infra: sort imports in all .py files with isort (#2225)

advanced_functionality/autogluon-tabular/container-training/inference.py

@@ -1,27 +1,28 @@
-import sys
-import os
 import argparse
-import logging
-import warnings
-import time
-import json
-import subprocess
 import copy
+import json
+import logging
+import os
+import subprocess
+import sys
+import time
+import warnings
 
 warnings.filterwarnings("ignore", category=FutureWarning)
 
+import pickle
+from collections import Counter
+from io import StringIO
+from itertools import islice
+from timeit import default_timer as timer
+
 import numpy as np
 import pandas as pd
-import pickle
-from io import StringIO
-from timeit import default_timer as timer
-from itertools import islice
-from collections import Counter
 
 with warnings.catch_warnings():
     warnings.filterwarnings("ignore", category=DeprecationWarning)
+    from autogluon.tabular import TabularDataset, TabularPredictor
     from prettytable import PrettyTable
-    from autogluon.tabular import TabularPredictor, TabularDataset
 
 
 def make_str_table(df):

advanced_functionality/autogluon-tabular/container-training/train.py

@@ -1,23 +1,23 @@
-import ast
 import argparse
-import logging
-import warnings
-import os
-import json
+import ast
 import glob
+import json
+import logging
+import os
+import pickle
+import shutil
 import subprocess
 import sys
-import boto3
-import pickle
-import pandas as pd
+import warnings
 from collections import Counter
 from timeit import default_timer as timer
 
-import numpy as np
-import seaborn as sns
+import boto3
 import matplotlib.pyplot as plt
-import shutil
 import networkx as nx
+import numpy as np
+import pandas as pd
+import seaborn as sns
 
 logging.basicConfig(level=logging.DEBUG)
 logging.info(subprocess.call("ls -lR /opt/ml/input".split()))
@@ -29,12 +29,10 @@ from smdebug.core.writer import FileWriter
 
 with warnings.catch_warnings():
     warnings.filterwarnings("ignore", category=DeprecationWarning)
-    from prettytable import PrettyTable
     import autogluon as ag
-
-    from autogluon.tabular import TabularDataset, TabularPredictor
-
     from autogluon.core.constants import BINARY, MULTICLASS, REGRESSION, SOFTCLASS
+    from autogluon.tabular import TabularDataset, TabularPredictor
+    from prettytable import PrettyTable
 
     # print(f'DEBUG AutoGluon version : {ag.__version__}')
 
@@ -74,11 +72,11 @@ def format_for_print(df):
 
 
 def get_roc_auc(y_test_true, y_test_pred, labels, class_labels_internal, model_output_dir):
-    from sklearn.preprocessing import label_binarize
-    from sklearn.metrics import roc_curve, auc
-
     from itertools import cycle
 
+    from sklearn.metrics import auc, roc_curve
+    from sklearn.preprocessing import label_binarize
+
     y_test_true_binalized = label_binarize(y_test_true, classes=labels)
 
     if len(labels) == 2:

advanced_functionality/autogluon-tabular/utils/ag_utils.py

@@ -1,13 +1,11 @@
-from IPython.display import display, IFrame
-from ipywidgets import interact, Image, VBox, HTML, GridspecLayout, Layout, widgets
-import ipywidgets as ipyw
-
-import boto3
-
 import os
 import tarfile
 
+import boto3
+import ipywidgets as ipyw
 import pandas as pd
+from IPython.display import IFrame, display
+from ipywidgets import HTML, GridspecLayout, Image, Layout, VBox, interact, widgets
 
 
 def search_training_jobs(job_tag_name, job_tag_value):

advanced_functionality/custom-training-containers/basic-training-container/docker/code/main.py

@@ -1,16 +1,16 @@
 from __future__ import absolute_import
 
+import os
 import sys
 import time
-import os
 
-from utils import ExitSignalHandler
 from utils import (
-    write_failure_file,
-    print_json_object,
+    ExitSignalHandler,
     load_json_object,
-    save_model_artifacts,
     print_files_in_path,
+    print_json_object,
+    save_model_artifacts,
+    write_failure_file,
 )
 
 hyperparameters_file_path = "/opt/ml/input/config/hyperparameters.json"

advanced_functionality/custom-training-containers/basic-training-container/docker/code/utils.py

@@ -1,7 +1,7 @@
-import signal
-import pprint
 import json
 import os
+import pprint
+import signal
 from os import path
 
 

advanced_functionality/custom-training-containers/framework-container/notebook/source_dir/train.py

@@ -1,11 +1,11 @@
 from __future__ import absolute_import
 
+import argparse
+import os
 import sys
 import time
-import os
-import argparse
 
-from utils import save_model_artifacts, print_files_in_path
+from utils import print_files_in_path, save_model_artifacts
 
 
 def train(hp1, hp2, hp3, train_channel, validation_channel):

advanced_functionality/custom-training-containers/framework-container/package/setup.py

@@ -1,9 +1,8 @@
 from __future__ import absolute_import
 
-from glob import glob
 import os
-from os.path import basename
-from os.path import splitext
+from glob import glob
+from os.path import basename, splitext
 
 from setuptools import find_packages, setup
 

advanced_functionality/custom-training-containers/framework-container/package/src/custom_framework_training/training.py

@@ -1,6 +1,7 @@
 from __future__ import absolute_import
 
 import logging
+
 from sagemaker_training import entry_point, environment
 
 logger = logging.getLogger(__name__)

advanced_functionality/custom-training-containers/script-mode-container-2/notebook/source_dir/train.py

@@ -1,11 +1,11 @@
 from __future__ import absolute_import
 
+import argparse
+import os
 import sys
 import time
-import os
-import argparse
 
-from utils import save_model_artifacts, print_files_in_path
+from utils import print_files_in_path, save_model_artifacts
 
 
 def train(hp1, hp2, hp3, train_channel, validation_channel):

advanced_functionality/custom-training-containers/script-mode-container/docker/code/train.py

@@ -1,11 +1,11 @@
 from __future__ import absolute_import
 
+import argparse
+import os
 import sys
 import time
-import os
-import argparse
 
-from utils import save_model_artifacts, print_files_in_path
+from utils import print_files_in_path, save_model_artifacts
 
 
 def train(hp1, hp2, hp3, train_channel, validation_channel):

advanced_functionality/distributed_tensorflow_mask_rcnn/aws-mask-rcnn.py

@@ -15,15 +15,15 @@ OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
 SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """
 
+import glob
 import json
 import os
 import shutil
+import signal
+import socket
 import subprocess
 import sys
 import time
-import signal
-import socket
-import glob
 
 
 def copy_files(src, dest):

advanced_functionality/distributed_tensorflow_mask_rcnn/container-optimized-script-mode/resources/train.py

