[tune] enable isort

.isort.cfg

@@ -19,7 +19,7 @@ filter_files=True
 # python/ray/setup-dev.py
 # For the rest we will gradually remove them from the blacklist as we
 # reformat the code to follow the style guide.
-skip_glob=doc/*,python/ray/__init__.py,python/ray/setup-dev.py,python/build/*,python/ray/cloudpickle/*,python/ray/thirdparty_files/*,python/ray/_private/thirdparty/*,python/ray/_private/runtime_env/agent/thirdparty_files/*,python/ray/dag/*.py,ci/*,python/ray/_private/*,python/ray/air/*,dashboard/*,python/ray/includes/*,python/ray/internal/*,python/ray/ray_operator/*,python/ray/scripts/*,python/ray/serve/generated/serve_pb2.py,python/ray/sgd/*,python/ray/streaming/*,python/ray/tests/*,python/ray/tests/*,python/ray/tune/*,python/ray/util/*,python/ray/workers/*,python/ray/workflow/*,rllib/*,release/*,
+skip_glob=doc/*,python/ray/__init__.py,python/ray/setup-dev.py,python/build/*,python/ray/cloudpickle/*,python/ray/thirdparty_files/*,python/ray/_private/thirdparty/*,python/ray/_private/runtime_env/agent/thirdparty_files/*,python/ray/dag/*.py,ci/*,python/ray/_private/*,python/ray/air/*,dashboard/*,python/ray/includes/*,python/ray/internal/*,python/ray/ray_operator/*,python/ray/scripts/*,python/ray/serve/generated/serve_pb2.py,python/ray/sgd/*,python/ray/streaming/*,python/ray/tests/*,python/ray/tests/*,python/ray/util/*,python/ray/workers/*,python/ray/workflow/*,rllib/*,release/*,
 
 known_local_folder=ray
 known_afterray=psutil,setproctitle

python/ray/tune/__init__.py

@@ -1,54 +1,53 @@
+# isort: off
 # Try import ray[tune] core requirements (defined in setup.py)
 try:
+    import fsspec  # noqa: F401
     import pandas  # noqa: F401
-    import requests  # noqa: F401
     import pyarrow  # noqa: F401
-    import fsspec  # noqa: F401
+    import requests  # noqa: F401
 except ImportError as exc:
     raise ImportError(
         "Can't import ray.tune as some dependencies are missing. "
         'Run `pip install "ray[tune]"` to fix.'
     ) from exc
+# isort: on
 
-
-from ray.tune.error import TuneError
-from ray.tune.tune_config import ResumeConfig, TuneConfig
-from ray.tune.tune import run_experiments, run
-from ray.tune.syncer import SyncConfig
-from ray.tune.experiment import Experiment
 from ray.tune.analysis import ExperimentAnalysis
-from ray.tune.stopper import Stopper
-from ray.tune.registry import register_env, register_trainable
-from ray.tune.trainable import Trainable
 from ray.tune.callback import Callback
-from ray.tune.search import grid_search
+from ray.tune.error import TuneError
+from ray.tune.execution.placement_groups import PlacementGroupFactory
+from ray.tune.experiment import Experiment
 from ray.tune.progress_reporter import (
-    ProgressReporter,
     CLIReporter,
     JupyterNotebookReporter,
+    ProgressReporter,
 )
+from ray.tune.registry import register_env, register_trainable
+from ray.tune.result_grid import ResultGrid
+from ray.tune.schedulers import create_scheduler
+from ray.tune.search import create_searcher, grid_search
 from ray.tune.search.sample import (
-    sample_from,
-    uniform,
-    quniform,
     choice,
-    randint,
     lograndint,
-    qrandint,
-    qlograndint,
-    randn,
-    qrandn,
     loguniform,
+    qlograndint,
     qloguniform,
+    qrandint,
+    qrandn,
+    quniform,
+    randint,
+    randn,
+    sample_from,
+    uniform,
 )
-from ray.tune.search import create_searcher
-from ray.tune.schedulers import create_scheduler
-from ray.tune.execution.placement_groups import PlacementGroupFactory
+from ray.tune.stopper import Stopper
+from ray.tune.syncer import SyncConfig
+from ray.tune.trainable import Trainable
 from ray.tune.trainable.util import with_parameters, with_resources
-from ray.tune.result_grid import ResultGrid
+from ray.tune.tune import run, run_experiments
+from ray.tune.tune_config import ResumeConfig, TuneConfig
 from ray.tune.tuner import Tuner
 
