def test_1():
x = HTEX(
label='htex',
provider=LocalProvider(channel=LocalChannel),
address="127.0.0.1",
)
task_p, result_p, command_p = x.start()
print(task_p, result_p, command_p)
print("Executor initialized : ", x)
args = [2]
kwargs = {}
f1 = x.submit(double, *args, **kwargs)
print("Sent task with :", f1)
args = [2]
kwargs = {}
f2 = x.submit(fail, *args, **kwargs)
print("hi")
while True:
stop = input("Stop ? (y/n)")
if stop == "y":
break
print("F1: {}, f2: {}".format(f1.done(), f2.done()))
x.shutdown()
from funcx_endpoint.endpoint.utils.config import Config
from funcx_endpoint.executors import HighThroughputExecutor
CONDA_ENV = os.environ["WORKER_CONDA_ENV"]
print(f"Using conda env:{CONDA_ENV} for worker_init")
config = Config(
executors=[
HighThroughputExecutor(
provider=LocalProvider(
init_blocks=1,
min_blocks=0,
max_blocks=1,
# FIX ME: Update conda.sh file to match your paths
worker_init=(
"source ~/anaconda3/etc/profile.d/conda.sh; "
f"conda activate {CONDA_ENV}; "
"python3 --version"
),
),
)
],
funcx_service_address="https://api2.funcx.org/v2",
)
# For now, visible_to must be a list of URNs for globus auth users or groups, e.g.:
# urn:globus:auth:identity:{user_uuid}
# urn:globus:groups:id:{group_uuid}
meta = {
"name": "0.3.3",
HighThroughputExecutor(
max_workers_per_node=2,
worker_debug=False,
address=address_by_interface('bond0.144'),
provider=SlurmProvider(
partition='debug', # Partition / QOS
# We request all hyperthreads on a node.
launcher=SrunLauncher(overrides='-c 272'),
# string to prepend to #SBATCH blocks in the submit
# script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache'
scheduler_options=user_opts['cori']['scheduler_options'],
# Command to be run before starting a worker, such as:
# 'module load Anaconda; source activate parsl_env'.
worker_init=user_opts['cori']['worker_init'],
# Increase timeout as Cori's scheduler may be slow
# to respond
cmd_timeout=120,
# Scale between 0-1 blocks with 2 nodes per block
nodes_per_block=2,
init_blocks=0,
min_blocks=0,
max_blocks=1,
# Hold blocks for 10 minutes
walltime='00:10:00',
),
),
from parsl.utils import RepresentationMixin
from funcx_endpoint.executors import HighThroughputExecutor
_DEFAULT_EXECUTORS = [HighThroughputExecutor()]
class Config(RepresentationMixin):
"""Specification of FuncX configuration options.
Parameters
----------
executors : list of Executors
A list of executors which serve as the backend for function execution.
As of 0.2.2, this list should contain only one executor.
Default: [HighThroughtputExecutor()]
funcx_service_address: str
URL address string of the funcX service to which the Endpoint should connect.
Default: 'https://api2.funcx.org/v2'
heartbeat_period: int (seconds)
The interval at which heartbeat messages are sent from the endpoint to the
funcx-web-service
Default: 30s
heartbeat_threshold: int (seconds)
Seconds since the last hearbeat message from the funcx-web-service after which
the connection is assumed to be disconnected.
