what is the procedure or how to plan how many nodes of dispy need for dsolve
differential system in amazon cloud in limited time such as 1 hour, 2 hours.etc
?
#For Amazon Linux, the user name is ec2-user. For RHEL5, the user name is
either root or ec2-user.
#For Ubuntu, the user name is ubuntu. For Fedora, the user name is either
fedora or ec2-user.
#For SUSE Linux, the user name is either root or ec2-user.
#Otherwise, if ec2-user and root don't work, check with your AMI provider.
import random, dispy
import ast
from __future__ import division
from sympy import *
x, y, z, t = symbols('x y z t')
k, m, n = symbols('k m n', integer=True)
f, g, h = symbols('f g h', cls=Function)
import inspect
def compute(n): # executed on nodes
import random, time, socket
name = socket.gethostname()
cur_best = 1
for ii in range(n[0],n[0]):
for jj in range(n[1],n[1]):
for kk in range(n[2],n[3],100):
#assume dsolve with sympy for differential system
dispy_provisional_result((name, r))
cur_best = r
time.sleep(0.1)
# final result
return (name, cur_best)
def job_callback(job): # executed at the client
if job.status == dispy.DispyJob.ProvisionalResult:
#if job.result[1] < 0.005:
# acceptable result; terminate jobs
print('%s computed: %s %s %s %s' % (job.result[0], job.result[1],
job.result[2], job.result[3], job.result[4]))
# 'jobs' and 'cluster' are created in '__main__' below
for j in jobs:
if j.status in [dispy.DispyJob.Created, dispy.DispyJob.Running,
dispy.DispyJob.ProvisionalResult]:
cluster.cancel(j)
if __name__ == '__main__':
#cluster = dispy.JobCluster(compute, callback=job_callback)
cluster = dispy.JobCluster(compute,
nodes=['ec2-35-162-137-237.us-west-2.compute.amazonaws.com'],
ip_addr='127.0.0.1', port=51347, node_port=51348, callback=job_callback,
keyfile=r"C:\Users\hello\datacenterusekey.ppk")
jobs = []
prevk = 1
count = 0
for ii in range(1,2):
for jj in range(1,2000):
for kk in range(1,2000,100):
if ii < jj and jj < kk:
job = cluster.submit([ii,jj,prevk,kk])
prevk = kk
if job is None:
print('creating job %s failed!' % n)
continue
job.id = count
count = count + 1
jobs.append(job)
cluster.wait()
cluster.print_status()
cluster.close()
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