Format files with pre-commit hooks and improve comments

.env.example

@@ -1,7 +1,6 @@
-# this is example of the file that can be used for storing private and user specific environment variables, like keys or system paths
-# create a file named .env (by default .env will be excluded from version control)
-# the variables declared in .env are loaded in train.py automatically
+# example of file for storing private and user specific environment variables, like keys or system paths
+# rename it to ".env" (excluded from version control by default)
+# .env is loaded by train.py automatically
 # hydra allows you to reference variables in .yaml configs with special syntax: ${oc.env:MY_VAR}
 
 MY_VAR="/home/user/my/system/path"
-MY_KEY="asdgjhawi8y23ihsghsueity23ihwd"

src/pipelines/_pipeline_wrapper.py

@@ -1,7 +1,9 @@
 import time
-from src import utils
+
 from omegaconf import DictConfig
 
+from src import utils
+
 log = utils.get_logger(__name__)
 
 

src/pipelines/testing_pipeline.py

@@ -1,26 +1,28 @@
-from typing import List, Dict, Tuple, Any
-
+from typing import Any, Dict, List, Tuple
 
 import hydra
 from omegaconf import DictConfig
 from pytorch_lightning import LightningDataModule, LightningModule, Trainer
 from pytorch_lightning.loggers import LightningLoggerBase
 
-from src.pipelines import pipeline_wrapper
 from src import utils
+from src.pipelines import pipeline_wrapper
 
 log = utils.get_logger(__name__)
 
 
 @pipeline_wrapper
 def test(cfg: DictConfig) -> Tuple[None, Dict[str, Any]]:
-    """Contains minimal example of the testing pipeline. Evaluates given checkpoint on a testset.
+    """Evaluates given checkpoint on a datamodule testset.
+
+    This method is wrapped in @pipeline_wrapper decorator which applies extra utilities
+    before and after the call.
 
     Args:
         cfg (DictConfig): Configuration composed by Hydra.
 
     Returns:
-        Tuple[None, Dict[str, Any]]
+        Tuple[None, Dict[str, Any]]: A tuple of None and dictionary with all instantiated objects.
     """
 
     assert cfg.ckpt_path

src/pipelines/training_pipeline.py

@@ -1,4 +1,4 @@
-from typing import Optional, List, Dict, Tuple, Any
+from typing import Any, Dict, List, Optional, Tuple
 
 import hydra
 from omegaconf import DictConfig
@@ -13,7 +13,9 @@
 
 @pipeline_wrapper
 def train(cfg: DictConfig) -> Tuple[Optional[float], Dict[str, Any]]:
-    """Contains the training pipeline. Can additionally evaluate model on a testset, using best
+    """Contains the training pipeline.
+
+    Can additionally evaluate model on a testset, using best
     weights obtained during training.
 
     This method is wrapped in @pipeline_wrapper decorator which applies extra utilities

src/test.py

@@ -1,8 +1,7 @@
-import pyrootutils
 import hydra
+import pyrootutils
 from omegaconf import DictConfig
 
-
 root = pyrootutils.setup_root(indicator=".git", search_from=__file__)
 
 

src/train.py

@@ -1,8 +1,7 @@
-import pyrootutils
 import hydra
+import pyrootutils
 from omegaconf import DictConfig
 
-
 # project root setup
 # allows for calling this script from any place
 root = pyrootutils.setup_root(