Skip to main content

luml.experiments.tracker

ExperimentTracker Objects​

class ExperimentTracker()

Local experiment tracking for ML experiments.

Tracks metrics, parameters, artifacts, and traces for machine learning experiments. Supports multiple backend storage options via connection strings.

Arguments:

  • connection_string - Backend connection string. Format: 'backend://config'. Default is 'sqlite://./experiments' for local SQLite storage.

Example:

tracker = ExperimentTracker("sqlite://./my_experiments")
exp_id = tracker.start_experiment(name="my_experiment", tags=["baseline"])
tracker.log_static("learning_rate", 0.001, experiment_id=exp_id)
tracker.log_dynamic("loss", 0.5, step=1, experiment_id=exp_id)
tracker.end_experiment(exp_id)

start_experiment​

def start_experiment(experiment_id: str | None = None,
                     name: str | None = None,
                     group: str | None = None,
                     tags: list[str] | None = None) -> str

Start a new experiment tracking session.

Arguments:

  • experiment_id - Unique experiment ID. Auto-generated if not provided.
  • name - Human-readable experiment name.
  • group - Group name to organize related experiments.
  • tags - List of tags for categorizing the experiment.

Returns:

  • str - The experiment ID.

Example:

tracker = ExperimentTracker()
exp_id = tracker.start_experiment(
    name="baseline_model",
    group="image_classification",
    tags=["resnet", "baseline"]
)

end_experiment​

def end_experiment(experiment_id: str | None = None) -> None

End an active experiment tracking session.

Arguments:

  • experiment_id - ID of experiment to end. Uses current experiment if not specified.

Example:

tracker = ExperimentTracker()
exp_id = tracker.start_experiment(name="my_exp")
tracker.end_experiment(exp_id)

log_static​

def log_static(key: str, value: Any, experiment_id: str | None = None) -> None

Log static parameters or metadata (values that don't change during training).

Arguments:

  • key - Parameter name.
  • value - Parameter value (can be any serializable type).
  • experiment_id - Experiment ID. Uses current experiment if not specified.

Example:

tracker = ExperimentTracker()
exp_id = tracker.start_experiment()
tracker.log_static("learning_rate", 0.001)
tracker.log_static("model_architecture", "resnet50")
tracker.log_static("batch_size", 32)

log_dynamic​

def log_dynamic(key: str,
                value: int | float,
                step: int | None = None,
                experiment_id: str | None = None) -> None

Log time-series metrics (values that change during training).

Arguments:

  • key - Metric name.
  • value - Metric value (numeric).
  • step - Training step/epoch number.
  • experiment_id - Experiment ID. Uses current experiment if not specified.

Example:

tracker = ExperimentTracker()
exp_id = tracker.start_experiment()
for epoch in range(10):
    loss = train_epoch()
    tracker.log_dynamic("train_loss", loss, step=epoch)

log_attachment​

def log_attachment(name: str,
                   data: Any,
                   binary: bool = False,
                   experiment_id: str | None = None) -> None

Log files or artifacts to the experiment.

Arguments:

  • name - Attachment name/filename.
  • data - Data to attach (string, bytes, or file path).
  • binary - Whether data is binary. Default is False.
  • experiment_id - Experiment ID. Uses current experiment if not specified.

Example:

tracker = ExperimentTracker()
exp_id = tracker.start_experiment()
tracker.log_attachment("model_config.json", config_json)
tracker.log_attachment("plot.png", image_bytes, binary=True)

list_experiments​

def list_experiments() -> list[dict[str, Any]]

List all experiments in the backend.

Returns:

List of experiment dictionaries with metadata.

Example:

tracker = ExperimentTracker()
experiments = tracker.list_experiments()
for exp in experiments:
    print(f"{exp['id']}: {exp['name']}")
def link_to_model(model_reference: ModelReference,
                  experiment_id: str | None = None) -> None

Link experiment data to a saved model.

Attaches experiment tracking data (metrics, parameters, artifacts) to a model for reproducibility and model versioning.

Arguments:

  • model_reference - ModelReference object to link to.
  • experiment_id - Experiment ID. Uses current experiment if not specified.

Example:

from luml.integrations.sklearn import save_sklearn

tracker = ExperimentTracker()
exp_id = tracker.start_experiment(name="sklearn_model")
tracker.log_static("model_type", "RandomForest")

model_ref = save_sklearn(model, X_train)
tracker.link_to_model(model_ref)

enable_tracing​

def enable_tracing() -> None

Enable OpenTelemetry tracing for the experiment.

Sets up automatic tracing of function calls and links traces to the experiment. Useful for tracking execution flow in ML pipelines.

Example:

tracker = ExperimentTracker()
tracker.enable_tracing()
exp_id = tracker.start_experiment()
# All traced functions will be logged to this experiment