def generate_ML_stage(num_ML=1): """ Function to generate the learning stage """ s3 = Stage() s3.name = 'learning' # learn task for i in range(num_ML): t3 = Task() # https://github.com/radical-collaboration/hyperspace/blob/MD/microscope/experiments/CVAE_exps/train_cvae.py t3.pre_exec = [] t3.pre_exec = ['module reset'] t3.pre_exec += [ '. /sw/summit/python/2.7/anaconda2/5.3.0/etc/profile.d/conda.sh' ] t3.pre_exec += ['module load cuda/9.1.85'] t3.pre_exec += ['conda activate rp.copy'] t3.pre_exec += ['export CUDA_VISIBLE_DEVICES=0'] t3.pre_exec += [ 'export PYTHONPATH=/gpfs/alpine/scratch/hrlee/bip179/hyperspace/microscope/experiments/CVAE_exps:$PYTHONPATH' ] t3.pre_exec += [ 'cd /gpfs/alpine/scratch/hrlee/bip179/hyperspace/microscope/experiments/CVAE_exps' ] time_stamp = int(time.time()) dim = i + 3 cvae_dir = 'cvae_runs_%.2d_%d' % (dim, time_stamp) t3.pre_exec += ['mkdir -p {0} && cd {0}'.format(cvae_dir)] t3.executable = ['/ccs/home/hrlee/.conda/envs/rp.copy/bin/python' ] # train_cvae.py t3.arguments = [ '/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/CVAE_exps/train_cvae.py', '-f', '/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/MD_to_CVAE/cvae_input.h5', '-d', dim ] t3.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 4, 'thread_type': 'OpenMP' } t3.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': 'CUDA' } # Add the learn task to the learning stage s3.add_tasks(t3) time.sleep(1) return s3
def get_pipeline(tasks): # Create a Pipeline object p = Pipeline() # Create a Stage 1 s1 = Stage() # Create a Task object according to the app_name t1 = Task() t1.pre_exec = ['module load gromacs/5.0/INTEL-140-MVAPICH2-2.0'] t1.executable = app_coll['grompp']['executable'] t1.arguments = app_coll['grompp']['arguments'] t1.cores = app_coll['grompp']['cores'] t1.link_input_data = [ '$SHARED/grompp.mdp > grompp.mdp', '$SHARED/input.gro > input.gro', '$SHARED/topol.top > topol.top' ] # Add the Task to the Stage s1.add_tasks(t1) # Add Stage to the Pipeline p.add_stages(s1) # Create a Stage 2 s2 = Stage() for cnt in range(tasks): # Create a Task object according to the app_name t2 = Task() t2.pre_exec = [ 'module load gromacs/5.0/INTEL-140-MVAPICH2-2.0', 'export OMP_NUM_THREADS=%s' % num_cores ] t2.executable = app_coll['mdrun']['executable'] t2.arguments = app_coll['mdrun']['arguments'] #t2.cores = app_coll['mdrun']['cores'] t2.cores = num_cores t2.copy_input_data = [ '$Pipeline_%s_Stage_%s_Task_%s/topol.tpr' % (p.uid, s1.uid, t1.uid) ] # Add the Task to the Stage s2.add_tasks(t2) # Add Stage to the Pipeline p.add_stages(s2) return p
def generate_interfacing_stage(): s4 = Stage() s4.name = 'scanning' # Scaning for outliers and prepare the next stage of MDs t4 = Task() t4.pre_exec = [] t4.pre_exec = ['module reset'] t4.pre_exec += [ '. /sw/summit/python/2.7/anaconda2/5.3.0/etc/profile.d/conda.sh' ] t4.pre_exec += ['module load cuda/9.1.85'] t4.pre_exec += ['conda activate rp.copy'] t4.pre_exec += ['export CUDA_VISIBLE_DEVICES=0'] t4.pre_exec += [ 'export PYTHONPATH=/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/CVAE_exps:$PYTHONPATH' ] t4.pre_exec += [ 'cd /gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/Outlier_search' ] # python outlier_locator.py -m ../MD_exps/fs-pep -c ../CVAE_exps -p ../MD_exps/fs-pep/pdb/100-fs-peptide-400K.pdb t4.executable = ['/ccs/home/hrlee/.conda/envs/rp.copy/bin/python'] t4.arguments = [ 'outlier_locator.py', '--md', '../MD_exps/fs-pep', '--cvae', '../CVAE_exps --pdb', '../MD_exps/fs-pep/pdb/100-fs-peptide-400K.pdb' ] # t4.arguments = ['/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/Outlier_search/outlier_locator.py', # '-m', '/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/MD_exps/fs-pep', # '-c', '/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/CVAE_exps', # '-p', '/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/MD_exps/fs-pep/pdb/100-fs-peptide-400K.pdb' # ] t4.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 12, 'thread_type': 'OpenMP' } t4.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': 'CUDA' } s4.add_tasks(t4) s4.post_exec = func_condition return s4
def generate_pipeline(): global CUR_TASKS, CUR_CORES, duration, MAX_NEW_STAGE def func_condition(): global CUR_NEW_STAGE, MAX_NEW_STAGE if CUR_NEW_STAGE < MAX_NEW_STAGE: return True return False def func_on_true(): global CUR_NEW_STAGE CUR_NEW_STAGE += 1 for t in p.stages[CUR_NEW_STAGE].tasks: cores = randint(1,16) t.arguments = ['-c', str(cores), '-t', str(duration)] def func_on_false(): print 'Done' # Create a Pipeline object p = Pipeline() for s in range(MAX_NEW_STAGE+1): # Create a Stage object s1 = Stage() for i in range(CUR_TASKS): t1 = Task() t1.pre_exec = ['export PATH=/u/sciteam/balasubr/modules/stress-ng-0.09.34:$PATH'] t1.executable = ['stress-ng'] t1.arguments = [ '-c', str(CUR_CORES), '-t', str(duration)] t1.cpu_reqs = { 'processes': 1, 'process_type': '', 'threads_per_process': CUR_CORES, 'thread_type': '' } # Add the Task to the Stage s1.add_tasks(t1) # Add post-exec to the Stage s1.post_exec = { 'condition': func_condition, 'on_true': func_on_true, 'on_false': func_on_false } # Add Stage to the Pipeline p.add_stages(s1) return p
def func_on_true(): global CUR_NEW_STAGE, CUR_TASKS, CUR_CORES, duration CUR_NEW_STAGE += 1 s = Stage() for i in range(CUR_TASKS): t = Task() t.pre_exec = [ 'export PATH=/u/sciteam/balasubr/modules/stress-ng-0.09.34:$PATH' ] t.executable = ['stress-ng'] t.arguments = ['-c', str(CUR_CORES), '-t', str(duration)] t.cpu_reqs = { 'processes': 1, 'process_type': '', 'threads_per_process': CUR_CORES, 'thread_type': '' } # Add the Task to the Stage s.add_tasks(t) # Add post-exec to the Stage s.post_exec = { 'condition': func_condition, 'on_true': func_on_true, 'on_false': func_on_false } p.add_stages(s)
def generate_aggregating_stage(): """ Function to concatenate the MD trajectory (h5 contact map) """ s2 = Stage() s2.name = 'aggregating' # Aggregation task t2 = Task() # https://github.com/radical-collaboration/hyperspace/blob/MD/microscope/experiments/MD_to_CVAE/MD_to_CVAE.py t2.pre_exec = [] #t2.pre_exec += ['. /sw/summit/python/2.7/anaconda2/5.3.0/etc/profile.d/conda.sh'] #t2.pre_exec += ['conda activate %s' % conda_path] t2.pre_exec += ['module unload python'] t2.pre_exec += ['module load ibm-wml-ce'] t2.pre_exec += ['cd %s' % agg_path] #t2.executable = ['%s/bin/python' % conda_path] # MD_to_CVAE.py t2.executable = [ '/sw/summit/ibm-wml-ce/anaconda-base/envs/ibm-wml-ce-1.7.0-2/bin/python' ] t2.arguments = ['%s/MD_to_CVAE.py' % agg_path, '--sim_path', md_path] # Add the aggregation task to the aggreagating stage s2.add_tasks(t2) return s2
def add_ex_stg(rid, cycle): #ex stg here ex_tsk = Task() ex_stg = Stage() ex_tsk.name = 'extsk-{replica}-{cycle}'.format(replica=rid, cycle=cycle) for rid in range(len(waiting_replicas)): ex_tsk.link_input_data += ['%s/mdinfo-{replica}-{cycle}'.format(replica=rid, cycle=self.cycle)%replica_sandbox] ex_tsk.arguments = ['t_ex_gibbs.py', len(waiting_replicas)] #This needs to be fixed ex_tsk.executable = ['python'] ex_tsk.cpu_reqs = { 'processes': 1, 'process_type': '', 'threads_per_process': 1, 'thread_type': None } ex_tsk.pre_exec = ['export dummy_variable=19'] ex_stg.add_tasks(ex_tsk) ex_stg.post_exec = { 'condition': post_ex, 'on_true': terminate_replicas, 'on_false': continue_md } return ex_stg
