Aws-What You Need to Know_Simon Elisha
- 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Simon Elisha. Head of Solution Architecture, ANZ Public Sector AWS. What You Need to Know
- 4. 2010 61 516 1,017 159 2012 2014 2016 * As of 1 January 2017 Innovating on Behalf of Our Customers
- 5. AWS Direct Connect AWS Elastic Beanstalk GovCloud Amazon CloudTrail CloudHSM WorkSpaces Amazon Kinesis Amazon AppStream Amazon SNS Identity & Access Management Amazon Route 53 AWS Import/Export Amazon SWF Redshift Dynamo DB CloudSearch AWS Data Pipeline AWS Certificate Manager AWS KMS Amazon Config Amazon RDS for Aurora WorkDocs Directory Service CodeCommit AWS CodePipeline AWS Service Catalog CloudWatch Logs Amazon EFS Amazon API Gateway Amazon Machine Learning AWS Device Farm AWS WAF Elasticsearch Service QuickSight Import/Export Snowball RDS for MariaDB Amazon Inspector AWS IoT EC2 Container Registry Amazon ElastiCache AWS CloudFormation Mobile Analytics AWS Mobile Hub AWS Storage Gateway AWS OpsWorks Elastic Transcoder Amazon SES EC2 Container Service Amazon Cognito AWS CodeDeploy Glacier Amazon WorkMail Lambda 2,913
- 6. ENTERPRISE APPS DEVELOPMENT & OPERATIONSMOBILE SERVICESAPP SERVICESANALYTICS Data Warehousing Hadoop/ Spark Streaming Data Collection Machine Learning Elastic Search Virtual Desktops Sharing & Collaboration Corporate Email Backup Queuing & Notifications Workflow Search Email Transcoding One-click App Deployment Identity Sync Single Integrated Console Push Notifications DevOps Resource Management Application Lifecycle Management Containers Triggers Resource Templates TECHNICAL & BUSINESS SUPPORT Account Management Support Professional Services Training & Certification Security & Pricing Reports Partner Ecosystem Solutions Architects MARKETPLACE Business Apps Business Intelligence Databases DevOps Tools NetworkingSecurity Storage Regions Availability Zones Points of Presence INFRASTRUCTURE CORE SERVICES Compute VMs, Auto-scaling, & Load Balancing Storage Object, Blocks, Archival, Import/Export Databases Relational, NoSQL, Caching, Migration Networking VPC, DX, DNS CDN Access Control Identity Management Key Management & Storage Monitoring & Logs Assessment and reporting Resource & Usage Auditing SECURITY & COMPLIANCE Configuration Compliance Web application firewall HYBRID ARCHITECTURE Data Backups Integrated App Deployments Direct Connect Identity Federation Integrated Resource Management Integrated Networking API Gateway IoT Rules Engine Device Shadows Device SDKs Registry Device Gateway Streaming Data Analysis Business Intelligence Mobile Analytics
- 7. What is New in the Sydney Region and What Does it Mean to You?
