` import numpy as np
import pandas as pd

from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

from lightautoml.tasks import Task
from lightautoml.automl.presets.tabular_presets import TabularAutoML

import clearml

cml_task = clearml.Task.get_task(clearml.config.get_remote_task_id())
logger = cml_task.get_logger()

data = pd.read_csv("./examples/data/sampled_app_train.csv")
....

automl = TabularAutoML(task=Task('binary'))
cml_task.connect(automl)
oof_pred = automl.fit_predict(train_data, roles={"target": "TARGET"})
logger.report_single_value("ROCAUC test", roc_auc_score(test_data["TARGET"].values, test_pred.data[:, 0]))
logger.flush() `
TabularAutoML is a custom class that uses some popular frameworks deep inside
https://github.com/sb-ai-lab/LightAutoML