def post_stage():
if (not os.path.exists(f'{run_dir}/aggregator/stop.aggregator')):
nstages = len(p.stages)
s = Stage()
s.name = f"{nstages}"
t = Task()
t.cpu_reqs = {
'processes': 1,
'process_type': None,
'threads_per_process': 4,
'thread_type': 'OpenMP'
}
t.gpu_reqs = {
'processes': 0,
'process_type': None,
'threads_per_process': 0,
'thread_type': None
}
t.name = f" {i}_{nstages} "
t.executable = PYTHON
t.arguments = [
f'{current_dir}/simulation.py',
f'{run_dir}/simulations/all/{i}_{nstages}', ADIOS_XML
]
subprocess.getstatusoutput(
f'ln -s {run_dir}/simulations/all/{i}_{nstages} {run_dir}/simulations/new/{i}_{nstages}'
)
s.add_tasks(t)
s.post_exec = post_stage
p.add_stages(s)
def generate_pipeline(name, stages):
# Create a Pipeline object
p = Pipeline()
p.name = name
for s_cnt in range(stages):
# Create a Stage object
s = Stage()
s.name = 'Stage %s' % s_cnt
for t_cnt in range(5):
# Create a Task object
t = Task()
t.name = 'my-task' # Assign a name to the task (optional)
t.executable = ['/bin/echo'] # Assign executable to the task
# Assign arguments for the task executable
t.arguments = ['I am task %s in %s in %s' % (t_cnt, s_cnt, name)]
# Add the Task to the Stage
s.add_tasks(t)
# Add Stage to the Pipeline
p.add_stages(s)
return p
def generate_pipeline(name, stages):
# Create a Pipeline object
p = Pipeline()
p.name = name
for s_cnt in range(stages):
# Create a Stage object
s = Stage()
s.name = 'Stage %s'%s_cnt
for t_cnt in range(5):
# Create a Task object
t = Task()
t.name = 'my-task' # Assign a name to the task (optional)
t.executable = '/bin/echo' # Assign executable to the task
# Assign arguments for the task executable
t.arguments = ['I am task %s in %s in %s'%(t_cnt, s_cnt, name)]
# Add the Task to the Stage
s.add_tasks(t)
# Add Stage to the Pipeline
p.add_stages(s)
return p
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 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_pipeline(nid):
# Create a Pipeline object
p = Pipeline()
p.name = 'p%s' % nid
# Create a Stage object
s1 = Stage()
s1.name = 's1'
# Create a Task object which creates a file named 'output.txt' of size 1 MB
t1 = Task()
t1.name = 't2'
t1.executable = ['/bin/echo']
t1.arguments = ['hello']
# Add the Task to the Stage
s1.add_tasks(t1)
# Add Stage to the Pipeline
p.add_stages(s1)
# Create another Stage object to hold character count tasks
s2 = Stage()
s2.name = 's2'
s2_task_uids = []
for cnt in range(10):
# Create a Task object
t2 = Task()
t2.name = 't%s' % (cnt + 1)
t2.executable = ['/bin/echo']
t2.arguments = ['world']
# Copy data from the task in the first stage to the current task's location
t2.copy_input_data = [
'$Pipeline_%s_Stage_%s_Task_%s/output.txt' % (p.name, s1.name, t1.name)]
# Add the Task to the Stage
s2.add_tasks(t2)
s2_task_uids.append(t2.name)
# Add Stage to the Pipeline
p.add_stages(s2)
return p
def create_single_task():
t1 = Task()
t1.name = 'simulation'
t1.executable = '/bin/date'
t1.copy_input_data = []
t1.copy_output_data = []
return t1
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 constructTask(url):
response = requests.get(url)
response = response.json()
t = Task()
t.name = str(response['name'])
t.executable = [str(response['executable'])]
t.arguments = [str(response['arguments'])]
return t
def create_single_task():