def add_md_stg(rid,cycle): #md stg h md_tsk = Task() md_stg = Stage() md_tsk.name = 'mdtsk-{replica}-{cycle}'.format(replica=rid, cycle=cycle) md_tsk.link_input_data += ['%s/inpcrd' %replica_sandbox, '%s/prmtop' %replica_sandbox, '%s/mdin-{replica}-{cycle}'.format(replica=rid, cycle=0) %replica_sandbox] md_tsk.arguments = ['-O', '-i', 'mdin-{replica}-{cycle}'.format(replica=rid, cycle=0), '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out', '-r', '%s/restrt-{replica}-{cycle}'.format(replica=rid, cycle=cycle) %replica_sandbox, '-x', 'mdcrd', '-inf', '%s/mdinfo-{replica}-{cycle}'.format(replica=rid, cycle=cycle) %replica_sandbox] md_tsk.executable = ['/home/scm177/mantel/AMBER/amber14/bin/sander'] md_tsk.cpu_reqs = { 'processes': replica_cores, 'process_type': '', 'threads_per_process': 1, 'thread_type': None } md_tsk.pre_exec = ['export dummy_variable=19', 'echo $SHARED'] md_stg.add_tasks(md_tsk) md_stg.post_exec = { 'condition': md_post, 'on_true': suspend, 'on_false': exchange_stg } return md_stg
def generate_aggregating_stage(self) -> Stage: stage = Stage() stage.name = "aggregating" cfg = self.cfg.aggregation_stage # Aggregation task task = Task() task.cpu_reqs = cfg.cpu_reqs.dict() task.pre_exec = cfg.pre_exec task.executable = cfg.executable task.arguments = cfg.arguments # Update base parameters cfg.run_config.experiment_directory = self.cfg.experiment_directory cfg.run_config.output_path = self.aggregated_data_path( self.cur_iteration) cfg_path = self.experiment_dirs["aggregation_runs"].joinpath( f"aggregation_{self.cur_iteration:03d}.yaml") cfg.run_config.dump_yaml(cfg_path) task.arguments += ["-c", cfg_path] stage.add_tasks(task) return stage
def generate_ml_stage(self) -> Stage: stage = Stage() stage.name = "learning" cfg = self.cfg.ml_stage task = Task() task.cpu_reqs = cfg.cpu_reqs.dict() task.gpu_reqs = cfg.gpu_reqs.dict() task.pre_exec = cfg.pre_exec task.executable = cfg.executable task.arguments = cfg.arguments # Update base parameters cfg.run_config.input_path = self.aggregated_data_path( self.cur_iteration) cfg.run_config.output_path = self.model_path(self.cur_iteration) if self.cur_iteration > 0: cfg.run_config.init_weights_path = self.latest_ml_checkpoint_path( self.cur_iteration - 1) cfg_path = self.experiment_dirs["ml_runs"].joinpath( f"ml_{self.cur_iteration:03d}.yaml") cfg.run_config.dump_yaml(cfg_path) task.arguments += ["-c", cfg_path] stage.add_tasks(task) return stage
def generate_aggregating_stage(): """ Function to concatenate the MD trajectory (h5 contact map) """ s2 = Stage() s2.name = 'aggregating' # Aggregation task t2 = Task() # https://github.com/radical-collaboration/hyperspace/blob/MD/microscope/experiments/MD_to_CVAE/MD_to_CVAE.py t2.pre_exec = [] t2.pre_exec += [ '. /sw/summit/python/2.7/anaconda2/5.3.0/etc/profile.d/conda.sh' ] t2.pre_exec += ['conda activate rp.copy'] t2.pre_exec += [ 'cd /gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/MD_to_CVAE' ] t2.executable = ['/ccs/home/hrlee/.conda/envs/rp.copy/bin/python' ] # MD_to_CVAE.py t2.arguments = [ '/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/MD_to_CVAE/MD_to_CVAE.py', '-f', '/gpfs/alpine/bip179/scratch/hrlee/hyperspace/microscope/experiments/MD_exps/fs-pep' ] # Add the aggregation task to the aggreagating stage s2.add_tasks(t2) return s2
def generate_outlier_detection_stage(self) -> Stage: stage = Stage() stage.name = "outlier_detection" cfg = self.cfg.od_stage task = Task() task.cpu_reqs = cfg.cpu_reqs.dict() task.gpu_reqs = cfg.gpu_reqs.dict() task.pre_exec = cfg.pre_exec task.executable = cfg.executable task.arguments = cfg.arguments self.outlier_pdbs_path(self.cur_iteration).mkdir() # Update base parameters cfg.run_config.experiment_directory = self.cfg.experiment_directory cfg.run_config.input_path = self.aggregated_data_path( self.cur_iteration) cfg.run_config.output_path = self.outlier_pdbs_path(self.cur_iteration) cfg.run_config.weights_path = self.latest_ml_checkpoint_path( self.cur_iteration) cfg.run_config.restart_points_path = self.restart_points_path( self.cur_iteration) cfg_path = self.experiment_dirs["od_runs"].joinpath( f"od_{self.cur_iteration:03d}.yaml") cfg.run_config.dump_yaml(cfg_path) task.arguments += ["-c", cfg_path] stage.add_tasks(task) return stage
def generate_aggregating_task(self): """ Function to concatenate the MD trajectory (h5 contact map) """ p = Pipeline() p.name = 'aggragating' s2 = Stage() s2.name = 'aggregating' # Aggregation task t2 = Task() # https://github.com/radical-collaboration/hyperspace/blob/MD/microscope/experiments/MD_to_CVAE/MD_to_CVAE.py t2.pre_exec = [] t2.pre_exec += ['. /sw/summit/python/2.7/anaconda2/5.3.0/etc/profile.d/conda.sh'] t2.pre_exec += ['conda activate %s' % conda_path] t2.pre_exec += ['cd %s' % agg_path] t2.executable = ['%s/bin/python' % conda_path] # MD_to_CVAE.py t2.arguments = [ '%s/MD_to_CVAE.py' % agg_path, '--sim_path', md_path, '--train_frames', 100000] # assign hardware the task t2.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 4, 'thread_type': 'OpenMP' } # Add the aggregation task to the aggreagating stage s2.add_tasks(t2) p.add_stages(s2) return p
def esmacs(self, rct_stage, stage, outdir="equilibration", name=None): for i in range(1, 13): t = Task() t.pre_exec = [ "export WDIR=\"{}/{}\"".format(self.run_dir, name), ". {}".format(self.conda_init), "conda activate {}".format(self.esmacs_tenv), "module load {}".format(self.esmacs_tmodules), "mkdir -p $WDIR/replicas/rep{}/{}".format(i, outdir), "cd $WDIR/replicas/rep{}/{}".format(i, outdir), "rm -f {}.log {}.xml {}.dcd {}.chk".format( stage, stage, stage, stage), "export OMP_NUM_THREADS=1" ] # t.executable = '/ccs/home/litan/miniconda3/envs/wf3/bin/python3.7' t.executable = 'python3' t.arguments = ['$WDIR/{}.py'.format(stage)] t.post_exec = [] t.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 4, 'thread_type': 'OpenMP' } t.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': 'CUDA' } getattr(self, rct_stage).add_tasks(t) print(getattr(self, rct_stage).to_dict())
def create_inversion_dict_stage(cmt_file_db, param_path, task_counter): """Creates stage for the creation of the inversion files. This stage is tiny, but required before the actual inversion. :param cmt_file_db: :param param_path: :param task_counter: :return: """ # Get database parameter path databaseparam_path = os.path.join(param_path, "Database/DatabaseParameters.yml") # Load Parameters DB_params = read_yaml_file(databaseparam_path) # Earthquake specific database parameters: Dir and Cid Cdir, Cid = get_Centry_path(DB_params["databasedir"], cmt_file_db) # Function inv_dict_func = os.path.join(bin_path, "write_inversion_dicts.py") # Create Process Paths Stage (CPP) # Create a Stage object inv_dict_stage = Stage() inv_dict_stage.name = "Creating" # Create Task inv_dict_task = Task() # This way the task gets the name of the path file inv_dict_task.name = "Inversion-Dictionaries" inv_dict_task.pre_exec = [ # Conda activate DB_params["conda-activate"] ] inv_dict_task.executable = [DB_params["bin-python"]] # Assign exec # to the task inv_dict_task.arguments = [ inv_dict_func, "-f", cmt_file_db, "-p", param_path ] # In the future maybe to database dir as a total log? inv_dict_task.stdout = os.path.join( "%s" % Cdir, "logs", "stdout.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), inv_dict_task.name)) inv_dict_task.stderr = os.path.join( "%s" % Cdir, "logs", "stderr.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), inv_dict_task.name)) inv_dict_stage.add_tasks(inv_dict_task) task_counter += 1 return inv_dict_stage, task_counter
