost-effective training of generative AI models.

3- Amazon EC2 Inf2 Instances
Get high performance at the lowest cost in Amazon EC2 for generative AI interference.

4- Amazon EC2 P5 Instances
Get the highest-performance GPU-based instances for deep learning and EFA applications.

5- Amazon EC2 G5 Instances
Get high-performance GPU-based instances for graphic-intensive applications and ML interfaces.

Resources to develop your machine learning skills

1- AWS Solution Library

2- AWS DeepRacer League

3- AWS SageMaker Studio Lab

4- Machine Learning University

5- Machine Learning Tutorial

6- AWS ML Community

Conclusion

In summary, AWS offers a wide range of tools and services to enable businesses and developers to leverage the potential of machine learning. From fully managed platforms such as Amazon SageMaker to customizable options like EC2 instances, AWS provides the flexibility to effectively scale ML projects. By understanding the various AWS ML services and their respective pricing models, organizations can make informed decisions to optimize their AI initiatives and spur innovation.
In our upcoming blog posts, we will explore specific AWS ML services in more detail, offering practical guidance and examples to assist you in successfully building and deploying your machine-learning models.
Keep an eye out for further insights and best practices from AWS!