t1 = Task()
t1.name = 'simulation'
t1.executable = ['/bin/date']
t1.copy_input_data = []
t1.copy_output_data = []
return t1
def generate_pipeline():
# Create a Pipeline object
p = Pipeline()
p.name = 'p1'
# Create a Stage object
s1 = Stage()
s1.name = 's1'
# Create a Task object which creates a file named 'output.txt' of size 1 MB
t1 = Task()
t1.name = 't1'
t1.executable = ['/bin/echo']
t1.arguments = ['"Hello World"']
t1.stdout = 'temp.txt'
# Add the Task to the Stage
s1.add_tasks(t1)
# Add Stage to the Pipeline
p.add_stages(s1)
# Create a Stage object
s2 = Stage()
s2.name = 's2'
# Create a Task object which creates a file named 'output.txt' of size 1 MB
t2 = Task()
t2.name = 't2'
t2.executable = ['/bin/cat']
t2.arguments = [
'$Pipeline_%s_Stage_%s_Task_%s/temp.txt' % (p.name, s1.name, t1.name)
]
t2.stdout = 'output.txt'
t2.download_output_data = ['output.txt']
# Add the Task to the Stage
s2.add_tasks(t2)
# Add Stage to the Pipeline
p.add_stages(s2)
return p
def create_single_task():
t1 = Task()
t1.name = 'simulation'
t1.executable = ['/bin/echo']
t1.arguments = ['hello']
t1.copy_input_data = []
t1.copy_output_data = []
return t1
def create_single_task():
t1 = Task()
t1.name = 'dummy_task'
t1.executable = ['placeholder']
t1.arguments = ['a','b','c']
t1.copy_input_data = []
t1.copy_output_data = []
return t1
def create_single_task():
t1 = Task()
t1.name = 'simulation'
t1.executable = ['/bin/echo']
t1.arguments = ['hello']
t1.copy_input_data = []
t1.copy_output_data = []
return t1
def create_single_task():
t1 = Task()
t1.name = 'simulation'
t1.executable = ['gmx mdrun']
t1.arguments = ['a', 'b', 'c']
t1.copy_input_data = []
t1.copy_output_data = []
return t1
def get_pipeline(shared_fs=False, size=1):
p = Pipeline()
p.name = 'p'
n = 4
s1 = Stage()
s1.name = 's1'
for x in range(n):
t = Task()
t.name = 't%s'%x
# dd if=/dev/random bs=<byte size of a chunk> count=<number of chunks> of=<output file name>
t.executable = 'dd'
if not shared_fs:
t.arguments = ['if=/dev/urandom','bs=%sM'%size, 'count=1', 'of=$NODE_LFS_PATH/s1_t%s.txt'%x]
else:
t.arguments = ['if=/dev/urandom','bs=%sM'%size, 'count=1', 'of=/home/vivek91/s1_t%s.txt'%x]
t.cpu_reqs['processes'] = 1
t.cpu_reqs['threads_per_process'] = 24
t.cpu_reqs['thread_type'] = ''
t.cpu_reqs['process_type'] = ''
t.lfs_per_process = 1024
s1.add_tasks(t)
p.add_stages(s1)
s2 = Stage()
s2.name = 's2'
for x in range(n):
t = Task()
t.executable = ['dd']
if not shared_fs:
t.arguments = ['if=$NODE_LFS_PATH/s1_t%s.txt'%x,'bs=%sM'%size, 'count=1', 'of=$NODE_LFS_PATH/s2_t%s.txt'%x]
else:
t.arguments = ['if=/home/vivek91/s1_t%s.txt'%x,'bs=%sM'%size, 'count=1', 'of=/home/vivek91/s2_t%s.txt'%x]
t.cpu_reqs['processes'] = 1
t.cpu_reqs['threads_per_process'] = 24
t.cpu_reqs['thread_type'] = ''
t.cpu_reqs['process_type'] = ''
t.tag = 't%s'%x
s2.add_tasks(t)
p.add_stages(s2)
return p
def get_pipeline(shared_fs=False, size=1):
p = Pipeline()
p.name = 'p'
n = 4
s1 = Stage()
s1.name = 's1'
for x in range(n):
t = Task()
t.name = 't%s'%x
# dd if=/dev/random bs=<byte size of a chunk> count=<number of chunks> of=<output file name>
t.executable = ['dd']
if not shared_fs:
t.arguments = ['if=/dev/urandom','bs=%sM'%size, 'count=1', 'of=$NODE_LFS_PATH/s1_t%s.txt'%x]
else:
t.arguments = ['if=/dev/urandom','bs=%sM'%size, 'count=1', 'of=/home/vivek91/s1_t%s.txt'%x]
