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precip 0.1

Pegasus Repeatable Experiments for the Cloud in Python - a simple module for setting up repeatable computer science

Latest Version: 0.3

precip - Pegasus Repeatable Experiments for the Cloud in Python

Overview

   Precip is a flexible exeperiment management API for running experiments
   on clouds. Precip was developed for use on FutureGrid infrastructures
   such as OpenStack, Eucalyptus (>=3.2), Nimbus, and at the same time
   commercial clouds such as Amazon EC2. The API allows you to easily
   provision resources, which you can then can run commands on and copy
   files to/from subsets of instances identified by tags. The goal of the
   API is to be flexible and simple to use in Python scripts to control
   your experiments.

   The API does not require any special images, which makes it easy to get
   going. Any basic Linux image will work. More complex images can be used
   if your experiment requires so, or you can use the experiment API to
   run bootstrap scripts on the images to install/configure required
   software.

   A concept which simplfies interacting with the API is instance tagging.
   When you start an instance, you can add arbitrary tags to it. The
   instance also gets a set of default tags. API methods such as running a
   remote command, or copying files, all use tags for specify which
   instances you want to target.

   Precip also handles ssh keys and security groups automatically. This is
   done to make sure the experiment management is not interfering with
   your existing cloud setup. The first time you use Precip, a directory
   will be created called ~/.precip. Inside this directory, a ssh keypair
   will be created and used for accessing instances. On clouds which
   supports it, the keypair is automatically registered as 'precip', and a
   'precip' security group is created. If your experiement requires more
   ports to be open you can use the cloud interface to add those ports to
   the precip security group.

   Precip is a fairly new API, and if you have questions or suggestions
   for improvements, please contact pegasus-support@isi.edu

Installation

   Prerequisites are the Paramiko and Boto Python modules. The Python
   source package and RPMs are available at:
   [8]http://pegasus.isi.edu/static/precip/software/

API

   provision(image_id, instance_type='m1.small', count=1, tags=None)
          Provision a new instance. Note that this method starts the
          provisioning cycle, but does not block for the instance to
          finish booting. For blocking on instance creation/booting, see
          wait()

          Parameters:

          + image_id - the id of the image to instanciate
          + instance_type - the type of instance. This is infrastructure
            specific, but usually follows the Amazon EC2 model with
            m1.small, m1.large, and so on.
          + count - number of instances to create
          + tags - these are used to manipulate the instance later. Use
            this to create logical groups of your instances.

   wait(tags=[], timeout=600)
          Barrier for all instances matching the tags argument. This
          method will block until the instances have finish booting and
          are accessible via their external hostnames.

          Parameters:

          + tags - tags specifying the subset of instances to block on.
            The default value is [] which means wait for all instances.

          + timeout - timeout in seconds for the instances to boot. If the
            timeout is reached, an ExperimentException is raised. The
            default is 600 seconds.

   deprovision(tags)
          Deprovisions (terminates) instances matching the tags argument

          Parameters:

          + tags - tags specifying the subset of instances to deprovision.

   list(tags)
          Returns a list of details about the instances matching the tags.
          The details include instance id, hostnames, and tags.

          Parameters:

          + tags - tags specifying the subset of instances to give
            information on. If you want details on all current instances,
            use [].

          Returns:

          + List of dictionaries, one for each instance.

   get_public_hostnames(tags)
          Provides a list of public hostnames for the instances matching
          the tags. The public hostnames can be provided to other
          instances in order to let the instances know about eachother.

          Parameters:

          + tags - tags specifying the subset of instances.

          Returns:

          + A list of public hostnames

   get_private_hostnames(tags)
          Provides a list of private hostnames for the instances matching
          the tags. The private hostnames can be provided to other
          instances in order to let the instances know about eachother.

          Parameters:

          + tags - tags specifying the subset of instances.

          Returns:

          + A list of private hostnames

   get(tags, remote_path, local_path, user="root")
          Transfers a file from a set of remote machines matching the
          tags, and stores the file locally. If more than one instance
          matches the tags, an instance id will be appended to the
          local_path.

