#ec2instance

Welcome to Naresh I Technologies!

In this practical video, Raham Shek walks you through how to create an AWS server (EC2 instance) using Terraform — from creating configuration files to understanding Terraform’s core structure.

🎯 What You'll Learn:

Creating a working directory for Terraform Writing your first main.tf file

What is a provider block in Terraform? Explanation of Terraform’s structure: Block, Block Label, and Arguments Real-time AWS example with region selection (us-east-1) Key syntax that beginners often miss

Best practices for infrastructure automation

🚀 Whether you're a beginner or practicing DevOps engineer, this video will help you confidently start using Terraform for real-world cloud provisioning.

💬 Drop your questions or doubts in the comments. We’ll personally reply!

Getting Started with AWS

Outpost Racks

Amazon's latest edge computing innovation, second-generation Outpost racks, are now available. This new version supports the latest x86-powered Amazon Elastic Compute Cloud (Amazon EC2) instances and features faster networking instances for ultra-low latency and high throughput applications and simpler network scalability and deployment. These enhancements boost on-premises workloads including telecom 5G Core and financial services core trading platforms.

For on-premises workloads. The second-generation at outpost racks process data locally and has low latency for multiplayer online gaming servers, consumer transaction data, medical records, industrial and manufacturing control systems, telecom BSS, edge inference of diverse applications, and machine learning (ML) models. Customers may now choose from the latest processor generation and Outposts rack configurations with faster processing, more memory, and more network bandwidth.

The latest EC2 instances

In AWS racks are compute-optimized C7i, general-purpose M7i, and memory-optimized R7i x86 instances. Older Outpost Rack C5, M5, and R5 instances had 40% less performance and double vCPU, RAM, and Internet bandwidth. Larger databases, real-time analytics, memory-intensive apps, on-premises workloads, CPU-based edge inference with complicated machine learning models. benefit tremendously from 4th Gen Intel Xeon Scalable CPUs. Newer EC2 instances, including GPU-enabled ones, will be supported.

Easy network scalability and configuration

Amazon has overhauled networking for its latest Outposts generation, making it easier and more scalable. This update centres on its new Outposts network rack, which centralises compute and storage traffic.

The new design has three key benefits. First, you may now grow compute capacity separately from networking infrastructure as workloads rise, increasing flexibility and lowering costs. Second, it started with network resiliency to keep your systems running smoothly. Network racks handle device failures automatically. Third, connecting to on-premises and AWS Regions is simple. You may configure IP addresses, VLANs, and BGP using a revamped console interface or simple APIs.

Amazon EC2 instances with faster networking

Enhanced Amazon EC2 instances with faster networking are being launched on Outpost racks. These instances are designed for mission-critical on-premises throughput, computation, and latency. A supplemental physical network with network accelerator cards attached to top-of-rack (TOR) switches is added to the Outpost logical network for best performance.

Bmn-sf2e instances, designed for ultra-low latency and predictable performance, are the first. The new instances use Intel's latest Sapphire Rapids processors (4th Gen Xeon Scalable) and 8GB of RAM per CPU core to sustain 3.9 GHz across all cores. Bmn-sf2e instances feature AMD Solarflare X2522 network cards that link to top-of-rack switches.

These examples provide deterministic networking for financial services customers, notably capital market companies, employing equal cable lengths, native Layer 2 (L2) multicast, and precision time protocol. Customers may simply connect to their trading infrastructure to meet fair trading and equitable access regulations.

The second instance type, Bmn-cx2, has low latency and high throughput. This example's NVIDIA ConnectX-7 400G NICs are physically coupled to fast top-of-rack switches, giving 800 Gbps bare metal network bandwidth at near line rate. This instance supports hardware PTP and native Layer 2 (L2) multicast, making it ideal for high-throughput workloads including risk analytics, real-time market data dissemination, and telecom 5G core network applications.

Overall, the next Outpost racks generation improves performance, scalability, and resilience for on-premises applications, particularly mission-critical workloads with rigorous throughput and latency constraints. AWS Management Console lets you pick and buy. The new instances preserve regional deployment consistency by supporting the same APIs, AWS Management Console, automation, governance policies, and security controls on-premises and in the cloud. improving IT and developer productivity.

Know something

What is Amazon CloudWatch?

Amazon CloudWatch allows you to monitor your Amazon Web Services (AWS) apps and resources in real time. CloudWatch may be used to collect and track metrics, which are characteristics you can measure for your resources and apps.

Every AWS service you use has metrics automatically displayed on the CloudWatch home page. Additionally, you may design your own dashboards to show analytics about your own apps as well as bespoke sets of metrics of your choosing.

When a threshold is crossed, you may set up alerts that monitor metrics and send messages or that automatically modify the resources you are keeping an eye on. For instance, you may keep an eye on your Amazon EC2 instances‘ CPU utilization and disk reads and writes, and then use that information to decide whether to start more instances to accommodate the increasing strain. To save money, you may also utilize this data to halt instances that aren’t being used.