t.cpu_reqs['processes'] = 1
t.cpu_reqs['threads_per_process'] = 24
t.cpu_reqs['thread_type'] = ''
t.cpu_reqs['process_type'] = ''
t.lfs_per_process = 1024
s1.add_tasks(t)
p.add_stages(s1)
s2 = Stage()
s2.name = 's2'
for x in range(n):
t = Task()
t.executable = ['dd']
if not shared_fs:
t.arguments = ['if=$NODE_LFS_PATH/s1_t%s.txt'%x,'bs=%sM'%size, 'count=1', 'of=$NODE_LFS_PATH/s2_t%s.txt'%x]
else:
t.arguments = ['if=/home/vivek91/s1_t%s.txt'%x,'bs=%sM'%size, 'count=1', 'of=/home/vivek91/s2_t%s.txt'%x]
t.cpu_reqs['processes'] = 1
t.cpu_reqs['threads_per_process'] = 24
t.cpu_reqs['thread_type'] = ''
t.cpu_reqs['process_type'] = ''
t.tag = 't%s'%x
s2.add_tasks(t)
p.add_stages(s2)
return p
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 create_task_from_cu(cu, prof=None):
"""
Purpose: Create a Task based on the Compute Unit.
Details: Currently, only the uid, parent_stage and parent_pipeline are retrieved. The exact initial Task (that was
converted to a CUD) cannot be recovered as the RP API does not provide the same attributes for a CU as for a CUD.
Also, this is not required for the most part.
TODO: Add exit code, stdout, stderr and path attributes to a Task. These can be extracted from a CU
:arguments:
:cu: RP Compute Unit
:return: Task
"""
try:
logger.debug('Create Task from CU %s' % cu.name)
if prof:
prof.prof('task from cu - create',
uid=cu.name.split(',')[0].strip())
task = Task()
task.uid = cu.name.split(',')[0].strip()
task.name = cu.name.split(',')[1].strip()
task.parent_stage['uid'] = cu.name.split(',')[2].strip()
task.parent_stage['name'] = cu.name.split(',')[3].strip()
task.parent_pipeline['uid'] = cu.name.split(',')[4].strip()
task.parent_pipeline['name'] = cu.name.split(',')[5].strip()
task.rts_uid = cu.uid
if cu.exit_code is not None:
task.exit_code = cu.exit_code
else:
if cu.state == rp.DONE:
task.exit_code = 0
else:
task.exit_code = 1
task.path = ru.Url(cu.sandbox).path
if prof:
prof.prof('task from cu - done', uid=cu.name.split(',')[0].strip())
logger.debug('Task %s created from CU %s' % (task.uid, cu.name))
return task
except Exception, ex:
logger.error('Task creation from CU failed, error: %s' % ex)
raise
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 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 generate_pipeline(nid):
p = Pipeline()
p.name = 'p%s' % nid
s1 = Stage()
s1.name = 's1'
t1 = Task()
t1.name = 't2'
t1.executable = '/bin/echo'
t1.arguments = ['hello']
s1.add_tasks(t1)
p.add_stages(s1)
s2 = Stage()
s2.name = 's2'
s2_task_uids = []
for cnt in range(10):
t2 = Task()
t2.name = 't%s' % (cnt + 1)
t2.executable = '/bin/echo'
t2.arguments = ['world']
# Copy data from the task in first stage to the current task's location
t2.copy_input_data = [
'$Pipeline_%s_Stage_%s_Task_%s/output.txt' %
(p.name, s1.name, t1.name)
]
s2.add_tasks(t2)
s2_task_uids.append(t2.name)
p.add_stages(s2)
return p
def create_task_from_cu(cu, prof=None):
"""
Purpose: Create a Task based on the Compute Unit.
Details: Currently, only the uid, parent_stage and parent_pipeline are retrieved. The exact initial Task (that was
converted to a CUD) cannot be recovered as the RP API does not provide the same attributes for a CU as for a CUD.
Also, this is not required for the most part.
