ci: add isort to the ruff lint rules (#4032)

Co-authored-by: aarnphm-ec2-dev <[email protected]>

.pre-commit-config.yaml

@@ -2,8 +2,8 @@ ci:
   autoupdate_schedule: monthly
 exclude: '.*\.(css|js|svg)$'
 repos:
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.0.274'
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: 'v0.0.277'
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --show-fixes]

docs/source/frameworks/diffusers.rst

@@ -18,7 +18,7 @@ You can import a pretrained diffusion model from huggingface hub or local direct
 	"sd2.1",  # model tag in BentoML model store
 	"stabilityai/stable-diffusion-2-1",  # huggingface model name
     )
-	      
+
 
 If you plan to use the model with a custom pipeline that has method other than :code:`__call__` (e.g. a :code:`StableDiffusionMegaPipeline`), you need to explicitly list them like this:
 

examples/custom_model_runner/locustfile.py

@@ -1,6 +1,6 @@
-from locust import task
-from locust import between
 from locust import HttpUser
+from locust import between
+from locust import task
 
 
 class MnistTestUser(HttpUser):

examples/custom_model_runner/train.py

@@ -4,12 +4,12 @@
 
 import net
 import torch
-import torch.optim as optim
 import torch.nn.functional as F
+import torch.optim as optim
+from torch.optim.lr_scheduler import StepLR
+from torch.utils.data import DataLoader
 from torchvision import datasets
 from torchvision import transforms
-from torch.utils.data import DataLoader
-from torch.optim.lr_scheduler import StepLR
 
 
 def train(args, model, device, train_loader, optimizer, epoch):

examples/custom_runner/nltk_pretrained_model/service.py

@@ -2,8 +2,8 @@
 
 import time
 import typing as t
-from typing import TYPE_CHECKING
 from statistics import mean
+from typing import TYPE_CHECKING
 
 import nltk
 from nltk.sentiment import SentimentIntensityAnalyzer

examples/custom_web_serving/fastapi_example/train.py

@@ -1,8 +1,8 @@
 import logging
 
 import pandas as pd
-from sklearn import svm
 from sklearn import datasets
+from sklearn import svm
 
 import bentoml
 

examples/custom_web_serving/flask_example/train.py

@@ -1,7 +1,7 @@
 import logging
 
-from sklearn import svm
 from sklearn import datasets
+from sklearn import svm
 
 import bentoml
 

examples/flax/MNIST/tests/conftest.py

@@ -1,26 +1,26 @@
 from __future__ import annotations
 
+import contextlib
 import os
+import subprocess
 import sys
 import typing as t
-import contextlib
-import subprocess
 from typing import TYPE_CHECKING
 
 import psutil
 import pytest
 
 import bentoml
-from bentoml.testing.server import host_bento
 from bentoml._internal.configuration.containers import BentoMLContainer
+from bentoml.testing.server import host_bento
 
 if TYPE_CHECKING:
     from contextlib import ExitStack
 
-    from _pytest.main import Session
-    from _pytest.nodes import Item
     from _pytest.config import Config
     from _pytest.fixtures import FixtureRequest as _PytestFixtureRequest
+    from _pytest.main import Session
+    from _pytest.nodes import Item
 
     class FixtureRequest(_PytestFixtureRequest):
         param: str

examples/flax/MNIST/tests/test_endpoints.py

@@ -7,8 +7,8 @@
 import pytest
 
 import bentoml
-from bentoml.testing.grpc import create_channel
 from bentoml.testing.grpc import async_client_call
+from bentoml.testing.grpc import create_channel
 
 if t.TYPE_CHECKING:
     import jax.numpy as jnp

examples/flax/MNIST/train.py

@@ -1,17 +1,17 @@
 # modified from https://github.com/google/flax/blob/main/examples/mnist/README.md
 from __future__ import annotations
 
