Revert 1023 (#1123)

* Revert "Raise error if resource limitation and parallelization is requested (#1023)"

This reverts commit cb1cd2f.

* update examples

* update daks_queue version

docs/advanced_usage/9_parallelism.rst

@@ -21,39 +21,6 @@ SMAC supports multiple workers natively via Dask. Just specify ``n_workers`` in
     When using multiple workers, SMAC is not reproducible anymore.
 
 
-.. warning ::
-
-    You cannot use resource limitation (pynisher, via the `scenario` arguments `trail_walltime_limit` and `trial_memory_limit`).
-    This is because pynisher works by running your function inside of a subprocess.
-    Once in the subprocess, the resources will be limited for that process before running your function. 
-    This does not work together with pickling - which is required by dask to schedule jobs on the cluster, even on a local one.
-
-
-.. warning ::
-
-    Start/run SMAC inside ``if __name__ == "__main__"`` in your script otherwise Dask is not able to correctly
-    spawn jobs and probably this runtime error will be raised:
-
-    .. code-block ::
-
-        RuntimeError: 
-            An attempt has been made to start a new process before the
-            current process has finished its bootstrapping phase.
-
-            This probably means that you are not using fork to start your
-            child processes and you have forgotten to use the proper idiom
-            in the main module:
-
-                if __name__ == '__main__':
-                    freeze_support()
-                    ...
-
-            The "freeze_support()" line can be omitted if the program
-            is not going to be frozen to produce an executable.
-
-
-
-
 Running on a Cluster
 --------------------
 You can also pass a custom dask client, e.g. to run on a slurm cluster.

examples/1_basics/7_parallelization_cluster.py

@@ -6,6 +6,9 @@
 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
+
 :warning: 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.
 
@@ -77,7 +80,7 @@ def train(self, config: Configuration, seed: int = 0) -> float:
     model = Branin()
 
     # Scenario object specifying the optimization "environment"
-    scenario = Scenario(model.configspace, deterministic=True, n_trials=100)
+    scenario = Scenario(model.configspace, deterministic=True, n_trials=100, trial_walltime_limit=100)
 
     # Create cluster
     n_workers = 4  # Use 4 workers on the cluster
@@ -97,6 +100,10 @@ def train(self, config: Configuration, seed: int = 0) -> float:
         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)
 

setup.py

@@ -66,7 +66,7 @@ def read_file(filepath: str) -> str:
         "scikit-learn>=1.1.2",
         "pyrfr>=0.9.0",
         "dask[distributed]",
-        "dask_jobqueue",
+        "dask_jobqueue>=0.8.2",
         "emcee>=3.0.0",
         "regex",
         "pyyaml",

smac/facade/abstract_facade.py

@@ -463,18 +463,6 @@ def _validate(self) -> None:
         # Make sure the same acquisition function is used
         assert self._acquisition_function == self._acquisition_maximizer._acquisition_function
 
-        if isinstance(self._runner, DaskParallelRunner) and (
-            self.scenario.trial_walltime_limit is not None or self.scenario.trial_memory_limit is not None
-        ):
-            # This is probably due to pickling dask jobs
-            raise ValueError(
-                "Parallelization via Dask cannot be used in combination with limiting "
-                "the resources "
-                "of the target function via `scenario.trial_walltime_limit` or "
-                "`scenario.trial_memory_limit`. Set those to `None` if you want "
-                "parallelization. "
-            )
-
     def _get_signature_arguments(self) -> list[str]:
         """Returns signature arguments, which are required by the intensifier."""
         arguments = []