Logger

Logger#

class braintools.trainer.Logger#

Abstract base class for all loggers.

Subclass this to implement custom logging backends.

name#

Name of the experiment/run.

Type:

str

version#

Version or run ID.

Type:

str

finalize(status='success')[source]#

Finalize logging (called at end of training).

Parameters:

status (str) – Final status (‘success’, ‘failed’, ‘interrupted’).

log_artifact(local_path, artifact_path=None)[source]#

Log an artifact (file).

Parameters:

  • local_path (str) – Path to the local file.

  • artifact_path (str | None) – Path in artifact storage.

property log_dir: str | None#

Directory for this specific run’s logs.

log_graph(model, input_array=None)[source]#

Log model graph/architecture.

Parameters:

  • model (Any) – The model to log.

  • input_array (Any) – Sample input for tracing.

abstractmethod log_hyperparams(params)[source]#

Log hyperparameters.

Parameters:

params (Dict[str, Any]) – Dictionary of hyperparameter names to values.

log_image(key, images, step=None)[source]#

Log images.

Parameters:

  • key (str) – Image key/tag.

  • images (Any) – Images to log (numpy array or list).

  • step (int | None) – Global step number.

abstractmethod log_metrics(metrics, step=None)[source]#

Log metrics.

Parameters:

  • metrics (Dict[str, float]) – Dictionary of metric names to values.

  • step (int | None) – Global step number.

log_text(key, text, step=None)[source]#

Log text.

Parameters:

  • key (str) – Text key/tag.

  • text (str) – Text to log.

  • step (int | None) – Global step number.

property name: str#

Experiment name.

property root_dir: str | None#

Root directory for logs.

save()[source]#

Save/flush any buffered data.

property version: str | None#

Experiment version/run ID.