def generate_ML_pipeline(): p = Pipeline() p.name = 'ML' s1 = Stage() s1.name = 'Generator-ML' # the generator/ML Pipeline will consist of 1 Stage, 2 Tasks Task 1 : # Generator; Task 2: ConvNet/Active Learning Model # NOTE: Generator and ML/AL are alive across the whole workflow execution. # For local testing, sleep time is longer than the total execution time of # the MD pipelines. t1 = Task() t1.name = "generator" t1.pre_exec = [ # 'module load python/2.7.15-anaconda2-5.3.0', # 'module load cuda/9.1.85', # 'module load gcc/6.4.0', # 'source activate snakes' ] # t1.executable = ['python'] # t1.arguments = ['/ccs/home/jdakka/tf.py'] t1.executable = ['sleep'] t1.arguments = ['5'] s1.add_tasks(t1) t2 = Task() t2.name = "ml-al" t2.pre_exec = [ # 'module load python/2.7.15-anaconda2-5.3.0', # 'module load cuda/9.1.85', # 'module load gcc/6.4.0', # 'source activate snakes' ] # t2.executable = ['python'] # t2.arguments = ['/ccs/home/jdakka/tf.py'] t2.executable = ['sleep'] t2.arguments = ['10'] s1.add_tasks(t2) # Add Stage to the Pipeline p.add_stages(s1) return p
def generate_task(cfg: BaseStageConfig) -> Task: task = Task() task.cpu_reqs = cfg.cpu_reqs.dict().copy() task.gpu_reqs = cfg.gpu_reqs.dict().copy() task.pre_exec = cfg.pre_exec.copy() task.executable = cfg.executable task.arguments = cfg.arguments.copy() return task
def main(): cmd = "{0} 'ls {1}'".format(ssh, dir_) p = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE) out, _ = p.communicate() out = out.decode('utf-8').strip().split(linesep) fullpaths = [op.join(dir_, p) for p in out] print(fullpaths) # Start radical entk pipeline p = Pipeline() for i in range(iterations): s = Stage() for fp in fullpaths: t = Task() t.name = 'Incrementation {}'.format(i) t.pre_exec = [ 'source /home/vhayot/miniconda3/etc/profile.d/conda.sh', 'conda activate radenv' ] t.executable = 'python /home/vhayot/inc.py' if i == 0: t.arguments = [fp, out_dir, i] else: # Note: assuming all data is accessible through shared dir # radical entk functions without sharedfs, however t.arguments = [ op.join(out_dir, "it-{0}-{1}".format(i - 1, op.basename(fp))), out_dir, i ] s.add_tasks(t) # Create a new stage everytime there's a dependency p.add_stages(s) appman = AppManager(hostname=hostname, port=port) appman.resource_desc = { 'resource': 'xsede.bridges', 'walltime': 20, 'cpus': 5, 'project': 'mc3bggp', 'schema': 'gsissh' } appman.workflow = set([p]) appman.run()
def create_inversion_stage(cmt_file_db, param_path, task_counter): """Creates inversion stage. :param cmt_file_db: :param param_path: :return: """ # Get database parameter path databaseparam_path = os.path.join(param_path, "Database/DatabaseParameters.yml") # Load Parameters DB_params = read_yaml_file(databaseparam_path) # Earthquake specific database parameters: Dir and Cid Cdir, Cid = get_Centry_path(DB_params["databasedir"], cmt_file_db) # Function inversion_func = os.path.join(bin_path, "inversion.py") # Create a Stage object inversion_stage = Stage() inversion_stage.name = "CMT3D" # Create Task inversion_task = Task() # This way the task gets the name of the path file inversion_task.name = "Inversion" inversion_task.pre_exec = [ # Conda activate DB_params["conda-activate"] ] inversion_task.executable = DB_params["bin-python"] # Assign exec # to the task inversion_task.arguments = [ inversion_func, "-f", cmt_file_db, "-p", param_path ] # In the future maybe to database dir as a total log? inversion_task.stdout = os.path.join( "%s" % Cdir, "logs", "stdout.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), inversion_task.name)) inversion_task.stderr = os.path.join( "%s" % Cdir, "logs", "stderr.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), inversion_task.name)) inversion_stage.add_tasks(inversion_task) return inversion_stage
def write_sources(cmt_file_db, param_path, task_counter): """ This function creates a stage that modifies the CMTSOLUTION files before the simulations are run. :param cmt_file_db: cmtfile in the database :param param_path: path to parameter file directory :param task_counter: total task count up until now in pipeline :return: EnTK Stage """ # Get Database parameters databaseparam_path = os.path.join(param_path, "Database/DatabaseParameters.yml") DB_params = read_yaml_file(databaseparam_path) # Earthquake specific database parameters: Dir and Cid Cdir, Cid = get_Centry_path(DB_params["databasedir"], cmt_file_db) # Path to function write_source_func = os.path.join(bin_path, "write_sources.py") # Create a Stage object w_sources = Stage() w_sources.name = "Write-Sources" # Create Task for stage w_sources_t = Task() w_sources_t.name = "Task-Sources" w_sources_t.pre_exec = [ # Conda activate DB_params["conda-activate"] ] w_sources_t.executable = DB_params["bin-python"] # # Assign executable # to the task w_sources_t.arguments = [write_source_func, cmt_file_db] # In the future maybe to database dir as a total log? w_sources_t.stdout = os.path.join( "%s" % Cdir, "logs", "stdout.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), w_sources_t.name)) w_sources_t.stderr = os.path.join( "%s" % Cdir, "logs", "stderr.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), w_sources_t.name)) # Add Task to the Stage w_sources.add_tasks(w_sources_t) task_counter += 1 return w_sources, task_counter
def create_process_path_files(cmt_file_db, param_path, task_counter): """This function creates the path files used for processing both synthetic and observed data in ASDF format, as well as the following windowing procedure. :param cmt_file_db: cmtfile in the database :param param_path: path to parameter file directory :param pipelinedir: path to pipeline directory :return: EnTK Stage """ # Get database parameter path databaseparam_path = os.path.join(param_path, "Database/DatabaseParameters.yml") # Load Parameters DB_params = read_yaml_file(databaseparam_path) # Earthquake specific database parameters: Dir and Cid Cdir, Cid = get_Centry_path(DB_params["databasedir"], cmt_file_db) # Process path function create_process_path_bin = os.path.join(bin_path, "create_path_files.py") # Create Process Paths Stage (CPP) # Create a Stage object cpp = Stage() cpp.name = "CreateProcessPaths" # Create Task cpp_t = Task() cpp_t.name = "CPP-Task" cpp_t.pre_exec = [ # Conda activate DB_params["conda-activate"] ] cpp_t.executable = DB_params["bin-python"] # Assign executable # to the task cpp_t.arguments = [create_process_path_bin, cmt_file_db] # In the future maybe to database dir as a total log? cpp_t.stdout = os.path.join( "%s" % Cdir, "logs", "stdout.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), cpp_t.name)) cpp_t.stderr = os.path.join( "%s" % Cdir, "logs", "stderr.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), cpp_t.name)) task_counter += 1 cpp.add_tasks(cpp_t) return cpp, task_counter
def generate_interfacing_stage(): s4 = Stage() s4.name = 'scanning' # Scaning for outliers and prepare the next stage of MDs t4 = Task() t4.pre_exec = [] #t4.pre_exec += ['. /sw/summit/python/2.7/anaconda2/5.3.0/etc/profile.d/conda.sh'] #t4.pre_exec += ['module load cuda/9.1.85'] #t4.pre_exec += ['conda activate %s' % conda_path] t4.pre_exec += [ 'module unload prrte', 'module unload python', 'module load xl', 'module load xalt', 'module load spectrum-mpi', 'module load cuda', 'module list' ] t4.pre_exec += [ '. /sw/summit/ibm-wml-ce/anaconda-base/etc/profile.d/conda.sh', 'source /sw/summit/ibm-wml-ce/anaconda-base/etc/profile.d/conda.sh', 'conda deactivate', 'conda deactivate', 'conda activate /gpfs/alpine/proj-shared/med110/wf-2/conda/envs/ibm-wml-ce-cloned' ] #'conda activate /sw/summit/ibm-wml-ce/anaconda-base/envs/ibm-wml-ce-1.7.0-2'] t4.pre_exec += [ 'export PYTHONPATH=%s/CVAE_exps:%s/CVAE_exps/cvae:$PYTHONPATH' % (base_path, base_path) ] t4.pre_exec += ['cd %s/Outlier_search' % base_path] #t4.executable = ['%s/bin/python' % conda_path] t4.executable = ['python'] t4.arguments = [ 'outlier_locator.py', '--md', md_path, '--cvae', cvae_path, '--pdb', pdb_file ] #'--ref', ref_pdb_file] t4.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 16, 'thread_type': 'OpenMP' } t4.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': 'CUDA' } s4.add_tasks(t4) s4.post_exec = func_condition return s4
