Generate a unique filename for generated experiment files to avoid collisions in parallel runs

Author: fjwillemsen

kernel_tuner/backends/hypertuner.py

@@ -111,7 +111,7 @@ def compile(self, kernel_instance):
 
         name = kernel_instance.name if len(kernel_instance.name) > 0 else kernel_instance.kernel_source.kernel_name
         experiments_filepath = generate_experiment_file(name, path, searchspace_strategies, applications, gpus, 
-                                                        override=override, overwrite_existing_file=True)
+                                                        override=override, generate_unique_file=True, overwrite_existing_file=True)
         return str(experiments_filepath)
 
     def start_event(self):
@@ -134,6 +134,9 @@ def run_kernel(self, func, gpu_args=None, threads=None, grid=None, stream=None):
         # run the methodology to get a fitness score for this configuration
         scores = get_strategy_scores(str(experiments_filepath))
         self.last_score = scores[list(scores.keys())[0]]['score']
+
+        # remove the experiments file
+        experiments_filepath.unlink()
 
     def memset(self, allocation, value, size):
         return super().memset(allocation, value, size)

kernel_tuner/hyper.py

@@ -75,8 +75,12 @@ def put_if_not_present(target_dict, key, value):
     kwargs['verify'] = None
     arguments = [target_strategy]
 
+    # IMPORTANT when running this script in parallel, always make sure the below name is unique among your runs!
+    # e.g. when parallalizing over the hypertuning of multiple strategies, use the strategy name
+    name = f"hyperparamtuning_{target_strategy.lower()}"
+
     # execute the hyperparameter tuning
-    result, env = kernel_tuner.tune_kernel('hyperparamtuning', None, [], arguments, hyper_params, *args, lang='Hypertuner',
+    result, env = kernel_tuner.tune_kernel(name, None, [], arguments, hyper_params, *args, lang='Hypertuner',
                                     objective='score', objective_higher_is_better=True, iterations=iterations, **kwargs)
 
     # remove the temporary cachefile and return only unique results in order