Introduction to Amazon Elastic Inference and its use cases

In this recipe, we will learn about Amazon Elastic Inference. We will also learn about the use cases of Amazon Elastic Inference.

Recipe Objective - Introduction to Amazon Elastic Inference and its use cases?

The Amazon Elastic Inference is widely used and allows users to attach low-cost GPU-powered acceleration to the Amazon EC2 and AWS Sagemaker instances or Amazon ECS tasks, to reduce the cost of running the deep learning inference by up to 75%. Amazon Elastic Inference further supports the PyTorch, TensorFlow, Apache MXNet and ONNX models. Also, Inference is defined as the process of making predictions using the trained model. Also, In deep learning applications, inference accounts for up to 90% of total operational costs for two reasons, Firstly, standalone GPU instances are typically designed for the model training and not for the inference So, While training jobs batch process, hundreds of data samples in parallel, inference jobs usually process a single input in real-time and thus consume a small amount of GPU compute and this makes standalone GPU inference cost-inefficient. Also, the standalone CPU instances are not specialized for matrix operations and thus are often too slow for deep learning inference. Secondly, different models have different GPU, CPU and memory requirements So, Optimizing for one resource can lead to underutilization of other resources and further higher costs. The Amazon Elastic Inference solves these problems by allowing users to attach just the right amount of GPU-powered inference acceleration to any Amazon EC2 or AWS SageMaker instance type or ECS task, with no code changes. Also, With Amazon Elastic Inference, users are allowed to choose any CPU instance in AWS which is best suited to the overall compute and memory needs of the application and then separately configure the right amount of GPU-powered inference acceleration, allowing users to efficiently utilize resources and reduce costs.

Build Log Analytics Application with Spark Streaming and Kafka

Benefits of Amazon Elastic Inference

  • The Amazon Elastic Inferences allows users to choose the instance type which is best suited to the overall compute and memory needs of the user's application. Users can separately specify the amount of inference acceleration that they need and this reduces inference costs by up to 75% because users no longer need to over-provision GPU compute for inference. The Amazon Elastic Inference can provide as little as the single-precision TFLOPS (trillion floating-point operations per second) of the inference acceleration or as much as 32 mixed-precision TFLOPS and this is a much more appropriate range of inference compute than the range of up to 1,000 TFLOPS provided by the standalone Amazon EC2 P3 instance and thus give users what they exactly need. The Amazon Elastic Inference can easily scale the amount of inference acceleration up and down using the Amazon EC2 Auto Scaling groups to meet the demands of users applications without over-provisioning capacity, when EC2 Auto Scaling increases, user's EC2 instances to meet increasing demand and it also automatically scales up the attached accelerator for each instance and thus responding the changes in demand.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Elastic Inference and Use cases of Amazon Inference.

Use cases of Amazon Elastic Inference

    • It provides Computer Vision services.

Using Amazon Elastic Inference, the computer vision can be performed which deals with how computers can gain high-level understanding from the digital images or videos and it seeks to understand and further automate tasks that the human visual system can do.

    • It provides Natural language processing services.

Using Amazon Elastic Inference, natural language processing(NLP) can be performed which is described as a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and the human language So, in particular, it defines how to program computers to process and further analyze large amounts of natural language data.

    • It provides Speech Recognition services.

Using Amazon Elastic Inference, speech recognition can be performed which is described as an interdisciplinary subfield of computer science and computational linguistics which develops methodologies and technologies which further enable the recognition and translation of spoken language into text by computers with searchability as the main benefit.

What Users are saying..

profile image

Abhinav Agarwal

Graduate Student at Northwestern University
linkedin profile url

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

Relevant Projects

How to deal with slowly changing dimensions using snowflake?
Implement Slowly Changing Dimensions using Snowflake Method - Build Type 1 and Type 2 SCD in Snowflake using the Stream and Task Functionalities

Hadoop Project to Perform Hive Analytics using SQL and Scala
In this hadoop project, learn about the features in Hive that allow us to perform analytical queries over large datasets.

Web Server Log Processing using Hadoop in Azure
In this big data project, you will use Hadoop, Flume, Spark and Hive to process the Web Server logs dataset to glean more insights on the log data.

Azure Stream Analytics for Real-Time Cab Service Monitoring
Build an end-to-end stream processing pipeline using Azure Stream Analytics for real time cab service monitoring

GCP Project-Build Pipeline using Dataflow Apache Beam Python
In this GCP Project, you will learn to build a data pipeline using Apache Beam Python on Google Dataflow.

Yelp Data Processing using Spark and Hive Part 2
In this spark project, we will continue building the data warehouse from the previous project Yelp Data Processing Using Spark And Hive Part 1 and will do further data processing to develop diverse data products.

PySpark Project to Learn Advanced DataFrame Concepts
In this PySpark Big Data Project, you will gain hands-on experience working with advanced functionalities of PySpark Dataframes and Performance Optimization.

Learn Efficient Multi-Source Data Processing with Talend ETL
In this Talend ETL Project , you will create a multi-source ETL Pipeline to load data from multiple sources such as MySQL Database, Azure Database, and API to Snowflake cloud using Talend Jobs.

Build an Incremental ETL Pipeline with AWS CDK
Learn how to build an Incremental ETL Pipeline with AWS CDK using Cryptocurrency data

Airline Dataset Analysis using PySpark GraphFrames in Python
In this PySpark project, you will perform airline dataset analysis using graphframes in Python to find structural motifs, the shortest route between cities, and rank airports with PageRank.