Default: 120s
config = Config(
executors=[
HighThroughputExecutor(
max_workers_per_node=2,
worker_debug=False,
address=address_by_hostname(),
provider=CobaltProvider(
queue='default',
account=user_opts['cooley']['account'],
launcher=MpiExecLauncher(),
# string to prepend to #COBALT blocks in the submit
# script to the scheduler eg: '#COBALT -t 50'
scheduler_options=user_opts['cooley']['scheduler_options'],
# Command to be run before starting a worker, such as:
# 'module load Anaconda; source activate funcx_env'.
worker_init=user_opts['cooley']['worker_init'],
# Scale between 0-1 blocks with 2 nodes per block
nodes_per_block=2,
init_blocks=0,
min_blocks=0,
max_blocks=1,
# Hold blocks for 30 minutes
walltime='00:30:00',
),
)
], )
# fmt: onrom funcx_endpoint.endpoint.utils.config import Config
config = Config(
executors=[
HighThroughputExecutor(
max_workers_per_node=10,
address=address_by_hostname(),
scheduler_mode='soft',
worker_mode='singularity_reuse',
container_type='singularity',
container_cmd_options="-H /home/$USER",
provider=SlurmProvider(
partition='broadwl',
launcher=SrunLauncher(),
# string to prepend to #SBATCH blocks in the submit
# script to the scheduler eg: '#SBATCH --constraint=knl,quad,cache'
scheduler_options=user_opts['midway']['scheduler_options'],
# Command to be run before starting a worker, such as:
# 'module load Anaconda; source activate parsl_env'.
worker_init=user_opts['midway']['worker_init'],
# Scale between 0-1 blocks with 2 nodes per block
nodes_per_block=2,
init_blocks=0,
min_blocks=0,
max_blocks=1,
# Hold blocks for 30 minutes
walltime='00:30:00'),
)
], )
from funcx_endpoint.endpoint.utils.config import Config
from funcx_endpoint.executors import HighThroughputExecutor
from parsl.providers import LocalProvider
...
config = Config(executors=[
HighThroughputExecutor(
label="fe.cs.uchicago",
address=address_by_hostname(),
provider=SlurmProvider(
channel=LocalChannel(),
nodes_per_block=NODES_PER_JOB,
init_blocks=1,
partition="general",
launcher=SrunLauncher(
overrides=(f"hostname; srun --ntasks={TOTAL_WORKERS} "
f"--ntasks-per-node={WORKERS_PER_NODE} "
f"--gpus-per-task=rtx2080ti:{GPUS_PER_WORKER} "
f"--gpu-bind=map_gpu:{GPU_MAP}")),
walltime="01:00:00",
),
)
], )
}
config = Config(
executors=[
HighThroughputExecutor(
max_workers_per_node=1,
worker_debug=False,
address=address_by_hostname(),
provider=TorqueProvider(
queue='normal',
launcher=AprunLauncher(overrides="-b -- bwpy-environ --"),
# string to prepend to #SBATCH blocks in the submit
scheduler_options=user_opts['bluewaters']['scheduler_options'],
# Command to be run before starting a worker, such as:
# 'module load bwpy; source activate funcx env'.
worker_init=user_opts['bluewaters']['worker_init'],
# Scale between 0-1 blocks with 2 nodes per block
nodes_per_block=2,
init_blocks=0,
min_blocks=0,
max_blocks=1,
# Hold blocks for 30 minutes
walltime='00:30:00'),
)
], )
# fmt: on
config = Config(
executors=[
HighThroughputExecutor(
label="fe.cs.uchicago",
worker_debug=False,
address=address_by_hostname(),
provider=SlurmProvider(
partition='general',
# Launch 4 managers per node, each bound to 1 GPU
# This is a hack. We use hostname ; to terminate the srun command, and
# start our own
#
# DO NOT MODIFY unless you know what you are doing.
launcher=SrunLauncher(
overrides=(f'hostname; srun --ntasks={TOTAL_WORKERS} '
f'--ntasks-per-node={WORKERS_PER_NODE} '
f'--gpus-per-task=rtx2080ti:{GPUS_PER_WORKER} '
f'--gpu-bind=map_gpu:{GPU_MAP}')),
# Scale between 0-1 blocks with 2 nodes per block
nodes_per_block=NODES_PER_JOB,
init_blocks=0,
min_blocks=0,
max_blocks=1,
# Hold blocks for 30 minutes
walltime='00:30:00',
),
)
], )
