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AWS Batch: Simplifying Batch Computing in the Cloud

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Docker enables you to create highly customized images that are used to execute your jobs. These images allow you to easily share complex applications between teams and even organizations

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AWS Batch: Simplifying Batch Computing in the Cloud

  1. 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Adrian Hornsby, Technical Evangelist @ AWS Twitter: @adhorn Email: adhorn@amazon.com AWS Batch: Simplifying Batch Computing in the Cloud
  2. 2. • Technical Evangelist, Developer Advocate, … Software Engineer • My @home is in Finland • Previously: • Solutions Architect @AWS • Lead Cloud Architect @Dreambroker • Director of Engineering, Software Engineer, DevOps, Manager, ... @Hdm • Researcher @Nokia Research Center • and a bunch of other stuff. • Love climbing and ginger shots.
  3. 3. What to expect from this session • Batch processing overview • AWS Batch platform walkthrough • API overview • Demo(s) • Show me the code! • Usage patterns
  4. 4. What is batch computing?
  5. 5. What is batch computing? Run jobs asynchronously and automatically across one or more computers. Jobs may have dependencies, making the sequencing and scheduling of multiple jobs complex and challenging.
  6. 6. Early Batch APIs (19th Century) • Processing of data stored on decks of punch card • Tabulating machine by Herman Hollerith, used for the 1890 United States Census. • Each card stored a separate record of data with different fields. • Cards were processed by the machine one by one, all in the same way, as a batch. IBM Type 285 tabulators (1936) being used for batch processing of punch cards (in stack on each machine) with human operators at U.S. Social Security Administration
  7. 7. Batch in Linux echo "cc -o foo foo.c" | at 1145 jan 31
  8. 8. Batch in Linux echo "cc -o foo foo.c" | at 1145 jan 31 > job 1 at Wed Jan 31 11:45:00 2018
  9. 9. Batch in Linux echo "cc -o foo foo.c" | at 1145 jan 31 > job 1 at Wed Jan 31 11:45:00 2018 $ at 1145 jan 31 at> cc -o foo foo.c at> ^D $ atq (list jobs) $ atrm <job_number>
  10. 10. Batch computing today • In-house compute clusters powered by open source or commercial job schedulers. • Often comprised of a large array of identical, undifferentiated processors, all of the same vintage and built to the same specifications.
  11. 11. It’s like trying to fit a square into a circle Batch computing today …
  12. 12. AWS Batch Overview & Concepts
  13. 13. AWS Batch in a nutshell • Fully managed batch primitives • Focus on your applications • Shell scripts, • Linux executables, • Docker images • and their resource requirements • We take care of the rest!
  14. 14. AWS Batch advantages Reduces operational complexities Saves time Reduces costs
  15. 15. AWS Batch Components • Jobs • Job definitions • Job queues • Job Scheduler • Compute environments
  16. 16. Components relation Batch Compute Environment ** Batch Queue (2) Batch Queue (1) Batch Queue (0) Job Definition 1 Job Definition 2 Job Definition 3 Job Definition n priority Job 1 Job 2 Container Property Compute Resources DependsOn Container Property Container Property Container Property ** regional service
  17. 17. Jobs Jobs are the unit of work executed by AWS Batch as containerized applications running on Amazon EC2. Containerized jobs can reference a container image, command, and parameters. Or, users can fetch a .zip containing their application and run it on a Amazon Linux container.
  18. 18. Submit Job aws batch submit-job --cli-input-json file://submit_job.json --region us-east-1
  19. 19. Submit Job with dependency aws batch submit-job --cli-input-json file://submit_job.json --region us-east-1
  20. 20. Job States Jobs submitted to a queue can have the following states: SUBMITTED: Accepted into the queue, but not yet evaluated for execution PENDING: Your job has dependencies on other jobs which have not yet completed RUNNABLE: Your job has been evaluated by the scheduler and is ready to run STARTING: Your job is in the process of being scheduled to a compute resource RUNNING: Your job is currently running SUCCEEDED: Your job has finished with exit code 0 FAILED: Your job finished with a non-zero exit code, was cancelled or terminated.
  21. 21. Job Definition AWS Batch job definitions specify how jobs are to be run. Some of the attributes specified in a job definition: • IAM role associated with the job • vCPU and memory requirements • Mount points • Container properties • Environment variables • Retry strategy • While each job must reference a job definition, many parameters can be overridden.
  22. 22. Create Job Definition aws batch register-job-definition --region us-east-1 --cli-input-json file://job_def.json
  23. 23. Job Queue Jobs are submitted to a job queue, where they reside until they are able to be scheduled to a compute resource. Information related to completed jobs persists in the queue for 24 hours. Job queues support priorities and multiple queues can schedule work to the same compute environment.
  24. 24. Create Job Queue aws batch create-job-queue --region us-east-1 --cli-input-json file://job_queue.json
  25. 25. Job Scheduler The scheduler evaluates when, where, and how to run jobs that have been submitted to a job queue. Jobs run in approximately the order in which they are submitted, as long as all dependencies on other jobs have been met.
  26. 26. Compute Environment Job queues are mapped to one or more compute environments. Managed compute environments enable you to describe your business requirements (instance types, min/max/desired vCPUs, and Spot Instance bid as a % of the On-Demand price) and we launch and scale resources on your behalf. You can choose specific instance types or choose “optimal” and AWS Batch launches appropriately sized instances.
  27. 27. Create Environment aws batch create-compute-environment --cli-input-json file://job_env.json --region us-east-1
  28. 28. Customer Provided AMIs Customer Provided AMIs let you set the AMI that is launched as part of a managed compute environment. Makes it possible to configure Docker settings, mount EBS/EFS volumes, and configure drivers for GPU jobs. AMIs must be Linux-based, HVM and have a working ECS agent installation.
  29. 29. Resource Limits
  30. 30. Deployment
  31. 31. Pricing
  32. 32. AWS Batch: Demo Fetch&Run
  33. 33. IAM Role AWS Batch Queue AWS Batch Compute Env. Read/Write Fetch & Run Demo Job definition AWS Batch execution Container AWS Batch Scheduler Amazon DynamoDB FetchScript Submit job Developer Amazon S3
  34. 34. Show me the code!
  35. 35. AWS Batch: Typical Use cases
  36. 36. AWS Batch Use Cases High Performance Computing Post-Trade Analytics Fraud Surveillance Drug Screening DNA Sequencing Rendering Transcoding Media Supply Chain
  37. 37. Financial Services: Automate the analysis of the day’s transaction for fraud surveillance.
  38. 38. Life Sciences: Drug Screening for Biopharma Rapidly search libraries of small molecules for drug discovery.
  39. 39. Digital Media: Visual Effects Rendering Automate content rendering workloads and reduce the need for human intervention due to execution dependencies or resource scheduling.
  40. 40. Thank you! Twitter: @adhorn Email: adhorn@amazon.com

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