Talk at NCRR P41 Director's Meeting
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Talk at NCRR P41 Director's Meeting

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Invited Talk given at the NCRR P41 Director's meeting on October 12, 2010

Invited Talk given at the NCRR P41 Director's meeting on October 12, 2010

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    Talk at NCRR P41 Director's Meeting Talk at NCRR P41 Director's Meeting Presentation Transcript

    • Amazon Web Services A platform for life science research Deepak Singh, Ph.D. Amazon Web Services NCRR P41 PI meeting, October 2010
    • the new reality
    • lots and lots and lots and lots and lots of data
    • lots and lots and lots and lots and lots of people
    • lots and lots and lots and lots and lots of places
    • constant change
    • science in a new reality
    • science in a new reality ^
    • data science in a new reality ^
    • Image: Drew Conway
    • goal
    • optimize the most valuable resource
    • compute, storage, workflows, memory, transmission, algorithms, cost, …
    • people Credit: Pieter Musterd a CC-BY-NC-ND license
    • enter the cloud
    • what is the cloud?
    • infrastructure
    • scalable
    • 3000 CPU’s for one firm’s risk management application 3444JJ' !"#$%&'()'*+,'-./01.2%/' 344'+567/'(.' 8%%9%.:/' 344'JJ' I%:.%/:1=' ;<"&/:1=' A&B:1=' C10"&:1=' C".:1=' E(.:1=' ;"%/:1=' >?,,?,44@' >?,3?,44@' >?,>?,44@' >?,H?,44@' >?,D?,44@' >?,F?,44@' >?,G?,44@'
    • highly available
    • US East Region Availability Availability Zone A Zone B Availability Availability Zone C Zone D
    • durable
    • 99.999999999%
    • dynamic
    • extensible
    • secure
    • a utility
    • on-demand instances reserved instances spot instances
    • infrastructure as code
    • class Instance attr_accessor :aws_hash, :elastic_ip def initialize(hash, elastic_ip = nil) @aws_hash = hash @elastic_ip = elastic_ip end def public_dns @aws_hash[:dns_name] || "" end def friendly_name public_dns.empty? ? status.capitalize : public_dns.split(".")[0] end def id @aws_hash[:aws_instance_id] end end
    • include_recipe "packages" include_recipe "ruby" include_recipe "apache2" if platform?("centos","redhat") if dist_only? # just the gem, we'll install the apache module within apache2 package "rubygem-passenger" return else package "httpd-devel" end else %w{ apache2-prefork-dev libapr1-dev }.each do |pkg| package pkg do action :upgrade end end end gem_package "passenger" do version node[:passenger][:version] end execute "passenger_module" do command 'echo -en "nnnn" | passenger-install-apache2-module' creates node[:passenger][:module_path] end
    • import boto import boto.emr from boto.emr.step import StreamingStep Connect to Elastic MapReduce from boto.emr.bootstrap_action import BootstrapAction import time # set your aws keys and S3 bucket, e.g. from environment or .boto AWSKEY= SECRETKEY= S3_BUCKET= NUM_INSTANCES = 1 conn = boto.connect_emr(AWSKEY,SECRETKEY) bootstrap_step = BootstrapAction("download.tst", "s3://elasticmapreduce/bootstrap-actions/download.sh",None) Install packages step = StreamingStep(name='Wordcount',                      mapper='s3n://elasticmapreduce/samples/wordcount/wordSplitter.py',                      cache_files = ["s3n://" + S3_BUCKET + "/boto.mod#boto.mod"],                      reducer='aggregate',                      input='s3n://elasticmapreduce/samples/wordcount/input',                      output='s3n://' + S3_BUCKET + '/output/wordcount_output') Set up mappers & jobid = conn.run_jobflow(     name="testbootstrap", reduces     log_uri="s3://" + S3_BUCKET + "/logs",     steps = [step],     bootstrap_actions=[bootstrap_step],     num_instances=NUM_INSTANCES) print "finished spawning job (note: starting still takes time)" state = conn.describe_jobflow(jobid).state print "job state = ", state print "job id = ", jobid while state != u'COMPLETED':     print time.localtime() job state     time.sleep(30)     state = conn.describe_jobflow(jobid).state     print "job state = ", state     print "job id = ", jobid print "final output can be found in s3://" + S3_BUCKET + "/output" + TIMESTAMP print "try: $ s3cmd sync s3://" + S3_BUCKET + "/output" + TIMESTAMP + " ."
    • a data science platform
    • dataspaces Further reading: Jeff Hammerbacher, Information Platforms and the rise of the data scientist, Beautiful Data
    • accept all data formats
    • evolve APIs
    • beyond the database and the data warehouse
    • move compute to the data
    • data is a royal garden
    • compute is a fungible commodity
    • “I terminate the instance and relaunch it. Thats my error handling” Source: @jtimberman on Twitter
    • the cloud is an architectural and cultural fit for data science
    • amazon web services
    • your data science platform
    • s3://1000genomes
    • http://aws.amazon.com/publicdatasets/
    • Credit: Angel Pizzaro, U. Penn
    • http://usegalaxy.org/cloud
    • mapreduce for genomics http://bowtie-bio.sourceforge.net/crossbow/index.shtml http://contrail-bio.sourceforge.net http://bowtie-bio.sourceforge.net/myrna/index.shtml
    • AWS knows scalable infrastructure
    • you know the science
    • we can make this work together
    • http://aws.amazon.com/education http://aws.amazon.com/publicdatasets
    • deesingh@amazon.com Twitter:@mndoci http://slideshare.net/mndoci http://mndoci.com Inspiration and ideas from Matt Wood, James Hamilton & Larry Lessig Credit” Oberazzi under a CC-BY-NC-SA license