+import argparse
 import os
 import typing as t
-import argparse
 from typing import TYPE_CHECKING
 
-import jax
 import attrs
-import numpy as np
-import optax
 import cattrs
+import jax
 import jax.numpy as jnp
+import numpy as np
+import optax
 import tensorflow_datasets as tfds
 from flax import linen as nn
 from flax import serialization

examples/fraud_detection/benchmark/fastapi_main_load_model.py

@@ -2,9 +2,9 @@
 
 import numpy as np
 import pandas as pd
-from sample import sample_input
 from fastapi import FastAPI
 from fastapi import Request
+from sample import sample_input
 
 import bentoml
 

examples/fraud_detection/benchmark/fastapi_main_local_runner.py

@@ -2,9 +2,9 @@
 
 import numpy as np
 import pandas as pd
-from sample import sample_input
 from fastapi import FastAPI
 from fastapi import Request
+from sample import sample_input
 
 import bentoml
 

examples/fraud_detection/benchmark/locustfile.py

@@ -1,8 +1,8 @@
 import numpy as np
 import pandas as pd
-from locust import task
-from locust import between
 from locust import HttpUser
+from locust import between
+from locust import task
 
 NUM_OF_ROWS = 500
 test_transactions = pd.read_csv("../data/test_transaction.csv")[0:NUM_OF_ROWS]

examples/kfserving/train.py

@@ -1,7 +1,7 @@
 import logging
 
-from sklearn import svm
 from sklearn import datasets
+from sklearn import svm
 
 import bentoml
 

examples/mlflow/keras/train.py

@@ -2,17 +2,16 @@
 on the Reuters newswire topic classification task.
 """
 # pylint: disable=no-name-in-module
-import numpy as np
-
 # The following import and function call are the only additions to code required
 # to automatically log metrics and parameters to MLflow.
 import mlflow.keras
+import numpy as np
 from tensorflow import keras
+from tensorflow.keras.datasets import reuters
+from tensorflow.keras.layers import Activation
 from tensorflow.keras.layers import Dense
 from tensorflow.keras.layers import Dropout
-from tensorflow.keras.layers import Activation
 from tensorflow.keras.models import Sequential
-from tensorflow.keras.datasets import reuters
 from tensorflow.keras.preprocessing.text import Tokenizer
 
 import bentoml

examples/mlflow/lightgbm/train.py

@@ -1,12 +1,12 @@
 import argparse
 
-import mlflow
 import lightgbm as lgb
 import matplotlib as mpl
+import mlflow
 import mlflow.lightgbm
 from sklearn import datasets
-from sklearn.metrics import log_loss
 from sklearn.metrics import accuracy_score
+from sklearn.metrics import log_loss
 from sklearn.model_selection import train_test_split
 
 import bentoml

examples/mlflow/pytorch/mnist.py

@@ -11,15 +11,15 @@
 #
 # BentoML example is based on https://github.com/mlflow/mlflow/blob/master/examples/pytorch/mnist_tensorboard_artifact.py
 #
-import os
 import argparse
+import os
 
-import torch
 import mlflow
-import torch.nn as nn
-import torch.optim as optim
 import mlflow.pytorch
+import torch
+import torch.nn as nn
 import torch.nn.functional as F
+import torch.optim as optim
 from torchvision import datasets
 from torchvision import transforms
 

examples/mlflow/sklearn_autolog/grid_search_cv.py

@@ -2,10 +2,10 @@
 
 import mlflow
 import pandas as pd
-from utils import fetch_logged_data
-from sklearn import svm
 from sklearn import datasets
+from sklearn import svm
 from sklearn.model_selection import GridSearchCV
+from utils import fetch_logged_data
 
 import bentoml
 

examples/mlflow/sklearn_autolog/linear_regression.py

@@ -1,9 +1,9 @@
 from pprint import pprint
 
-import numpy as np
 import mlflow
-from utils import fetch_logged_data
+import numpy as np
 from sklearn.linear_model import LinearRegression
+from utils import fetch_logged_data
 
