reference/generated/torchrl.record.loggers.get_logger

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get_logger

torchrl.record.loggers.get_logger(logger_type: Literal['tensorboard', 'csv', 'wandb', 'mlflow', 'trackio', ''] | None, logger_name: str, experiment_name: str, *, state_dict: Mapping[str, Any] | None = None, service_backend: Literal['direct', 'process', 'ray'] = 'direct', service_backend_options: dict[str, Any] | None = None, use_ray_service: bool = False, ray_actor_options: dict[str, Any] | None = None, **kwargs) → Logger | None

Get a logger instance of the provided logger_type.

Parameters:

• logger_type (str) – One of tensorboard / csv / wandb / mlflow / trackio. If empty, None is returned.

• logger_name (str) – Name to be used as a log_dir

• experiment_name (str) – Name of the experiment

Keyword Arguments:

• state_dict (Mapping[str, Any] or None, optional) – Saved logger state from state_dict(). Restores the saved name, directory and counters for CSV/TensorBoard, or resumes the saved W&B run with strict resume="must" semantics. Other logger types currently reject this option before opening a service. Defaults to None (create a logger normally).

• service_backend – One of "direct", "process", or "ray".

• service_backend_options – Process or Ray initialization options.

• use_ray_service – Deprecated compatibility flag for the Ray backend.

• ray_actor_options – Deprecated spelling for Ray actor options.

• **kwargs – May contain wandb_kwargs, mlflow_kwargs, or trackio_kwargs.