Add logging config

src/batdetect2/train/config.py

@@ -9,6 +9,7 @@ from batdetect2.train.augmentations import (
     AugmentationsConfig,
 )
 from batdetect2.train.clips import ClipingConfig
+from batdetect2.train.logging import CSVLoggerConfig, LoggerConfig
 from batdetect2.train.losses import LossConfig
 
 __all__ = [
@@ -59,6 +60,8 @@ class TrainingConfig(BaseConfig):
 
     trainer: TrainerConfig = Field(default_factory=TrainerConfig)
 
+    logger: LoggerConfig = Field(default_factory=CSVLoggerConfig)
+
 
 def load_train_config(
     path: data.PathLike,

src/batdetect2/train/logging.py

@@ -0,0 +1,104 @@
+from typing import Annotated, Literal, Optional, Union
+
+from lightning.pytorch.loggers import Logger
+from pydantic import Field
+from soundevent.data import PathLike
+
+from batdetect2.configs import BaseConfig, load_config
+
+DEFAULT_LOGS_DIR: str = "logs"
+
+
+class DVCLiveConfig(BaseConfig):
+    logger_type: Literal["dvclive"] = "dvclive"
+    dir: str = DEFAULT_LOGS_DIR
+    run_name: Optional[str] = None
+    prefix: str = ""
+    log_model: Union[bool, Literal["all"]] = False
+    monitor_system: bool = False
+
+
+class CSVLoggerConfig(BaseConfig):
+    logger_type: Literal["csv"] = "csv"
+    save_dir: str = DEFAULT_LOGS_DIR
+    name: Optional[str] = "logs"
+    version: Optional[str] = None
+    flush_logs_every_n_steps: int = 100
+
+
+class TensorBoardLoggerConfig(BaseConfig):
+    logger_type: Literal["tensorboard"] = "tensorboard"
+    save_dir: str = DEFAULT_LOGS_DIR
+    name: Optional[str] = "default"
+    version: Optional[str] = None
+    log_graph: bool = False
+    flush_logs_every_n_steps: Optional[int] = None
+
+
+LoggerConfig = Annotated[
+    Union[DVCLiveConfig, CSVLoggerConfig, TensorBoardLoggerConfig],
+    Field(discriminator="logger_type"),
+]
+
+
+def create_dvclive_logger(config: DVCLiveConfig) -> Logger:
+    try:
+        from dvclive.lightning import DVCLiveLogger  # type: ignore
+    except ImportError as error:
+        raise ValueError(
+            "DVCLive is not installed and cannot be used for logging"
+            "Make sure you have it installed by running `pip install dvclive`"
+            "or `uv add dvclive`"
+        ) from error
+
+    return DVCLiveLogger(
+        dir=config.dir,
+        run_name=config.run_name,
+        prefix=config.prefix,
+        log_model=config.log_model,
+        monitor_system=config.monitor_system,
+    )
+
+
+def create_csv_logger(config: CSVLoggerConfig) -> Logger:
+    from lightning.pytorch.loggers import CSVLogger
+
+    return CSVLogger(
+        save_dir=config.save_dir,
+        name=config.name,
+        version=config.version,
+        flush_logs_every_n_steps=config.flush_logs_every_n_steps,
+    )
+
+
+def create_tensorboard_logger(config: TensorBoardLoggerConfig) -> Logger:
+    from lightning.pytorch.loggers import TensorBoardLogger
+
+    return TensorBoardLogger(
+        save_dir=config.save_dir,
+        name=config.name,
+        version=config.version,
+        log_graph=config.log_graph,
+        flush_logs_every_n_steps=config.flush_logs_every_n_steps,
+    )
+
+
+LOGGER_FACTORY = {
+    "dvclive": create_dvclive_logger,
+    "csv": create_csv_logger,
+    "tensorboard": create_tensorboard_logger,
+}
+
+
+def build_logger(config: LoggerConfig) -> Logger:
+    """
+    Creates a logger instance from a validated Pydantic config object.
+    """
+    logger_type = config.logger_type
+
+    if logger_type not in LOGGER_FACTORY:
+        raise ValueError(f"Unknown logger type: {logger_type}")
+
+    creation_func = LOGGER_FACTORY[logger_type]
+
+    return creation_func(config)

src/batdetect2/train/train.py

@@ -23,6 +23,7 @@ from batdetect2.train.dataset import (
     collate_fn,
 )
 from batdetect2.train.lightning import TrainingModule
+from batdetect2.train.logging import build_logger
 from batdetect2.train.losses import build_loss
 
 __all__ = [
@@ -47,6 +48,7 @@ def train(
     **trainer_kwargs,
 ) -> None:
     config = config or TrainingConfig()
+
     if model_path is None:
         if preprocessor is None:
             preprocessor = build_preprocessor()
@@ -81,9 +83,12 @@ def train(
         config=config,
     )
 
+    logger = build_logger(config.logger)
+
     trainer = Trainer(
-        **config.trainer.model_dump(exclude_none=True),
+        **config.trainer.model_dump(exclude_none=True, exclude={"logger"}),
         callbacks=callbacks,
+        logger=logger,
         **trainer_kwargs,
     )