-
 __all__ = [
     "Trainable",
     "Callback",

python/ray/tune/analysis/experiment_analysis.py

@@ -2,34 +2,24 @@
 import io
 import json
 import logging
-from numbers import Number
 import os
+from numbers import Number
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import pyarrow.fs
 
-from ray.util.annotations import PublicAPI
-from ray.air.constants import (
-    EXPR_PROGRESS_FILE,
-    EXPR_RESULT_FILE,
-    TRAINING_ITERATION,
-)
+from ray.air.constants import EXPR_PROGRESS_FILE, EXPR_RESULT_FILE, TRAINING_ITERATION
 from ray.train import Checkpoint
-from ray.train._internal.storage import (
-    _exists_at_fs_path,
-    get_fs_and_path,
-)
+from ray.train._internal.storage import _exists_at_fs_path, get_fs_and_path
 from ray.tune.execution.experiment_state import _find_newest_experiment_checkpoint
 from ray.tune.execution.tune_controller import TuneController
 from ray.tune.experiment import Trial
-from ray.tune.result import (
-    DEFAULT_METRIC,
-    CONFIG_PREFIX,
-)
+from ray.tune.result import CONFIG_PREFIX, DEFAULT_METRIC
 from ray.tune.utils import flatten_dict
 from ray.tune.utils.serialization import TuneFunctionDecoder
-from ray.tune.utils.util import is_nan_or_inf, is_nan, unflattened_lookup
+from ray.tune.utils.util import is_nan, is_nan_or_inf, unflattened_lookup
+from ray.util.annotations import PublicAPI
 
 try:
     import pandas as pd

python/ray/tune/callback.py

@@ -1,11 +1,11 @@
-from abc import ABCMeta
 import glob
+import warnings
+from abc import ABCMeta
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
-import warnings
 
-from ray.util.annotations import PublicAPI, DeveloperAPI
 from ray.tune.utils.util import _atomic_save, _load_newest_checkpoint
+from ray.util.annotations import DeveloperAPI, PublicAPI
 
 if TYPE_CHECKING:
     from ray.train import Checkpoint

python/ray/tune/cli/commands.py

@@ -1,25 +1,25 @@
-from pathlib import Path
-from typing import Optional, List
-
-import click
 import logging
 import operator
 import os
 import shutil
 import subprocess
 from datetime import datetime
+from pathlib import Path
+from typing import List, Optional
 
+import click
 import pandas as pd
-from pandas.api.types import is_string_dtype, is_numeric_dtype
+from pandas.api.types import is_numeric_dtype, is_string_dtype
+
+from ray._private.thirdparty.tabulate.tabulate import tabulate
 from ray.air.constants import EXPR_RESULT_FILE
+from ray.tune import TuneError
+from ray.tune.analysis import ExperimentAnalysis
 from ray.tune.result import (
+    CONFIG_PREFIX,
     DEFAULT_EXPERIMENT_INFO_KEYS,
     DEFAULT_RESULT_KEYS,
-    CONFIG_PREFIX,
 )
-from ray.tune.analysis import ExperimentAnalysis
-from ray.tune import TuneError
-from ray._private.thirdparty.tabulate.tabulate import tabulate
 
 logger = logging.getLogger(__name__)
 

python/ray/tune/cli/scripts.py

@@ -1,4 +1,5 @@
 import click
+
 import ray.tune.cli.commands as commands
 
 

python/ray/tune/examples/ax_example.py

@@ -4,9 +4,11 @@
 
 Requires the Ax library to be installed (`pip install ax-platform sqlalchemy`).
 """
-import numpy as np
+
 import time
 
+import numpy as np
+
 from ray import train, tune
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.search.ax import AxSearch

python/ray/tune/examples/bohb_example.py

@@ -7,8 +7,8 @@
 """
 
 import json
-import time
 import os
+import time
 
 import numpy as np
 

python/ray/tune/examples/cifar10_pytorch.py

@@ -2,22 +2,25 @@
 # fmt: off
 
 # __import_begin__
-import numpy as np
 import os
 import tempfile
+from typing import Dict
+
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from filelock import FileLock
-from torch.utils.data import random_split
 import torchvision
 import torchvision.transforms as transforms
-from typing import Dict
+from filelock import FileLock
+from torch.utils.data import random_split
+
 import ray
 from ray import train, tune
 from ray.train import Checkpoint
 from ray.tune.schedulers import ASHAScheduler
+
 # __import_end__
 