@@ -1,13 +1,12 @@
+import glob
 import json
 import os
 import shutil
+import signal
+import socket
 import subprocess
 import sys
 import time
-import signal
-import socket
-import glob
-
 from contextlib import contextmanager
 
 

advanced_functionality/distributed_tensorflow_mask_rcnn/container-optimized/resources/train.py

@@ -1,13 +1,12 @@
+import glob
 import json
 import os
 import shutil
+import signal
+import socket
 import subprocess
 import sys
 import time
-import signal
-import socket
-import glob
-
 from contextlib import contextmanager
 
 

advanced_functionality/distributed_tensorflow_mask_rcnn/container-script-mode/resources/train.py

@@ -1,13 +1,12 @@
+import glob
 import json
 import os
 import shutil
+import signal
+import socket
 import subprocess
 import sys
 import time
-import signal
-import socket
-import glob
-
 from contextlib import contextmanager
 
 

advanced_functionality/distributed_tensorflow_mask_rcnn/container-serving-optimized/resources/predict.py

@@ -1,21 +1,22 @@
-import os, glob
+import glob
+import os
 import sys
 
 sys.path.append("/mask-rcnn-tensorflow/MaskRCNN")
 
-from model.generalized_rcnn import ResNetFPNModel
-from config import finalize_configs, config as cfg
-from dataset import DetectionDataset
-
-from tensorpack.predict.base import OfflinePredictor
-from tensorpack.tfutils.sessinit import get_model_loader
-from tensorpack.predict.config import PredictConfig
-
-import numpy as np
-import cv2
 from itertools import groupby
 from threading import Lock
 
+import cv2
+import numpy as np
+from config import config as cfg
+from config import finalize_configs
+from dataset import DetectionDataset
+from model.generalized_rcnn import ResNetFPNModel
+from tensorpack.predict.base import OfflinePredictor
+from tensorpack.predict.config import PredictConfig
+from tensorpack.tfutils.sessinit import get_model_loader
+
 
 class MaskRCNNService:
 
@@ -25,7 +26,7 @@ class MaskRCNNService:
     # class method to load trained model and create an offline predictor
     @classmethod
     def get_predictor(cls):
-        """ load trained model"""
+        """load trained model"""
 
         with cls.lock:
             # check if model is already loaded
@@ -172,11 +173,10 @@ class MaskRCNNService:
 # create predictor
 MaskRCNNService.get_predictor()
 
-import json
-from flask import Flask
-from flask import request
-from flask import Response
 import base64
+import json
+
+from flask import Flask, Response, request
 
 app = Flask(__name__)
 

advanced_functionality/distributed_tensorflow_mask_rcnn/container-serving-optimized/resources/serve.py

@@ -12,12 +12,12 @@
 # timeout                  MODEL_SERVER_TIMEOUT              70 seconds
 
 from __future__ import print_function
+
 import os
 import signal
 import subprocess
 import sys
 
-
 model_server_timeout = os.environ.get("MODEL_SERVER_TIMEOUT", 70)
 model_server_workers = int(os.environ.get("MODEL_SERVER_WORKERS", 1))
 

advanced_functionality/distributed_tensorflow_mask_rcnn/container-serving/resources/predict.py

@@ -1,22 +1,23 @@
-import os, glob
+import glob
+import os
 import sys
 
 sys.path.append("/tensorpack/examples/FasterRCNN")
 
-from modeling.generalized_rcnn import ResNetFPNModel
-from config import finalize_configs, config as cfg
-from eval import predict_image, DetectionResult
-from dataset import register_coco
-
-from tensorpack.predict.base import OfflinePredictor
-from tensorpack.tfutils.sessinit import get_model_loader
-from tensorpack.predict.config import PredictConfig
-
-import numpy as np
-import cv2
 from itertools import groupby
 from threading import Lock
 
+import cv2
+import numpy as np
+from config import config as cfg
+from config import finalize_configs
+from dataset import register_coco
+from eval import DetectionResult, predict_image
+from modeling.generalized_rcnn import ResNetFPNModel
+from tensorpack.predict.base import OfflinePredictor
+from tensorpack.predict.config import PredictConfig
+from tensorpack.tfutils.sessinit import get_model_loader
+
 
 class MaskRCNNService:
 
@@ -26,7 +27,7 @@ class MaskRCNNService:
     # class method to load trained model and create an offline predictor
     @classmethod
     def get_predictor(cls):
-        """ load trained model"""
+        """load trained model"""
 
         with cls.lock:
             # check if model is already loaded
@@ -139,11 +140,10 @@ class MaskRCNNService:
 # create predictor
 MaskRCNNService.get_predictor()
 
-import json
-from flask import Flask
-from flask import request
-from flask import Response
 import base64
+import json
+
+from flask import Flask, Response, request
 
 app = Flask(__name__)
 

advanced_functionality/distributed_tensorflow_mask_rcnn/container-serving/resources/serve.py

@@ -12,6 +12,7 @@
 # timeout                  MODEL_SERVER_TIMEOUT              70 seconds
 
 from __future__ import print_function
+
 import os
 import signal
 import subprocess

advanced_functionality/distributed_tensorflow_mask_rcnn/container/resources/train.py

@@ -1,13 +1,12 @@
+import glob
 import json
 import os
 import shutil
+import signal
+import socket
 import subprocess
 import sys
 import time
-import signal
-import socket
-import glob
-
 from contextlib import contextmanager
 
 

advanced_functionality/distributed_tensorflow_mask_rcnn/tensorpack-mask-rcnn.py

@@ -15,15 +15,15 @@ OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
 SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """
 
+import glob
 import json
 import os
 import shutil
+import signal
+import socket
 import subprocess
 import sys
 import time
-import signal
-import socket
-import glob
 
 
 def copy_files(src, dest):

advanced_functionality/fairseq_translation/fairseq/distributed_train.py

@@ -6,17 +6,15 @@
 # the root directory of this source tree. An additional grant of patent rights
 # can be found in the PATENTS file in the same directory.
 