TODO: Add exit code, stdout, stderr and path attributes to a Task. These can be extracted from a CU
:arguments:
:cu: RP Compute Unit
:return: Task
"""
try:
logger.debug('Create Task from CU %s' % cu.name)
if prof:
prof.prof('task from cu - create',
uid=cu.name.split(',')[0].strip())
task = Task()
task.uid = cu.name.split(',')[0].strip()
task.name = cu.name.split(',')[1].strip()
task.parent_stage['uid'] = cu.name.split(',')[2].strip()
task.parent_stage['name'] = cu.name.split(',')[3].strip()
task.parent_pipeline['uid'] = cu.name.split(',')[4].strip()
task.parent_pipeline['name'] = cu.name.split(',')[5].strip()
task.rts_uid = cu.uid
if cu.state == rp.DONE:
task.exit_code = 0
else:
task.exit_code = 1
task.path = ru.Url(cu.sandbox).path
if prof:
prof.prof('task from cu - done', uid=cu.name.split(',')[0].strip())
logger.debug('Task %s created from CU %s' % (task.uid, cu.name))
return task
except Exception, ex:
logger.exception('Task creation from CU failed, error: %s' % ex)
raise
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 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_task(self, **ensembles):
""" Generate a `radical.entk` task.
Parameters
----------
ensembles: dict, OrderedDict
Dictionary of the *current* values of variables that are ensembles. All the variables
that were declared with `add_ensemble` should be specified here so that a correct
task object can be generated.
"""
[setattr(self, k, w) for k, w in ensembles.iteritems()]
if not self.all_variables_defined():
raise ValueError('Some variables are not defined!')
task = Task()
task.name = ensembles['task_name']
task.pre_exec += self.engine.pre_exec
task.executable += str(self.engine.executable)
task.arguments += self.engine.arguments
task.cpu_reqs = {
'processes': self._processes,
'process_type': 'MPI' if self.engine.uses_mpi else None,
'threads_per_process': self._threads_per_process,
'thread_type': None
}
task.gpu_reqs = {
'processes': self._gpu_processes,
'process_type': 'MPI' if self.engine.gpu_uses_mpi else None,
'threads_per_process': self._gpu_threads_per_process,
'thread_type': None
}
task.arguments.extend(self.arguments)
task.copy_input_data.extend(self.copied_files)
task.copy_input_data.extend(self.system.copied_files)
task.post_exec.append('echo "{}" > sim_desc.txt'.format(task.name))
task.link_input_data.extend(self.input_data(**ensembles))
task.link_input_data.extend(self.system.linked_files)
task.pre_exec.extend(
self._sed.format(n, v, f)
for f, vs in self.get_variables().items() for n, v in vs)
return task
def sendr(qname, bulk_size, num_tasks):
try:
tasks = list()
for cnt in range(num_tasks):
task = Task()
task.name = str(cnt)
tasks.append(task)
connection = pika.BlockingConnection(pika.ConnectionParameters( host=hostname,
port=port,
heartbeat=0))
channel = connection.channel()
cur_task_cnt = 0
f = open('sendr.txt','w')
f.write('start: %f\n'%time.time())
while(cur_task_cnt < num_tasks):
workload = list()
wld_size = 0
# tasks = copy_tasks
for task in tasks:
workload.append(task.to_dict())
# copy_tasks.remove(task)
wld_size+=1
if wld_size == bulk_size:
break
cur_task_cnt += wld_size
wld_as_json = json.dumps(workload)
channel.basic_publish( exchange = '',
routing_key = qname,
body = wld_as_json,
# properties=pika.BasicProperties(
# delivery_mode = 2, # make message persistent
# )
)
f.write('stop: %f\n'%time.time())
except Exception as ex:
print 'Error in sendr: %s'%ex
print traceback.format_exc()
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
def create_pipeline():
p = Pipeline()
s = Stage()
t1 = Task()
t1.name = 'simulation'
t1.executable = ['sleep']
t1.arguments = ['10']
s.add_tasks(t1)
p.add_stages(s)
return p
def create_pipeline():
p = Pipeline()
s = Stage()
t1 = Task()
t1.name = 'simulation'
t1.executable = 'sleep'
t1.arguments = ['10']
s.add_tasks(t1)
p.add_stages(s)
return p
def create_pipeline():
p = Pipeline()
s = Stage()
t = Task()
t.name = 'simulation'
t.executable = ['/bin/echo']
t.arguments = ['hello']
t.copy_input_data = []
t.copy_output_data = []
s.add_tasks(t)
p.add_stages(s)
return p
def create_pipeline():
p = Pipeline()
s = Stage()
t1 = Task()
t1.name = 'simulation'
t1.executable = ['/bin/echo']
t1.arguments = ['hello']
t1.copy_input_data = []
t1.copy_output_data = []
s.add_tasks(t1)
p.add_stages(s)
return p
def create_entry(cmt_filename, param_path, task_counter):
"""This function creates the Entk stage for creation of a database entry.
