def fresh_config():
return Config(
executors=[
HighThroughputExecutor(
label="htex_Local",
working_dir=working_dir,
storage_access=[FTPInTaskStaging(), HTTPInTaskStaging(), NoOpFileStaging()],
worker_debug=True,
cores_per_worker=1,
heartbeat_period=2,
heartbeat_threshold=5,
poll_period=100,
provider=LocalProvider(
channel=LocalChannel(),
init_blocks=0,
min_blocks=0,
max_blocks=5,
launcher=SingleNodeLauncher(),
),
)
],
strategy='simple',
app_cache=True, checkpoint_mode='task_exit',
retries=2,
monitoring=MonitoringHub(
hub_address="localhost",
hub_port=55055,
monitoring_debug=False,
resource_monitoring_interval=1,
)
)
def configure_parsl(n_threads, monitoring, **kwargs):
from parsl.config import Config
from parsl.executors.threads import ThreadPoolExecutor
from parsl.addresses import address_by_hostname
if monitoring:
from parsl.monitoring import MonitoringHub
monitoring = MonitoringHub(
hub_address=address_by_hostname(),
hub_port=55055,
logging_level=logging.INFO,
resource_monitoring_interval=10,
)
else:
monitoring = None
local_threads = ThreadPoolExecutor(max_threads=n_threads,
label='local_threads')
config = Config(
executors=[local_threads],
monitoring=monitoring,
strategy=None,
app_cache=True,
)
return config
def theta_nwchem_config(log_dir: str,
nodes_per_nwchem: int = 2,
total_nodes: int = int(
os.environ.get("COBALT_JOBSIZE", 1)),
ml_prefetch: int = 0) -> Config:
"""Theta configuration where QC workers sit on the launch node (to be able to aprun)
and ML workers are placed on compute nodes
Args:
nodes_per_nwchem: Number of nodes per NWChem computation
log_dir: Path to store monitoring DB and parsl logs
total_nodes: Total number of nodes available. Default: COBALT_JOBSIZE
ml_prefetch: Number of tasks for ML workers to prefetch for inference
Returns:
(Config) Parsl configuration
"""
assert total_nodes % nodes_per_nwchem == 0, "NWChem node count not a multiple of nodes per task"
nwc_workers = total_nodes // nodes_per_nwchem
return Config(
executors=[
ThreadPoolExecutor(label='qc', max_threads=nwc_workers),
HighThroughputExecutor(
address=address_by_hostname(),
label="ml",
max_workers=1,
prefetch_capacity=ml_prefetch,
provider=LocalProvider(
nodes_per_block=
nodes_per_nwchem, # Minimum increment in blcoks
init_blocks=0,
max_blocks=total_nodes //
nodes_per_nwchem, # Limits the number of manager processes,
launcher=AprunLauncher(
overrides='-d 256 --cc depth -j 4'
), # Places worker on the compute node
worker_init='''
module load miniconda-3
conda activate /lus/theta-fs0/projects/CSC249ADCD08/edw/env
''',
),
)
],
monitoring=MonitoringHub(
hub_address=address_by_hostname(),
monitoring_debug=False,
resource_monitoring_interval=10,
logdir=log_dir,
logging_endpoint=
f'sqlite:///{os.path.join(log_dir, "monitoring.db")}'),
run_dir=log_dir,
strategy='simple',
max_idletime=15.)
import logging
from parsl import ThreadPoolExecutor
from parsl.config import Config
from parsl.monitoring import MonitoringHub
config = Config(executors=[ThreadPoolExecutor(label='threads', max_threads=4)],
monitoring=MonitoringHub(
hub_address="localhost",
hub_port=55055,
logging_level=logging.INFO,
resource_monitoring_interval=3,
))
def theta_xtb_config(log_dir: str,
xtb_per_node: int = 1,
ml_tasks_per_node: int = 1,
total_nodes: int = int(os.environ.get(
"COBALT_JOBSIZE", 1))):
"""Theta configuration where QC tasks and ML tasks run on single nodes.
There are no MPI tasks in this configuration.