 

python/ray/tune/examples/hyperband_example.py

@@ -4,8 +4,8 @@
 
 import ray
 from ray import train, tune
-from ray.tune.utils.mock_trainable import MyTrainableClass
 from ray.tune.schedulers import HyperBandScheduler
+from ray.tune.utils.mock_trainable import MyTrainableClass
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

python/ray/tune/examples/hyperopt_conditional_search_space_example.py

@@ -7,14 +7,16 @@
 For an example of using a Tune search space, see
 :doc:`/tune/examples/hyperopt_example`.
 """
+
 import time
 
+from hyperopt import hp
+
 import ray
 from ray import train, tune
-from ray.tune.search import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
+from ray.tune.search import ConcurrencyLimiter
 from ray.tune.search.hyperopt import HyperOptSearch
-from hyperopt import hp
 
 
 def f_unpack_dict(dct):
@@ -35,7 +37,7 @@ def f_unpack_dict(dct):
     """
 
     res = {}
-    for (k, v) in dct.items():
+    for k, v in dct.items():
         if isinstance(v, dict):
             res = {**res, **f_unpack_dict(v)}
         else:

python/ray/tune/examples/lightgbm_example.py

@@ -4,8 +4,8 @@
 from sklearn.model_selection import train_test_split
 
 from ray import tune
-from ray.tune.schedulers import ASHAScheduler
 from ray.tune.integration.lightgbm import TuneReportCheckpointCallback
+from ray.tune.schedulers import ASHAScheduler
 
 
 def train_breast_cancer(config: dict):

python/ray/tune/examples/mlflow_ptl.py

@@ -1,16 +1,16 @@
 """An example showing how to use Pytorch Lightning training, Ray Tune
 HPO, and MLflow autologging all together."""
+
 import os
 import tempfile
 
-import pytorch_lightning as pl
-
 import mlflow
+import pytorch_lightning as pl
 
 from ray import train, tune
 from ray.air.integrations.mlflow import setup_mlflow
-from ray.tune.integration.pytorch_lightning import TuneReportCallback
 from ray.tune.examples.mnist_ptl_mini import LightningMNISTClassifier, MNISTDataModule
+from ray.tune.integration.pytorch_lightning import TuneReportCallback
 
 
 def train_mnist_tune(config, data_dir=None, num_epochs=10, num_gpus=0):

python/ray/tune/examples/mnist_ptl_mini.py

@@ -1,21 +1,17 @@
 import math
-
 import os
-import torch
-from filelock import FileLock
 
 import pytorch_lightning as pl
-
-
+import torch
+from filelock import FileLock
 from torch.nn import functional as F
 from torch.utils.data import DataLoader, random_split
 from torchmetrics import Accuracy
 from torchvision import transforms
 from torchvision.datasets import MNIST
-from ray.tune.integration.pytorch_lightning import TuneReportCheckpointCallback
 
 from ray import train, tune
-
+from ray.tune.integration.pytorch_lightning import TuneReportCheckpointCallback
 
 PATH_DATASETS = os.environ.get("PATH_DATASETS", ".")
 

python/ray/tune/examples/mnist_pytorch.py

@@ -1,13 +1,15 @@
 # Original Code here:
 # https://github.com/pytorch/examples/blob/master/mnist/main.py
-import os
+
 import argparse
-from filelock import FileLock
+import os
 import tempfile
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
+from filelock import FileLock
 from torchvision import datasets, transforms
 
 import ray

python/ray/tune/examples/mnist_pytorch_trainable.py

@@ -4,18 +4,19 @@
 
 import argparse
 import os
+
 import torch
 import torch.optim as optim
 
 import ray
 from ray import train, tune
-from ray.tune.schedulers import ASHAScheduler
 from ray.tune.examples.mnist_pytorch import (
-    train_func,
-    test_func,
-    get_data_loaders,
     ConvNet,
+    get_data_loaders,
+    test_func,
+    train_func,
 )
+from ray.tune.schedulers import ASHAScheduler
 
 # Change these values if you want the training to run quicker or slower.
 EPOCH_SIZE = 512

python/ray/tune/examples/nevergrad_example.py

@@ -4,11 +4,12 @@
 
 Requires the Nevergrad library to be installed (`pip install nevergrad`).
 """
+
 import time
 
 from ray import train, tune
-from ray.tune.search import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
+from ray.tune.search import ConcurrencyLimiter
 from ray.tune.search.nevergrad import NevergradSearch
 
 
@@ -30,6 +31,7 @@ def easy_objective(config):
 
 if __name__ == "__main__":
     import argparse
+
     import nevergrad as ng
 
     parser = argparse.ArgumentParser()