          Parameters:

          + tags - these are used to manipulate the instance later. Use
            this to create logical groups of your instances.
          + remote_path - the path of the file on the remote instance
          + local_path - the local path to tranfer to
          + user - remote user. If not specified, the default is 'root'

   put(tags, local_path, remote_path, user="root")
          Transfers a local file to a set of remote machines matching the
          tags.

          Parameters:

          + tags - these are used to manipulate the instance later. Use
            this to create logical groups of your instances.
          + local_path - the local path to tranfer from
          + remote_path - the path on the remote instance to store the
            file as
          + user - remote user. If not specified, the default is 'root'

   run(tags, cmd, user="root", check_exit_code=True)
          Runs a command on the instances matches the tags. The commands
          are run in series, on one instance after the other.

          Parameters:

          + tags - these are used to manipulate the instance later. Use
            this to create logical groups of your instances.
          + cmd - the command to run
          + user - remote user. If not specified, the default is 'root'.
            If you need to run commands as another user, you will have to
            make sure that user accepts the ssh key in ~/.precip/
          + check_exit_code - If set to True (default), commands returning
            non-zero exit codes will result in a ExperimentException being
            raised.

          Returns:

          + A list of lists, containing exit_code[], stdout[] and stderr[]
            for the commands run

   copy_and_run(tags, local_script, args=[], user="root",
          check_exit_code=True)
          Copies a script from the local machine to the remote instances
          and executes the script. The script is run in series, on one
          instance after the other.

          Parameters:

          + tags - these are used to manipulate the instance later. Use
            this to create logical groups of your instances.
          + local_script - the local script to run
          + args - arguments for the script
          + user - remote user. If not specified, the default is 'root'.
            If you need to run commands as another user, you will have to
            make sure that user accepts the ssh key in ~/.precip/
          + check_exit_code - If set to True (default), commands returning
            non-zero exit codes will result in a ExperimentException being
            raised.

          Returns:

          + A list of lists, containing exit_code[], stdout[] and stderr[]
            for the commands run

   The basic methods above are standard across all the Cloud
   infrastructures. What is different is the constructors as each
   infrastructure handles initialization a little bit different. For
   example, to create a new OpenStack using the EC2_* environment provided
   automatically by FutureGrid:

    exp = OpenStackExperiment(
            os.environ['EC2_URL'],
            os.environ['EC2_ACCESS_KEY'],
            os.environ['EC2_SECRET_KEY'])


   i For Amazon EC2, you have to specify region, endpoint, and
   access/secret keys. Note that it is not required to use environment
   variables for your credentials, but seperating the crenditals from the
   code prevents the credentials from being check in to source control
   systems.

    exp = EC2Experiment(
            "us-west-2c",
            "ec2.us-west-2.amazonaws.com",
            os.environ['AMAZON_EC2_ACCESS_KEY'],
            os.environ['AMAZON_EC2_SECRET_KEY'])


Examples

Hello World


#!/usr/bin/python

import os
import time
from pprint import pprint

from precip import *

exp = None

# Use try/except liberally in your experiments - the api is set up to
# raise ExperimentException on most errors
try:

    # Create a new OpenStack based experiment. In this case we pick
    # up endpoints and access/secret cloud keys from the environment
    # as exposing those is the common setup on FutureGrid
    exp = OpenStackExperiment(
            os.environ['EC2_URL'],
            os.environ['EC2_ACCESS_KEY'],
            os.environ['EC2_SECRET_KEY'])

    # Provision an instance based on the ami-0000004c. Note that tags are
    # used throughout the api to identify and manipulate instances. You
    # can give an instance an arbitrary number of tags.
    exp.provision("ami-0000004c", tags=["test1"], count=1)

    # Wait for all instances to boot and become accessible. The provision
    # method only starts the provisioning, and can be used to start a large
    # number of instances at the same time. The wait method provides a
    # barrier to when it is safe to start the actual experiment.
    exp.wait()

