upgrade to 2.0-style for ray.tune

search_params.py

@@ -171,13 +171,11 @@ def prepare_retrain_config(best_config, best_log_dir, retrain):
         best_config.merge_train_val = False
 
 
-def load_static_data(config, merge_train_val=False):
+def load_static_data(config):
     """Preload static data once for multiple trials.
 
     Args:
         config (AttributeDict): Config of the experiment.
-        merge_train_val (bool, optional): Whether to merge the training and validation data.
-            Defaults to False.
 
     Returns:
         dict: A dict of static data containing datasets, classes, and word_dict.
@@ -187,7 +185,7 @@ def load_static_data(config, merge_train_val=False):
         test_data=config.test_file,
         val_data=config.val_file,
         val_size=config.val_size,
-        merge_train_val=merge_train_val,
+        merge_train_val=config.merge_train_val,
         tokenize_text="lm_weight" not in config.network_config,
         remove_no_label_data=config.remove_no_label_data,
     )
@@ -231,7 +229,7 @@ def retrain_best_model(exp_name, best_config, best_log_dir, retrain):
     with open(os.path.join(checkpoint_dir, "params.yml"), "w") as fp:
         yaml.dump(dict(best_config), fp)
 
-    data = load_static_data(best_config, merge_train_val=best_config.merge_train_val)
+    data = load_static_data(best_config)
 
     if retrain:
         logging.info(f"Re-training with best config: \n{best_config}")
@@ -303,7 +301,7 @@ def main():
     config = init_search_params_spaces(config, parameter_columns, prefix="")
     parser.set_defaults(**config)
     config = AttributeDict(vars(parser.parse_args()))
-    # no need to include validation during parameter search
+    # Validation sets are mandatoray during parameter search
     config.merge_train_val = False
     config.mode = "min" if config.val_metric == "Loss" else "max"
 
@@ -344,20 +342,32 @@ def main():
         Path(config.config).stem if config.config else config.model_name,
         datetime.now().strftime("%Y%m%d%H%M%S"),
     )
-    analysis = tune.run(
-        tune.with_parameters(train_libmultilabel_tune, **data),
-        search_alg=init_search_algorithm(config.search_alg, metric=f"val_{config.val_metric}", mode=config.mode),
-        scheduler=scheduler,
-        storage_path=config.result_dir,
-        num_samples=config.num_samples,
-        resources_per_trial={"cpu": config.cpu_count, "gpu": config.gpu_count},
-        progress_reporter=reporter,
-        config=config,
-        name=exp_name,
+
+    tuner = tune.Tuner(
+        tune.with_resources(
+            tune.with_parameters(train_libmultilabel_tune, **data),
+            resources={"cpu": config.cpu_count, "gpu": config.gpu_count},
+        ),
+        param_space=config,
+        tune_config=tune.TuneConfig(
+            scheduler=scheduler,
+            num_samples=config.num_samples,
+            search_alg=init_search_algorithm(
+                search_alg=config.search_alg,
+                metric=f"val_{config.val_metric}",
+                mode=config.mode,
+            ),
+        ),
+        run_config=ray_train.RunConfig(
+            name=exp_name,
+            storage_path=config.result_dir,
+            progress_reporter=reporter,
+        ),
     )
+    results = tuner.fit()
     # Save best model after parameter search.
-    best_trial = analysis.get_best_trial(metric=f"val_{config.val_metric}", mode=config.mode, scope="all")
-    retrain_best_model(exp_name, best_trial.config, best_trial.local_path, retrain=not config.no_retrain)
+    best_result = results.get_best_result(metric=f"val_{config.val_metric}", mode=config.mode, scope="all")
+    retrain_best_model(exp_name, best_result.config, best_result.path, retrain=not config.no_retrain)
 
 
 if __name__ == "__main__":