tessl/pypi-lightning

The Deep Learning framework to train, deploy, and ship AI products Lightning fast.

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Logging and Monitoring

Name: tessl/pypi-lightning
Author: tessl

Integration with popular experiment tracking platforms and comprehensive logging capabilities for monitoring training progress, metrics, hyperparameters, and model artifacts.

Capabilities

TensorBoard Logger

Integration with TensorBoard for visualizing training metrics, model graphs, and embeddings.

class TensorBoardLogger:
    def __init__(
        self,
        save_dir: str,
        name: str = "lightning_logs",
        version: Optional[Union[int, str]] = None,
        log_graph: bool = False,
        default_hp_metric: bool = True,
        prefix: str = "",
        sub_dir: Optional[str] = None,
        **kwargs
    ):
        """Initialize TensorBoard logger."""

Weights & Biases Logger

Integration with Weights & Biases for experiment tracking and collaboration.

class WandbLogger:
    def __init__(
        self,
        name: Optional[str] = None,
        save_dir: Optional[str] = None,
        offline: bool = False,
        id: Optional[str] = None,
        anonymous: Optional[bool] = None,
        version: Optional[str] = None,
        project: Optional[str] = None,
        log_model: Union[str, bool] = False,
        experiment: Optional[Run] = None,
        prefix: str = "",
        checkpoint_name: Optional[str] = None,
        **kwargs
    ):
        """Initialize Weights & Biases logger."""

MLFlow Logger

Integration with MLFlow for experiment tracking and model registry.

class MLFlowLogger:
    def __init__(
        self,
        experiment_name: Optional[str] = None,
        run_name: Optional[str] = None,
        tracking_uri: Optional[str] = None,
        tags: Optional[Dict[str, Any]] = None,
        save_dir: Optional[str] = "./mlruns",
        log_model: Union[str, bool] = False,
        prefix: str = "",
        artifact_location: Optional[str] = None,
        run_id: Optional[str] = None,
        **kwargs
    ):
        """Initialize MLFlow logger."""

CSV Logger

Simple CSV file logging for basic experiment tracking.

class CSVLogger:
    def __init__(
        self,
        save_dir: str,
        name: str = "lightning_logs",
        version: Optional[Union[int, str]] = None,
        prefix: str = "",
        flush_logs_every_n_steps: int = 100
    ):
        """Initialize CSV logger."""

Comet Logger

Integration with Comet ML for experiment tracking and model comparison.

class CometLogger:
    def __init__(
        self,
        api_key: Optional[str] = None,
        workspace: Optional[str] = None,
        save_dir: Optional[str] = None,
        project_name: Optional[str] = None,
        rest_api_key: Optional[str] = None,
        experiment_name: Optional[str] = None,
        experiment_key: Optional[str] = None,
        offline: bool = False,
        prefix: str = "",
        **kwargs
    ):
        """Initialize Comet logger."""

Neptune Logger

Integration with Neptune AI for experiment management and model registry.

class NeptuneLogger:
    def __init__(
        self,
        api_key: Optional[str] = None,
        project: Optional[str] = None,
        name: Optional[str] = None,
        run: Optional[Run] = None,
        log_model_checkpoints: bool = True,
        prefix: str = "",
        **kwargs
    ):
        """Initialize Neptune logger."""

Base Logger

Base class for implementing custom loggers.

class Logger:
    def __init__(self):
        """Initialize base logger."""
    
    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
        """Log metrics to the logger."""
    
    def log_hyperparams(self, params: Dict[str, Any]) -> None:
        """Log hyperparameters to the logger."""
    
    def save(self) -> None:
        """Save logged data."""
    
    def finalize(self, status: str) -> None:
        """Finalize the logger with status."""

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