super-gradients/src/super_gradients/examples/loggers_examples/clearml_logger_example.py

from super_gradients.training import Trainer, models
from super_gradients.training.metrics.classification_metrics import Accuracy, Top5
from super_gradients.training.dataloaders.dataloaders import cifar10_train, cifar10_val


trainer = Trainer(experiment_name="demo-clearml-logger")
model = models.get("resnet18", num_classes=10)

training_params = {
    "max_epochs": 20,
    "lr_updates": [5, 10, 15],
    "lr_decay_factor": 0.1,
    "lr_mode": "step",
    "initial_lr": 0.1,
    "loss": "cross_entropy",
    "optimizer": "SGD",
    "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
    "train_metrics_list": [Accuracy(), Top5()],
    "valid_metrics_list": [Accuracy(), Top5()],
    "metric_to_watch": "Accuracy",
    "greater_metric_to_watch_is_better": True,
    "sg_logger": "clearml_sg_logger",
    "sg_logger_params": {
        "project_name": "project_name",  # ClearML project name
        "save_checkpoints_remote": True,
        "save_tensorboard_remote": True,
        "save_logs_remote": True,
    },
}

trainer.train(model=model, training_params=training_params, train_loader=cifar10_train(), valid_loader=cifar10_val())