Args:
ml_workers: Number of nodes dedicated to ML tasks
xtb_per_node: Number of XTB calculations
ml_tasks_per_node: Number of ML tasks to place on each node
log_dir: Path to store monitoring DB and parsl logs
total_nodes: Total number of nodes available. Default: COBALT_JOBSIZE
Returns:
(Config) Parsl configuration
"""
return Config(
executors=[
HighThroughputExecutor(
address=address_by_hostname(),
label="qc",
max_workers=xtb_per_node,
cpu_affinity='block',
provider=LocalProvider(
nodes_per_block=total_nodes,
init_blocks=0,
max_blocks=1,
launcher=AprunLauncher(
overrides='-d 64 --cc depth'
), # Places worker on the compute node
worker_init='''
module load miniconda-3
conda activate /lus/theta-fs0/projects/CSC249ADCD08/edw/env
''',
),
),
HighThroughputExecutor(
address=address_by_hostname(),
label="ml",
max_workers=ml_tasks_per_node,
cpu_affinity='block',
provider=LocalProvider(
nodes_per_block=total_nodes,
init_blocks=1,
max_blocks=1,
launcher=AprunLauncher(
overrides='-d 64 --cc depth'
), # Places worker on the compute node
worker_init='''
module load miniconda-3
conda activate /lus/theta-fs0/projects/CSC249ADCD08/edw/env
''',
),
)
],
monitoring=MonitoringHub(
hub_address=address_by_hostname(),
hub_port=55055,
monitoring_debug=False,
resource_monitoring_interval=10,
logdir=log_dir,
logging_endpoint=
f'sqlite:///{os.path.join(log_dir, "monitoring.db")}'),
run_dir=log_dir,
strategy='simple',
max_idletime=15.)
obs_lsst_configs="/opt/lsst/software/stack/obs_lsst/config/",
# this is the butler repo to use
repo_dir="/global/cscratch1/sd/bxc/lsst-dm-repo-1",
root_softs="/global/homes/b/bxc/dm/",
# what is ROOT_SOFTS in general? this has come from the SRS workflow,
# probably the path to this workflow's repo, up one level.
# This specifies a function (str -> str) which rewrites a bash command into
# one appropriately wrapper for whichever container/environment is being used
# with this configuration (for example, wrap_shifter_container writes the
# command to a temporary file and then invokes that file inside shifter)
wrap=partial(wrap_shifter_container,
image_id="lsstdesc/desc-drp-stack:v19-dc2-run2.2-v4"),
wrap_sql=wrap_no_op,
parsl_config=Config(executors=[
cori_queue_executor_1, cori_queue_executor_2, local_executor
],
strategy='htex',
app_cache=True,
checkpoint_mode='task_exit',
checkpoint_files=get_all_checkpoints(),
retries=2,
monitoring=MonitoringHub(
hub_address=address_by_hostname(),
hub_port=55055,
monitoring_debug=True,
resource_monitoring_interval=10)))
configuration = cori_shifter_debug_config
from parsl import ThreadPoolExecutor
from parsl.config import Config
from parsl.monitoring import MonitoringHub
config = Config(executors=[ThreadPoolExecutor(label='threads', max_threads=4)],
monitoring=MonitoringHub(
hub_address="localhost",
hub_port=55055,
resource_monitoring_interval=3,
))
def theta_nwchem_config(ml_workers: int, log_dir: str, nodes_per_nwchem: int = 2,
total_nodes: int = int(os.environ.get("COBALT_JOBSIZE", 1))) -> Config:
"""Theta configuration where QC workers sit on the launch node (to be able to aprun)
and ML workers are placed on compute nodes
Args:
ml_workers: Number of nodes dedicated to ML tasks
nodes_per_nwchem: Number of nodes per NWChem computation
log_dir: Path to store monitoring DB and parsl logs
total_nodes: Total number of nodes available. Default: COBALT_JOBSIZE
Returns:
(Config) Parsl configuration
