else:
resource = 'local.localhost'
try:
with open('%s/config.json'%os.path.dirname(os.path.abspath(__file__))) as data_file:
config = json.load(data_file)
# Create a new resource hande with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource=resource,
cores=config[resource]["cores"],
walltime=15,
#username=None,
project=config[resource]['project'],
access_schema = config[resource]['schema'],
queue = config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
# Allocate the resources.
cluster.allocate()
# We set the simulation 'instances' to 16 and analysis 'instances' to 1. We set the adaptive
# simulation to True and specify the simulation extraction script to be used.
cur_path = os.path.dirname(os.path.abspath(__file__))
mssa = MSSA(iterations=2, simulation_instances=16, analysis_instances=1, adaptive_simulation=True, sim_extraction_script='{0}/extract.py'.format(cur_path))
cluster.run(mssa)
resource = 'local.localhost'
try:
with open('%s/config.json'%os.path.dirname(os.path.abspath(__file__))) as data_file:
config = json.load(data_file)
# Create a new resource handle with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource=resource,
cores=config[resource]["cores"],
walltime=15,
#username=None,
project=config[resource]['project'],
access_schema = config[resource]['schema'],
queue = config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
os.system('/bin/echo Welcome! > input_file.txt')
# Allocate the resources.
cluster.allocate()
# Set the 'instances' of the BagofTasks to 16. This means that 16 instances
# of each BagofTasks step are executed.
app = MyApp(stages=1,instances=16)
else:
resource = 'local.localhost'
try:
with open('%s/config.json'%os.path.dirname(os.path.abspath(__file__))) as data_file:
config = json.load(data_file)
# Create a new resource handle with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource=resource,
cores=config[resource]["cores"],
walltime=15,
#username=None,
project=config[resource]['project'],
access_schema = config[resource]['schema'],
queue = config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
# Allocate the resources.
cluster.allocate()
# We set both the the simulation and the analysis stage 'instances' to 16.
# This means that 16 instances of the simulation stage and 16 instances of
# the analysis stage are executed every iteration.
randomsa = RandomSA(maxiterations=1, simulation_instances=16, analysis_instances=16)
cluster.run(randomsa)
else:
resource = 'local.localhost'
try:
with open('%s/config.json' %
os.path.dirname(os.path.abspath(__file__))) as data_file:
config = json.load(data_file)
# Create a new resource handle with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource=resource,
cores=config[resource]["cores"],
walltime=15,
#username=None,
project=config[resource]['project'],
access_schema=config[resource]['schema'],
queue=config[resource]['queue'],
#database_url='mongodb://138.201.86.166:27017/ee_exp_4c',
)
# Allocate the resources.
cluster.allocate()
# Set the 'instances' of the pipeline to 16. This means that 16 instances
# of each pipeline stage are executed.
#
# Execution of the 16 pipeline instances can happen concurrently or
# sequentially, depending on the resources (cores) available in the
# SingleClusterEnvironment.
ccount = RunExchange(stages=3, instances=2)
resource = 'local.localhost'
try:
with open('%s/config.json'%os.path.dirname(os.path.abspath(__file__))) as data_file:
config = json.load(data_file)
# Create a new resource handle with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource=resource,
cores=config[resource]["cores"],
walltime=15,
#username=None,
project=config[resource]['project'],
access_schema = config[resource]['schema'],
queue = config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
# Allocate the resources.
cluster.allocate()
# Set the 'instances' of the BagofTasks to 16. This means that 16 instances
# of each BagofTasks stage are executed.
#
# Execution of the 16 BagofTasks instances can happen concurrently or
# sequentially, depending on the resources (cores) available in the
# SingleClusterEnvironment.
else:
resource = 'local.localhost'
try:
with open('%s/config.json' %
os.path.dirname(os.path.abspath(__file__))) as data_file:
config = json.load(data_file)
# Create a new resource handle with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource=resource,
cores=config[resource]["cores"],
walltime=60,
username='******',
project=config[resource]['project'],
access_schema=config[resource]['schema'],
queue=config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
# Allocate the resources.
cluster.allocate()
# Set the 'instances' of the BagofTasks to 16. This means that 16 instances
# of each BagofTasks step are executed.
app = MyApp(stages=1, instances=1)
cluster.run(app)
except EnsemblemdError, er:
try:
workdir_local = os.getcwd()
with open('%s/config.json'%os.path.dirname(os.path.abspath(__file__))) as data_file:
config = json.load(data_file)
# Create a new static execution context with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource=resource,
cores=config[resource]["cores"],
walltime=15,
#username=None,
project=config[resource]['project'],
access_schema = config[resource]['schema'],
queue = config[resource]['queue'],
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
#database_name='myexps',
)
# Allocate the resources.
cluster.allocate()
# creating RE pattern object
re_pattern = RePattern(workdir_local)
# set number of replicas
k.download_output_data = "checksum{0}.sha1".format(instance)
return k
# ------------------------------------------------------------------------------
#
if __name__ == "__main__":
try:
# Create a new resource handle with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource="localhost",
cores=1,
walltime=15,
database_url='mongodb://*****:*****@ds015335.mlab.com:15335/rp',
)
# Allocate the resources.
cluster.allocate()
ccount = CalculateChecksums(stages=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
pipe = Test(ensemble_size=ENSEMBLE_SIZE + 1, pipeline_size=1)
# Create an application manager
app = AppManager(name='Adap_sampling')
# Register kernels to be used
app.register_kernels(rand_kernel)
app.register_kernels(sleep_kernel)
# Add workload to the application manager
app.add_workload(pipe)
# Create a resource handle for target machine
res = ResourceHandle(
resource="local.localhost",
cores=4,
# username=,
# project =,
# queue=,
walltime=10,
database_url='mongodb://ensembletk.imp.fu-berlin.de:27017/rp')
# Submit request for resources + wait till job becomes Active
res.allocate(wait=True)
# Run the given workload
res.run(app)
# Deallocate the resource
res.deallocate()
sys.exit(1)
if args.Kconfig is None:
parser.error('Please enter a Kernel configuration file')
sys.exit(0)
RPconfig = imp.load_source('RPconfig', args.RPconfig)
Kconfig = imp.load_source('Kconfig', args.Kconfig)
# Create a new static execution context with one resource and a fixed
# number of cores and runtime.
cluster = ResourceHandle(
resource=RPconfig.REMOTE_HOST,
cores=RPconfig.PILOTSIZE,
walltime=RPconfig.WALLTIME,
username=RPconfig.UNAME, #username
project=RPconfig.ALLOCATION, #project
queue=RPconfig.QUEUE,
database_url=RPconfig.DBURL,
access_schema='gsissh')
cluster.shared_data = [
Kconfig.initial_crd_file, Kconfig.grompp_1_mdp,
Kconfig.grompp_2_mdp, Kconfig.grompp_3_mdp,
Kconfig.grompp_1_itp_file, Kconfig.grompp_2_itp_file,
Kconfig.top_file, Kconfig.restr_file
]
cluster.allocate()
coco_gromacs_static = Extasy_CocoGromacs_Static(