+import json
 import os
 import socket
 import subprocess
-import json
-
-from train_driver import main as single_process_main
-from fairseq import distributed_utils, options
-
-from multiprocessing_train import ErrorHandler
 
 import torch
+from fairseq import distributed_utils, options
+from multiprocessing_train import ErrorHandler
+from train_driver import main as single_process_main
 
 
 def run(args, error_queue):

advanced_functionality/fairseq_translation/fairseq/predictor.py

@@ -1,8 +1,7 @@
-from sagemaker_translate import model_fn, input_fn, output_fn, predict_fn
-
-import flask
 import os
 
+import flask
+from sagemaker_translate import input_fn, model_fn, output_fn, predict_fn
 
 prefix = "/opt/ml/"
 model_path = os.path.join(prefix, "model")

advanced_functionality/fairseq_translation/fairseq/sagemaker_translate.py

@@ -9,20 +9,18 @@
 Translate raw text with a trained model. Batches data on-the-fly.
 """
 
-from collections import namedtuple
-import numpy as np
-import sys
-import os
-import logging
-import json
 import copy
+import json
+import logging
+import os
+import sys
+from collections import namedtuple
 
+import numpy as np
 import torch
-
 from fairseq import data, options, tasks, tokenizer, utils
 from fairseq.sequence_generator import SequenceGenerator
 
-
 Batch = namedtuple("Batch", "srcs tokens lengths")
 Translation = namedtuple("Translation", "src_str hypos pos_scores alignments")
 

advanced_functionality/fairseq_translation/fairseq/serve

@@ -14,6 +14,7 @@
 # timeout                  MODEL_SERVER_TIMEOUT              60 seconds
 
 from __future__ import print_function
+
 import multiprocessing
 import os
 import signal

advanced_functionality/fairseq_translation/fairseq/train

@@ -1,22 +1,18 @@
 #!/opt/conda/bin/python
 from __future__ import print_function
 
-import sys
-
-import os
+import copy
 import json
+import os
 import pickle
+import shutil
 import sys
 import traceback
-import copy
-import shutil
 from glob import glob
 
 from fairseq import distributed_utils, options
-
 from train_driver import main
 
-
 # These are the paths to where SageMaker mounts interesting things in your container.
 
 prefix = '/opt/ml/'

advanced_functionality/fairseq_translation/fairseq/train_driver.py

@@ -14,14 +14,14 @@ Train a new model on one or across multiple GPUs.
 
 import collections
 import itertools
-import os
 import math
-import torch
+import os
 
+import torch
 from fairseq import distributed_utils, options, progress_bar, tasks, utils
 from fairseq.data import iterators
-from fairseq.trainer import Trainer
 from fairseq.meters import AverageMeter, StopwatchMeter
+from fairseq.trainer import Trainer
 
 
 def main(args):

advanced_functionality/fastai_oxford_pets/source/pets.py

@@ -1,13 +1,12 @@
 import argparse
-import logging
-import sagemaker_containers
-import requests
-
-import os
-import io
 import glob
+import io
+import logging
+import os
 import time
 
+import requests
+import sagemaker_containers
 from fastai.vision import *
 
 logger = logging.getLogger(__name__)

advanced_functionality/inference_pipeline_sparkml_blazingtext_dbpedia/dbpedia_processing.py

@@ -1,23 +1,19 @@
-from __future__ import print_function
-from __future__ import unicode_literals
+from __future__ import print_function, unicode_literals
 
-import time
-import sys
+import csv
 import os
 import shutil
-import csv
+import sys
+import time
+
 import boto3
-
-from awsglue.utils import getResolvedOptions
-
 import pyspark
-from pyspark.sql import SparkSession
-from pyspark.sql.types import IntegerType, StructField, StructType, StringType
-from pyspark.ml.feature import Tokenizer
-from pyspark.sql.functions import *
-from mleap.pyspark.spark_support import SimpleSparkSerializer
-
 from awsglue.utils import getResolvedOptions
+from mleap.pyspark.spark_support import SimpleSparkSerializer
+from pyspark.ml.feature import Tokenizer
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import *
+from pyspark.sql.types import IntegerType, StringType, StructField, StructType
 
 
 def csv_line(data):

advanced_functionality/inference_pipeline_sparkml_xgboost_abalone/abalone_processing.py

@@ -1,22 +1,20 @@
-from __future__ import print_function
-from __future__ import unicode_literals
+from __future__ import print_function, unicode_literals
 
-import time
-import sys
+import csv
 import os
 import shutil
-import csv
+import sys
+import time
+
 import boto3
-
-from awsglue.utils import getResolvedOptions
-
 import pyspark
-from pyspark.sql import SparkSession
-from pyspark.ml import Pipeline
-from pyspark.sql.types import StructField, StructType, StringType, DoubleType
-from pyspark.ml.feature import StringIndexer, VectorIndexer, OneHotEncoder, VectorAssembler
-from pyspark.sql.functions import *
+from awsglue.utils import getResolvedOptions
 from mleap.pyspark.spark_support import SimpleSparkSerializer
+from pyspark.ml import Pipeline
+from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler, VectorIndexer
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import *
+from pyspark.sql.types import DoubleType, StringType, StructField, StructType
 
 
 def csv_line(data):

advanced_functionality/inference_pipeline_sparkml_xgboost_car_evaluation/preprocessor.py

@@ -1,27 +1,26 @@
 from __future__ import print_function
 
-import time
-import sys
+import csv
 import os
 import shutil
-import csv
+import sys
+import time
+
 import boto3
-
-from awsglue.utils import getResolvedOptions
-
 import pyspark
-from pyspark.sql import SparkSession
-from pyspark.ml import Pipeline
-from pyspark.ml.feature import (
-    StringIndexer,
-    VectorIndexer,
-    OneHotEncoder,
-    VectorAssembler,
-    IndexToString,
-)
-from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.sql.functions import *
+from awsglue.utils import getResolvedOptions
 from mleap.pyspark.spark_support import SimpleSparkSerializer
+from pyspark.ml import Pipeline
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.ml.feature import (
+    IndexToString,
+    OneHotEncoder,
+    StringIndexer,
+    VectorAssembler,
+    VectorIndexer,
+)
+from pyspark.sql import SparkSession
+from pyspark.sql.functions import *
 
 
 def toCSVLine(data):

advanced_functionality/jax_bring_your_own/sagemaker_jax.py

@@ -14,8 +14,8 @@
 Custom Framework Estimator for JAX
 """
 from sagemaker.estimator import Framework
-from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 from sagemaker.tensorflow.model import TensorFlowModel
+from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
 
 class JaxEstimator(Framework):

advanced_functionality/jax_bring_your_own/training_scripts/train_jax.py

@@ -13,9 +13,9 @@
 """
 Train JAX model and serialize as TF SavedModel
 """
-import time
 import argparse
 import functools
+import time
 
 import jax
 import jax.numpy as jnp

advanced_functionality/jax_bring_your_own/training_scripts/train_trax.py

@@ -15,8 +15,8 @@ Train Trax model and serialize as TF SavedModel
 """
 import argparse
 
-import trax
 import tensorflow as tf
+import trax
 from trax import layers as tl
 from trax.supervised import training
 

advanced_functionality/multi_model_bring_your_own/container/dockerd-entrypoint.py