"""
nwc_nodes = total_nodes - ml_workers
assert nwc_nodes % nodes_per_nwchem == 0, "NWChem node count not a multiple of nodes per task"
nwc_workers = nwc_nodes // nodes_per_nwchem
return Config(
executors=[
HighThroughputExecutor(
address=address_by_hostname(),
label="qc",
max_workers=nwc_workers,
cores_per_worker=1e-6,
provider=LocalProvider(
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(), # Places worker on the launch node
worker_init='''
module load miniconda-3
conda activate /lus/theta-fs0/projects/CSC249ADCD08/edw/env
''',
),
),
HighThroughputExecutor(
address=address_by_hostname(),
label="ml",
max_workers=1,
provider=LocalProvider(
nodes_per_block=ml_workers,
init_blocks=1,
max_blocks=1,
launcher=AprunLauncher(overrides='-d 64 --cc depth'), # Places worker on the compute node
worker_init='''
module load miniconda-3
conda activate /lus/theta-fs0/projects/CSC249ADCD08/edw/env
''',
),
)
],
monitoring=MonitoringHub(
hub_address=address_by_hostname(),
monitoring_debug=False,
resource_monitoring_interval=10,
logdir=log_dir,
logging_endpoint=f'sqlite:///{os.path.join(log_dir, "monitoring.db")}'
),
run_dir=log_dir,
strategy=None,
)
working_dir=working_dir,
storage_access=[
FTPInTaskStaging(),
HTTPInTaskStaging(),
NoOpFileStaging()
],
worker_debug=True,
cores_per_worker=1,
heartbeat_period=2,
heartbeat_threshold=5,
poll_period=100,
provider=LocalProvider(
channel=LocalChannel(),
init_blocks=0,
min_blocks=0,
max_blocks=5,
launcher=SingleNodeLauncher(),
),
)
],
strategy='simple',
app_cache=True,
checkpoint_mode='task_exit',
retries=2,
monitoring=MonitoringHub(
hub_address="localhost",
hub_port=55055,
monitoring_debug=False,
resource_monitoring_interval=1,
))
## To discover the hash, run the command "$ shifterimg lookup <image name>"
## Image name = lsstdesc/desc-drp-stack:v19-dc2-run2.2-v5
## corresponds to
## Hash = 2d1db8fd83d62956ca0fbbe544c7f194f7aee72c106afd58ad2f1094d4c77435
##
## --image=id:$(shifterimg lookup <image name>)
## OLD WAY wrap=partial(wrap_shifter_container, image_id="lsstdesc/desc-drp-stack:v19-dc2-run2.2-v5"),
wrap=partial(
wrap_shifter_container,
image_id=
"id:2d1db8fd83d62956ca0fbbe544c7f194f7aee72c106afd58ad2f1094d4c77435"),
wrap_sql=wrap_no_op,
parsl_config=Config(
executors=[
local_executor, cori_knl_1, cori_knl_2, cori_knl_3, cori_knl_4,
cori_knl_5
],
app_cache=True,
checkpoint_mode='task_exit',
checkpoint_files=get_all_checkpoints(),
retries=2, # plus the original attempt
monitoring=MonitoringHub(
hub_address=address_by_hostname(),
hub_port=55055,
monitoring_debug=False,
resource_monitoring_enabled=True,
resource_monitoring_interval=100, # seconds
workflow_name="DRPtest")))
configuration = cori_shifter_debug_config
from parsl.addresses import address_by_hostname
# Define a configuration for using local threads and pilot jobs
#parsl.set_stream_logger()
FILENAME = 'log_monitor.txt'
parsl.set_file_logger(FILENAME, level=logging.DEBUG)
config = Config(
executors=[
ThreadPoolExecutor(
max_threads=8,
label='local_threads'
)
],
monitoring =MonitoringHub(
hub_address=address_by_hostname(),
hub_port=55055,
logging_level=logging.INFO,
resource_monitoring_interval=10,
),
strategy=None
)
parsl.clear()
parsl.load(config)
print( 'Modules Imported!')
# App that generates
@bash_app
def echo1(inputs=[], outputs=[], stdout=parsl.AUTO_LOGNAME, stderr=parsl.AUTO_LOGNAME):
command = '{exe} {output}'.format(
exe = inputs[0],