    # Print out the details of the instance. The details include instance id,
    # private and public hostnames, and tags both defined by you and some
    # added by the api
    pprint(exp.list())

    # Run a command on the instances having the "test1" tag. In this case we
    # only have one instance, but if you had multiple instances with that
    # tag, the command would run on each one.
    exp.run(["test1"], "echo 'Hello world from a experiment instance'")

except ExperimentException as e:
    # This is the default exception for most errors in the api
    print "ERROR: %s" % e

finally:
    # Be sure to always deprovision the instances we have started. Putting
    # the deprovision call under finally: make the deprovisioning happening
    # even in the case of failure.
    if exp is not None:
        exp.deprovision()


Resources from mulitple infrastructures


#!/usr/bin/python

import os
import time

from precip import *

ec2 = None
fg = None

try:

    # This example show how to run an experiment between Amazon EC2
    # and an OpenStack resource on FutureGrid. The setup is pretty
    # similar to the HelloWorld example, except that we now have to
    # experiment to handle. The first step is to get the experiments
    # initialized. Note that it is not required to use environment
    # variables for your credentials, but seperating the crenditals
    # from the code prevents the credentials from being check in to
    # source control systems.

    ec2 = EC2Experiment(
            "us-west-2c",
            "ec2.us-west-2.amazonaws.com",
            os.environ['AMAZON_EC2_ACCESS_KEY'],
            os.environ['AMAZON_EC2_SECRET_KEY'])

    fg = OpenStackExperiment(
            os.environ['EC2_URL'],
            os.environ['EC2_ACCESS_KEY'],
            os.environ['EC2_SECRET_KEY'])

    # Next we provision two instances, one on Amazon EC2 and one of
    # FutureGrid
    ec2.provision("ami-8a1e92ba", tags=["id=ec2_1"])
    fg.provision("ami-0000004c", tags=["id=fg_1"])

    # Wait for all instances to boot and become accessible. The provision
    # method only starts the provisioning, and can be used to start a large
    # number of instances at the same time. The wait method provides a
    # barrier to when it is safe to start the actual experiment.
    ec2.wait([])
    fg.wait([])

    # Run commands on the remote instances
    ec2.run([], "echo 'Hello world Amazon EC2'")
    fg.run([], "echo 'Hello world FutureGrid OpenStack'")

except ExperimentException as e:
    # This is the default exception for most errors in the api
    print "ERROR: %s" % e
    raise e
finally:
    # Be sure to always deprovision the instances we have started. Putting
    # the deprovision call under finally: make the deprovisioning happening
    # even in the case of failure.
    if ec2 is not None:
        ec2.deprovision([])
    if fg is not None:
        fg.deprovision([])


Setting up a Condor pool and run a Pegasus workflow

   This is a more complex example in which a small Condor pool is set up
   and then a Pegasus workflow is run and benchmarked. The Precip script
   is similar to what we have seen before, but it has two groups of
   instances, one master acting as the Condor central manager, and a set
   of Condor worker nodes.

#!/usr/bin/python

import os
import time

from precip import *

try:

    # This experiment is targeted to run on OpenStack
    exp = OpenStackExperiment(
            os.environ['OPENSTACK_URL'],
            os.environ['OPENSTACK_ACCESS_KEY'],
            os.environ['OPENSTACK_SECRET_KEY'])

    # We need a master Condor node and a set of workers
    exp.provision("ami-0000004c", tags=["master"],
                  instance_type="m1.large")
    exp.provision("ami-0000004c", tags=["compute"],
                  instance_type="m1.large", count=2)
    exp.wait()

    # The workers need to know what the private hostname of the master is
    master_priv_addr = exp.get_private_hostnames(["master"])[0]

    # Bootstrap the instances. This includes installing Condor and Pegasus,
    # downloading and settup the workflow.
    exp.copy_and_run(["master"], "./bootstrap.sh")
    exp.copy_and_run(["compute"], "./bootstrap.sh", args=[master_priv_addr])