- 8. A system or service which can perform tasks that usually require human intelligence Artificial Intelligence
- 9. The Challenge For Artificial Intelligence: SCALE Data
- 10. Data Training The Challenge For Artificial Intelligence: SCALE
- 11. Data Training Prediction The Challenge For Artificial Intelligence: SCALE
- 12. AI Services AI Platform AI Engines Amazon Rekognition Amazon Polly Amazon Lex More to come in 2017 More to come in 2017 Apache MXNet TensorFlow Caffe Theano KerasTorch CNTK Amazon AI: Democratized Artificial Intelligence Amazon Machine Learning EMR & Spark AWS Batch
- 13. New P2 GPU Instance Types • New EC2 GPU instance type for accelerated computing • Offers up to 16 NVIDIA K80 GPUs (8 K80 cards) in a single instance • The 16xlarge size provides: • A combined 192 GB of GPU memory, 40 thousand CUDA cores • 70 teraflops of single precision floating point performance • Over 23 teraflops of double precision floating point performance • Example workloads include: • Deep learning, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, genomics, VR content rendering, accelerated databases
- 14. P2 Instance Types Three P2 instance sizes: Instance Size GPUs GPU Peer to Peer vCPU s Memory (GiB) Network Bandwidth * p2.xlarge 1 - 4 61 1.25Gbps p2.8xlarge 8 Y 32 488 10Gbps p2.16xlarge 16 Y 64 732 20Gbps *In a placement group
- 15. Apache MXNet Programmable Portable High Performance Near linear scaling across hundreds of GPUs Highly efficient models for mobile and IoT Simple syntax, multiple languages Most Open Best On AWS Optimized for deep learning on AWS Accepted into the Apache Incubator
- 16. 0 4 8 12 16 1 2 3 4 5 Ideal Inception v3 Resnet Alexnet 91% Efficiency Multi-GPU Scaling With MXNet
- 17. One-Click GPU or CPU Deep Learning AWS Deep Learning AMI Up to~40k CUDA cores Apache MXNet TensorFlow Theano Caffe Torch Keras Pre-configured CUDA drivers, MKL Anaconda, Python3 Ubuntu and Amazon Linux + AWS CloudFormation template + Container image
- 18. Application Examples | Jupyter Notebooks • https://github.com/dmlc/mxnet-notebooks • Basic concepts • NDArray - multi-dimensional array computation • Symbol - symbolic expression for neural networks • Module - neural network training and inference • Applications • MNIST: recognize handwritten digits • Check out the distributed training results • Predict with pre-trained models • LSTMs for sequence learning • Recommender systems • Train a state of the art Computer Vision model (CNN) • Lots more..
- 19. Serverless is changing how we deliver and run software
- 20. What is serverless computing? • VMs • Machine as the unit of scale • Abstracts the hardware • Containers • Application as the unit of scale • Abstracts the OS • Serverless • Functions as the unit of scale • Abstracts the language runtime Amazon ECS Amazon EC2 AWS Lambda
- 21. Continuous scaling No servers to manage Never pay for idle – no cold servers (only happy accountants) Benefits of AWS Lambda
- 22. AWS Lambda Programming Model Bring your own code • Node.js, Java, Python, .NET • Bring your own libraries (even native ones) Simple resource model • Select power rating from 128 MB to 1.5 GB • CPU and network allocated proportionately • Reports actual usage Programming model • AWS SDK built in (Python and Node.js) • Lambda is the “webserver” • Use processes, threads, /tmp, sockets normally Stateless • Persist data using Amazon DynamoDB, S3, or Amazon ElastiCache • No affinity to infrastructure (can’t “log in to the box”)
- 25. “I want to sequence functions” “I want to select functions based on data” “I want to retry functions” “I want try/catch/finally” Functions into apps “I have code that runs for hours” “I want to run functions in parallel”
- 26. Coordination must-haves • Scales out • Doesn’t lose state • Deals with errors/timeouts • Easy to build & operate • Auditable
- 28. Application Lifecycle in AWS Step Functions Visualize in the Console Define in JSON Monitor Executions
- 29. Integrate with other AWS services • Create state machines and Activities with AWS CloudFormation • Call AWS Step Functions with Amazon API Gateway • Start state machines in response to events, or on a schedule, with Amazon CloudWatch Events • Monitor state machine executions with Amazon CloudWatch • Log API calls with AWS CloudTrail
- 30. Image Recognition and Processing Backend Task Choice Fail Parallel https://github.com/awslabs/lambda-refarch-imagerecognition
- 31. Insight into the Customer Experience
- 32. AWS X-Ray – GA and Available for Lambda Analyze and debug production, distributed applications Simple setup: instrument your application with X- Ray SDK and install X-Ray Daemon End-to-End Tracing, cross-service view of requests made to your application.
