lint: pre-commit linter application

configs/model/mnist.yaml

@@ -22,4 +22,4 @@ net:
   output_size: 10
 
 # compile model for faster training with pytorch 2.0
-compile: false
+compile_model: false

src/eval.py

@@ -1,11 +1,13 @@
 """Main evaluation script."""
 
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import hydra
 import torch
-from lightning import LightningDataModule, LightningModule, Trainer
-from lightning.pytorch.loggers import Logger
+
+if TYPE_CHECKING:
+    from lightning import LightningDataModule, LightningModule, Trainer
+    from lightning.pytorch.loggers import Logger
 from omegaconf import DictConfig
 
 from src.utils import (

src/models/components/simple_dense_net.py

@@ -1,3 +1,5 @@
+"""Simple dense neural network."""
+
 import torch
 from torch import nn
 

src/models/mnist_module.py

@@ -1,3 +1,5 @@
+"""Mnist simple model."""
+
 from typing import Any
 
 import torch
@@ -44,14 +46,15 @@ def __init__(
         net: torch.nn.Module,
         optimizer: torch.optim.Optimizer,
         scheduler: torch.optim.lr_scheduler,
-        compile: bool,
+        compile_model: bool,
     ) -> None:
         """Initialize a `MNISTLitModule`.
 
         Args:
             net: The model to train.
             optimizer: The optimizer to use for training.
             scheduler: The learning rate scheduler to use for training.
+            compile_model: Whether or not compile the model.
         """
         super().__init__()
 
@@ -185,20 +188,20 @@ def on_test_epoch_end(self) -> None:
         pass
 
     def setup(self, stage: str) -> None:
-        """Lightning hook that is called at the beginning of fit (train + validate), validate,
-        test, or predict.
+        """Lightning hook that is called at the beginning of fit (train + validate), validate, test, or predict.
 
         This is a good hook when you need to build models dynamically or adjust something about
         them. This hook is called on every process when using DDP.
 
         Args:
             stage: Either `"fit"`, `"validate"`, `"test"`, or `"predict"`.
         """
-        if self.hparams.compile and stage == "fit":
+        if self.hparams.compile_model and stage == "fit":
             self.net = torch.compile(self.net)
 
     def configure_optimizers(self) -> dict[str, Any]:
         """Choose what optimizers and learning-rate schedulers to use in your optimization.
+
         Normally you'd need one. But in the case of GANs or similar you might have multiple.
 
         Examples:

src/train.py

@@ -1,12 +1,15 @@
 """Main training script."""
 
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import hydra
-import lightning as L
+import lightning
 import torch
-from lightning import Callback, LightningDataModule, LightningModule, Trainer
-from lightning.pytorch.loggers import Logger
+
+if TYPE_CHECKING:
+    from lightning import Callback, LightningDataModule, LightningModule, Trainer
+    from lightning.pytorch.loggers import Logger
+
 from omegaconf import DictConfig
 
 from src.utils import (
@@ -37,7 +40,7 @@ def train(cfg: DictConfig) -> tuple[dict[str, Any], dict[str, Any]]:
     """
     # set seed for random number generators in pytorch, numpy and python.random
     if cfg.get("seed"):
-        L.seed_everything(cfg.seed, workers=True)
+        lightning.seed_everything(cfg.seed, workers=True)
 
     log.info(f"Instantiating datamodule <{cfg.data._target_}>")
     datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)

src/utils/__init__.py

@@ -1,7 +1,7 @@
 """This module contains utility functions and classes for the project."""
 