config = Config(
executors=[
HighThroughputExecutor(
max_workers_per_node=2,
worker_debug=False,
address=address_by_hostname(),
provider=SlurmProvider(
partition=user_opts['frontera']['partition'],
launcher=SrunLauncher(),
# Enter scheduler_options if needed
scheduler_options=user_opts['frontera']['scheduler_options'],
# Command to be run before starting a worker, such as:
# 'module load Anaconda; source activate parsl_env'.
worker_init=user_opts['frontera']['worker_init'],
# Add extra time for slow scheduler responses
cmd_timeout=60,
# Scale between 0-1 blocks with 2 nodes per block
nodes_per_block=2,
init_blocks=0,
min_blocks=0,
max_blocks=1,
# Hold blocks for 30 minutes
walltime='00:30:00',
),
)
], )
HighThroughputExecutor(
max_workers_per_node=1,
worker_debug=False,
address=address_by_hostname(),
scheduler_mode='soft',
worker_mode='singularity_reuse',
container_type='singularity',
container_cmd_options="-H /home/$USER",
provider=CobaltProvider(
queue='debug-flat-quad',
account=user_opts['theta']['account'],
launcher=AprunLauncher(overrides="-d 64"),
# string to prepend to #COBALT blocks in the submit
# script to the scheduler eg: '#COBALT -t 50'
scheduler_options=user_opts['theta']['scheduler_options'],
# Command to be run before starting a worker, such as:
# 'module load Anaconda; source activate funcx_env'.
worker_init=user_opts['theta']['worker_init'],
# Scale between 0-1 blocks with 2 nodes per block
nodes_per_block=2,
init_blocks=0,
min_blocks=0,
max_blocks=1,
# Hold blocks for 30 minutes
walltime='00:30:00'
),
)
}
}
config = Config(
executors=[
HighThroughputExecutor(
label='Kubernetes_funcX',
max_workers_per_node=1,
address=address_by_route(),
scheduler_mode='hard',
container_type='docker',
strategy=KubeSimpleStrategy(max_idletime=3600),
provider=KubernetesProvider(
init_blocks=0,
min_blocks=0,
max_blocks=2,
init_cpu=1,
max_cpu=4,
init_mem="1024Mi",
max_mem="4096Mi",
image=user_opts['kube']['image'],
worker_init=user_opts['kube']['worker_init'],
namespace=user_opts['kube']['namespace'],
incluster_config=False,
),
)
],
heartbeat_period=15,
heartbeat_threshold=200,
log_dir='.',
)
from parsl.providers import LocalProvider
from funcx_endpoint.endpoint.utils.config import Config
from funcx_endpoint.executors import HighThroughputExecutor
config = Config(
executors=[
HighThroughputExecutor(provider=LocalProvider(
init_blocks=1,
min_blocks=0,
max_blocks=1,
), )
],
funcx_service_address="https://api2.funcx.org/v2",
)
# For now, visible_to must be a list of URNs for globus auth users or groups, e.g.:
# urn:globus:auth:identity:{user_uuid}
# urn:globus:groups:id:{group_uuid}
meta = {
"name": "$name",
"description": "",
"organization": "",
"department": "",
"public": False,
"visible_to": [],
}
# Set ncpus=32, otherwise it defaults to 1 on Polaris
'scheduler_options': '',
}
}
config = Config(
executors=[
HighThroughputExecutor(
max_workers_per_node=1,
strategy=SimpleStrategy(max_idletime=300),
# IP of Polaris testbed login node
address='10.230.2.72',
provider=PBSProProvider(
launcher=SingleNodeLauncher(),
queue='workq',
scheduler_options=user_opts['polaris']['scheduler_options'],
# Command to be run before starting a worker, such as:
# 'module load Anaconda; source activate parsl_env'.
worker_init=user_opts['polaris']['worker_init'],
cpus_per_node=32,
walltime='01:00:00',
nodes_per_block=1,
init_blocks=0,
min_blocks=0,
max_blocks=1,
),
)
], )
# fmt: on