restructure parallel runner

src/irace/__init__.py

@@ -14,6 +14,7 @@
 from rpy2.robjects.conversion import localconverter
 from rpy2 import rinterface as ri
 from rpy2.rinterface_lib import na_values
+from multiprocessing import Queue, Process
 
 @ro.default_converter.rpy2py.register(ri.IntSexpVector)
 def to_int(obj):
@@ -80,29 +81,57 @@ def r_to_python(data):
             raise KeyError(f'Could not proceed, type {type(data)} of rclass ({data.rclass[0]}) is not defined!')
     return data  # We reached the end of recursion
 
-def make_target_runner(py_target_runner):
-    @ri.rternalize
-    def tmp_r_target_runner(experiment, scenario):
-        py_experiment = r_to_python(experiment)
-        py_scenario = r_to_python(scenario)
-        # FIXME: How to skip this conversion?
-        py_experiment['configuration'] = py_experiment['configuration'].to_dict('records')[0]
-        # FIXME: We should also filter 'switches'
-        # Filter all the NaN from keys in the dictionary
-        py_experiment['configuration'] = OrderedDict(
-            (k,v) for k,v in py_experiment['configuration'].items() if not pd.isna(v)
-        )
+def make_target_runner(py_target_runner, check_output_target_runner, scenario):
+    def tmp_target_runner(experiment, _):
         try:
-            ret = py_target_runner(py_experiment, py_scenario)
+            ret = py_target_runner(experiment, scenario)
         except:
             traceback.print_exc()
             ret = dict(error=traceback.format_exc())
+
+        ret = check_output_target_runner(ListVector(ret), ListVector(scenario))
         return ListVector(ret)
-    return tmp_r_target_runner
+    return tmp_target_runner
 
-def check_windows(scenario):
-    if scenario.get('parallel', 1) != 1 and os.name == 'nt':
-        raise NotImplementedError('Parallel running on windows is not supported yet. Follow https://github.com/auto-optimization/iracepy/issues/16 for updates. Alternatively, use Linux or MacOS or the irace R package directly.')
+def make_target_runner_parallel(aq: Queue, rq: Queue, check_output_target_runner, scenario_a):
+    @ri.rternalize
+    def parallel_runner(*args, **kwargs):
+        try:
+            experiments = list(r_to_python(args[0]).values())
+            n = len(experiments)
+
+            for i, experiment in enumerate(experiments):
+                # FIXME: How to skip this conversion?
+                experiment['configuration'] = experiment['configuration'].to_dict('records')[0]
+                # FIXME: We should also filter 'switches'
+                # Filter all the NaN from keys in the dictionary
+                experiment['configuration'] = OrderedDict(
+                    (k,v) for k,v in experiment['configuration'].items() if not pd.isna(v)
+                )
+                aq.put((i, experiment, scenario_a[0]))
+            ans = [None for i in range(n)]
+            for _ in range(n):
+                i, res = rq.get()
+                res = check_output_target_runner(ListVector(res), scenario_a[1])
+                ans[i] = res
+            return ListVector(zip(range(len(ans)), ans)) 
+        except:
+            traceback.print_exc()
+    return parallel_runner
+
+def runner_worker(target_runner, aq: Queue, rq: Queue):
+    while True:
+        i, experiment, scenario = aq.get()
+        if i == -1:
+            break
+        res = target_runner(experiment, scenario)
+        rq.put((i, res))
+
+def check_unsupported_scenarios(scenario):
+    if scenario.get('targetRunnerRetries', 1) > 1:
+        raise NotImplementedError("targetRunnerRetries is not yet supported by the python binding although it's supported in the irace R package. We recommend you to implement retries in your target runner.")
+    if 'targetRunnerParallel' in scenario:
+        raise NotImplementedError("targetRunnerParallel is not yet supported. If you need this feature, consider opening an issue to show us some people actually want to use this.")
 
 class irace:
     # Imported R package
@@ -118,8 +147,16 @@ def __init__(self, scenario, parameters_table, target_runner):
         self.parameters = self._pkg.readParameters(text = parameters_table, digits = scenario.get('digits', 4))
         # IMPORTANT: We need to save this in a variable or it will be garbage
         # collected by Python and crash later.
-        self.r_target_runner = make_target_runner(target_runner)
-        check_windows(scenario)
+        self.target_runner = target_runner
+        self.target_aq = Queue()
+        self.target_rq = Queue()
+        self.worker_count = max(self.scenario.get('parallel', 1), 1)
+        self.workers: list[Process] = []
+        for i in range(self.worker_count):
+            self.workers.append(Process(target=runner_worker, args=(self.target_runner, self.target_aq, self.target_rq), daemon=True))
+        for worker in self.workers:
+            worker.start()
+
 
     def read_configurations(self, filename=None, text=None):
         if text is None:
@@ -151,10 +188,21 @@ def set_initial(self, x):
 
     def run(self):
         """Returns a Pandas DataFrame, one column per parameter and the row index are the configuration ID."""
-        self.scenario['targetRunner'] = self.r_target_runner
-        res = self._pkg.irace(ListVector(self.scenario), self.parameters)
+        scenario_a = [None, None]
+        self.r_target_runner_parallel = make_target_runner_parallel(self.target_aq, self.target_rq, self._pkg.check_output_target_runner, scenario_a)
+        self.scenario['targetRunnerParallel'] = self.r_target_runner_parallel
+        self.r_scenario = self._pkg.checkScenario(ListVector(self.scenario))
+        self.scenario = r_to_python(self.r_scenario)
+        self.scenario.pop('targetRunnerParallel', None)
+        scenario_a[0] = self.scenario
+        scenario_a[1] = self.r_scenario
+        res = self._pkg.irace(self.r_scenario, self.parameters)
         with localconverter(irace_converter):
             res = ro.conversion.rpy2py(res)
         # Remove metadata columns.
         res = res.loc[:, ~res.columns.str.startswith('.')]
+        for i in range(self.worker_count):
+            self.target_aq.put((-1, None, None))
+        self.target_aq.close()
+        self.target_rq.close()
         return res

tests/test_dual_annealing.py

@@ -36,18 +36,13 @@ def target_runner(experiment, scenario, lb = LB, ub = UB):
 
 # See https://mlopez-ibanez.github.io/irace/reference/defaultScenario.html
 
-if os.name == 'nt':
-    parallel = 1
-else:
-    parallel = 2
-
 scenario = dict(
     instances = instances,
     maxExperiments = 180,
     debugLevel = 3,
     seed = 123,
     digits = 5,
-    parallel= parallel, # It can run in parallel ! 
+    parallel= 2, # It can run in parallel ! 
     logFile = "")
 
 def test_run():
@@ -56,19 +51,3 @@ def test_run():
     best_confs = tuner.run()
     # FIXME: assert type Pandas DataFrame
     print(best_confs)
-
-def test_fail_windows():
-    # FIXME: remove when https://github.com/auto-optimization/iracepy/issues/16 is closed. 
-    if os.name == 'nt':
-        with pytest.raises(NotImplementedError):
-            scenario = dict(
-                instances = instances,
-                maxExperiments = 180,
-                debugLevel = 3,
-                seed = 123,
-                digits = 5,
-                parallel= 2, # It can run in parallel ! 
-                logFile = "")
-            tuner = irace(scenario, parameters_table, target_runner)
-            tuner.run()
-