-from src.utils.instantiators import instantiate_callbacks, instantiate_loggers
-from src.utils.logging_utils import log_hyperparameters
-from src.utils.pylogger import RankedLogger
-from src.utils.rich_utils import enforce_tags, print_config_tree
-from src.utils.utils import extras, get_metric_value, task_wrapper
+from src.utils.instantiators import instantiate_callbacks, instantiate_loggers  # noqa
+from src.utils.logging_utils import log_hyperparameters  # noqa
+from src.utils.pylogger import RankedLogger  # noqa
+from src.utils.rich_utils import enforce_tags, print_config_tree  # noqa
+from src.utils.utils import extras, get_metric_value, task_wrapper  # noqa

src/utils/download_utils.py

@@ -1,3 +1,5 @@
+"""Utility functions aimed at downloading any data from external sources."""
+
 import cloudpathlib
 
 from src.utils import RankedLogger

src/utils/instantiators.py

@@ -1,3 +1,5 @@
+"""Module to instantiate different objects types."""
+
 import hydra
 from lightning import Callback
 from lightning.pytorch.loggers import Logger
@@ -21,7 +23,7 @@ def instantiate_callbacks(callbacks_cfg: DictConfig) -> list[Callback]:
         return callbacks
 
     if not isinstance(callbacks_cfg, DictConfig):
-        raise TypeError("Callbacks config must be a DictConfig!")
+        raise TypeError("Callbacks config must be a DictConfig!")  # noqa: TRY003
 
     for _, cb_conf in callbacks_cfg.items():
         if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf:
@@ -44,7 +46,7 @@ def instantiate_loggers(logger_cfg: DictConfig) -> list[Logger]:
         return logger
 
     if not isinstance(logger_cfg, DictConfig):
-        raise TypeError("Logger config must be a DictConfig!")
+        raise TypeError("Logger config must be a DictConfig!")  # noqa: TRY003
 
     for _, lg_conf in logger_cfg.items():
         if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:

src/utils/logging_utils.py

@@ -1,3 +1,5 @@
+"""Logging utility instantiator."""
+
 from typing import Any
 
 from lightning_utilities.core.rank_zero import rank_zero_only

src/utils/pylogger.py

@@ -1,3 +1,5 @@
+"""Code for logging on multi-GPU-friendly."""
+
 import logging
 from collections.abc import Mapping
 

src/utils/rich_utils.py

@@ -1,3 +1,5 @@
+"""Rich utils to print config tree."""
+
 from collections.abc import Sequence
 from pathlib import Path
 
@@ -85,7 +87,7 @@ def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None:
     """
     if not cfg.get("tags"):
         if "id" in HydraConfig().cfg.hydra.job:
-            raise ValueError("Specify tags before launching a multirun!")
+            raise ValueError("Specify tags before launching a multirun!")  # noqa
 
         log.warning("No tags provided in config. Prompting user to input tags...")
         tags = Prompt.ask("Enter a list of comma separated tags", default="dev")

src/utils/utils.py

@@ -78,14 +78,14 @@ def wrap(cfg: DictConfig) -> tuple[dict[str, Any], dict[str, Any]]:
             metric_dict, object_dict = task_func(cfg=cfg)
 
         # things to do if exception occurs
-        except Exception as ex:
+        except Exception as e:
             # save exception to `.log` file
             log.exception("")
 
             # some hyperparameter combinations might be invalid or cause out-of-memory errors
             # so when using hparam search plugins like Optuna, you might want to disable
             # raising the below exception to avoid multirun failure
-            raise ex
+            raise e  # noqa: TRY201
 
         # things to always do after either success or exception
         finally:
@@ -120,11 +120,7 @@ def get_metric_value(metric_dict: dict[str, Any], metric_name: str | None) -> No
         return None
 
     if metric_name not in metric_dict:
-        raise Exception(
-            f"Metric value not found! <metric_name={metric_name}>\n"
-            "Make sure metric name logged in LightningModule is correct!\n"
-            "Make sure `optimized_metric` name in `hparams_search` config is correct!"
-        )
+        raise ValueError(f"Metric value not found! <metric_name={metric_name}>\n")  # noqa: TRY003
 
     metric_value = metric_dict[metric_name].item()
     log.info(f"Retrieved metric value! <{metric_name}={metric_value}>")

tests/conftest.py

@@ -0,0 +1 @@
+"""Fixtures for your unit tests."""