CloudWatch gives you system-wide insight into operational health, application performance, and resource usage.

How Amazon CloudWatch works

In essence, Amazon CloudWatch is a storehouse for measurements. Metrics are entered into the repository by an AWS service like Amazon EC2, and statistics are retrieved using those metrics. Statistics on your own custom metrics may also be retrieved if you add them to the repository.

Metrics may be used to compute statistics, and the CloudWatch interface can then display the data graphically.

When specific conditions are fulfilled, you may set up alert actions to stop, start, or terminate an Amazon EC2 instance. Additionally, you may set up alerts to start Amazon Simple Notification Service (Amazon SNS) and Amazon EC2 Auto Scaling on your behalf. See Alarms for further details on setting up CloudWatch alarms.

Resources for AWS Cloud computing are kept in highly accessible data center buildings. Each data center facility is situated in a particular geographic area, referred to as a Region, to offer extra scalability and dependability. To achieve the highest level of failure isolation and stability, each region is designed to be totally separated from the others. Although metrics are kept independently in Regions, you may combine information from many Regions using CloudWatch’s cross-Region feature.

Why Use CloudWatch?

A service called Amazon CloudWatch keeps an eye on apps, reacts to changes in performance, maximizes resource use, and offers information on the state of operations. CloudWatch provides a consistent picture of operational health, enables users to create alarms, and automatically responds to changes by gathering data from various AWS services.

Advantages Of Amazon CloudWatch

Visualize and analyze your data with end-to-end observability

Utilize robust visualization tools to gather, retrieve, and examine your resource and application data.

Operate efficiently with automation

Utilize automated actions and alerts that are programmed to trigger at preset thresholds to enhance operational effectiveness.

Quickly obtain an integrated view of your AWS or other resources

Connect with over 70 AWS services with ease for streamlined scalability and monitoring.

Proactively monitor and get actional insights to enhance end user experiences

Use relevant information from your CloudWatch dashboards’ logs and analytics to troubleshoot operational issues.

Amazon CloudWatch Use cases

Monitor application performance

To identify and fix the underlying cause of performance problems with your AWS resources, visualize performance statistics, generate alarms, and correlate data.

Perform root cause analysis

To expedite debugging and lower the total mean time to resolution, examine metrics, logs, log analytics, and user requests.

Optimize resources proactively

By establishing actions that take place when thresholds are reached according to your requirements or machine learning models, you may automate resource planning and save expenses.

Test website impacts

By looking at images, logs, and web requests at any moment, you can determine precisely when and how long your website is affected.

Run ML and HPC applications at scale with the Elastic Fabric Adapter.

What is Elastic Fabric Adapter?

Customers can execute applications that need high volumes of inter-node communications at scale on AWS by using the Elastic Fabric Adapter (EFA), a network interface for Amazon EC2 instances. The performance of inter-instance communications is improved by its specially designed operating system (OS) bypass hardware interface, which is essential for scaling these applications. EFA enables machine learning (ML) applications using the NVIDIA Collective Communications Library (NCCL) and high performance computing (HPC) applications utilizing the Message Passing Interface (MPI) to grow to thousands of CPUs or GPUs. This gives you the on-demand elasticity and flexibility of the AWS cloud along with the application performance of on-premises HPC clusters.

Any compatible EC2 instance can have EFA enabled as an optional EC2 networking feature at no extra cost. Additionally, you may move your HPC apps to AWS with minimal changes because it integrates with the most widely used interfaces, APIs, and libraries for inter-node interactions.

EFA supports Nvidia Collective Communications Library (NCCL) for AI and ML applications, Open MPI 4 and later, and Intel MPI 2019 Update 5 and later for HPC applications. It also interfaces with Libfabric 1.7.0 and later.

AWS Elastic Fabric Adapter

EFA fundamentals

There are two methods for connecting an EFA device to an EC2 instance:

Creating both an EFA device and an ENA device by using a conventional EFA interface, also known as EFA plus ENA.

By using an EFA-only interface, only the Elastic Fabric Adapter device is created.

Features like built-in OS-bypass and congestion control via the Scalable Reliable Datagram (SRD) protocol are offered by the Elastic Fabric Adapter device. The low-latency, dependable transport functionality made possible by the EFA device features enables the EFA interface to improve application performance for HPC and ML workloads running on Amazon EC2. On the other hand, the ENA device provides conventional IP networking.

Traditionally, HPC applications use the Message Passing connect (MPI) to connect with the system’s network transport, while AI/ML applications employ NCCL. Applications in the AWS cloud have to interact with NCCL or MPI, which subsequently leverages the TCP/IP stack of the operating system and the ENA device driver to allow network connection between instances.

AI/ML applications use NCCL and HPC programs use MPI to connect directly with the Libfabric API using a typical EFA (EFA with ENA) or EFA-only interface. To send packets to the network, the Libfabric API talks directly with the EFA device, avoiding the operating system kernel. This lowers overhead and improves the performance of HPC and AI/ML applications.

