# type: ignore
# flake8: noqa

import modin.pandas as pd
import ray
from ray import tune

if not ray.is_initialized():
    ray.init(num_cpus=4)


def inner_experiment(max_depth):
    # to actually use the max_depth config
    print(max_depth)

    X = pd.DataFrame({"a": [1, 2, 3, 4], "b": [4, 3, 2, 1]})

    # this line breaks everything
    Z = X + X


def hyper_param_tuning():
    hyper_params = {"max_depth": tune.grid_search([1])}

    tune.run(inner_experiment,
             config=hyper_params,
             resources_per_trial=tune.PlacementGroupFactory([
                 {
                     "CPU": 1,
                     "GPU": 0
                 },
             ], ))


if __name__ == "__main__":
    hyper_param_tuning()

5a8fb5573def7162b9395dd7de381b94d2b18b2d
<mask token>


def inner_experiment(max_depth):
    print(max_depth)
    X = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [4, 3, 2, 1]})
    Z = X + X


<mask token>

<mask token>


def inner_experiment(max_depth):
    print(max_depth)
    X = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [4, 3, 2, 1]})
    Z = X + X


def hyper_param_tuning():
    hyper_params = {'max_depth': tune.grid_search([1])}
    tune.run(inner_experiment, config=hyper_params, resources_per_trial=
        tune.PlacementGroupFactory([{'CPU': 1, 'GPU': 0}]))


<mask token>

<mask token>
if not ray.is_initialized():
    ray.init(num_cpus=4)


def inner_experiment(max_depth):
    print(max_depth)
    X = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [4, 3, 2, 1]})
    Z = X + X


def hyper_param_tuning():
    hyper_params = {'max_depth': tune.grid_search([1])}
    tune.run(inner_experiment, config=hyper_params, resources_per_trial=
        tune.PlacementGroupFactory([{'CPU': 1, 'GPU': 0}]))


if __name__ == '__main__':
    hyper_param_tuning()

import modin.pandas as pd
import ray
from ray import tune
if not ray.is_initialized():
    ray.init(num_cpus=4)


def inner_experiment(max_depth):
    print(max_depth)
    X = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [4, 3, 2, 1]})
    Z = X + X


def hyper_param_tuning():
    hyper_params = {'max_depth': tune.grid_search([1])}
    tune.run(inner_experiment, config=hyper_params, resources_per_trial=
        tune.PlacementGroupFactory([{'CPU': 1, 'GPU': 0}]))


if __name__ == '__main__':
    hyper_param_tuning()

# type: ignore
# flake8: noqa

import modin.pandas as pd
import ray
from ray import tune

if not ray.is_initialized():
    ray.init(num_cpus=4)


def inner_experiment(max_depth):
    # to actually use the max_depth config
    print(max_depth)

    X = pd.DataFrame({"a": [1, 2, 3, 4], "b": [4, 3, 2, 1]})

    # this line breaks everything
    Z = X + X


def hyper_param_tuning():
    hyper_params = {"max_depth": tune.grid_search([1])}

    tune.run(inner_experiment,
             config=hyper_params,
             resources_per_trial=tune.PlacementGroupFactory([
                 {
                     "CPU": 1,
                     "GPU": 0
                 },
             ], ))


if __name__ == "__main__":
    hyper_param_tuning()