:param cmt_filename: cmt_filename
:param param_path: parameter directory
:param pipelinedir: Directory of the 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_filename)
# Create a Stage object
database_entry = Stage()
t1 = Task()
t1.name = "database-entry"
t1.pre_exec = PRE_EXECS
t1.executable = 'create-entry' # Assign
# executable to the task
t1.arguments = ['-f %s' % cmt_filename, '-p %s' % param_path]
# In the future maybe to database dir as a total log?
t1.stdout = os.path.join(
"%s" % Cdir, "logs", "stdout.pipeline_%s.task_%s.%s" %
(Cid, str(task_counter).zfill(4), t1.name))
t1.stderr = os.path.join(
"%s" % Cdir, "logs", "stderr.pipeline_%s.task_%s.%s" %
(Cid, str(task_counter).zfill(4), t1.name))
# Increase task-counter
task_counter += 1
# Add Task to the Stage
database_entry.add_tasks(t1)
return database_entry, task_counter
def generate_pipline(stages, tasks_per_stage=1):
p = Pipeline()
##Create 8 stages each with one task
for s_cnt in range(stages):
s = Stage()
s.name = 'stage %s' % (s_cnt + 1)
for t_cnt in range(tasks_per_stage):
t = Task()
t.name = 'task %s' % (t_cnt + 1)
t.executable = '/bin/sleep'
t.arguments = ['100']
# Add the Task to the Stage
s.add_tasks(t)
# Add Stage to the Pipeline
p.add_stages(s)
return p
def generate_pipeline(name, stages): #generate the pipeline of prediction and blob detection
# Create a Pipeline object
p = Pipeline()
p.name = name
for s_cnt in range(stages):
# Create a Stage object
s = Stage()
s.name = 'Stage %s'%s_cnt
if(stage==1)
# Create Task 1, training
t = Task()
t.name = 'my-task1'
t.executable = ['sbatch'] # Assign executable to the task
# Assign arguments for the task executable
t.arguments = ['/Code/trainbatch.bat']
def generate_pipeline():
# Create a Pipeline object
p = Pipeline()
p.name = 'p1'
# Create a Stage object
s1 = Stage()
s1.name = 's1'
# Create a Task object which creates a file named 'output.txt' of size 1 MB
t1 = Task()
t1.name = 't1'
t1.executable = ['/bin/false']
# t1.arguments = ['"Hello World"','>>','temp.txt']
# Add the Task to the Stage
s1.add_tasks(t1)
# Add Stage to the Pipeline
p.add_stages(s1)
return p
def test_amgr_run_mock():
p = Pipeline()
s = Stage()
t = Task()
t.name = 'simulation'
t.executable = ['/bin/date']
s.tasks = t
p.add_stages(s)
res_dict = {
'resource': 'local.localhost',
'walltime': 5,
'cpus': 1,
'project': ''
}
appman = Amgr(hostname=hostname, port=port, rts="mock")
appman.resource_desc = res_dict
appman.workflow = [p]
appman.run()
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
}
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 GeneralCycle(self, Replicas, Replica_Cores, Cycle, MD_Executable, ExchangeMethod):
"""
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
ExchangeArray = []
for line in f:
ExchangeArray.append(int(line.split()[1]))
#ExchangeArray.append(line)
#print ExchangeArray
q = Pipeline()
q.name = 'genpipeline{0}'.format(Cycle)
#Bookkeeping
stage_uids = list()
task_uids = list() ## = dict()
md_dict = dict()
#Create initial 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)
md_tsk.name = 'mdtsk-{replica}-{cycle}'.format(replica=r,cycle=Cycle)