@@ -1,9 +1,10 @@
+import os
+import shlex
 import subprocess
 import sys
-import shlex
-import os
-from retrying import retry
 from subprocess import CalledProcessError
+
+from retrying import retry
 from sagemaker_inference import model_server
 
 

advanced_functionality/multi_model_bring_your_own/container/model_handler.py

@@ -1,12 +1,12 @@
 """
 ModelHandler defines an example model handler for load and inference requests for MXNet CPU models
 """
-from collections import namedtuple
 import glob
 import json
 import logging
 import os
 import re
+from collections import namedtuple
 
 import mxnet as mx
 import numpy as np

advanced_functionality/multi_model_linear_learner_home_value/sklearn_preprocessor.py

@@ -1,23 +1,16 @@
 from __future__ import print_function
 
-import time
-import sys
-from io import StringIO
-import os
-import shutil
-
 import argparse
 import csv
 import json
+import os
+import shutil
+import sys
+import time
+from io import StringIO
+
 import numpy as np
 import pandas as pd
-
-from sklearn.compose import ColumnTransformer
-from sklearn.externals import joblib
-from sklearn.impute import SimpleImputer
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import Binarizer, StandardScaler, OneHotEncoder
-
 from sagemaker_containers.beta.framework import (
     content_types,
     encoders,
@@ -26,6 +19,11 @@ from sagemaker_containers.beta.framework import (
     transformer,
     worker,
 )
+from sklearn.compose import ColumnTransformer
+from sklearn.externals import joblib
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import Binarizer, OneHotEncoder, StandardScaler
 
 # Since we get a headerless CSV file we specify the column names here.
 feature_columns_names = [

advanced_functionality/multi_model_pytorch/code/inference.py

@@ -11,7 +11,6 @@ import torch
 # Local Dependencies:
 from model import MNISTNet
 
-
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 logger.addHandler(logging.StreamHandler(sys.stdout))

advanced_functionality/multi_model_pytorch/code/train.py

@@ -1,6 +1,5 @@
 # Python Built-Ins:
 import argparse
-from distutils.dir_util import copy_tree
 import gzip
 import json
 import logging
@@ -8,22 +7,22 @@ import os
 import shutil
 import subprocess
 import sys
+from distutils.dir_util import copy_tree
 from tempfile import TemporaryDirectory
 
 # External Dependencies:
 import numpy as np
-from packaging import version as pkgversion
-from sagemaker_pytorch_serving_container import handler_service as default_handler_service
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from torch.utils.data import Dataset, DataLoader
+from model import MNISTNet
+from packaging import version as pkgversion
+from sagemaker_pytorch_serving_container import handler_service as default_handler_service
+from torch.utils.data import DataLoader, Dataset
 
 # Local Dependencies:
 from inference import *
-from model import MNISTNet
-
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)

advanced_functionality/mxnet_mnist_byom/mnist.py

@@ -1,11 +1,11 @@
-import logging
-
 import gzip
-import mxnet as mx
-import numpy as np
+import logging
 import os
 import struct
 
+import mxnet as mx
+import numpy as np
+
 
 def find_file(root_path, file_name):
     for root, dirs, files in os.walk(root_path):

advanced_functionality/pytorch_extending_our_containers/container/cifar10/cifar10.py

@@ -10,15 +10,15 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-import ast
 import argparse
+import ast
 import logging
-
 import os
 
 import torch
 import torch.distributed as dist
 import torch.nn as nn
+import torch.nn.functional as F
 import torch.nn.parallel
 import torch.optim
 import torch.utils.data
@@ -26,7 +26,6 @@ import torch.utils.data.distributed
 import torchvision
 import torchvision.models
 import torchvision.transforms as transforms
-import torch.nn.functional as F
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)

advanced_functionality/pytorch_extending_our_containers/utils/utils_cifar.py

@@ -10,11 +10,11 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
+import matplotlib.pyplot as plt
+import numpy as np
 import torch
 import torchvision
 import torchvision.transforms as transforms
-import matplotlib.pyplot as plt
-import numpy as np
 
 classes = ("plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck")
 

advanced_functionality/scikit_bring_your_own/container/decision_trees/predictor.py

@@ -3,16 +3,15 @@
 
 from __future__ import print_function
 
-import os
-import json
-import pickle
 import io
-import sys
+import json
+import os
+import pickle
 import signal
+import sys
 import traceback
 
 import flask
-
 import pandas as pd
 
 prefix = "/opt/ml/"

advanced_functionality/scikit_bring_your_own/container/decision_trees/train

@@ -6,14 +6,13 @@
 
 from __future__ import print_function
 
-import os
 import json
+import os
 import pickle
 import sys
 import traceback
 
 import pandas as pd
-
 from sklearn import tree
 
 # These are the paths to where SageMaker mounts interesting things in your container.

advanced_functionality/tensorflow_bring_your_own/container/cifar10/cifar10.py

@@ -10,17 +10,14 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import argparse
 import functools
 import os
 
-import tensorflow as tf
-
 import resnet_model
+import tensorflow as tf
 
 INPUT_TENSOR_NAME = "inputs"
 SIGNATURE_NAME = "serving_default"

advanced_functionality/tensorflow_bring_your_own/container/cifar10/resnet_model.py

@@ -29,9 +29,7 @@ The key difference of the full preactivation 'v2' variant compared to the
 'v1' variant in [1] is the use of batch normalization before every weight layer
 rather than after.
 """
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import tensorflow as tf
 

advanced_functionality/tensorflow_bring_your_own/container/cifar10/train

@@ -19,10 +19,10 @@
 
 from __future__ import print_function
 
-import os
 import json
-import sys
+import os
 import subprocess
+import sys
 import traceback
 
 # These are the paths to where SageMaker mounts interesting things in your container.

advanced_functionality/tensorflow_bring_your_own/utils/generate_cifar10_tfrecords.py

@@ -16,9 +16,7 @@ Generates tf.train.Example protos and writes them to TFRecord files from the
 python version of the CIFAR-10 dataset downloaded from
 https://www.cs.toronto.edu/~kriz/cifar.html.
 """
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import argparse
 import os
@@ -26,12 +24,12 @@ import shutil
 import sys
 import tarfile
 
-from six.moves import cPickle as pickle
-from six.moves import urllib
-from six.moves import xrange  # pylint: disable=redefined-builtin
-from ipywidgets import FloatProgress
-from IPython.display import display
 import tensorflow as tf
+from IPython.display import display
+from ipywidgets import FloatProgress
+from six.moves import cPickle as pickle
+from six.moves import xrange  # pylint: disable=redefined-builtin
+from six.moves import urllib
 