    # Give the workers some time to register with the Condor central
    # manager
    time.sleep(60)

    # Make sure Condor came up correctly
    exp.run(["master"], "condor_status")

    # Run the workflow
    exp.run(["master"], "cd ~/montage && ./run-montage", user="wf")

    # At this point, in a real experiment, you could for example provision
    # more resources and run the workflow again, or run the workflow with
    # different parameters/settings.

except ExperimentException as e:
    print "ERROR: %s" % e
finally:
    # always want to clean up all the instances we have started
    exp.deprovision([])


   We also need a bootstrap.sh which sets up the instances:

#!/bin/bash

# This script bootstraps a basic RHEL6 instance to be have working
# Condor and Pegasus installs. The script takes one optional
# argument which is the address of the master instance (central
# manager in Condor terminology). If the argument is not given,
# the script sets up the instance to be the master.

MASTER_ADDR=$1

# for images with condor already installed, stop condor
/etc/init.d/condor stop >/dev/null 2>&1 || /bin/true

# correct clock is important for most projects
yum -q -y install ntpdate
/etc/init.d/ntpdate start

# Add the EPEL repository
wget -nv http://mirror.utexas.edu/epel/6/x86_64/epel-release-6-7.noarch.rpm
rpm -Uh epel-release-*.noarch.rpm

# Add the Condor repository
cat >/etc/yum.repos.d/condor.repo <<EOF
[condor-stable]
name=Condor Stable RPM Repository for Redhat Enterprise Linux 6
baseurl=http://www.cs.wisc.edu/condor/yum/stable/rhel6
enabled=1
gpgcheck=0
EOF

# Add the Pegasus repository
cat >/etc/yum.repos.d/pegasus.repo <<EOF
[Pegasus]
name=Pegasus
baseurl=http://download.pegasus.isi.edu/wms/download/rhel/6/x86_64
gpgcheck=0
enabled=1
priority=50
EOF

# Install required software
yum -q -y clean all
yum -q -y install gcc gcc-g++ gcc-gfortran make gawk bison diffutils \
                  java-1.7.0-openjdk.x86_64 \
                  java-1.7.0-openjdk-devel.x86_64 \
                  ganglia-gmond condor pegasus

# Clear the Condor local config file - we use config.d instead
cat /dev/null >/etc/condor/condor_config.local

# Common Condor config between master and workers
cat >/etc/condor/config.d/50-main.conf <<EOF

CONDOR_HOST = $MASTER_ADDR

FILESYSTEM_DOMAIN = \$(FULL_HOSTNAME)
TRUST_UID_DOMAIN = True

DAEMON_LIST = MASTER, STARTD

# security
ALLOW_WRITE = 10.*
ALLOW_READ = \$(ALLOW_WRITE)

# default policy
START = True
SUSPEND = False
CONTINUE = True
PREEMPT = False
KILL = False

EOF

# Master gets extra packages/configs
if [ "x$MASTER_ADDR" = "x" ]; then
    yum -q -y install ganglia-gmetad ganglia-web

    cat >/etc/condor/config.d/90-master.conf <<EOF
CONDOR_HOST = \$(FULL_HOSTNAME)
DAEMON_LIST = MASTER, COLLECTOR, NEGOTIATOR, SCHEDD
EOF
fi

# Restarting daemons
/etc/init.d/condor start

# User to run the workflows as, and allow the experiment management
# ssh key to authenticate
adduser wf
mkdir -p ~wf/.ssh
cp ~/.ssh/authorized_keys ~wf/.ssh/
chown -R wf: ~wf/.ssh

# Master is the submit host, so deploy our workflow on it
if [ "x$MASTER_ADDR" = "x" ]; then
    # install the workflow tarball and wait script
    cd ~wf
    wget -q http://pegasus.isi.edu/static/precip/wf-experiment/montage.tar.gz
    tar xzf montage.tar.gz
    chown -R wf: montage*
fi
 
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