def selection(self, ps_file, select_file): tasks = [] t = Task() t.pre_exec = ['/bin/cp {0} {1}'.format(self.param_space, ps_file)] t.executable = self.analysis t.arguments = [ps_file, self.n_samples, select_file] t.download_output_data = [select_file] tasks.append(t) return tasks
def specfem_clean_up(cmt_file_db, param_path, task_counter): """ Cleaning up the simulation directories since we don"t need all the files for the future. :param cmt_file_db: cmtfile in the database :param param_path: path to parameter file directory :param pipelinedir: path to pipeline directory :return: EnTK Stage """ # Get Database parameters databaseparam_path = os.path.join(param_path, "Database/DatabaseParameters.yml") # Database parameters. DB_params = read_yaml_file(databaseparam_path) # Earthquake specific database parameters: Dir and Cid Cdir, Cid = get_Centry_path(DB_params["databasedir"], cmt_file_db) # Path to function clean_up_func = os.path.join(bin_path, "clean_up_simdirs.py") # Create a Stage object clean_up = Stage() clean_up.name = "Clean-Up" # Create Task for stage clean_up_t = Task() clean_up_t.name = "Task-Clean-Up" clean_up_t.pre_exec = [ # Conda activate DB_params["conda-activate"] ] clean_up_t.executable = DB_params["bin-python"] # Assign executable # to the task clean_up_t.arguments = [clean_up_func, cmt_file_db] # In the future maybe to database dir as a total log? clean_up_t.stdout = os.path.join( "%s" % Cdir, "logs", "stdout.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), clean_up_t.name)) clean_up_t.stderr = os.path.join( "%s" % Cdir, "logs", "stderr.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), clean_up_t.name)) # Add Task to the Stage clean_up.add_tasks(clean_up_t) return clean_up, task_counter
def generate_MD_stage(num_MD=1): """ Function to generate MD stage. """ s1 = Stage() s1.name = 'MD' # MD tasks time_stamp = int(time.time()) for i in range(num_MD): t1 = Task() t1.pre_exec = [ '. /sw/summit/python/2.7/anaconda2/5.3.0/etc/profile.d/conda.sh' ] t1.pre_exec += ['module load cuda/9.1.85'] t1.pre_exec += ['conda activate %s' % conda_path] t1.pre_exec += [ 'export PYTHONPATH=%s/MD_exps:$PYTHONPATH' % base_path ] t1.pre_exec += ['cd %s/MD_exps/fs-pep' % base_path] t1.pre_exec += [ 'mkdir -p omm_runs_%d && cd omm_runs_%d' % (time_stamp + i, time_stamp + i) ] t1.executable = ['%s/bin/python' % conda_path] # run_openmm.py t1.arguments = ['%s/MD_exps/fs-pep/run_openmm.py' % base_path] # t1.arguments += ['--topol', '%s/MD_exps/fs-pep/pdb/topol.top' % base_path] t1.arguments += [ '--pdb_file', '%s/MD_exps/fs-pep/pdb/100-fs-peptide-400K.pdb' % base_path, '--length', LEN_sim ] # assign hardware the task t1.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 4, 'thread_type': 'OpenMP' } t1.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': 'CUDA' } # Add the MD task to the simulating stage s1.add_tasks(t1) return s1
def data_request(cmt_file_db, param_path, task_counter): """ This function creates the request for the observed data and returns it as an EnTK Stage :param cmt_file_db: cmt_file in the database :param param_path: path to parameter file directory :param task_counter: total task count up until now in pipeline :return: EnTK Stage """ # Get Database parameters databaseparam_path = os.path.join(param_path, "Database/DatabaseParameters.yml") DB_params = read_yaml_file(databaseparam_path) # Earthquake specific database parameters: Dir and Cid Cdir, Cid = get_Centry_path(DB_params["databasedir"], cmt_file_db) # # Path to function request_data_func = os.path.join(bin_path, "request_data.py") # Create a Stage object datarequest = Stage() datarequest_t = Task() datarequest_t.name = "data-request" datarequest_t.pre_exec = [ # Conda activate DB_params["conda-activate"] ] datarequest_t.executable = DB_params["bin-python"] # Assign executable # to the task datarequest_t.arguments = [request_data_func, cmt_file_db] # In the future maybe to database dir as a total log? datarequest_t.stdout = os.path.join( "%s" % Cdir, "logs", "stdout.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), datarequest_t.name)) datarequest_t.stderr = os.path.join( "%s" % Cdir, "logs", "stderr.pipeline_%s.task_%s.%s" % (Cid, str(task_counter).zfill(4), datarequest_t.name)) # Add Task to the Stage datarequest.add_tasks(datarequest_t) # Increase task-counter task_counter += 1 return datarequest, task_counter
def generate_ML_stage(num_ML=1): """ Function to generate the learning stage """ s3 = Stage() s3.name = 'learning' # learn task time_stamp = int(time.time()) for i in range(num_ML): t3 = Task() # https://github.com/radical-collaboration/hyperspace/blob/MD/microscope/experiments/CVAE_exps/train_cvae.py t3.pre_exec = [] t3.pre_exec += [ '. /sw/summit/python/2.7/anaconda2/5.3.0/etc/profile.d/conda.sh' ] t3.pre_exec += ['module load cuda/9.1.85'] t3.pre_exec += ['conda activate %s' % conda_path] t3.pre_exec += [ 'export PYTHONPATH=%s/CVAE_exps:$PYTHONPATH' % base_path ] t3.pre_exec += ['cd %s' % cvae_path] dim = i + 3 cvae_dir = 'cvae_runs_%.2d_%d' % (dim, time_stamp + i) t3.pre_exec += ['mkdir -p {0} && cd {0}'.format(cvae_dir)] t3.executable = ['%s/bin/python' % conda_path] # train_cvae.py t3.arguments = [ '%s/train_cvae.py' % cvae_path, '--h5_file', '%s/cvae_input.h5' % agg_path, '--dim', dim ] t3.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 4, 'thread_type': 'OpenMP' } t3.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': 'CUDA' } # Add the learn task to the learning stage s3.add_tasks(t3) return s3
def generate_interfacing_stage(): s4 = Stage() s4.name = 'scanning' # Scaning for outliers and prepare the next stage of MDs t4 = Task() t4.pre_exec = ['. /sw/summit/python/3.6/anaconda3/5.3.0/etc/profile.d/conda.sh'] t4.pre_exec += ['conda activate %s' % cfg['conda_pytorch']] t4.pre_exec += ['mkdir -p %s/Outlier_search/outlier_pdbs' % cfg['base_path']] t4.pre_exec += ['export models=""; for i in `ls -d %s/CVAE_exps/model-cvae_runs*/`; do if [ "$models" != "" ]; then models=$models","$i; else models=$i; fi; done;cat /dev/null' % cfg['base_path']] t4.pre_exec += ['export LANG=en_US.utf-8', 'export LC_ALL=en_US.utf-8'] t4.pre_exec += ['unset CUDA_VISIBLE_DEVICES', 'export OMP_NUM_THREADS=4'] cmd_cat = 'cat /dev/null' cmd_jsrun = 'jsrun -n %s -a 6 -g 6 -r 1 -c 7' % cfg['node_counts'] #molecules_path = '/gpfs/alpine/world-shared/ven201/tkurth/molecules/' t4.executable = [' %s; %s %s/examples/outlier_detection/run_optics_dist_summit_entk.sh' % (cmd_cat, cmd_jsrun, cfg['molecules_path'])] t4.arguments = ['%s/bin/python' % cfg['conda_pytorch']] t4.arguments += ['%s/examples/outlier_detection/optics.py' % cfg['molecules_path'], '--sim_path', '%s/MD_exps/%s' % (cfg['base_path'], cfg['system_name']), '--pdb_out_path', '%s/Outlier_search/outlier_pdbs' % cfg['base_path'], '--restart_points_path', '%s/Outlier_search/restart_points.json' % cfg['base_path'], '--data_path', '%s/MD_to_CVAE/cvae_input.h5' % cfg['base_path'], '--model_paths', '$models', '--model_type', cfg['model_type'], '--min_samples', 10, '--n_outliers', 500, '--dim1', cfg['residues'], '--dim2', cfg['residues'], '--cm_format', 'sparse-concat', '--batch_size', cfg['batch_size'], '--distributed', '-iw', cfg['init_weights']] t4.cpu_reqs = {'processes' : 1, 'process_type' : None, 'threads_per_process': 12, 'thread_type' : 'OpenMP'} t4.gpu_reqs = {'processes' : 1, 'process_type' : None, 'threads_per_process': 1, 'thread_type' : 'CUDA'} s4.add_tasks(t4) s4.post_exec = func_condition return s4
