def __init__(self,
channels=[],
worker_init='',
cmd_timeout=30,
parallelism=1,
move_files=None):
self.channels = channels
self._label = 'ad-hoc'
self.worker_init = worker_init
self.cmd_timeout = cmd_timeout
self.parallelism = 1
self.move_files = move_files
self.launcher = SimpleLauncher()
self.init_blocks = self.min_blocks = self.max_blocks = len(channels)
# This will be overridden by the DFK to the rundirs.
self.script_dir = "."
# In ad-hoc mode, nodes_per_block should be 1
self.nodes_per_block = 1
# Dictionary that keeps track of jobs, keyed on job_id
self.resources = {}
self.least_loaded = self._least_loaded()
logger.debug("AdHoc provider initialized")
def test_one_block():
oneshot_provider = OneShotLocalProvider(
channel=LocalChannel(),
init_blocks=0,
min_blocks=0,
max_blocks=10,
launcher=SimpleLauncher(),
)
config = Config(
executors=[
HighThroughputExecutor(
label="htex_local",
worker_debug=True,
cores_per_worker=1,
provider=oneshot_provider,
)
],
strategy='simple',
)
parsl.load(config)
f = app()
f.result()
parsl.clear()
assert oneshot_provider.recorded_submits == 1
def local_config(log_dir: str, max_workers: int, prefetch: int = 0) -> Config:
"""Single node with a single task per worker
Args:
log_dir: Path to store monitoring DB and parsl logs
max_workers: Maximum number of concurrent tasks
prefetch: Number of tasks for ML workers to prefetch for inference
Returns:
(Config) Parsl configuration
"""
return Config(
executors=[
HighThroughputExecutor(
address=address_by_hostname(),
label="qc-worker",
max_workers=max_workers,
prefetch_capacity=prefetch,
cpu_affinity='block',
provider=LocalProvider(
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(), # Places worker on the launch node
),
),
HighThroughputExecutor(
address=address_by_hostname(),
label="ml-worker",
max_workers=1,
prefetch_capacity=prefetch,
provider=LocalProvider(
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(), # Places worker on the launch node
)
)
],
run_dir=log_dir,
strategy='simple',
max_idletime=15.
)
def fresh_config():
return Config(
executors=[
HighThroughputExecutor(
label="htex_local",
worker_debug=True,
cores_per_worker=1,
provider=LocalProvider(
channel=LocalChannel(),
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(),
),
)
],
strategy=None,
)
def theta_nwchem_config(
choice: str,
log_dir: str,
nodes_per_nwchem: int = 2,
total_nodes: int = int(os.environ.get("COBALT_JOBSIZE", 1))
) -> Config:
"""Theta configuration to run NWChem
Args:
choice: Choice of the runtime configuration
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
"""
assert total_nodes % nodes_per_nwchem == 0, "NWChem node count not a multiple of nodes per task"
nwc_workers = total_nodes // nodes_per_nwchem
if choice == "htex":
qc_exec = HighThroughputExecutor(
address=address_by_hostname(),
label="qc",
max_workers=nwc_workers,
cores_per_worker=1e-6,
provider=LocalProvider(
nodes_per_block=1,
init_blocks=0,
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
''',
),
)
elif choice == 'thread':
qc_exec = ThreadPoolExecutor(label='qc', max_threads=nwc_workers)
else:
raise ValueError(f'Choice "{choice}" not recognized ')
return Config(executors=[qc_exec],
run_dir=log_dir,
strategy='simple',
max_idletime=15.)
from parsl.providers import LocalProvider
from parsl.channels import LocalChannel
from parsl.launchers import SimpleLauncher
from parsl.config import Config
from parsl.executors import ExtremeScaleExecutor
config = Config(
executors=[
ExtremeScaleExecutor(label="Extreme_Local",
worker_debug=True,
ranks_per_node=4,
provider=LocalProvider(
channel=LocalChannel(),
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(),
))
],
strategy=None,
)
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:
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
ml_prefetch: Number of tasks for ML workers to prefect
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=[
HighThroughputExecutor(
address=address_by_hostname(),
label="qc",
max_workers=nwc_workers,
cores_per_worker=1e-6,
provider=LocalProvider(
nodes_per_block=1,
init_blocks=0,
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,
prefetch_capacity=ml_prefetch,
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(),
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.)