 CIFAR_FILENAME = "cifar-10-python.tar.gz"
 CIFAR_DOWNLOAD_URL = "https://www.cs.toronto.edu/~kriz/" + CIFAR_FILENAME

advanced_functionality/tensorflow_iris_byom/iris_dnn_classifier.py

@@ -1,4 +1,5 @@
 import os
+
 import numpy as np
 import tensorflow as tf
 

advanced_functionality/unity_ml_agents/src/train.py

@@ -1,12 +1,13 @@
 import argparse
-import numpy as np
-import os
-import tensorflow as tf
-from mlagents_envs.environment import UnityEnvironment
-import mlagents
-import subprocess
-import yaml
 import json
+import os
+import subprocess
+
+import mlagents
+import numpy as np
+import tensorflow as tf
+import yaml
+from mlagents_envs.environment import UnityEnvironment
 
 
 def parse_args():

autopilot/custom-feature-selection/sklearn_feature_selection.py

@@ -1,23 +1,16 @@
 from __future__ import print_function
 
-import time
-import sys
-from io import StringIO
-import os
-import shutil
-
 import argparse
 import csv
 import json
+import os
+import shutil
+import sys
+import time
+from io import StringIO
+
 import numpy as np
 import pandas as pd
-
-from sklearn.externals import joblib
-from sklearn.impute import SimpleImputer
-from sklearn.pipeline import Pipeline
-from sklearn.svm import SVR
-from sklearn.feature_selection import f_regression, mutual_info_regression, SelectKBest, RFE
-
 from sagemaker_containers.beta.framework import (
     content_types,
     encoders,
@@ -26,6 +19,11 @@ from sagemaker_containers.beta.framework import (
     transformer,
     worker,
 )
+from sklearn.externals import joblib
+from sklearn.feature_selection import RFE, SelectKBest, f_regression, mutual_info_regression
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
+from sklearn.svm import SVR
 
 label_column = "y"
 INPUT_FEATURES_SIZE = 100

aws_marketplace/creating_marketplace_products/container/decision_trees/predictor.py

@@ -3,17 +3,16 @@
 
 from __future__ import print_function
 
-import os
 import json
+import os
 import pickle
-import StringIO
-import sys
 import signal
+import sys
 import traceback
 
 import flask
-
 import pandas as pd
+import StringIO
 
 prefix = "/opt/ml/"
 model_path = os.path.join(prefix, "model")

aws_marketplace/creating_marketplace_products/container/decision_trees/serve

@@ -14,6 +14,7 @@
 # timeout                  MODEL_SERVER_TIMEOUT              60 seconds
 
 from __future__ import print_function
+
 import multiprocessing
 import os
 import signal

aws_marketplace/creating_marketplace_products/container/decision_trees/train

@@ -6,17 +6,16 @@
 
 from __future__ import print_function
 
-import os
 import json
+import os
 import pickle
 import sys
 import traceback
 
 import pandas as pd
-
+from numpy import mean
 from sklearn import tree
 from sklearn.model_selection import cross_val_score
-from numpy import mean
 
 # These are the paths to where SageMaker mounts interesting things in your container.
 

aws_sagemaker_studio/frameworks/keras_pipe_mode_horovod/generate_tensorboard_command.py

@@ -10,9 +10,10 @@
 # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
-import boto3
-from datetime import datetime, timedelta
 import re
+from datetime import datetime, timedelta
+
+import boto3
 
 client = boto3.client("sagemaker")
 running_jobs = client.list_training_jobs(CreationTimeAfter=datetime.utcnow() - timedelta(hours=1))

aws_sagemaker_studio/frameworks/keras_pipe_mode_horovod/source/keras_cifar10.py

@@ -15,24 +15,24 @@ from __future__ import absolute_import, division, print_function
 import argparse
 import json
 import logging
-import re
 import os
+import re
 
 import keras
 import tensorflow as tf
 from keras import backend as K
-from keras.callbacks import TensorBoard, ModelCheckpoint
+from keras.callbacks import ModelCheckpoint, TensorBoard
 from keras.layers import (
     Activation,
+    BatchNormalization,
     Conv2D,
     Dense,
     Dropout,
     Flatten,
     MaxPooling2D,
-    BatchNormalization,
 )
 from keras.models import Sequential
-from keras.optimizers import Adam, SGD, RMSprop
+from keras.optimizers import SGD, Adam, RMSprop
 
 logging.getLogger().setLevel(logging.INFO)
 tf.logging.set_verbosity(tf.logging.INFO)

aws_sagemaker_studio/frameworks/mxnet_gluon_sentiment/sentiment.py

@@ -12,8 +12,8 @@ from itertools import chain, islice
 
 import mxnet as mx
 import numpy as np
-from mxnet import gluon, autograd, nd
-from mxnet.io import DataIter, DataBatch, DataDesc
+from mxnet import autograd, gluon, nd
+from mxnet.io import DataBatch, DataDesc, DataIter
 
 logging.basicConfig(level=logging.DEBUG)
 

aws_sagemaker_studio/frameworks/mxnet_onnx_ei/resnet152.py

@@ -2,6 +2,7 @@ from __future__ import print_function
 
 import json
 import logging
+
 import mxnet as mx
 import mxnet.contrib.onnx as onnx_mxnet
 import numpy as np

aws_sagemaker_studio/getting_started/preprocessor.py

@@ -1,6 +1,7 @@
 import json
 import random
 
+
 # sample preprocess_handler (to be implemented by customer)
 # This is a trivial example, where we demonstrate an echo preprocessor for json data
 # for others though, we are generating random data (real customers would not do that obviously/hopefully)

aws_sagemaker_studio/introduction_to_amazon_algorithms/xgboost_abalone/abalone.py

@@ -16,14 +16,13 @@ import argparse
 import json
 import logging
 import os
-import pandas as pd
 import pickle as pkl
 
-from sagemaker_containers import entry_point
-from sagemaker_xgboost_container.data_utils import get_dmatrix
-from sagemaker_xgboost_container import distributed
-
+import pandas as pd
 import xgboost as xgb
+from sagemaker_containers import entry_point
+from sagemaker_xgboost_container import distributed
+from sagemaker_xgboost_container.data_utils import get_dmatrix
 
 
 def _xgb_train(params, dtrain, evals, num_boost_round, model_dir, is_master):

aws_sagemaker_studio/sagemaker-python-sdk/scikit_learn_iris/scikit_learn_iris.py

@@ -15,12 +15,11 @@ from __future__ import print_function
 
 import argparse
 import os
-import pandas as pd
 
+import pandas as pd
 from sklearn import tree
 from sklearn.externals import joblib
 
