[Revert] Recombine toml files (#21)

endure.py

@@ -12,8 +12,8 @@
 
 
 class EndureDriver:
-    def __init__(self, config):
-        self.config = config
+    def __init__(self, conf):
+        self.config = conf
 
         logging.basicConfig(
             format=config["log"]["format"], datefmt=config["log"]["datefmt"]
@@ -33,20 +33,14 @@ def run(self):
             "LTuneTrain": LTuneTrainJob,
             "BayesianBaseline": BayesianPipeline,
         }
-
         jobs_list = self.config["job"]["to_run"]
         for job_name in jobs_list:
-            job = jobs.get(job_name)
+            job = jobs.get(job_name, None)
             if job is None:
-                driver.log.warn(f"No job associated with {job_name}")
+                self.log.warn(f"No job associated with {job_name}")
                 continue
-
-            conf_path = os.path.join("jobs", "config", f"{job_name}.toml")
-            with open(conf_path) as jobfid:
-                job_config = toml.load(jobfid)
-
-            job_instance = job(job_config)
-            job_instance.run()
+            job = job(config)
+            job.run()
 
         self.log.info("All jobs finished, exiting")
 

endure.toml

@@ -175,8 +175,12 @@ acquisition_function = "ExpectedImprovement"
 beta_value = 0.3
 # model_type can take values - "Classic", "QFixed", "YZHybrid", "KHybrid"
 model_type = "KHybrid"
+# determines how many workloads do we want to test using the bayesian pipeline
+multi_jobs_number = 100
+multi_job_file = "design_comparison.csv"
 
 [job.BayesianOptimization.database]
+data_dir = "databases"
 # This will take value 0 and 1 where 1 means write each cost and run details into the MySqlLite database
 # and 0 means run details are not stored in the database
 write_to_db = 1
@@ -199,12 +203,6 @@ z1 = 0.190
 q = 0.545
 w = 0.202
 
-[job.BayesianOptimization.bounds]
-h_min = 1.0
-h_max = 10.0
-T_min = 2.0
-T_max = 31.0
-
 # =============================================================================
 # HEADER LSM
 #   Generic LSM settings including maximum bounds, system settings, starting

jobs/bayesian_pipeline.py

@@ -51,7 +51,7 @@ def __init__(self, conf: dict) -> None:
         self.bounds = LSMBounds()
         self.max_levels = self.bounds.max_considered_levels
         self.cf: EndureCost = EndureCost(self.max_levels)
-        # self.log: logging.Logger = logging.getLogger(self.config["log"]["name"])
+        self.log: logging.Logger = logging.getLogger(self.config["log"]["name"])
 
         self.system: System = System(**self.bayesian_setting["system"])
         self.workload: Workload = Workload(**self.bayesian_setting["workload"])
@@ -129,8 +129,8 @@ def optimization_loop(self, train_x, train_y, best_y):
             new_designs, costs = self.evaluate_new_candidates(new_candidates)
             train_x, train_y, best_y, best_designs = self.update_training_data(train_x, train_y, new_candidates, costs,
                                                                                best_designs)
-        #     self.log.debug(f"Iteration {i + 1}/{self.num_iterations} complete")
-        # self.log.debug("Bayesian Optimization completed")
+            self.log.debug(f"Iteration {i + 1}/{self.num_iterations} complete")
+        self.log.debug("Bayesian Optimization completed")
         return best_designs
 
     def _initialize_feature_list(self, bounds):