luxonis_ml.tracker

package documentation

Experiment tracking facade for Luxonis ML workflows.

The luxonis_ml.tracker package exports LuxonisTracker, a unified logging interface for TensorBoard, Weights & Biases, and MLflow. Training and evaluation code can log metrics, hyperparameters, images, matrices, and artifacts through one API while choosing the enabled backends at runtime.

Example

Start a TensorBoard-backed run and log a scalar metric.

from luxonis_ml.tracker import LuxonisTracker

tracker = LuxonisTracker(
    project_name="training",
    run_name="baseline",
    is_tensorboard=True,
)
tracker.log_metric("loss", 0.42, step=1)
tracker.close()

Note

The tracker uses optional dependencies. Install luxonis-ml[tracker] and the backend SDKs required by the integrations you enable.

See Also

luxonis_ml.tracker.tracker for the logging implementation and luxonis_ml.tracker.mlflow_plugins for MLflow request-header support.

Module	`mlflow_plugins`	Undocumented
Module	`tracker`	Undocumented

From __init__.py:

Class	`LuxonisRequestHeaderProvider`	Undocumented
Class	`LuxonisTracker`	Logger wrapper for TensorBoard, WandB, and MLflow.