def esmacs(cfg, names, stage, outdir): s = Stage() s.name = 'S3.%s' % stage #print("DEBUG:instantiation: %s" % len(s._tasks)) for comp in names: #print("DEBUG:first loop: %s" % len(s._tasks)) for i in range(1, cfg['n_replicas']): #print("DEBUG:second loop:start: %s" % len(s._tasks)) t = Task() # RCT native t.pre_exec = [ #". /sw/summit/lmod/lmod/init/profile", "export WDIR=\"{}\"".format(comp), ". {}".format(cfg['conda_init']), "conda activate {}".format(cfg['conda_esmacs_task_env']), "module load {}".format(cfg['esmacs_task_modules']), "mkdir -p $WDIR/replicas/rep{}/{}".format(i, outdir), "cd $WDIR/replicas/rep{}/{}".format(i, outdir), #"rm -f {}.log {}.xml {}.dcd {}.chk".format(stage, stage, stage, stage), "export OMP_NUM_THREADS=1"] t.executable = 'python3' t.arguments = ['$WDIR/{}.py'.format(stage)] # Bash wrapper #t.executable = '%s/wf3.sh' % comp #t.arguments = [comp, i, outdir, stage, # cfg['conda_init'], # cfg['conda_esmacs_task_env'], # cfg['esmacs_task_modules']] t.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 4, 'thread_type': 'OpenMP'} t.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': 'CUDA'} s.add_tasks(t) #print("DEBUG:second loop:end: %s" % len(s._tasks)) return s
def describe_MD_pipeline(): p = Pipeline() p.name = 'MD' # MD stage s1 = Stage() s1.name = 'OpenMM' # Each Task() is an OpenMM executable that will run on a single GPU. # Set sleep time for local testing # for i in range(18): task = Task() task.name = 'md' task.pre_exec = [] # task.pre_exec += ['export MINICONDA=/gpfs/alpine/scratch/jdakka/bip178/miniconda'] # task.pre_exec += ['export PATH=$MINICONDA/bin:$PATH'] # task.pre_exec += ['export LD_LIBRARY_PATH=$MINICONDA/lib:$LD_LIBRARY_PATH'] task.pre_exec += ['module load python/2.7.15-anaconda2-5.3.0'] task.pre_exec += ['module load cuda/9.1.85'] task.pre_exec += ['module load gcc/6.4.0'] task.pre_exec += ['source activate openmm'] task.pre_exec += ['cd /gpfs/alpine/scratch/jdakka/bip178/benchmarks/MD_exps/fs-pep/results_2'] task.executable = '/ccs/home/jdakka/.conda/envs/openmm/bin/python' task.arguments = ['run_openmm.py', '-f', '/gpfs/alpine/scratch/jdakka/bip178/benchmarks/MD_exps/fs-pep/pdb/100-fs-peptide-400K.pdb'] task.cpu_reqs = {'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': None } task.gpu_reqs = {'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': 'CUDA' } # Add the MD task to the Docking Stage s1.add_tasks(task) # Add MD stage to the MD Pipeline p.add_stages(s1) return p
# Create a Pipeline object p = Pipeline() # Bookkeeping stage_uids = list() task_uids = dict() Stages = 3 Replicas = 4 for N_Stg in range(Stages): stg = Stage() ## initialization task_uids['Stage_%s'%N_Stg] = list() if N_Stg == 0: for n0 in range(Replicas): t = Task() t.executable = ['/usr/local/packages/gromacs/5.1.4/INTEL-140-MVAPICH2-2.0/bin/gmx_mpi_d'] #MD Engine t.upload_input_data = ['in.gro', 'in.top', 'FNF.itp', 'martini_v2.2.itp', 'in.mdp'] t.pre_exec = ['module load gromacs', '/usr/local/packages/gromacs/5.1.4/INTEL-140-MVAPICH2-2.0/bin/gmx_mpi_d grompp -f in.mdp -c in.gro -o in.tpr -p in.top'] t.arguments = ['mdrun', '-s', 'in.tpr', '-deffnm', 'out'] t.cores = 32 stg.add_tasks(t) task_uids['Stage_%s'%N_Stg].append(t.uid) p.add_stages(stg) stage_uids.append(stg.uid) else: for n0 in range(Replicas): t = Task() t.executable = ['/usr/local/packages/gromacs/5.1.4/INTEL-140-MVAPICH2-2.0/bin/gmx_mpi_d'] #MD Engine t.copy_input_data = ['$Pipeline_%s_Stage_%s_Task_%s/out.gro > in.gro'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0]), '$Pipeline_%s_Stage_%s_Task_%s/in.top'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0]), '$Pipeline_%s_Stage_%s_Task_%s/FNF.itp'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0]), '$Pipeline_%s_Stage_%s_Task_%s/martini_v2.2.itp'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0]), '$Pipeline_%s_Stage_%s_Task_%s/in.mdp'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0])]
def test_create_cud_from_task(): """ **Purpose**: Test if the 'create_cud_from_task' function generates a RP ComputeUnitDescription with the complete Task description """ pipeline = 'p1' stage = 's1' task = 't1' placeholder_dict = { pipeline: { stage: { task: '/home/vivek/some_file.txt' } } } t1 = Task() t1.name = 't1' t1.pre_exec = ['module load gromacs'] t1.executable = ['grompp'] t1.arguments = ['hello'] t1.cpu_reqs = {'processes': 4, 'process_type': 'MPI', 'threads_per_process': 1, 'thread_type': 'OpenMP' } t1.gpu_reqs = {'processes': 4, 'process_type': 'MPI', 'threads_per_process': 2, 'thread_type': 'OpenMP' } t1.post_exec = ['echo test'] t1.upload_input_data = ['upload_input.dat'] t1.copy_input_data = ['copy_input.dat'] t1.link_input_data = ['link_input.dat'] t1.copy_output_data = ['copy_output.dat'] t1.download_output_data = ['download_output.dat'] p = Pipeline() p.name = 'p1' s = Stage() s.name = 's1' s.tasks = t1 p.stages = s p._assign_uid('test') cud = create_cud_from_task(t1, placeholder_dict) assert cud.name == '%s,%s,%s,%s,%s,%s' % (t1.uid, t1.name, t1.parent_stage['uid'], t1.parent_stage['name'], t1.parent_pipeline['uid'], t1.parent_pipeline['name']) assert cud.pre_exec == t1.pre_exec # rp returns executable as a string regardless of whether assignment was using string or list assert cud.executable == t1.executable assert cud.arguments == t1.arguments assert cud.cpu_processes == t1.cpu_reqs['processes'] assert cud.cpu_threads == t1.cpu_reqs['threads_per_process'] assert cud.cpu_process_type == t1.cpu_reqs['process_type'] assert cud.cpu_thread_type == t1.cpu_reqs['thread_type'] assert cud.gpu_processes == t1.gpu_reqs['processes'] assert cud.gpu_threads == t1.gpu_reqs['threads_per_process'] assert cud.gpu_process_type == t1.gpu_reqs['process_type'] assert cud.gpu_thread_type == t1.gpu_reqs['thread_type'] assert cud.post_exec == t1.post_exec assert {'source': 'upload_input.dat', 'target': 'upload_input.dat'} in cud.input_staging assert {'source': 'copy_input.dat', 'action': rp.COPY, 'target': 'copy_input.dat'} in cud.input_staging assert {'source': 'link_input.dat', 'action': rp.LINK, 'target': 'link_input.dat'} in cud.input_staging assert {'source': 'copy_output.dat', 'action': rp.COPY, 'target': 'copy_output.dat'} in cud.output_staging assert {'source': 'download_output.dat', 'target': 'download_output.dat'} in cud.output_staging