 import bentoml
 

examples/mlflow/sklearn_autolog/pipeline.py

@@ -1,11 +1,11 @@
 from pprint import pprint
 
-import numpy as np
 import mlflow
-from utils import fetch_logged_data
-from sklearn.pipeline import Pipeline
+import numpy as np
 from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import StandardScaler
+from utils import fetch_logged_data
 
 import bentoml
 

examples/mlflow/sklearn_logistic_regression/train.py

@@ -1,6 +1,6 @@
-import numpy as np
 import mlflow
 import mlflow.sklearn
+import numpy as np
 from sklearn.linear_model import LogisticRegression
 
 import bentoml

examples/mlflow/torchscript/IrisClassification/iris_classification.py

@@ -1,13 +1,13 @@
 # pylint: disable=abstract-method,redefined-outer-name
 import argparse
 
+import mlflow.pytorch
 import numpy as np
 import torch
 import torch.nn as nn
-import mlflow.pytorch
 import torch.nn.functional as F
-from sklearn.metrics import accuracy_score
 from sklearn.datasets import load_iris
+from sklearn.metrics import accuracy_score
 from sklearn.model_selection import train_test_split
 
 import bentoml

examples/mlflow/torchscript/MNIST/mnist_torchscript.py

@@ -2,16 +2,16 @@
 
 import argparse
 
+import mlflow
+import mlflow.pytorch
 import numpy as np
 import torch
-import mlflow
 import torch.nn as nn
-import torch.optim as optim
-import mlflow.pytorch
 import torch.nn.functional as F
+import torch.optim as optim
+from torch.optim.lr_scheduler import StepLR
 from torchvision import datasets
 from torchvision import transforms
-from torch.optim.lr_scheduler import StepLR
 
 import bentoml
 

examples/monitoring/task_classification/locustfile.py

@@ -1,7 +1,7 @@
 import numpy as np
-from locust import task
-from locust import between
 from locust import HttpUser
+from locust import between
+from locust import task
 from sklearn import datasets
 
 test_data = datasets.load_iris().data

examples/monitoring/task_classification/service.py

@@ -5,8 +5,8 @@
 import numpy as np
 
 import bentoml
-from bentoml.io import Text
 from bentoml.io import NumpyNdarray
+from bentoml.io import Text
 
 CLASS_NAMES = ["setosa", "versicolor", "virginica"]
 

examples/monitoring/task_classification/tests/conftest.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
+import contextlib
 import os
+import subprocess
 import sys
 import typing as t
-import contextlib
-import subprocess
 from pathlib import Path
 
 import numpy as np
@@ -14,11 +14,11 @@
 from bentoml._internal.configuration.containers import BentoMLContainer
 
 if t.TYPE_CHECKING:
+    from _pytest.config import Config
+    from _pytest.fixtures import FixtureRequest
     from _pytest.main import Session
     from _pytest.nodes import Item
-    from _pytest.config import Config
     from _pytest.tmpdir import TempPathFactory
-    from _pytest.fixtures import FixtureRequest
 
 PROJECT_DIR = Path(__file__).parent.parent
 

examples/monitoring/task_classification/train.py

@@ -1,7 +1,7 @@
 import logging
 
-from sklearn import svm
 from sklearn import datasets
+from sklearn import svm
 
 import bentoml
 

examples/pydantic_validation/train.py

@@ -1,8 +1,8 @@
 import logging
 
 import pandas as pd
-from sklearn import svm
 from sklearn import datasets
+from sklearn import svm
 
 import bentoml
 

examples/pytorch_mnist/locustfile.py

@@ -1,6 +1,6 @@
-from locust import task
-from locust import between
 from locust import HttpUser
+from locust import between
+from locust import task
 
 with open("samples/1.png", "rb") as f:
     test_image_bytes = f.read()