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(