def test_task_to_dict(): """ **Purpose**: Test if the 'to_dict' function of Task class converts all expected attributes of the Task into a dictionary """ t = Task() d = t.to_dict() assert d == { 'uid': None, 'name': None, 'state': states.INITIAL, 'state_history': [states.INITIAL], 'pre_exec': [], 'executable': str(), 'arguments': [], 'post_exec': [], 'cpu_reqs': { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': None }, 'gpu_reqs': { 'processes': 0, 'process_type': None, 'threads_per_process': 0, 'thread_type': None }, 'lfs_per_process': 0, 'upload_input_data': [], 'copy_input_data': [], 'link_input_data': [], 'move_input_data': [], 'copy_output_data': [], 'move_output_data': [], 'download_output_data': [], 'stdout': None, 'stderr': None, 'exit_code': None, 'path': None, 'tag': None, 'parent_stage': {'uid':None, 'name': None}, 'parent_pipeline': {'uid':None, 'name': None}} t = Task() t.uid = 'test.0000' t.name = 'new' t.pre_exec = ['module load abc'] t.executable = ['sleep'] t.arguments = ['10'] t.cpu_reqs['processes'] = 10 t.cpu_reqs['threads_per_process'] = 2 t.gpu_reqs['processes'] = 5 t.gpu_reqs['threads_per_process'] = 3 t.lfs_per_process = 1024 t.upload_input_data = ['test1'] t.copy_input_data = ['test2'] t.link_input_data = ['test3'] t.move_input_data = ['test4'] t.copy_output_data = ['test5'] t.move_output_data = ['test6'] t.download_output_data = ['test7'] t.stdout = 'out' t.stderr = 'err' t.exit_code = 1 t.path = 'a/b/c' t.tag = 'task.0010' t.parent_stage = {'uid': 's1', 'name': 'stage1'} t.parent_pipeline = {'uid': 'p1', 'name': 'pipeline1'} d = t.to_dict() assert d == { 'uid': 'test.0000', 'name': 'new', 'state': states.INITIAL, 'state_history': [states.INITIAL], 'pre_exec': ['module load abc'], 'executable': 'sleep', 'arguments': ['10'], 'post_exec': [], 'cpu_reqs': { 'processes': 10, 'process_type': None, 'threads_per_process': 2, 'thread_type': None }, 'gpu_reqs': { 'processes': 5, 'process_type': None, 'threads_per_process': 3, 'thread_type': None }, 'lfs_per_process': 1024, 'upload_input_data': ['test1'], 'copy_input_data': ['test2'], 'link_input_data': ['test3'], 'move_input_data': ['test4'], 'copy_output_data': ['test5'], 'move_output_data': ['test6'], 'download_output_data': ['test7'], 'stdout': 'out', 'stderr': 'err', 'exit_code': 1, 'path': 'a/b/c', 'tag': 'task.0010', 'parent_stage': {'uid': 's1', 'name': 'stage1'}, 'parent_pipeline': {'uid': 'p1', 'name': 'pipeline1'}} t.executable = 'sleep' d = t.to_dict() assert d == { 'uid': 'test.0000', 'name': 'new', 'state': states.INITIAL, 'state_history': [states.INITIAL], 'pre_exec': ['module load abc'], 'executable': 'sleep', 'arguments': ['10'], 'post_exec': [], 'cpu_reqs': { 'processes': 10, 'process_type': None, 'threads_per_process': 2, 'thread_type': None }, 'gpu_reqs': { 'processes': 5, 'process_type': None, 'threads_per_process': 3, 'thread_type': None }, 'lfs_per_process': 1024, 'upload_input_data': ['test1'], 'copy_input_data': ['test2'], 'link_input_data': ['test3'], 'move_input_data': ['test4'], 'copy_output_data': ['test5'], 'move_output_data': ['test6'], 'download_output_data': ['test7'], 'stdout': 'out', 'stderr': 'err', 'exit_code': 1, 'path': 'a/b/c', 'tag': 'task.0010', 'parent_stage': {'uid': 's1', 'name': 'stage1'}, 'parent_pipeline': {'uid': 'p1', 'name': 'pipeline1'}}
def test_task_exceptions(s,l,i,b): """ **Purpose**: Test if all attribute assignments raise exceptions for invalid values """ t = Task() data_type = [s,l,i,b] for data in data_type: if not isinstance(data,str): with pytest.raises(TypeError): t.name = data with pytest.raises(TypeError): t.path = data with pytest.raises(TypeError): t.parent_stage = data with pytest.raises(TypeError): t.parent_pipeline = data with pytest.raises(TypeError): t.stdout = data with pytest.raises(TypeError): t.stderr = data if not isinstance(data,list): with pytest.raises(TypeError): t.pre_exec = data with pytest.raises(TypeError): t.arguments = data with pytest.raises(TypeError): t.post_exec = data with pytest.raises(TypeError): t.upload_input_data = data with pytest.raises(TypeError): t.copy_input_data = data with pytest.raises(TypeError): t.link_input_data = data with pytest.raises(TypeError): t.move_input_data = data with pytest.raises(TypeError): t.copy_output_data = data with pytest.raises(TypeError): t.download_output_data = data with pytest.raises(TypeError): t.move_output_data = data if not isinstance(data, str) and not isinstance(data, list): with pytest.raises(TypeError): t.executable = data if not isinstance(data, str) and not isinstance(data, unicode): with pytest.raises(ValueError): t.cpu_reqs = { 'processes': 1, 'process_type': data, 'threads_per_process': 1, 'thread_type': None } t.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': data } t.gpu_reqs = { 'processes': 1, 'process_type': data, 'threads_per_process': 1, 'thread_type': None } t.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': 1, 'thread_type': data } if not isinstance(data, int): with pytest.raises(TypeError): t.cpu_reqs = { 'processes': data, 'process_type': None, 'threads_per_process': 1, 'thread_type': None } t.cpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': data, 'thread_type': None } t.gpu_reqs = { 'processes': data, 'process_type': None, 'threads_per_process': 1, 'thread_type': None } t.gpu_reqs = { 'processes': 1, 'process_type': None, 'threads_per_process': data, 'thread_type': None }
# Bookkeeping stage_uids = list() task_uids = dict() Stages = 1 Replicas = 2 for N_Stg in range(Stages): stg = Stage() ## initialization task_uids['Stage_%s'%N_Stg] = list() if N_Stg == 0: for n0 in range(Replicas): t = Task() t.executable = ['/u/sciteam/mushnoor/amber/amber14/bin/sander.MPI'] #MD Engine t.upload_input_data = ['inpcrd', 'prmtop', 'mdin'] t.pre_exec = ['export AMBERHOME=$HOME/amber/amber14/'] t.arguments = ['-O', '-i', 'mdin', '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out'] t.cores = 32 t.mpi = True stg.add_tasks(t) task_uids['Stage_%s'%N_Stg].append(t.uid) p.add_stages(stg) stage_uids.append(stg.uid) else: for n0 in range(Replicas): t = Task() t.executable = ['/u/sciteam/mushnoor/amber/amber14/bin/sander.MPI'] #MD Engine t.copy_input_data = ['$Pipeline_%s_Stage_%s_Task_%s/out.gro > in.gro'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0]), '$Pipeline_%s_Stage_%s_Task_%s/in.top'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0]), '$Pipeline_%s_Stage_%s_Task_%s/FNF.itp'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0]), '$Pipeline_%s_Stage_%s_Task_%s/martini_v2.2.itp'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0]), '$Pipeline_%s_Stage_%s_Task_%s/in.mdp'%(p.uid, stage_uids[N_Stg-1], task_uids['Stage_%s'%(N_Stg-1)][n0])]
def Cycle(Replicas, Replica_Cores, Cycles, MD_Executable, ExchangeMethod): """ All cycles after the initial cycle """ with open("exchangePairs.dat","r") as f: # Read exchangePairs.dat ExchangeArray = [] for line in f: ExchangeArray.append(int(line.split()[1])) #ExchangeArray.append(line) #print ExchangeArray q = Pipeline() #Bookkeeping stage_uids = list() task_uids = list() ## = dict() md_dict = dict() #Create initial MD stage md_stg = Stage() for r in range (Replicas): md_tsk = Task() md_tsk.executable = [MD_Executable] #MD Engine, Blue Waters md_tsk.link_input_data = ['%s/restrt > inpcrd'%(Book[Cycle-1][ExchangeArray[r]]), '%s/prmtop'%(Book[Cycle-1][r]), #'%s/mdin_{0}'.format(r)%(Book[k-1][r])] '%s/mdin'%(Book[Cycle-1][r])] md_tsk.pre_exec = ['export AMBERHOME=$HOME/amber/amber14/'] # Should be abstracted from user? #md_tsk.pre_exec = ['module load amber'] #md_tsk.arguments = ['-O', '-i', 'mdin_{0}'.format(n0), '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out_{0}'.format(n0),'-inf', 'mdinfo_{0}'.format(n0)] md_tsk.arguments = ['-O', '-i', 'mdin', '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out_{0}'.format(r),'-inf', 'mdinfo_{0}'.format(r)] md_tsk.cores = Replica_Cores md_tsk.mpi = True md_dict[r] = '$Pipeline_%s_Stage_%s_Task_%s'%(p.uid, md_stg.uid, md_tsk.uid) md_stg.add_tasks(md_tsk) #task_uids.append(md_tsk.uid) q.add_stages(md_stg) ex_stg= Stage() #Create Exchange Task ex_tsk = Task() ex_tsk.executable = ['python'] ex_tsk.upload_input_data = ['exchangeMethods/TempEx.py'] for n1 in range (Replicas): #print d[n1] ex_tsk.link_input_data += ['%s/mdinfo_%s'%(d[n1],n1)] ex_tsk.arguments = ['TempEx.py','{0}'.format(Replicas)] ex_tsk.cores = 1 ex_tsk.mpi = False ex_tsk.download_output_data = ['exchangePairs.dat'] ex_stg.add_tasks(ex_tsk) #task_uids.append(ex_tsk.uid) q.add_stages(ex_stg) #stage_uids.append(ex_stg.uid) Book.append(md_dict) #print d #print Book return q
