Why Amazon EC2 P3 Instances?
Amazon EC2 P3 instances deliver high performance compute in the cloud with up to 8 NVIDIA® V100 Tensor Core GPUs and up to 100 Gbps of networking throughput for machine learning and HPC applications. These instances deliver up to one petaflop of mixed-precision performance per instance to significantly accelerate machine learning and high performance computing applications. Amazon EC2 P3 instances have been proven to reduce machine learning training times from days to minutes, as well as increase the number of simulations completed for high performance computing by 3-4x.
With up to 4x the network bandwidth of P3.16xlarge instances, Amazon EC2 P3dn.24xlarge instances are the latest addition to the P3 family, optimized for distributed machine learning and HPC applications. These instances provide up to 100 Gbps of networking throughput, 96 custom Intel® Xeon® Scalable (Skylake) vCPUs, 8 NVIDIA® V100 Tensor Core GPUs with 32 GiB of memory each, and 1.8 TB of local NVMe-based SSD storage. P3dn.24xlarge instances also support Elastic Fabric Adapter (EFA) which accelerates distributed machine learning applications that use NVIDIA Collective Communications Library (NCCL). EFA can scale to thousands of GPUs, significantly improving the throughput and scalability of deep learning training models, which leads to faster results.
Overview of Amazon EC2 P3 Instances
Benefits
Customer testimonials
Here are some examples of how customers and partners have achieved their business goals with Amazon EC2 P3 instances.
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Airbnb
Airbnb is using machine learning to optimize search recommendations and improve dynamic pricing guidance for hosts, both of which translate to increased booking conversions. With Amazon EC2 P3 instances, Airbnb can run training workloads faster, go through more iterations, build better machine learning models and reduce costs.
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Celgene
Celgene is a global biotechnology company that is developing targeted therapies that match treatment with the patient. The company runs their HPC workloads for next-generation genomic sequencing and chemical simulations on Amazon EC2 P3 instances. With this compute power, Celgene can train deep learning models to distinguish between malignant cells and benign cells. Before using P3 instances, it took two months to run large scale computational jobs, now it takes just four hours. AWS technology has enabled Celgene to accelerate development of drug therapies for cancer and inflammatory diseases.
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Hyperconnect
Hyperconnect specializes in applying new technologies based on machine learning to image and video processing and was the first company to develop webRTC for mobile platforms.
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NerdWallet
NerdWallet is a personal finance startup that provides tools and advice that make it easy for customers to pay off debt, choose the best financial products and services, and tackle major life goals like buying a house or saving for retirement. The company relies heavily on data science and machine learning (ML) to connect customers with personalized financial products.
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PathWise Solutions Group
A leader in quality systems solutions, Aon’s PathWise is a cloud-based SaaS application suite geared toward enterprise risk-management modeling that delivers speed, reliability, security, and on-demand service to an array of customers.
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Pinterest
Pinterest uses mixed precision training in P3 instances on AWS to speed up training of deep learning models, and also uses these instances for faster inference of these models, to enable fast and unique discovery experience for users. Pinterest uses PinSage, made by using PyTorch on AWS. This AI model groups images together based on certain themes. With 3 billion images on the platform, there are 18 billion different associations that connect images. These associations help Pinterest contextualize themes, styles and produce more personalized user experiences.
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Salesforce
Salesforce is using machine learning to power Einstein Vision, enabling developers to harness the power of image recognition for use cases such as visual search, brand detection, and product identification. Amazon EC2 P3 instances enable developers to train deep learning models much faster so that they can achieve their machine learning goals quickly.
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Schrodinger
Schrodinger uses high performance computing (HPC) to develop predictive models to extend the scale of discovery and optimization and give their customers the ability to bring lifesaving drugs to market more quickly. Amazon EC2 P3 instances allows Schrodinger to perform four times as many simulations in a day as they could with P2 instances.
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Subtle Medical
Subtle Medical is a healthcare technology company working to improve medical imaging efficiency and patient experience with innovative deep-learning solutions. Its team is made up of renowned imaging scientists, radiologists, and AI experts from Stanford, MIT, MD Anderson, and more.
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Western Digital
Western Digital uses HPC to run tens of thousands of simulations for materials sciences, heat flows, magnetics and data transfer to improve disk drive and storage solution performance and quality. Based on early testing, P3 instances allow engineering teams to run simulations at least three times faster than previously deployed solutions.
