“Add GPU acceleration to any Amazon EC2 instance for faster inference at much lower cost (up to 75% savings)”
So you’ve just kicked off the training phase of your multilayered deep neural network. The training phase is leveraging Amazon EC2 P3 instances to keep the training time to a minimum, but it’s still going to take a while. With time in hand, you begin to contemplate what infrastructure you’ll use to run your inferences.
You’re already familiar with the merits of using GPUs for the training phase. GPUs have the ability to parallelize massive amounts of simple math computations, which makes them perfect for training neural networks. GPUs are more expensive to run than CPUs, but because they can parallelize the number crunching, you don’t need to run them as long as you would the equivalent training performed on CPUs. In fact, training on GPUs can be orders-of-magnitude quicker. So it may cost you more per hour to run a GPU, but you won’t need to run it anywhere nearly as long when on a CPU. Besides factoring in cost, training your models faster allows you to get them into production quicker to perform inferences. So in terms of the training phase, it makes complete sense to go with GPUs.
So your contemplation now focuses on whether to use GPU or CPU infrastructure to perform inferencing once the training completes and your model is ready. We know that GPUs cost more per hour to run. Performing inferences through a trained neural network are far less taxing in terms of required computation and data volume that needs to be ingested and processed. Therefore, CPUs seem to be the way to go. However, you know from past experiences that over time, your CPU hosted inferencing tends to bottleneck due to overwhelming demand and this makes you reconsider running the inferencing on GPUs, but you now need to budget in the extra cost as a project consideration. This dilemma of whether to use GPUs versus CPUs for inferencing, with respect to both cost and performance is all too familiar for many organizations. The choice of using a GPU or CPU was a fairly mutually exclusive upfront decision made when using EC2. As of today, this is no longer the case.
Amazon Elastic Inference is a new service from AWS which allows you to complement your EC2 CPU instances with GPU acceleration, which is perfect for hosting your inferencing models. You can now select the appropriate CPU sized EC2 instance and boost its number crunching ability with GPU processing. Like with many other AWS services, you only pay for the actual accelerator hours you use. What this means is that you can get full GPU processing power but being up to 75% cheaper than running an equivalent GPU sized EC2 instance.
(You might also want to read up on this year’s announcements from re:Invent, particularly our blog post on how Amazon FSx for Lustre Makes High Performance Computing More Accessible.)
For starters, Amazon Elastic Inference is launching with 3 types of Teraflop mixed precision powered accelerators: eia1.medium, eia1.large, and the eia1.xlarge
Amazon Elastic Inference has been seamlessly integrated into both the AWS EC2 console and the AWS CLI. In the following EC2 console screenshot, attaching GPU acceleration, is as simple as enabling the “Add an Elastic Inference accelerator” option:
The equivalent AWS CLI command looks like the following, noting that the existing API has been extended with a new optional elastic-inference-accelerator parameter:
aws ec2 run-instances \ --image-id ami-00ffbd996ef2211e3 \ --key-name DNN_Key --security-group-ids sg-12345678 \ --subnet-id subnet-12345678 \ --instance-type c5.xlarge \ --elastic-inference-accelerator Type=eia1.large --iam-instance-profile Name="InferenceAcceleratorProfile"
The following list itemizes several prerequisites that need to be in place to leverage Amazon Elastic Inference:
- A Private Link endpoint configured for Elastic Inference must be present
- An IAM role with the necessary policies to connect to the Elastic Inference accelerator
- Build your models using TensorFlow, Apache MXNet, and/or ONNX
- Use the latest AWS Deep learning AMIs, which have been updated with Amazon Elastic Inference support baked directly into the TensorFlow, Apache MXNet deep learning frameworks
As you can see with a few extra configuration options in place you can have the best of both worlds, CPU hosted inferencing with GPU acceleration. You no longer need to spend time contemplating CPUs over GPUs – take both!!
Another game changer in the machine learning space from AWS – give it a try and check out our Lab on Analyzing CPU vs GPU Performance for AWS Machine Learning.
