Parallelization on Cluster#
Expand to copy examples/1_basics/7_parallelization_cluster.py (top right)
import numpy as np
from ConfigSpace import Configuration, ConfigurationSpace, Float
from dask.distributed import Client
from dask_jobqueue import SLURMCluster
from smac import BlackBoxFacade, Scenario
__copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
__license__ = "3-clause BSD"
class Branin(object):
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x0 = Float("x0", (-5, 10), default=-5, log=False)
x1 = Float("x1", (0, 15), default=2, log=False)
cs.add([x0, x1])
return cs
def train(self, config: Configuration, seed: int = 0) -> float:
"""Branin function
Parameters
----------
config : Configuration
Contains two continuous hyperparameters, x0 and x1
seed : int, optional
Not used, by default 0
Returns
-------
float
Branin function value
"""
x0 = config["x0"]
x1 = config["x1"]
a = 1.0
b = 5.1 / (4.0 * np.pi**2)
c = 5.0 / np.pi
r = 6.0
s = 10.0
t = 1.0 / (8.0 * np.pi)
ret = a * (x1 - b * x0**2 + c * x0 - r) ** 2 + s * (1 - t) * np.cos(x0) + s
return ret
if __name__ == "__main__":
model = Branin()
# Scenario object specifying the optimization "environment"
scenario = Scenario(model.configspace, deterministic=True, n_trials=100, trial_walltime_limit=100)
# Create cluster
n_workers = 4 # Use 4 workers on the cluster
# Please note that the number of workers is directly set in the
# cluster / client. `scenario.n_workers` is ignored in this case.
cluster = SLURMCluster(
# This is the partition of our slurm cluster.
queue="cpu_short",
# Your account name
# account="myaccount",
cores=1,
memory="1 GB",
# Walltime limit for each worker. Ensure that your function evaluations
# do not exceed this limit.
# More tips on this here: https://jobqueue.dask.org/en/latest/advanced-tips-and-tricks.html#how-to-handle-job-queueing-system-walltime-killing-workers
walltime="00:10:00",
processes=1,
log_directory="tmp/smac_dask_slurm",
# if you would like to limit the resources consumption of each function evaluation with pynisher, you need to
# set nanny as False
# Otherwise, an error `daemonic processes are not allowed to have children` will raise!
nanny=False, # if you do not use pynisher to limit the memory/time usage, feel free to set this one as True
)
cluster.scale(jobs=n_workers)
# Dask will create n_workers jobs on the cluster which stay open.
# Then, SMAC/Dask will schedule individual runs on the workers like on your local machine.
client = Client(
address=cluster,
)
# Instead, you can also do
# client = cluster.get_client()
# Now we use SMAC to find the best hyperparameters
smac = BlackBoxFacade(
scenario,
model.train, # We pass the target function here
overwrite=True, # Overrides any previous results that are found that are inconsistent with the meta-data
dask_client=client,
)
incumbent = smac.optimize()
# Get cost of default configuration
default_cost = smac.validate(model.configspace.get_default_configuration())
print(f"Default cost: {default_cost}")
# Let's calculate the cost of the incumbent
incumbent_cost = smac.validate(incumbent)
print(f"Incumbent cost: {incumbent_cost}")
Description#
An example of applying SMAC to optimize Branin using parallelization via Dask client on a
SLURM cluster. If you do not want to use a cluster but your local machine, set dask_client
to None and pass n_workers to the Scenario.
Sometimes, the submitted jobs by the slurm client might be cancelled once it starts. In that case, you could try to start your job from a computing node
On some clusters you cannot spawn new jobs when running a SLURMCluster inside a
job instead of on the login node. No obvious errors might be raised but it can hang silently.
Sometimes you need to modify your launch command which can be done with
SLURMCluster.job_class.submit_command.
Here we optimize the synthetic 2d function Branin. We use the black-box facade because it is designed for black-box function optimization. The black-box facade uses a Gaussian Process as its surrogate model. The facade works best on a numerical hyperparameter configuration space and should not be applied to problems with large evaluation budgets (up to 1000 evaluations).
import numpy as np
from ConfigSpace import Configuration, ConfigurationSpace, Float
from dask.distributed import Client
from dask_jobqueue import SLURMCluster
from smac import BlackBoxFacade, Scenario
__copyright__ = "Copyright 2025, Leibniz University Hanover, Institute of AI"
__license__ = "3-clause BSD"
class Branin(object):
@property
def configspace(self) -> ConfigurationSpace:
cs = ConfigurationSpace(seed=0)
x0 = Float("x0", (-5, 10), default=-5, log=False)
x1 = Float("x1", (0, 15), default=2, log=False)
cs.add([x0, x1])
return cs
def train(self, config: Configuration, seed: int = 0) -> float:
"""Branin function
Parameters
----------
config : Configuration
Contains two continuous hyperparameters, x0 and x1
seed : int, optional
Not used, by default 0
Returns
-------
float
Branin function value
"""
x0 = config["x0"]
x1 = config["x1"]
a = 1.0
b = 5.1 / (4.0 * np.pi**2)
c = 5.0 / np.pi
r = 6.0
s = 10.0
t = 1.0 / (8.0 * np.pi)
ret = a * (x1 - b * x0**2 + c * x0 - r) ** 2 + s * (1 - t) * np.cos(x0) + s
return ret
if __name__ == "__main__":
model = Branin()
# Scenario object specifying the optimization "environment"
scenario = Scenario(model.configspace, deterministic=True, n_trials=100, trial_walltime_limit=100)
# Create cluster
n_workers = 4 # Use 4 workers on the cluster
# Please note that the number of workers is directly set in the
# cluster / client. `scenario.n_workers` is ignored in this case.
cluster = SLURMCluster(
# This is the partition of our slurm cluster.
queue="cpu_short",
# Your account name
# account="myaccount",
cores=1,
memory="1 GB",
# Walltime limit for each worker. Ensure that your function evaluations
# do not exceed this limit.
# More tips on this here: https://jobqueue.dask.org/en/latest/advanced-tips-and-tricks.html#how-to-handle-job-queueing-system-walltime-killing-workers
walltime="00:10:00",
processes=1,
log_directory="tmp/smac_dask_slurm",
# if you would like to limit the resources consumption of each function evaluation with pynisher, you need to
# set nanny as False
# Otherwise, an error `daemonic processes are not allowed to have children` will raise!
nanny=False, # if you do not use pynisher to limit the memory/time usage, feel free to set this one as True
)
cluster.scale(jobs=n_workers)
# Dask will create n_workers jobs on the cluster which stay open.
# Then, SMAC/Dask will schedule individual runs on the workers like on your local machine.
client = Client(
address=cluster,
)
# Instead, you can also do
# client = cluster.get_client()
# Now we use SMAC to find the best hyperparameters
smac = BlackBoxFacade(
scenario,
model.train, # We pass the target function here
overwrite=True, # Overrides any previous results that are found that are inconsistent with the meta-data
dask_client=client,
)
incumbent = smac.optimize()
# Get cost of default configuration
default_cost = smac.validate(model.configspace.get_default_configuration())
print(f"Default cost: {default_cost}")
# Let's calculate the cost of the incumbent
incumbent_cost = smac.validate(incumbent)
print(f"Incumbent cost: {incumbent_cost}")