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
 

aws_sagemaker_studio/sagemaker_debugger/mnist_tensor_analysis/src/mnist.py

@@ -1,5 +1,7 @@
 # Standard Library
 import argparse
+import os
+import time
 
 # Third Party
 import mxnet as mx
@@ -7,8 +9,6 @@ import numpy as np
 from mxnet import autograd, gluon, init
 from mxnet.gluon import nn
 from mxnet.gluon.data.vision import datasets, transforms
-import os
-import time
 
 
 def parse_args():

aws_sagemaker_studio/sagemaker_debugger/mnist_tensor_analysis/utils.py

@@ -1,6 +1,7 @@
-import numpy as np
 import math
+
 import matplotlib.pyplot as plt
+import numpy as np
 from matplotlib.animation import FuncAnimation
 
 plt.rcParams.update({"font.size": 8})

aws_sagemaker_studio/sagemaker_debugger/tensorflow_builtin_rule/src/mnist_byoc.py

@@ -11,14 +11,13 @@ For more information, please refer to https://github.com/awslabs/sagemaker-debug
 
 # Standard Library
 import argparse
+import logging
 import random
 
 # Third Party
 import numpy as np
-import tensorflow as tf
 import smdebug.tensorflow as smd
-
-import logging
+import tensorflow as tf
 
 logging.getLogger().setLevel(logging.INFO)
 

aws_sagemaker_studio/sagemaker_debugger/tensorflow_builtin_rule/src/mnist_zerocodechange.py

@@ -8,14 +8,13 @@ For more information, please refer to https://github.com/awslabs/sagemaker-debug
 
 # Standard Library
 import argparse
+import logging
 import random
 
 # Third Party
 import numpy as np
 import tensorflow as tf
 
-import logging
-
 logging.getLogger().setLevel(logging.INFO)
 
 parser = argparse.ArgumentParser()

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/deploy_tensorflow_model_on_Inf1_instance/mnist.py

@@ -1,4 +1,5 @@
 import os
+
 import tensorflow as tf
 from tensorflow.python.estimator.model_fn import ModeKeys as Modes
 
@@ -110,9 +111,10 @@ def _input_fn(training_dir, training_filename, batch_size=100):
 
 
 def neo_preprocess(payload, content_type):
-    import logging
-    import numpy as np
     import io
+    import logging
+
+    import numpy as np
 
     logging.info("Invoking user-defined pre-processing function")
 
@@ -132,9 +134,10 @@ def neo_preprocess(payload, content_type):
 
 ### NOTE: this function cannot use MXNet
 def neo_postprocess(result):
-    import logging
-    import numpy as np
     import json
+    import logging
+
+    import numpy as np
 
     logging.info("Invoking user-defined post-processing function")
 

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/deploy_tensorflow_model_on_Inf1_instance/utils.py

@@ -1,5 +1,6 @@
 """Converts MNIST data to TFRecords file format with Example protos."""
 import os
+
 import tensorflow as tf
 
 

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/gluoncv_ssd_mobilenet/ssd_entry_point.py

@@ -1,9 +1,10 @@
 import io
-import os
-import PIL.Image
 import json
 import logging
+import os
+
 import numpy as np
+import PIL.Image
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
@@ -12,14 +13,13 @@ logger.setLevel(logging.DEBUG)
 # Training methods                                             #
 # ------------------------------------------------------------ #
 
+import argparse
 import glob
 import time
-import argparse
 import warnings
+
 import mxnet as mx
-from mxnet import nd
-from mxnet import gluon
-from mxnet import autograd
+from mxnet import autograd, gluon, nd
 
 
 def parse_args():
@@ -74,7 +74,7 @@ def get_dataloader(net, data_shape, batch_size, num_workers, ctx):
     """Get dataloader."""
 
     from gluoncv import data as gdata
-    from gluoncv.data.batchify import Tuple, Stack, Pad
+    from gluoncv.data.batchify import Pad, Stack, Tuple
     from gluoncv.data.transforms.presets.ssd import SSDDefaultTrainTransform
 
     width, height = data_shape, data_shape

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/gluoncv_ssd_mobilenet/tools/concat_db.py

@@ -15,9 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 
-from imdb import Imdb
 import random
 
+from imdb import Imdb
+
 
 class ConcatDB(Imdb):
     """

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/gluoncv_ssd_mobilenet/tools/im2rec.py

@@ -18,18 +18,20 @@
 # under the License.
 
 from __future__ import print_function
+
 import os
 import sys
 
 curr_path = os.path.abspath(os.path.dirname(__file__))
 sys.path.append(os.path.join(curr_path, "../python"))
-import mxnet as mx
-import random
 import argparse
-import cv2
+import random
 import time
 import traceback
 
+import cv2
+import mxnet as mx
+
 try:
     import multiprocessing
 except ImportError:

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/gluoncv_ssd_mobilenet/tools/imdb.py

@@ -15,9 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 
-import numpy as np
 import os.path as osp
 
+import numpy as np
+
 
 class Imdb(object):
     """

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/gluoncv_ssd_mobilenet/tools/pascal_voc.py

@@ -16,11 +16,13 @@
 # under the License.
 
 from __future__ import print_function
+
 import os
+import xml.etree.ElementTree as ET
+
+import cv2
 import numpy as np
 from imdb import Imdb
-import xml.etree.ElementTree as ET
-import cv2
 
 
 class PascalVoc(Imdb):

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/gluoncv_ssd_mobilenet/tools/prepare_dataset.py

@@ -19,14 +19,16 @@
 # under the License.
 
 from __future__ import print_function
-import sys, os
+
 import argparse
+import os
 import subprocess
+import sys
 
 curr_path = os.path.abspath(os.path.dirname(__file__))
 sys.path.append(os.path.join(curr_path, ".."))
-from pascal_voc import PascalVoc
 from concat_db import ConcatDB
+from pascal_voc import PascalVoc
 
 
 def load_pascal(image_set, year, devkit_path, shuffle=False):

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/imageclassification_caltech/inference.py

@@ -1,12 +1,13 @@
-import numpy as np
+import io
 import json
+import logging
+import os
+
 import mxnet as mx
 
 # Please make sure to import neomx
 import neomx  # noqa: F401
-import io
-import os
-import logging
+import numpy as np
 
 # Change the context to mx.gpu() if deploying to a GPU endpoint
 ctx = mx.cpu()

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/pytorch_torchvision/code/resnet18.py

@@ -4,9 +4,9 @@ import logging
 import os
 import pickle
 
+import neopytorch
 import numpy as np
 import torch
-import neopytorch
 import torchvision.transforms as transforms
 from PIL import Image  # Training container doesn't have this package
 

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/pytorch_vgg19_bn/code/vgg19_bn_compiled.py