- 33. AWS X-Ray Service Map provides a view of connections between services in your application and aggregated data for each service, including average latency and failure rates. Data Annotation and Filtering
- 34. X-Ray Graph
- 35. But What about the State Layer?
- 36. DynamoDB AutoScaling Track storage performance to customer demand. Data Annotation and Filtering
- 37. DynamoDB AutoScaling Policy Based Scale Read and/or Write & Global Secondary Indexes
- 38. Building and Releasing Software Faster Than Ever Faster “Time-to-Value”
- 39. • Integration tests with other systems • Load testing • UI tests • Penetration testing Release processes have four major phases Source Build Test Production • Check-in source code such as .java files. • Peer review new code • Compile code • Unit tests • Style checkers • Code metrics • Create container images • Deployment to production environments
- 40. Release processes levels Source Build Test Production Continuous integration Continuous delivery Continuous deployment
- 41. AWS Code* Services AWS CodeCommit AWS CodeBuild AWS CodeDeploy AWS CodePipeline
- 42. AWS Code Services Source Build Test Production Software Release Steps:
- 43. AWS Code Services Source Build Test Production Software Release Steps: AWS CodeCommit
- 44. AWS Code Services Source Build Test Production Software Release Steps: AWS CodeBuild
- 45. AWS Code Services Source Build Test Production Software Release Steps: Third Party Tooling
- 46. AWS Code Services Source Build Test Production Software Release Steps: AWS CodeDeploy
- 47. AWS Code Services Source Build Test Production Software Release Steps: EC2 On-Premesis AWS CodeDeploy
- 48. AWS Code Services Source Build Test Production Software Release Steps: AWS CodePipeline
- 49. AWS Code Services Source Build Test Production Third Party Tooling Software Release Steps: AWS CodeCommit AWS CodeBuild AWS CodeDeploy AWS CodePipeline
- 50. Simple Solutions for Simple Use Cases
- 51. Amazon Lightsail - Everything you need to launch a simple website or web app Lightsail virtual private server SSD-based storage Networking & data transfer DNS management Static IP Access to AWS services Monash University is using Amazon Lightsail to rapidly re-platform a number of CMS services in a simple and cost-effective manner. They have already migrated 50 workloads and are now thinking of creating an internal CMS service based on Lightsail to allow staff and students to create their own CMS instances in a self-service manner.
- 52. Lightsail pricing starts at $5 per month
- 53. Choose from a dozen preconfigured instances Operating Systems Applications Dev Stacks
- 54. A Couple of Other Goodies!
- 55. AWS Direct Connect in Canberra
- 56. • A fully managed file system for Amazon EC2 instances • A file system interface with file system access semantics that works with standard operating system APIs • Sharable across thousands of instances • Designed to grow elastically to petabyte scale • Built for performance across a wide variety of workloads • Highly available and durable • Strong consistency Amazon Elastic File System (EFS)
- 57. We are growing by leaps and bounds, and our core offering is all about better support delivery. During the course of developing our next-generation internal support system, we never wanted to worry about scale again, yet we had existing architectural commitments that meant a distributed file solution was required. We chose Amazon EFS because it was the only option available that scaled both capacity and performance – without the up-front payments or the management overhead of traditional models. Now we can just focus on supporting our customers to help them support their customers. - Sam Caldwell, Sr. Systems Engineer ” “ Customer Example
- 58. One More Thing
- 59. Amazon CloudFront POP 2H 2017
- 60. The Amazon CloudFront Service Global Content Delivery Network with Massive Capacity and Scale Optimized for Performance and Scale Built in Security Features Self-Service Full Control Configurations Robust Real Time Reporting Amazon CloudFront Static and Dynamic Object and Video Delivery
- 61. Eliminate the Undifferentiated Heavy Lifting of IT and Focus on Citizen Outcomes
- 62. Thank you