Merge pull request #24 from boostcampaitech7/feat-23/hotfix-data-path

Feat 23/hotfix data path

config/config.py

@@ -20,14 +20,14 @@ def __init__(self):
 class DatasetConfig:
     """Dataset-related configuration."""
     def __init__(self):
-        self.data_path = "/home/data/"
+        self.data_path = "/data/ephemeral/home/data/"
         # self.transform_mode = 'albumentation'
 
 
 class ExperimentConfig:
     """Experiment-related configuration."""
     def __init__(self):
-        self.save_dir = "/home/logs/"
+        self.save_dir = "/data/ephemeral/home/logs/"
         self.num_gpus = 1
         self.max_epochs = 100
         self.num_workers = 2  # number of cpus workers in dataloader

engine/test_runner.py

@@ -10,7 +10,7 @@ def run_test(config, ckpt_dir):
 
     # Define the trainer for testing
     pred_callback = PredictionCallback(f"{config.dataset.data_path}/test.csv", ckpt_dir, config.model.model_name)
-    trainer_test = Trainer(callbacks=[pred_callback], logger=False)
+    trainer_test = Trainer(callbacks=[pred_callback], logger=False, enable_progress_bar=False,)
     best_model = LightningModule.load_from_checkpoint(f"{ckpt_dir}/pltrainer.ckpt")
     # Conduct testing with the loaded model
     trainer_test.test(best_model, dataloaders=test_loader)

engine/tuner.py

@@ -25,10 +25,12 @@ def train_func(config_dict):  # Note that config_dict is dict here passed by pbt
         accelerator='gpu',
         devices=config_dict['experiment']['num_gpus'],
         strategy='ddp',
+        logger=False,
         callbacks=[TuneReportCheckpointCallback(
             metrics={"val_loss": "val_loss", "val_acc": "val_acc"},
             filename="pltrainer.ckpt", on="validation_end",
             )],
+        enable_progress_bar=False,
         )
 
     trainer.fit(model, train_dataloaders=train_loader, val_dataloaders=val_loader)
@@ -72,7 +74,7 @@ def _define_run_config(self):
                 num_to_keep=4,
                 checkpoint_score_attribute="val_loss",
             ),
-            storage_path="/tmp/ray_results",
+            storage_path=f"{self.config.experiment.save_dir}/ray_results",
             callbacks=[WandbLoggerCallback(project=self.config.model.model_name)],
             verbose=1,
         )