Reference for ultralytics/utils/callbacks/clearml.py

def _log_debug_samples(files, title='Debug Samples') -> None:
    """
    Log files (images) as debug samples in the ClearML task.

    Args:
        files (list): A list of file paths in PosixPath format.
        title (str): A title that groups together images with the same values.
    """
    import re

    if task := Task.current_task():
        for f in files:
            if f.exists():
                it = re.search(r'_batch(\d+)', f.name)
                iteration = int(it.groups()[0]) if it else 0
                task.get_logger().report_image(title=title,
                                               series=f.name.replace(it.group(), ''),
                                               local_path=str(f),
                                               iteration=iteration)

def _log_plot(title, plot_path) -> None:
    """
    Log an image as a plot in the plot section of ClearML.

    Args:
        title (str): The title of the plot.
        plot_path (str): The path to the saved image file.
    """
    import matplotlib.image as mpimg
    import matplotlib.pyplot as plt

    img = mpimg.imread(plot_path)
    fig = plt.figure()
    ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[])  # no ticks
    ax.imshow(img)

    Task.current_task().get_logger().report_matplotlib_figure(title=title,
                                                              series='',
                                                              figure=fig,
                                                              report_interactive=False)

def on_pretrain_routine_start(trainer):
    """Runs at start of pretraining routine; initializes and connects/ logs task to ClearML."""
    try:
        if task := Task.current_task():
            # Make sure the automatic pytorch and matplotlib bindings are disabled!
            # We are logging these plots and model files manually in the integration
            PatchPyTorchModelIO.update_current_task(None)
            PatchedMatplotlib.update_current_task(None)
        else:
            task = Task.init(project_name=trainer.args.project or 'YOLOv8',
                             task_name=trainer.args.name,
                             tags=['YOLOv8'],
                             output_uri=True,
                             reuse_last_task_id=False,
                             auto_connect_frameworks={
                                 'pytorch': False,
                                 'matplotlib': False})
            LOGGER.warning('ClearML Initialized a new task. If you want to run remotely, '
                           'please add clearml-init and connect your arguments before initializing YOLO.')
        task.connect(vars(trainer.args), name='General')
    except Exception as e:
        LOGGER.warning(f'WARNING ⚠️ ClearML installed but not initialized correctly, not logging this run. {e}')

def on_train_epoch_end(trainer):
    """Logs debug samples for the first epoch of YOLO training and report current training progress."""
    if task := Task.current_task():
        # Log debug samples
        if trainer.epoch == 1:
            _log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
        # Report the current training progress
        for k, v in trainer.validator.metrics.results_dict.items():
            task.get_logger().report_scalar('train', k, v, iteration=trainer.epoch)

def on_fit_epoch_end(trainer):
    """Reports model information to logger at the end of an epoch."""
    if task := Task.current_task():
        # You should have access to the validation bboxes under jdict
        task.get_logger().report_scalar(title='Epoch Time',
                                        series='Epoch Time',
                                        value=trainer.epoch_time,
                                        iteration=trainer.epoch)
        if trainer.epoch == 0:
            from ultralytics.utils.torch_utils import model_info_for_loggers
            for k, v in model_info_for_loggers(trainer).items():
                task.get_logger().report_single_value(k, v)

def on_val_end(validator):
    """Logs validation results including labels and predictions."""
    if Task.current_task():
        # Log val_labels and val_pred
        _log_debug_samples(sorted(validator.save_dir.glob('val*.jpg')), 'Validation')

def on_train_end(trainer):
    """Logs final model and its name on training completion."""
    if task := Task.current_task():
        # Log final results, CM matrix + PR plots
        files = [
            'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',
            *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
        files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()]  # filter
        for f in files:
            _log_plot(title=f.stem, plot_path=f)
        # Report final metrics
        for k, v in trainer.validator.metrics.results_dict.items():
            task.get_logger().report_single_value(k, v)
        # Log the final model
        task.update_output_model(model_path=str(trainer.best), model_name=trainer.args.name, auto_delete_file=False)