Amazon EC2 P3 instances and Amazon SageMaker
Amazon EC2 P3 instances and AWS Deep Learning AMIs
Pre-configured development environments to quickly start building deep learning applications
An alternative to Amazon SageMaker for developers who have more customized requirements, the AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. You can quickly launch Amazon EC2 P3 instances pre-installed with popular deep learning frameworks such as TensorFlow, PyTorch, Apache MXNet, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, Chainer, Gluon, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or learn new skills and techniques. Learn more
Amazon EC2 P3 instances and high performance computing
Solve large computational problems and gain new insights using the power of HPC on AWS
Amazon EC2 P3 instances are an ideal platform to run engineering simulations, computational finance, seismic analysis, molecular modeling, genomics, rendering, and other GPU compute workloads. High performance computing (HPC) allows scientists and engineers to solve these complex, compute-intensive problems. HPC applications often require high network performance, fast storage, large amounts of memory, high compute capabilities, or all of the above. AWS enables you to increase the speed of research and reduce time-to-results by running HPC in the cloud and scaling to larger numbers of parallel tasks than would be practical in most on-premises environments. For example, P3dn.24xlarge instances support Elastic Fabric Adapter (EFA) that enables HPC applications using the Message Passing Interface (MPI) to scale to thousands of GPUs. AWS helps to reduce costs by providing solutions optimized for specific applications, and without the need for large capital investments. Learn more
Support for NVIDIA RTX Virtual Workstation
NVIDIA RTX Virtual Workstation AMIs deliver high graphics performance using powerful P3 instances with NVIDIA Volta V100 GPUs running in the AWS cloud. These AMIs have the latest NVIDIA GPU graphics software preinstalled along with the latest RTX drivers and NVIDIA ISV certifications with support for up to four 4K desktop resolutions. P3 instances with NVIDIA V100 GPUs combined with RTX vWS deliver a high performance workstation in the cloud with up to 32 GiB of GPU memory, fast ray tracing, and AI-powered rendering.
The new AMIs are available on the AWS Marketplace with support for Windows Server 2016 and Windows Server 2019.
Amazon EC2 P3dn.24xlarge instances
Amazon EC2 P3 instance product details
Instance Size | GPUs - Tesla V100 | GPU Peer to Peer | GPU Memory (GB) | vCPUs | Memory (GB) | Network Bandwidth | EBS Bandwidth | On-Demand Price/hr* | 1-yr Reserved Instance Effective Hourly* | 3-yr Reserved Instance Effective Hourly* |
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p3.2xlarge | 1 | N/A | 16 | 8 | 61 | Up to 10 Gbps | 1.5 Gbps | $3.06 | $1.99 | $1.05 |
p3.8xlarge | 4 |
NVLink | 64 | 32 | 244 | 10 Gbps | 7 Gbps | $12.24 | $7.96 | $4.19 |
p3.16xlarge | 8 | NVLink | 128 | 64 | 488 | 25 Gbps | 14 Gbps | $24.48 | $15.91 | $8.39 |
p3dn.24xlarge | 8 | NVLink | 256 | 96 | 768 | 100 Gbps | 19 Gbps | $31.218 | $18.30 | $9.64 |
* - Prices shown are for Linux/Unix in the US East (Northern Virginia) AWS Region and rounded to the nearest cent. For full pricing details, see the Amazon EC2 pricing page.
Customers can purchase P3 instances as On-Demand Instances, Reserved Instances, Spot Instances, and Dedicated Hosts.
Billing by the second
One of the many advantages of cloud computing is the elastic nature of provisioning or deprovisioning resources as you need them. By billing usage down to the second, we enable customers to level up their elasticity, save money, and enable them to optimize allocation of resources toward achieving their machine learning goals.
Reserved Instance pricing
Reserved Instances provide you with a significant discount (up to 75%) compared to On-Demand Instance pricing. In addition, when Reserved Instances are assigned to a specific Availability Zone, they provide a capacity reservation, giving you additional confidence in your ability to launch instances when you need them.
Spot pricing
With Spot Instances, you pay the Spot price that's in effect for the time period that your instances are running. Spot Instance prices are set by Amazon EC2 and adjust gradually based on long-term trends in supply and demand for Spot Instance capacity. Spot Instances are available at a discount of up to 90% off compared to On-Demand pricing.
The broadest global availability
Amazon EC2 P3.2xlarge, P3.8xlarge and P3.16xlarge instances are available in 14 AWS Regions so that customers have the flexibility to train and deploy their machine learning models wherever their data is stored. Available regions for P3 are the US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Europe (Ireland), Europe (Frankfurt), Europe (London), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Singapore), China (Beijing), China (Ningxia), and GovCloud (US-West) AWS Regions.
P3dn.24xlarge instances are available in the Asia Pacific (Tokyo), Europe (Ireland), US East (N. Virginia), US West (Oregon), GovCloud (US-West), and GovCloud (US-East) AWS regions.
Get started with Amazon EC2 P3 instances for machine learning
To get started within minutes, learn more about Amazon SageMaker or use the AWS Deep Learning AMI, pre-installed with popular deep learning frameworks such as Caffe2 and MXNet. Alternatively, you can also use the NVIDIA AMI with GPU driver and CUDA toolkit pre-installed.