from autogluon.tabular import TabularDataset, TabularPredictor

train_data = TabularDataset("https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv")
test_data = TabularDataset("https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv")

predictor = TabularPredictor(label="class", path=str("./ag_path")).fit(
    train_data=train_data,
    time_limit=int(60 * 2),
    presets="best_quality",
    num_bag_folds=2,
    num_bag_sets=1,
    verbosity=2,
    dynamic_stacking=False,
    fit_strategy="parallel",
)

predictor.predict(
    test_data, resource_config=dict(num_cpus=8, num_gpus=1, usage_strategy="parallel")
)
predictor.predict_proba(
    test_data, resource_config=dict(num_cpus=8, num_gpus=1, usage_strategy="parallel")
)

# Also works for the LB
predictor.leaderboard(
    test_data, resource_config=dict(num_cpus=8, usage_strategy="parallel")
)