def cycle(k): #read exchangePairs.dat # with open("exchangePairs.dat","r") as f: ExchangeArray = [] for line in f: ExchangeArray.append(int(line.split()[1])) #ExchangeArray.append(line) #print ExchangeArray p = Pipeline() #Bookkeeping stage_uids = list() task_uids = list() ## = dict() d = dict() #Create initial MD stage md_stg = Stage() #Create MD task for n0 in range (Replicas): md_tsk = Task() md_tsk.executable = ['/u/sciteam/mushnoor/amber/amber14/bin/sander.MPI'] #MD Engine, Blue Waters #md_tsk.executable = ['/usr/local/packages/amber/16/INTEL-140-MVAPICH2-2.0/bin/pmemd.MPI'] #MD Engine, SuperMIC #md_tsk.executable = ['/opt/amber/bin/pmemd.MPI'] md_tsk.link_input_data = ['%s/restrt > inpcrd'%(Book[k-1][ExchangeArray[n0]]), '%s/prmtop'%(Book[k-1][n0]), #'%s/mdin_{0}'.format(n0)%(Book[k-1][n0])] '%s/mdin'%(Book[k-1][n0])] ##Above: Copy from previous PIPELINE, make sure bookkeeping is correct md_tsk.pre_exec = ['export AMBERHOME=$HOME/amber/amber14/'] #Preexec, BLue Waters #md_tsk.pre_exec = ['module load amber'] #md_tsk.arguments = ['-O', '-i', 'mdin_{0}'.format(n0), '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out_{0}'.format(n0),'-inf', 'mdinfo_{0}'.format(n0)] md_tsk.arguments = ['-O', '-i', 'mdin', '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out_{0}'.format(n0),'-inf', 'mdinfo_{0}'.format(n0)] md_tsk.cores = Replica_Cores md_tsk.mpi = True d[n0] = '$Pipeline_%s_Stage_%s_Task_%s'%(p.uid, md_stg.uid, md_tsk.uid) #print d md_stg.add_tasks(md_tsk) task_uids.append(md_tsk.uid) p.add_stages(md_stg) stage_uids.append(md_stg.uid) #Create exchange stage ex_stg= Stage() #Create Exchange Task ex_tsk = Task() ex_tsk.executable = ['python'] ex_tsk.upload_input_data = ['exchangeMethods/TempEx.py'] for n1 in range (Replicas): #print d[n1] ex_tsk.link_input_data += ['%s/mdinfo_%s'%(d[n1],n1)] ex_tsk.arguments = ['TempEx.py','{0}'.format(Replicas)] ex_tsk.cores = 1 ex_tsk.mpi = False ex_tsk.download_output_data = ['exchangePairs.dat'] ex_stg.add_tasks(ex_tsk) task_uids.append(ex_tsk.uid) p.add_stages(ex_stg) stage_uids.append(ex_stg.uid) Book.append(d) #print d #print Book return p
Pilot_Cores = Replicas * Replica_Cores for N_Stg in range(Stages): stg = Stage() ## initialization task_uids['Stage_%s'%N_Stg] = list() #####Initial MD stage if N_Stg == 0: for n0 in range(Replicas): t = Task() t.executable = ['/u/sciteam/mushnoor/amber/amber14/bin/sander.MPI'] #MD Engine t.upload_input_data = ['inpcrd', 'prmtop', 'mdin_{0}'.format(n0)] t.pre_exec = ['export AMBERHOME=$HOME/amber/amber14/'] t.arguments = ['-O', '-i', 'mdin_{0}'.format(n0), '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out'] t.cores = Replica_Cores stg.add_tasks(t) task_uids['Stage_%s'%N_Stg].append(t.uid) p.add_stages(stg) stage_uids.append(stg.uid) #####Exchange Stages elif N_Stg != 0 and N_Stg%2 = 1: t = Task() t.executable = ['python'] t.upload_input_data = ['exchangeMethods/RandEx.py'] #t.link_input_data = ['']
def init_cycle(self, replicas, replica_cores, python_path, md_executable, exchange_method, min_temp, max_temp, timesteps, basename, pre_exec): # "cycle" = 1 MD stage plus the subsequent exchange computation """ Initial cycle consists of: 1) Create tarball of MD input data 2) Transfer the tarball to pilot sandbox 3) Untar the tarball 4) Run first cycle """ #Initialize Pipeline self._prof.prof('InitTar', uid=self._uid) p = Pipeline() p.name = 'initpipeline' md_dict = dict() #bookkeeping tar_dict = dict() #bookkeeping #Write the input files self._prof.prof('InitWriteInputs', uid=self._uid) writeInputs.writeInputs( max_temp=max_temp, min_temp=min_temp, replicas=replicas, timesteps=timesteps, basename=basename) self._prof.prof('EndWriteInputs', uid=self._uid) self._prof.prof('InitTar', uid=self._uid) #Create Tarball of input data tar = tarfile.open("input_files.tar", "w") for name in [ basename + ".prmtop", basename + ".inpcrd", basename + ".mdin" ]: tar.add(name) for r in range(replicas): tar.add('mdin_{0}'.format(r)) tar.close() #delete all input files outside the tarball for r in range(replicas): os.remove('mdin_{0}'.format(r)) self._prof.prof('EndTar', uid=self._uid) #Create Untar Stage repo = git.Repo('.', search_parent_directories=True) aux_function_path = repo.working_tree_dir untar_stg = Stage() untar_stg.name = 'untarStg' #Untar Task untar_tsk = Task() untar_tsk.name = 'untartsk' untar_tsk.executable = ['python'] untar_tsk.upload_input_data = [ str(aux_function_path)+'/repex/untar_input_files.py', 'input_files.tar' ] untar_tsk.arguments = ['untar_input_files.py', 'input_files.tar'] untar_tsk.cpu_reqs = 1 #untar_tsk.post_exec = [''] untar_stg.add_tasks(untar_tsk) p.add_stages(untar_stg) tar_dict[0] = '$Pipeline_%s_Stage_%s_Task_%s' % ( p.name, untar_stg.name, untar_tsk.name) # First MD stage: needs to be defined separately since workflow is not built from a predetermined order, also equilibration needs to happen first. md_stg = Stage() md_stg.name = 'mdstg0' self._prof.prof('InitMD_0', uid=self._uid) # MD tasks for r in range(replicas): md_tsk = AMBERTask(cores=replica_cores, md_executable=md_executable, pre_exec=pre_exec) md_tsk.name = 'mdtsk-{replica}-{cycle}'.format(replica=r, cycle=0) md_tsk.link_input_data += [ '%s/inpcrd' % tar_dict[0], '%s/prmtop' % tar_dict[0], '%s/mdin_{0}'.format(r) % tar_dict[0] #Use for full temperature exchange ] md_tsk.arguments = [ '-O', '-p', 'prmtop', '-i', 'mdin_{0}'.format(r), '-c', 'inpcrd', '-o', 'out-{replica}-{cycle}'.format(replica=r, cycle=0), '-r', 'restrt'.format(replica=r, cycle=0), #'-r', 'rstrt-{replica}-{cycle}'.format(replica=r,cycle=0), '-x', 'mdcrd-{replica}-{cycle}'.format(replica=r, cycle=0), #'-o', '$NODE_LFS_PATH/out-{replica}-{cycle}'.format(replica=r,cycle=0), #'-r', '$NODE_LFS_PATH/rstrt-{replica}-{cycle}'.format(replica=r,cycle=0), #'-x', '$NODE_LFS_PATH/mdcrd-{replica}-{cycle}'.format(replica=r,cycle=0), '-inf', 'mdinfo_{0}'.format(r) ] md_dict[r] = '$Pipeline_%s_Stage_%s_Task_%s' % ( p.name, md_stg.name, md_tsk.name) md_stg.add_tasks(md_tsk) self.md_task_list.append(md_tsk) #print md_tsk.uid p.add_stages(md_stg) #stage_uids.append(md_stg.uid) # First Exchange Stage ex_stg = Stage() ex_stg.name = 'exstg0' self._prof.prof('InitEx_0', uid=self._uid) # Create Exchange Task ex_tsk = Task() ex_tsk.name = 'extsk0' #ex_tsk.pre_exec = ['module load python/2.7.10'] ex_tsk.executable = [python_path] ex_tsk.upload_input_data = [exchange_method] for r in range(replicas): ex_tsk.link_input_data += ['%s/mdinfo_%s' % (md_dict[r], r)] ex_tsk.pre_exec = ['mv *.py exchange_method.py'] ex_tsk.arguments = ['exchange_method.py', '{0}'.format(replicas), '0'] ex_tsk.cores = 1 ex_tsk.mpi = False ex_tsk.download_output_data = ['exchangePairs_0.dat'] ex_stg.add_tasks(ex_tsk) #task_uids.append(ex_tsk.uid) p.add_stages(ex_stg) self.ex_task_list.append(ex_tsk) #self.ex_task_uids.append(ex_tsk.uid) self.book.append(md_dict) return p
