)
# Allocate the resources.
cluster.allocate()
# Set the 'instances' of the pipeline to 16. This means that 16 instances
# of each pipeline step are executed.
#
# Execution of the 16 pipeline instances can happen concurrently or
# sequentially, depending on the resources (cores) available in the
# SingleClusterEnvironment.
ccount = CalculateChecksums(steps=1,instances=16)
os.system('wget -q -o UTF-8-demo.txt http://gist.githubusercontent.com/oleweidner/6084b9d56b04389717b9/raw/611dd0c184be5f35d75f876b13604c86c470872f/gistfile1.txt')
cluster.run(ccount)
# Print the checksums
print "\nResulting checksums:"
import glob
for result in glob.glob("checksum*.sha1"):
print " * {0}".format(open(result, "r").readline().strip())
cluster.deallocate()
cluster.profile(ccount)
except EnsemblemdError, er:
print "Ensemble MD Toolkit Error: {0}".format(str(er))
raise # Just raise the execption again to get the backtrace
cores=1,
walltime=15,
# username=None,
# project=None,
# queue = None,
# database_url='',
# database_name='',
)
# Allocate the resources.
cluster.allocate()
# We set both the the simulation and the analysis step 'instances' to 16.
# If they
mssa = MSSA(iterations=1, simulation_instances=16, analysis_instances=1)
# mssa_2 = MSSA(iterations=2, simulation_instances=32, analysis_instances = 2)
# print mssa_1
# print mssa_2
# print mssa_1 == mssa_2
cluster.run(mssa)
cluster.deallocate()
cluster.profile(mssa)
except EnsemblemdError, er:
print "Ensemble MD Toolkit Error: {0}".format(str(er))
raise # Just raise the execption again to get the backtrace