@@ -4,9 +4,9 @@ import logging
 import os
 import pickle
 
+import neopytorch
 import numpy as np
 import torch
-import neopytorch
 import torchvision.transforms as transforms
 from PIL import Image  # Training container doesn't have this package
 

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/pytorch_vgg19_bn/code/vgg19_bn_uncompiled.py

@@ -4,9 +4,9 @@ import logging
 import os
 import pickle
 
+import neopytorch
 import numpy as np
 import torch
-import neopytorch
 import torchvision.transforms as transforms
 from PIL import Image  # Training container doesn't have this package
 

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/tensorflow_distributed_mnist/mnist.py

@@ -1,4 +1,5 @@
 import os
+
 import tensorflow as tf
 from tensorflow.python.estimator.model_fn import ModeKeys as Modes
 
@@ -111,9 +112,10 @@ def _input_fn(training_dir, training_filename, batch_size=100):
 
 
 def neo_preprocess(payload, content_type):
-    import logging
-    import numpy as np
     import io
+    import logging
+
+    import numpy as np
 
     logging.info("Invoking user-defined pre-processing function")
 
@@ -133,9 +135,10 @@ def neo_preprocess(payload, content_type):
 
 ### NOTE: this function cannot use MXNet
 def neo_postprocess(result):
-    import logging
-    import numpy as np
     import json
+    import logging
+
+    import numpy as np
 
     logging.info("Invoking user-defined post-processing function")
 

aws_sagemaker_studio/sagemaker_neo_compilation_jobs/tensorflow_distributed_mnist/utils.py

@@ -1,5 +1,6 @@
 """Converts MNIST data to TFRecords file format with Example protos."""
 import os
+
 import tensorflow as tf
 
 

aws_sagemaker_studio/sagemaker_studio_image_build/tensorflow_bring_your_own/cifar10/cifar10.py

@@ -10,17 +10,14 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import argparse
 import functools
 import os
 
-import tensorflow as tf
-
 import resnet_model
+import tensorflow as tf
 
 INPUT_TENSOR_NAME = "inputs"
 SIGNATURE_NAME = "serving_default"

aws_sagemaker_studio/sagemaker_studio_image_build/tensorflow_bring_your_own/cifar10/resnet_model.py

@@ -29,9 +29,7 @@ The key difference of the full preactivation 'v2' variant compared to the
 'v1' variant in [1] is the use of batch normalization before every weight layer
 rather than after.
 """
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import tensorflow as tf
 

aws_sagemaker_studio/sagemaker_studio_image_build/tensorflow_bring_your_own/cifar10/train

@@ -19,10 +19,10 @@
 
 from __future__ import print_function
 
-import os
 import json
-import sys
+import os
 import subprocess
+import sys
 import traceback
 
 # These are the paths to where SageMaker mounts interesting things in your container.

aws_sagemaker_studio/sagemaker_studio_image_build/tensorflow_bring_your_own/utils/generate_cifar10_tfrecords.py

@@ -16,9 +16,7 @@ Generates tf.train.Example protos and writes them to TFRecord files from the
 python version of the CIFAR-10 dataset downloaded from
 https://www.cs.toronto.edu/~kriz/cifar.html.
 """
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import argparse
 import os
@@ -26,12 +24,12 @@ import shutil
 import sys
 import tarfile
 
-from six.moves import cPickle as pickle
-from six.moves import urllib
-from six.moves import xrange  # pylint: disable=redefined-builtin
-from ipywidgets import FloatProgress
-from IPython.display import display
 import tensorflow as tf
+from IPython.display import display
+from ipywidgets import FloatProgress
+from six.moves import cPickle as pickle
+from six.moves import xrange  # pylint: disable=redefined-builtin
+from six.moves import urllib
 
 CIFAR_FILENAME = "cifar-10-python.tar.gz"
 CIFAR_DOWNLOAD_URL = "https://www.cs.toronto.edu/~kriz/" + CIFAR_FILENAME

aws_sagemaker_studio/sagemaker_studio_image_build/xgboost_bring_your_own/xgboost/predictor.py

@@ -3,16 +3,15 @@
 
 from __future__ import print_function
 
-import os
 import json
+import os
 import pickle
-from io import StringIO
-import sys
 import signal
+import sys
 import traceback
+from io import StringIO
 
 import flask
-
 import pandas as pd
 import xgboost
 

aws_sagemaker_studio/sagemaker_studio_image_build/xgboost_bring_your_own/xgboost/serve

@@ -14,6 +14,7 @@
 # timeout                  MODEL_SERVER_TIMEOUT              60 seconds
 
 from __future__ import print_function
+
 import multiprocessing
 import os
 import signal

aws_sagemaker_studio/sagemaker_studio_image_build/xgboost_bring_your_own/xgboost/train

@@ -6,15 +6,15 @@
 
 from __future__ import print_function
 
-import os
 import json
+import os
 import pickle
 import sys
 import traceback
 
 import pandas as pd
-import xgboost as xgb
 import smdebug.xgboost as smd
+import xgboost as xgb
 
 print("Libraries imported")
 

contrib/inference_pipeline_custom_containers/containers/postprocessor/docker/code/main.py

@@ -1,44 +1,26 @@
-from __future__ import absolute_import
-from __future__ import print_function
-from string import Template
-
-import sys
-import time
-import os
-import multiprocessing
-import signal
-import subprocess
-from urllib.parse import urlparse
-
-from utils import ExitSignalHandler
-from utils import (
-    write_failure_file,
-    print_json_object,
-    load_json_object,
-    save_model_artifacts,
-    print_files_in_path,
-)
-
-import traceback
-from io import StringIO
-import os
-import shutil
+from __future__ import absolute_import, print_function
 
 import argparse
 import csv
+import decimal
 import json
-import numpy as np
-import pandas as pd
-
-from joblib import dump, load
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.preprocessing import LabelEncoder
+import multiprocessing
+import os
+import shutil
+import signal
+import subprocess
+import sys
+import time
+import traceback
+from io import StringIO
+from string import Template
+from urllib.parse import urlparse
 
 import boto3
-import decimal
-
+import numpy as np
+import pandas as pd
 from botocore.exceptions import ClientError
-
+from joblib import dump, load
 from sagemaker_containers.beta.framework import (
     content_types,
     encoders,
@@ -47,6 +29,17 @@ from sagemaker_containers.beta.framework import (
     transformer,
     worker,
 )
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import LabelEncoder
+
+from utils import (
+    ExitSignalHandler,
+    load_json_object,
+    print_files_in_path,
+    print_json_object,
+    save_model_artifacts,
+    write_failure_file,
+)
 
 cpu_count = multiprocessing.cpu_count()
 

contrib/inference_pipeline_custom_containers/containers/postprocessor/docker/code/predictor.py