md_tsk.link_input_data = ['%s/restrt > inpcrd'%(self.Book[Cycle-1][ExchangeArray[r]]),
'%s/prmtop'%(self.Book[0][r]),
#'%s/prmtop'%(self.Tarball_path[0]),
'%s/mdin_{0}'.format(r)%(self.Book[0][r])]
#'%s/mdin'%(self.Book[0][r])]
#'%s/mdin'%(self.Tarball_path[0])]
md_tsk.arguments = ['-O', '-i', 'mdin_{0}'.format(r), '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out_{0}'.format(r),'-inf', 'mdinfo_{0}'.format(r)]
#md_tsk.arguments = ['-O', '-i', 'mdin', '-p', 'prmtop', '-c', 'inpcrd', '-o', 'out_{0}'.format(r),'-inf', 'mdinfo_{0}'.format(r)]
md_dict[r] = '$Pipeline_%s_Stage_%s_Task_%s'%(q.name, md_stg.name, md_tsk.name)
self.md_task_list.append(md_tsk)
md_stg.add_tasks(md_tsk)
q.add_stages(md_stg)
ex_stg = Stage()
ex_stg.name = 'exstg{0}'.format(Cycle+1)
#Create Exchange Task
ex_tsk = Task()
ex_tsk.name = 'extsk{0}'.format(Cycle+1)
ex_tsk.executable = ['python']
ex_tsk.upload_input_data = [ExchangeMethod]
for r in range (Replicas):
ex_tsk.link_input_data += ['%s/mdinfo_%s'%(md_dict[r],r)]
ex_tsk.arguments = ['TempEx.py','{0}'.format(Replicas), '{0}'.format(Cycle+1)]
ex_tsk.cores = 1
ex_tsk.mpi = False
ex_tsk.download_output_data = ['exchangePairs_{0}.dat'.format(Cycle+1)] # Finds exchange partners, also Generates exchange history trace
ex_stg.add_tasks(ex_tsk)
#task_uids.append(ex_tsk.uid)
self.ex_task_list.append(ex_tsk)
q.add_stages(ex_stg)
#stage_uids.append(ex_stg.uid)
self.Book.append(md_dict)
#self._prof.prof('EndEx_{0}'.format(Cycle), uid=self._uid)
#print d
#print self.Book
return q
def generate_pipeline():
# Create a Pipeline object
p = Pipeline()
p.name = 'p1'
# Create a Stage object
s1 = Stage()
s1.name = 's1'
# Create a Task object which creates a file named 'output.txt' of size 1 MB
t1 = Task()
t1.name = 't1'
t1.executable = '/bin/bash'
t1.arguments = ['-l', '-c', 'base64 /dev/urandom | head -c 1000000 > output.txt']
# Add the Task to the Stage
s1.add_tasks(t1)
# Add Stage to the Pipeline
p.add_stages(s1)
# Create another Stage object to hold character count tasks
s2 = Stage()
s2.name = 's2'
s2_task_uids = []
for cnt in range(30):
# Create a Task object
t2 = Task()
t2.name = 't%s' % (cnt + 1)
t2.executable = '/bin/bash'
t2.arguments = ['-l', '-c', 'grep -o . output.txt | sort | uniq -c > ccount.txt']
# Copy data from the task in the first stage to the current task's location
t2.copy_input_data = ['$Pipeline_%s_Stage_%s_Task_%s/output.txt' % (p.name, s1.name, t1.name)]
# Add the Task to the Stage
s2.add_tasks(t2)
s2_task_uids.append(t2.name)
# Add Stage to the Pipeline
p.add_stages(s2)
# Create another Stage object to hold checksum tasks
s3 = Stage()
s3.name = 's3'
for cnt in range(30):
# Create a Task object
t3 = Task()
t3.name = 't%s' % (cnt + 1)
t3.executable = '/bin/bash'
t3.arguments = ['-l', '-c', 'sha1sum ccount.txt > chksum.txt']
# Copy data from the task in the first stage to the current task's location
t3.copy_input_data = ['$Pipeline_%s_Stage_%s_Task_%s/ccount.txt' % (p.name, s2.name, s2_task_uids[cnt])]
# Download the output of the current task to the current location
t3.download_output_data = ['chksum.txt > chksum_%s.txt' % cnt]
# Add the Task to the Stage
s3.add_tasks(t3)
# Add Stage to the Pipeline
p.add_stages(s3)