def local_config(log_dir: str, max_workers: int, prefetch: int = 0) -> Config:
"""Single node with a single task per worker
Args:
log_dir: Path to store monitoring DB and parsl logs
max_workers: Maximum number of concurrent tasks
prefetch: Number of tasks for ML workers to prefetch for inference
Returns:
(Config) Parsl configuration
"""
return Config(
executors=[
HighThroughputExecutor(
address=address_by_hostname(),
label="qc-worker",
max_workers=max_workers,
prefetch_capacity=prefetch,
cpu_affinity='block',
provider=LocalProvider(
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(
), # Places worker on the launch node
),
),
HighThroughputExecutor(
address=address_by_hostname(),
label="ml-worker-tensorflow",
max_workers=1,
prefetch_capacity=prefetch,
provider=LocalProvider(
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
worker_init=
'sleep 30', # Give enough time for other workers to exit (memory!)
launcher=SimpleLauncher(
), # Places worker on the launch node
)),
HighThroughputExecutor(
address=address_by_hostname(),
label=
"ml-worker-tensorflow-infer", # Something about training and then running a model causes issues?
max_workers=1,
prefetch_capacity=prefetch,
provider=LocalProvider(
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
worker_init=
'sleep 30', # Give enough time for other workers to exit (memory!)
launcher=SimpleLauncher(
), # Places worker on the launch node
)),
HighThroughputExecutor(
address=address_by_hostname(),
label="ml-worker-torch",
max_workers=1,
prefetch_capacity=prefetch,
provider=LocalProvider(
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
worker_init=
'sleep 30', # Give enough time for other workers to exit (memory!)
launcher=SimpleLauncher(
), # Places worker on the launch node
))
],
run_dir=log_dir,
strategy='simple',
max_idletime=15.)
def multisite_nwchem_config() -> Config:
"""Experimental multi-site configuration"""
return Config(
retries=1,
executors=[
HighThroughputExecutor(
address=address_by_hostname(),
label="qc",
max_workers=8, # One task per node
provider=CobaltProvider(
cmd_timeout=120,
nodes_per_block=8,
account='CSC249ADCD08',
queue='debug-cache-quad',
walltime="1:00:00",
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(), # Places worker on the launch node
scheduler_options='#COBALT --attrs enable_ssh=1',
worker_init='''
module load miniconda-3
export PATH=~/software/psi4/bin:$PATH
conda activate /lus/theta-fs0/projects/CSC249ADCD08/edw/env
# NWChem settings
export PATH="/home/lward/software/nwchem-6.8.1/bin/LINUX64:$PATH"
module load atp
export MPICH_GNI_MAX_EAGER_MSG_SIZE=16384
export MPICH_GNI_MAX_VSHORT_MSG_SIZE=10000
export MPICH_GNI_MAX_EAGER_MSG_SIZE=131072
export MPICH_GNI_NUM_BUFS=300
export MPICH_GNI_NDREG_MAXSIZE=16777216
export MPICH_GNI_MBOX_PLACEMENT=nic
export MPICH_GNI_LMT_PATH=disabled
export COMEX_MAX_NB_OUTSTANDING=6
export LD_LIBRARY_PATH=/opt/intel/compilers_and_libraries_2018.0.128/linux/compiler/lib/intel64_lin:$LD_LIBRARY_PATH
''',
),
),
HighThroughputExecutor(
address='localhost', # Using an SSH tunnel
worker_ports=(54382, 54008),
label="ml",
max_workers=1,
working_dir='/homes/lward/parsl',
worker_logdir_root='/homes/lward/parsl',
provider=LocalProvider(
channel=SSHChannel('lambda5.cels.anl.gov', script_dir='/home/lward/parsl'),
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(),
worker_init='''
source /homes/lward/miniconda3/etc/profile.d/conda.sh
conda activate colmena_full
export CUDA_VISIBLE_DEVICES=17 # Pins to a GPU worker
''',
),
)
],
strategy=None,
)
ThreadPoolExecutor(label="local_threads", max_threads=4)
],
strategy=None,
)
theta_nwchem_config = Config(
executors=[
HighThroughputExecutor(
address=address_by_hostname(),
label="htex",
max_workers=int(os.environ.get("COBALT_JOBSIZE", 1)),
provider=LocalProvider(
nodes_per_block=1,
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher(), # Places worker on the launch node
worker_init='''
module load miniconda-3
export PATH=~/software/psi4/bin:$PATH
conda activate /lus/theta-fs0/projects/CSC249ADCD08/colmena/env