@@ -3,21 +3,22 @@
 
 from __future__ import print_function
 
-import os
-import sys
-import stat
-import json
-import shutil
-import flask
-from flask import Flask, jsonify, request, Response
-import glob
-import pandas as pd
-import numpy as np
-import random
 import csv
+import glob
+import json
+import os
+import random
+import shutil
+import stat
+import sys
 from io import StringIO
-from joblib import dump, load
+
 import boto3
+import flask
+import numpy as np
+import pandas as pd
+from flask import Flask, Response, jsonify, request
+from joblib import dump, load
 from sagemaker_containers.beta.framework import (
     content_types,
     encoders,

contrib/inference_pipeline_custom_containers/containers/postprocessor/docker/code/utils.py

@@ -1,7 +1,7 @@
-import signal
-import pprint
 import json
 import os
+import pprint
+import signal
 from os import path
 
 

contrib/inference_pipeline_custom_containers/containers/preprocessor/docker/code/main.py

@@ -1,44 +1,25 @@
-from __future__ import absolute_import
-from __future__ import print_function
-from string import Template
-
-import sys
-import time
-import os
-import shutil
-import multiprocessing
-import signal
-import subprocess
-
-from utils import ExitSignalHandler
-from utils import (
-    write_failure_file,
-    print_json_object,
-    load_json_object,
-    save_model_artifacts,
-    print_files_in_path,
-)
-
-
-import traceback
-from io import StringIO
-import os
+from __future__ import absolute_import, print_function
 
 import argparse
 import csv
+import decimal
 import json
-import numpy as np
-import pandas as pd
-
-from joblib import dump, load
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.preprocessing import LabelEncoder
+import multiprocessing
+import os
+import shutil
+import signal
+import subprocess
+import sys
+import time
+import traceback
+from io import StringIO
+from string import Template
 
 import boto3
-import decimal
-
+import numpy as np
+import pandas as pd
 from botocore.exceptions import ClientError
-
+from joblib import dump, load
 from sagemaker_containers.beta.framework import (
     content_types,
     encoders,
@@ -47,6 +28,17 @@ from sagemaker_containers.beta.framework import (
     transformer,
     worker,
 )
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import LabelEncoder
+
+from utils import (
+    ExitSignalHandler,
+    load_json_object,
+    print_files_in_path,
+    print_json_object,
+    save_model_artifacts,
+    write_failure_file,
+)
 
 hyperparameters_file_path = "/opt/ml/input/config/hyperparameters.json"
 inputdataconfig_file_path = "/opt/ml/input/config/inputdataconfig.json"

contrib/inference_pipeline_custom_containers/containers/preprocessor/docker/code/predictor.py

@@ -3,18 +3,19 @@
 
 from __future__ import print_function
 
-import os
-import sys
-import stat
-import json
-import shutil
-import flask
-from flask import Flask, jsonify, request, make_response, Response
-import glob
-import pandas as pd
-import numpy as np
 import csv
+import glob
+import json
+import os
+import shutil
+import stat
+import sys
 from io import StringIO
+
+import flask
+import numpy as np
+import pandas as pd
+from flask import Flask, Response, jsonify, make_response, request
 from joblib import dump, load
 from sagemaker_containers.beta.framework import (
     content_types,
@@ -24,12 +25,13 @@ from sagemaker_containers.beta.framework import (
     transformer,
     worker,
 )
+
 from utils import (
-    write_failure_file,
-    print_json_object,
     load_json_object,
-    save_model_artifacts,
     print_files_in_path,
+    print_json_object,
+    save_model_artifacts,
+    write_failure_file,
 )
 
 model_artifacts_path = "/opt/ml/model/"

contrib/inference_pipeline_custom_containers/containers/preprocessor/docker/code/utils.py

@@ -1,7 +1,7 @@
-import signal
-import pprint
 import json
 import os
+import pprint
+import signal
 from os import path
 
 

contrib/inference_pipeline_custom_containers/generate-training-data.py

@@ -1,6 +1,6 @@
 import argparse
-import random
 import csv
+import random
 
 parser = argparse.ArgumentParser(description="Generate sample data")
 parser.add_argument("--samples", type=int, default=10000, help="Number of samples to generate")

contrib/inference_pipeline_custom_containers/load-ddb-data.py

@@ -1,5 +1,6 @@
 import argparse
 import csv
+
 import boto3
 
 parser = argparse.ArgumentParser(description="Load DynamoDB data")

end_to_end/create_dataset.py

@@ -1,8 +1,9 @@
-import time
-import boto3
 import argparse
-import pandas as pd
 import pathlib
+import time
+
+import boto3
+import pandas as pd
 
 # Parse argument variables passed via the CreateDataset processing step
 parser = argparse.ArgumentParser()

end_to_end/demo_helpers.py

@@ -1,6 +1,7 @@
-import boto3
 import time
 
+import boto3
+
 
 def delete_project_resources(
     sagemaker_boto_client,

end_to_end/deploy_model.py

@@ -1,7 +1,7 @@
-import time
-import boto3
 import argparse
+import time
 
+import boto3
 
 # Parse argument variables passed via the DeployModel processing step
 parser = argparse.ArgumentParser()

end_to_end/xgboost_starter_script.py

@@ -1,10 +1,11 @@
-import os
-import sys
-import pickle
-import xgboost as xgb
 import argparse
-import pandas as pd
 import json
+import os
+import pickle
+import sys
+
+import pandas as pd
+import xgboost as xgb
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

frameworks/mxnet/code/inference.py

@@ -15,15 +15,14 @@
 
 from __future__ import print_function
 
-import logging
-
-from mxnet import gluon
-import mxnet as mx
-
-import numpy as np
 import json
+import logging
 import os
 
+import mxnet as mx
+import numpy as np
+from mxnet import gluon
+
 logging.basicConfig(level=logging.DEBUG)
 
 

frameworks/mxnet/code/test_inference.py

@@ -13,16 +13,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from inference import transform_fn, model_fn
-import os
 import json
+import os
 import shutil
+import tarfile
+
 import boto3
 import botocore
-import tarfile
 import numpy as np
 import sagemaker
 
+from inference import model_fn, transform_fn
+
 
 def fetch_model(model_data):
     """Untar the model.tar.gz object either from local file system

frameworks/mxnet/code/test_train.py

@@ -13,12 +13,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from train import train, parse_args
-
-import sys
-import os
-import boto3
 import json
+import os
+import sys
+
+import boto3
+from train import parse_args, train
 
 dirname = os.path.dirname(os.path.abspath(__file__))
 with open(os.path.join(dirname, "config.json"), "r") as f: