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Referenz fĂźr ultralytics/utils/callbacks/mlflow.py

Hinweis

Diese Datei ist verfügbar unter https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/utils/callbacks/mlflow .py. Wenn du ein Problem entdeckst, hilf bitte mit, es zu beheben, indem du einen Pull Request 🛠️ einreichst. Vielen Dank 🙏!



ultralytics.utils.callbacks.mlflow.sanitize_dict(x)

Sanitize dictionary keys by removing parentheses and converting values to floats.

Quellcode in ultralytics/utils/callbacks/mlflow.py
def sanitize_dict(x):
    """Sanitize dictionary keys by removing parentheses and converting values to floats."""
    return {k.replace("(", "").replace(")", ""): float(v) for k, v in x.items()}



ultralytics.utils.callbacks.mlflow.on_pretrain_routine_end(trainer)

Protokolliere die Trainingsparameter am Ende der Pre-Training-Routine in MLflow.

Diese Funktion richtet die MLflow-Protokollierung anhand von Umgebungsvariablen und Trainer-Argumenten ein. Sie setzt die Tracking-URI, Name des Experiments und den Namen des Laufs ein und startet dann den MLflow-Lauf, falls er noch nicht aktiv ist. Schließlich protokolliert sie die Parameter des Trainers.

Parameter:

Name Typ Beschreibung Standard
trainer BaseTrainer

Das Trainingsobjekt mit Argumenten und Parametern zum Protokollieren.

erforderlich
Global

mlflow: Das importierte mlflow-Modul, das fĂźr die Protokollierung verwendet werden soll.

Umgebungsvariablen

MLFLOW_TRACKING_URI: Die URI für das MLflow-Tracking. Wenn sie nicht gesetzt ist, lautet der Standardwert "runs/mlflow". MLFLOW_EXPERIMENT_NAME: Der Name des MLflow-Experiments. Wenn er nicht gesetzt ist, ist er standardmäßig trainer.args.project. MLFLOW_RUN: Der Name des MLflow-Laufs. Wenn er nicht gesetzt ist, ist der Standardwert trainer.args.name. MLFLOW_KEEP_RUN_ACTIVE: Boolescher Wert, der angibt, ob der MLflow-Lauf nach dem Ende des Trainings aktiv bleiben soll.

Quellcode in ultralytics/utils/callbacks/mlflow.py
def on_pretrain_routine_end(trainer):
    """
    Log training parameters to MLflow at the end of the pretraining routine.

    This function sets up MLflow logging based on environment variables and trainer arguments. It sets the tracking URI,
    experiment name, and run name, then starts the MLflow run if not already active. It finally logs the parameters
    from the trainer.

    Args:
        trainer (ultralytics.engine.trainer.BaseTrainer): The training object with arguments and parameters to log.

    Global:
        mlflow: The imported mlflow module to use for logging.

    Environment Variables:
        MLFLOW_TRACKING_URI: The URI for MLflow tracking. If not set, defaults to 'runs/mlflow'.
        MLFLOW_EXPERIMENT_NAME: The name of the MLflow experiment. If not set, defaults to trainer.args.project.
        MLFLOW_RUN: The name of the MLflow run. If not set, defaults to trainer.args.name.
        MLFLOW_KEEP_RUN_ACTIVE: Boolean indicating whether to keep the MLflow run active after the end of training.
    """
    global mlflow

    uri = os.environ.get("MLFLOW_TRACKING_URI") or str(RUNS_DIR / "mlflow")
    LOGGER.debug(f"{PREFIX} tracking uri: {uri}")
    mlflow.set_tracking_uri(uri)

    # Set experiment and run names
    experiment_name = os.environ.get("MLFLOW_EXPERIMENT_NAME") or trainer.args.project or "/Shared/YOLOv8"
    run_name = os.environ.get("MLFLOW_RUN") or trainer.args.name
    mlflow.set_experiment(experiment_name)

    mlflow.autolog()
    try:
        active_run = mlflow.active_run() or mlflow.start_run(run_name=run_name)
        LOGGER.info(f"{PREFIX}logging run_id({active_run.info.run_id}) to {uri}")
        if Path(uri).is_dir():
            LOGGER.info(f"{PREFIX}view at http://127.0.0.1:5000 with 'mlflow server --backend-store-uri {uri}'")
        LOGGER.info(f"{PREFIX}disable with 'yolo settings mlflow=False'")
        mlflow.log_params(dict(trainer.args))
    except Exception as e:
        LOGGER.warning(f"{PREFIX}WARNING ⚠️ Failed to initialize: {e}\n" f"{PREFIX}WARNING ⚠️ Not tracking this run")



ultralytics.utils.callbacks.mlflow.on_train_epoch_end(trainer)

Protokolliere die Trainingsmetriken am Ende jeder Trainingsepoche in MLflow.

Quellcode in ultralytics/utils/callbacks/mlflow.py
def on_train_epoch_end(trainer):
    """Log training metrics at the end of each train epoch to MLflow."""
    if mlflow:
        mlflow.log_metrics(
            metrics={
                **sanitize_dict(trainer.lr),
                **sanitize_dict(trainer.label_loss_items(trainer.tloss, prefix="train")),
            },
            step=trainer.epoch,
        )



ultralytics.utils.callbacks.mlflow.on_fit_epoch_end(trainer)

Protokolliere die Trainingsmetriken am Ende jeder Anpassungsepoche in MLflow.

Quellcode in ultralytics/utils/callbacks/mlflow.py
def on_fit_epoch_end(trainer):
    """Log training metrics at the end of each fit epoch to MLflow."""
    if mlflow:
        mlflow.log_metrics(metrics=sanitize_dict(trainer.metrics), step=trainer.epoch)



ultralytics.utils.callbacks.mlflow.on_train_end(trainer)

Protokolliere die Modellartefakte am Ende des Trainings.

Quellcode in ultralytics/utils/callbacks/mlflow.py
def on_train_end(trainer):
    """Log model artifacts at the end of the training."""
    if not mlflow:
        return
    mlflow.log_artifact(str(trainer.best.parent))  # log save_dir/weights directory with best.pt and last.pt
    for f in trainer.save_dir.glob("*"):  # log all other files in save_dir
        if f.suffix in {".png", ".jpg", ".csv", ".pt", ".yaml"}:
            mlflow.log_artifact(str(f))
    keep_run_active = os.environ.get("MLFLOW_KEEP_RUN_ACTIVE", "False").lower() == "true"
    if keep_run_active:
        LOGGER.info(f"{PREFIX}mlflow run still alive, remember to close it using mlflow.end_run()")
    else:
        mlflow.end_run()
        LOGGER.debug(f"{PREFIX}mlflow run ended")

    LOGGER.info(
        f"{PREFIX}results logged to {mlflow.get_tracking_uri()}\n{PREFIX}disable with 'yolo settings mlflow=False'"
    )





Created 2023-11-12, Updated 2024-06-17
Authors: glenn-jocher (7), Burhan-Q (1), Laughing-q (1)