def general_cycle(self, replicas, replica_cores, cycle, python_path, md_executable, exchange_method, pre_exec): """ All cycles after the initial cycle Pulls up exchange pairs file and generates the new workflow """ self._prof.prof('InitcreateMDwokflow_{0}'.format(cycle), uid=self._uid) with open('exchangePairs_{0}.dat'.format(cycle), 'r') as f: # Read exchangePairs.dat exchange_array = [] for line in f: exchange_array.append(int(line.split()[1])) #exchange_array.append(line) #print exchange_array q = Pipeline() q.name = 'genpipeline{0}'.format(cycle) #bookkeeping stage_uids = list() task_uids = list() ## = dict() md_dict = dict() #Create MD stage md_stg = Stage() md_stg.name = 'mdstage{0}'.format(cycle) self._prof.prof('InitMD_{0}'.format(cycle), uid=self._uid) for r in range(replicas): md_tsk = AMBERTask(cores=replica_cores, md_executable=md_executable, pre_exec=pre_exec) md_tsk.name = 'mdtsk-{replica}-{cycle}'.format( replica=r, cycle=cycle) md_tsk.link_input_data = [ '%s/restrt > inpcrd' % (self.book[cycle - 1][exchange_array[r]]), '%s/prmtop' % (self.book[0][r]), '%s/mdin_{0}'.format(r) % (self.book[0][r]) ] ### The Following softlinking scheme is to be used ONLY if node local file system is to be used: not fully supported yet. #md_tsk.link_input_data = ['$NODE_LFS_PATH/rstrt-{replica}-{cycle}'.format(replica=exchange_array[r],cycle=cycle-1) > '$NODE_LFS_PATH/inpcrd', # #'%s/restrt > inpcrd'%(self.book[cycle-1][exchange_array[r]]), # '%s/prmtop'%(self.book[0][r]), # '%s/mdin_{0}'.format(r)%(self.Book[0][r])] md_tsk.arguments = [ '-O', '-i', 'mdin_{0}'.format(r), '-p', 'prmtop', '-c', 'inpcrd', #'-c', 'rstrt-{replica}-{cycle}'.format(replica=r,cycle=cycle-1), '-o', 'out-{replica}-{cycle}'.format(replica=r, cycle=cycle), '-r', 'restrt', #'-r', 'rstrt-{replica}-{cycle}'.format(replica=r,cycle=cycle), '-x', 'mdcrd-{replica}-{cycle}'.format(replica=r, cycle=cycle), '-inf', 'mdinfo_{0}'.format(r) ] #md_tsk.tag = 'mdtsk-{replica}-{cycle}'.format(replica=r,cycle=0) md_dict[r] = '$Pipeline_%s_Stage_%s_Task_%s' % ( q.name, md_stg.name, md_tsk.name) self.md_task_list.append(md_tsk) md_stg.add_tasks(md_tsk) q.add_stages(md_stg) ex_stg = Stage() ex_stg.name = 'exstg{0}'.format(cycle + 1) #Create Exchange Task ex_tsk = Task() ex_tsk.name = 'extsk{0}'.format(cycle + 1) ex_tsk.executable = [python_path]#['/usr/bin/python'] #['/opt/python/bin/python'] ex_tsk.upload_input_data = [exchange_method] for r in range(replicas): ex_tsk.link_input_data += ['%s/mdinfo_%s' % (md_dict[r], r)] ex_tsk.pre_exec = ['mv *.py exchange_method.py'] ex_tsk.arguments = [ 'exchange_method.py', '{0}'.format(replicas), '{0}'.format(cycle + 1) ] ex_tsk.cores = 1 ex_tsk.mpi = False ex_tsk.download_output_data = [ 'exchangePairs_{0}.dat'.format(cycle + 1) ] # Finds exchange partners, also Generates exchange history trace ex_stg.add_tasks(ex_tsk) #task_uids.append(ex_tsk.uid) self.ex_task_list.append(ex_tsk) q.add_stages(ex_stg) #stage_uids.append(ex_stg.uid) self.book.append(md_dict) #self._prof.prof('EndEx_{0}'.format(cycle), uid=self._uid) #print d #print self.book return q
def init_cycle(): # Create Pipeline Obj p = Pipeline() #Bookkeeping stage_uids = list() task_uids = list() ## = dict() d = dict() dict_tarball = dict() #Create Tarball stage tar_stg = Stage() #Create Tar/untar task tar_tsk = Task() tar_tsk.executable = ['python'] tar_tsk.upload_input_data = ['Input_Files.tar', 'untar_input_files.py'] tar_tsk.arguments = ['untar_input_files.py','Input_Files.tar'] tar_tsk.cores = 1 tar_stg.add_tasks(tar_tsk) #task_uids.append(tar_tsk.uid) p.add_stages(tar_stg) #stage_uids.append(tar_stg.uid) dict_tarball[0] = '$Pipeline_%s_Stage_%s_Task_%s'%(p.uid,tar_stg.uid,tar_tsk.uid) #Create initial MD stage md_stg = Stage() #Create MD task for n0 in range (Replicas): md_tsk = Task() md_tsk.executable = ['/u/sciteam/mushnoor/amber/amber14/bin/sander.MPI'] #MD Engine, BW #md_tsk.executable = ['/usr/local/packages/amber/16/INTEL-140-MVAPICH2-2.0/bin/pmemd.MPI'] #MD Engine, SuperMIC #md_tsk.executable = ['/opt/amber/bin/pmemd.MPI'] #md_tsk.upload_input_data = ['inpcrd', 'prmtop', 'mdin_{0}'.format(n0)] #md_tsk.upload_input_data = ['inpcrd','prmtop','mdin'] md_tsk.link_input_data += ['%s/inpcrd'%dict_tarball[0], '%s/prmtop'%dict_tarball[0], '%s/mdin'%dict_tarball[0]] md_tsk.pre_exec = ['export AMBERHOME=$HOME/amber/amber14/'] #md_tsk.pre_exec = ['module load amber'] #md_tsk.arguments = ['-O', '-i', 'mdin_{0}'.format(n0), '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out_{0}'.format(n0), '-inf', 'mdinfo_{0}'.format(n0)] md_tsk.arguments = ['-O', '-i', 'mdin', '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out_{0}'.format(n0), '-inf', 'mdinfo_{0}'.format(n0)] md_tsk.cores = Replica_Cores md_tsk.mpi = True d[n0] = '$Pipeline_%s_Stage_%s_Task_%s'%(p.uid, md_stg.uid, md_tsk.uid) md_stg.add_tasks(md_tsk) task_uids.append(md_tsk.uid) p.add_stages(md_stg) stage_uids.append(md_stg.uid) #print d #Create Exchange Stage ex_stg = Stage() #Create Exchange Task ex_tsk = Task() ex_tsk.executable = ['python'] ex_tsk.upload_input_data = ['exchangeMethods/TempEx.py'] for n1 in range (Replicas): ex_tsk.link_input_data += ['%s/mdinfo_%s'%(d[n1],n1)] ex_tsk.arguments = ['TempEx.py','{0}'.format(Replicas)] ex_tsk.cores = 1 ex_tsk.mpi = False ex_tsk.download_output_data = ['exchangePairs.dat'] ex_stg.add_tasks(ex_tsk) task_uids.append(ex_tsk.uid) p.add_stages(ex_stg) stage_uids.append(ex_stg.uid) Book.append(d) #print Book return p
def InitCycle(Replicas, Replica_Cores, MD_Executable, ExchangeMethod): # "Cycle" = 1 MD stage plus the subsequent exchange computation #Initialize Pipeline p = Pipeline() md_dict = dict() #Bookkeeping tar_dict = dict() #Bookkeeping #Create Tarball of input data #Create Untar Stage untar_stg = Stage() #Untar Task untar_tsk = Task() untar_tsk.executable = ['python'] untar_tsk.upload_input_data = ['untar_input_files.py','../../Input_Files.tar'] untar_tsk.arguments = ['untar_input_files.py','Input_Files.tar'] untar_tsk.cores = 1 untar_stg.add_tasks(untar_tsk) p.add_stages(untar_stg) tar_dict[0] = '$Pipeline_%s_Stage_%s_Task_%s'%(p.uid, untar_stg.uid, untar_tsk.uid) print tar_dict[0] # First MD stage: needs to be defined separately since workflow is not built from a predetermined order md_stg = Stage() # MD tasks for r in range (Replicas): md_tsk = Task() md_tsk.executable = [MD_Executable] md_tsk.link_input_data += ['%s/inpcrd'%tar_dict[0], '%s/prmtop'%tar_dict[0], #'%s/mdin_{0}'.format(r)%tar_dict[0] '%s/mdin'%tar_dict[0] ] md_tsk.pre_exec = ['export AMBERHOME=$HOME/amber/amber14/'] #Should be abstracted from the user? md_tsk.arguments = ['-O','-p','prmtop', '-i', 'mdin', #'mdin_{0}'.format(r), # Use this for full Temperature Exchange '-c','inpcrd','-o','out_{0}'.format(r), '-inf','mdinfo_{0}'.format(r)] md_tsk.cores = Replica_Cores md_tsk.mpi = True md_dict[r] = '$Pipeline_%s_Stage_%s_Task_%s'%(p.uid, md_stg.uid, md_tsk.uid) md_stg.add_tasks(md_tsk) #task_uids.append(md_tsk.uid) p.add_stages(md_stg) #stage_uids.append(md_stg.uid) # First Exchange Stage ex_stg = Stage() # Create Exchange Task. Exchange task performs a Metropolis Hastings thermodynamic balance condition # and spits out the exchangePairs.dat file that contains a sorted list of ordered pairs. # Said pairs then exchange configurations by linking output configuration files appropriately. ex_tsk = Task() ex_tsk.executable = ['python'] #ex_tsk.upload_input_data = ['exchangeMethods/TempEx.py'] ex_tsk.upload_input_data = [ExchangeMethod] for r in range (Replicas): ex_tsk.link_input_data += ['%s/mdinfo_%s'%(md_dict[r],r)] ex_tsk.arguments = ['TempEx.py','{0}'.format(Replicas)] ex_tsk.cores = 1 ex_tsk.mpi = False ex_tsk.download_output_data = ['exchangePairs.dat'] ex_stg.add_tasks(ex_tsk) #task_uids.append(ex_tsk.uid) p.add_stages(ex_stg) #stage_uids.append(ex_stg.uid) Book.append(md_dict) #print Book return p