Amazon Elastic Fabric Adapter

ENA, EFA, and EFA-only network interface differences

There are two kinds of network interfaces offered by Amazon EC2:

All of the conventional IP networking and routing functionalities needed to provide IP networking for a VPC are offered via ENA interfaces.

Both the EFA device for low-latency, high-throughput communication and the ENA device for IP networking are provided by EFA (EFA with ENA) interfaces.

EFA-only interfaces do not support the ENA device for conventional IP networking; they only support the EFA device’s features.

Interfaces and libraries that are supported

The following libraries and interfaces are supported by Elastic Fabric Adapters:

Launch MPI 4 and beyond.

Take note: For Graviton-based instances, Open MPI 4.0 or later is recommended.

Update 5 and later for Intel MPI 2019.

2.4.2 or later of the NVIDIA Collective Communications Library (NCCL)

Version 2.3 or higher of the AWS Neuron SDK

Types of instances that are supported

To see the available instance types that support EFAs in a specific Region

The available instance types vary by Region. To see the available instance types that support Elastic Fabric Adapters in a Region, use the describe-instance-types command with the --region parameter. Include the --filters parameter to scope the results to the instance types that support EFA and the --query parameter to scope the output to the value of InstanceType.

aws ec2 describe-instance-types –region us-east-1 –filters Name=network-info.efa-supported,Values=true –query “InstanceTypes[*].[InstanceType]” –output text | sort

Operating systems that are supported

Depending on the CPU type, different operating systems are supported. The supported operating systems are displayed in the following table.

Note: Ubuntu 20.04 supports peer direct support when used with dl1.24xlarge instances.

Limitations of Elastic Fabric Adapter

The following are the limits of EFAs:

Note: Traffic sent through an EFA (EFA plus ENA) or EFA-only interface’s EFA device is referred to as EFA traffic.

There is currently no support for EFA traffic between P4d/P4de/DL1 instances and other instance types.

One EFA per network card can be set up for instance types that allow multiple network cards. Only one Elastic Fabric Adapter per instance is supported by the other supported instance types.

Dedicated instances and dedicated hosts for c7g.16xlarge, m7g.16xlarge, and r7g.16xlarge are not supported when an EFA is attached.

Availability Zones and VPCs cannot be traversed by EFA traffic. Normal IP traffic from an EFA interface’s ENA device is exempt from this.

Routable EFA traffic does not exist. Routable IP traffic from an EFA interface’s ENA device is still available.

AWS Outposts does not support Elastic Fabric Adapter.

Only applications based on the AWS Cloud Digital Interface Software Development Kit (AWS CDI SDK) can use the EFA device of an EFA (EFA with ENA) interface on Windows instances. Without the additional EFA device capabilities, an EFA (EFA with ENA) interface works as an ENA interface when it is connected to a Windows instance for programs that are not based on the CDI SDK. Windows and Linux applications built using AWS CDI do not support the EFA-only interface.

Advantages

Quicker outcomes

For inter-instance communications, EFA’s special OS bypass networking technology offers a low-latency, low-jitter connection. This makes it possible for your distributed machine learning or tightly coupled HPC systems to grow to thousands of cores, which speeds up their operation.

Adaptable setup

You have the freedom to select the best computing configuration for your workload by turning on Elastic Fabric Adapter support on an expanding list of EC2 instances. Just enable EFA support on your new compute machines and adjust your cluster configurations as your needs evolve. There is no need for advance planning or bookings.

Smooth migration

Elastic Fabric Adapter communicates via the libfabric interface and libfabric APIs. This interface is supported by nearly all HPC programming models, so you may move your current HPC apps to the cloud with minimal changes.

How it operates

Use cases

Fluid Dynamics Computation

Engineers can now model ever-more complicated flow phenomena because to developments in Computational Fluid Dynamics (CFD) methods, while HPC speeds up turnaround times. Design engineers may now experiment with more adjustable parameters by scaling out their simulation jobs using Elastic Fabric Adapter, which produces faster and more accurate results.

Modeling the weather

To get accurate results, complex weather models need fast interconnects, large memory bandwidth, and reliable parallel file systems. Results are more accurate when the model’s grid spacing is closer, but it also uses more processing power. With the help of EFA’s quick interconnect, weather modeling apps may benefit from the AWS cloud’s nearly infinite scalability and produce more precise forecasts faster.

Learning Machines

Distributed computing on GPUs can greatly speed up the training of deep learning models. NCCL has already been implemented into top deep learning frameworks like Caffe, Caffe2, Chainer, MxNet, TensorFlow, and PyTorch to utilize its multi-GPU collectives for communications between nodes. Because EFA is tailored for NCCL on AWS, these training models have higher throughput and scalability, which produces quicker outcomes.

Elastic Fabric Adapter pricing

EFA is a free optional networking capability offered by Amazon EC2 that you can activate on any supported instance.