return p
# VM, set "RMQ_HOSTNAME" and "RMQ_PORT" in the session where you are running
# this script.
hostname = os.environ.get('RMQ_HOSTNAME', 'localhost')
port = os.environ.get('RMQ_PORT', 5672)
if __name__ == '__main__':
# Create a Pipeline object
p = Pipeline()
# Create a Stage object
s = Stage()
# Create a Task object
t = Task()
t.name = 'my-first-task' # Assign a name to the task (optional, do not use ',' or '_')
t.executable = '/bin/echo' # Assign executable to the task
t.arguments = ['Hello World'] # Assign arguments for the task executable
# Add Task to the Stage
s.add_tasks(t)
# Add Stage to the Pipeline
p.add_stages(s)
# Create Application Manager
appman = AppManager(hostname=hostname, port=port)
# Create a dictionary describe four mandatory keys:
# resource, walltime, and cpus
# resource is 'local.localhost' to execute locally
def InitCycle(self, Replicas, Replica_Cores, md_executable, ExchangeMethod, timesteps): # "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=350,min_temp=250,replicas=Replicas,timesteps=timesteps)
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 ["prmtop", "inpcrd", "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
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 = ['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.name,
untar_stg.name,
untar_tsk.name)
# First MD stage: needs to be defined separately since workflow is not built from a predetermined order
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)
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
#'%s/mdin'%tar_dict[0] #Testing only
]
md_tsk.arguments = ['-O','-p','prmtop', '-i', 'mdin_{0}'.format(r), # Use this for full Temperature Exchange
'-c','inpcrd','-o','out_{0}'.format(r),
'-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)
#with open('logfile.log', 'a') as logfile:
# logfile.write( '%.5f' %time.time() + ',' + 'InitEx0' + '\n')
# Create Exchange Task. Exchange task performs a Metropolis Hastings thermodynamic balance condition
# check 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.name = 'extsk0'
ex_tsk.executable = ['python']
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), '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(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 test_rp_da_scheduler_bw():
"""
**Purpose**: Run an EnTK application on localhost
"""
p1 = Pipeline()
p1.name = 'p1'
n = 10
s1 = Stage()
s1.name = 's1'
for x in range(n):
t = Task()
t.name = 't%s'%x
t.executable = ['/bin/hostname']
t.arguments = ['>','hostname.txt']
t.cpu_reqs['processes'] = 1
t.cpu_reqs['threads_per_process'] = 16
t.cpu_reqs['thread_type'] = ''
t.cpu_reqs['process_type'] = ''
t.lfs_per_process = 10
t.download_output_data = ['hostname.txt > s1_t%s_hostname.txt'%(x)]
s1.add_tasks(t)
p1.add_stages(s1)
s2 = Stage()
s2.name = 's2'
for x in range(n):
t = Task()
t.executable = ['/bin/hostname']
t.arguments = ['>','hostname.txt']
t.cpu_reqs['processes'] = 1
t.cpu_reqs['threads_per_process'] = 16
t.cpu_reqs['thread_type'] = ''
t.cpu_reqs['process_type'] = ''
t.download_output_data = ['hostname.txt > s2_t%s_hostname.txt'%(x)]
t.tag = 't%s'%x
s2.add_tasks(t)
p1.add_stages(s2)
res_dict = {
'resource' : 'ncsa.bw_aprun',
'walltime' : 10,
'cpus' : 128,
'project' : 'gk4',
'queue' : 'high'
}
os.environ['RADICAL_PILOT_DBURL'] = MLAB
appman = AppManager(hostname=hostname, port=port)
appman.resource_desc = res_dict
appman.workflow = [p1]
appman.run()
for i in range(n):
assert open('s1_t%s_hostname.txt'%i,'r').readline().strip() == open('s2_t%s_hostname.txt'%i,'r').readline().strip()
txts = glob('%s/*.txt' % os.getcwd())
for f in txts:
os.remove(f)
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'}}