''',
),
),
ThreadPoolExecutor(label="local_threads", max_threads=4)
],
strategy=None,
)
theta_interleaved_config = Config(
executors=[
HighThroughputExecutor(
def test():
import parsl
from pyscf import lib, gto, scf
import numpy as np
import pandas as pd
import logging
from parsl.config import Config
from parsl.providers import LocalProvider
from parsl.channels import LocalChannel
from parsl.launchers import SimpleLauncher
from parsl.executors import ExtremeScaleExecutor
ncore = 4
config = Config(
executors=[
ExtremeScaleExecutor(label="Extreme_Local",
worker_debug=True,
ranks_per_node=ncore,
provider=LocalProvider(
channel=LocalChannel(),
init_blocks=1,
max_blocks=1,
launcher=SimpleLauncher()))
],
strategy=None,
)
parsl.load(config)
mol = gto.M(atom='H 0. 0. 0.; H 0. 0. 2.0',
unit='bohr',
ecp='bfd',
basis='bfd_vtz')
mf = scf.RHF(mol).run()
mol.output = None
mol.stdout = None
mf.output = None
mf.stdout = None
mf.chkfile = None
from pyqmc import ExpCuspFunction, GaussianFunction, MultiplyWF, PySCFSlaterRHF, JastrowSpin, initial_guess, EnergyAccumulator
from pyqmc.accumulators import PGradTransform, LinearTransform
nconf = 1600
basis = [
ExpCuspFunction(2.0, 1.5),
GaussianFunction(0.5),
GaussianFunction(2.0),
GaussianFunction(.25),
GaussianFunction(1.0),
GaussianFunction(4.0),
GaussianFunction(8.0)
]
wf = MultiplyWF(PySCFSlaterRHF(mol, mf), JastrowSpin(mol, basis, basis))
coords = initial_guess(mol, nconf)
energy_acc = EnergyAccumulator(mol)
pgrad_acc = PGradTransform(
energy_acc, LinearTransform(wf.parameters, ['wf2acoeff', 'wf2bcoeff']))
from pyqmc.optsr import gradient_descent
gradient_descent(wf,
coords,
pgrad_acc,
vmc=distvmc,
vmcoptions={
'npartitions': ncore,
'nsteps': 100,
'nsteps_per': 100
})
def theta_persistent(log_dir: str,
nodes_per_nwchem: int = 1,
qc_nodes: int = 8,
ml_nodes: int = 8,
ml_prefetch: int = 0) -> Config:
"""Configuration where the application is persistent and sits on the Theta login node.
Nodes will be requested from Cobalt using separate jobs for ML and QC tasks.
Args:
nodes_per_nwchem: Number of nodes per NWChem computation
log_dir: Path to store monitoring DB and parsl logs
qc_nodes: Number of nodes dedicated to QC tasks
ml_prefetch: Number of tasks for ML workers to prefetch for inference
Returns:
(Config) Parsl configuration
"""
return Config(
retries=8,
executors=[
HighThroughputExecutor(
address=address_by_hostname(),
label="qc",
max_workers=qc_nodes // nodes_per_nwchem,
prefetch_capacity=ml_prefetch,
provider=CobaltProvider(
account='CSC249ADCD08',
queue='debug-cache-quad' if qc_nodes <= 8 else None,
walltime='00:60:00',
nodes_per_block=qc_nodes,
init_blocks=0,
max_blocks=1,
launcher=SimpleLauncher(),
cmd_timeout=360,
worker_init='''
module load miniconda-3
conda activate /lus/theta-fs0/projects/CSC249ADCD08/edw/env
export OMP_NUM_THREADS=64
export KMP_INIT_AT_FORK=FALSE
export PYTHONPATH=$PYTHONPATH:$(pwd)
export PATH="/lus/theta-fs0/projects/CSC249ADCD08/software/nwchem-6.8.1/bin/LINUX64:$PATH"
mkdir -p scratch # For the NWChem tasks
pwd
which nwchem
hostname
module load atp
export MPICH_GNI_MAX_EAGER_MSG_SIZE=16384
export MPICH_GNI_MAX_VSHORT_MSG_SIZE=10000
export MPICH_GNI_MAX_EAGER_MSG_SIZE=131072
export MPICH_GNI_NUM_BUFS=300
export MPICH_GNI_NDREG_MAXSIZE=16777216
export MPICH_GNI_MBOX_PLACEMENT=nic
export MPICH_GNI_LMT_PATH=disabled
export COMEX_MAX_NB_OUTSTANDING=6
export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64_lin/:/opt/intel/compilers_and_libraries_2020.0.166/linux/compiler/lib/intel64_lin:$LD_LIBRARY_PATH
''',
),
),
HighThroughputExecutor(
address=address_by_hostname(),
label="ml",
max_workers=1,
prefetch_capacity=ml_prefetch,
provider=CobaltProvider(
account='CSC249ADCD08',
queue='debug-flat-quad',
nodes_per_block=ml_nodes,
scheduler_options='#COBALT --attrs enable_ssh=1',
walltime='00:60:00',
init_blocks=0,
max_blocks=1,
cmd_timeout=360,
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''',
),
)
],
run_dir=log_dir,
strategy='simple',
max_idletime=15.)