New Content: AWS Terraform, Java Programming Lab Challenges, Azure DP-900 & DP-300 Certification Exam Prep, Plus Plenty More Amazon, Google, Microsoft, and Big Data Courses
This month our Content Team continues building the catalog of courses for everyone learning about AWS, GCP, and Microsoft Azure. In addition, this month’s updates include several Java programming lab challenges and a couple of courses on big data. In total, we released five new learning...
Where Should You Be Focusing Your AWS Security Efforts?
Another day, another re:Invent session! This time I listened to Stephen Schmidt’s session, “AWS Security: Where we've been, where we're going.” Amongst covering the highlights of AWS security during 2020, a number of newly added AWS features/services were discussed, including: AWS Audit...
AWS re:Invent: 2020 Keynote Top Highlights and More
We’ve gotten through the first five days of the special all-virtual 2020 edition of AWS re:Invent. It’s always a really exciting time for practitioners in the field to see what features and services AWS has cooked up for the year ahead. This year’s conference is a marathon and not a...
WARNING: Great Cloud Content Ahead
At Cloud Academy, content is at the heart of what we do. We work with the world’s leading cloud and operations teams to develop video courses and learning paths that accelerate teams and drive digital transformation. First and foremost, we listen to our customers’ needs and we stay ahea...
Excelling in AWS, Azure, and Beyond – How Danut Prisacaru Prepares for the Future
Meet Danut Prisacaru. Danut has been a Software Architect for the past 10 years and has been involved in Software Engineering for 30 years. He’s passionate about software and learning, and jokes that coding is basically the only thing he can do well (!). We think his enthusiasm shines t...
New Content: AWS Data Analytics – Specialty Certification, Azure AI-900 Certification, Plus New Learning Paths, Courses, Labs, and More
This month our Content Team released two big certification Learning Paths: the AWS Certified Data Analytics - Speciality, and the Azure AI Fundamentals AI-900. In total, we released four new Learning Paths, 16 courses, 24 assessments, and 11 labs. New content on Cloud Academy At any ...
New Content: Azure DP-100 Certification, Alibaba Cloud Certified Associate Prep, 13 Security Labs, and Much More
This past month our Content Team served up a heaping spoonful of new and updated content. Not only did our experts release the brand new Azure DP-100 Certification Learning Path, but they also created 18 new hands-on labs — and so much more! New content on Cloud Academy At any time, y...
AWS Certification Practice Exam: What to Expect from Test Questions
If you’re building applications on the AWS cloud or looking to get started in cloud computing, certification is a way to build deep knowledge in key services unique to the AWS platform. AWS currently offers 12 certifications that cover major cloud roles including Solutions Architect, De...
Overcoming Unprecedented Business Challenges with AWS
From auto-scaling applications with high availability to video conferencing that’s used by everyone, every day — cloud technology has never been more popular or in-demand. But what does this mean for experienced cloud professionals and the challenges they face as they carve out a new p...
Constant Content: Cloud Academy’s Q3 2020 Roadmap
Hello — Andy Larkin here, VP of Content at Cloud Academy. I am pleased to release our roadmap for the next three months of 2020 — August through October. Let me walk you through the content we have planned for you and how this content can help you gain skills, get certified, and...
New Content: Alibaba, Azure AZ-303 and AZ-304, Site Reliability Engineering (SRE) Foundation, Python 3 Programming, 16 Hands-on Labs, and Much More
This month our Content Team did an amazing job at publishing and updating a ton of new content. Not only did our experts release the brand new AZ-303 and AZ-304 Certification Learning Paths, but they also created 16 new hands-on labs — and so much more! New content on Cloud Academy At...
Blog Digest: Which Certifications Should I Get?, The 12 Microsoft Azure Certifications, 6 Ways to Prevent a Data Breach, and More
This month, we were excited to announce that Cloud Academy was recognized in the G2 Summer 2020 reports! These reports highlight the top-rated solutions in the industry, as chosen by the source that matters most: customers. We're grateful to have been nominated as a High Performer in se...