infra: sort imports in all .py files with isort (aws#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 copy
 import json
+import logging
+import os
 import subprocess
-import copy
+import sys
+import time
+import warnings
 
 warnings.filterwarnings("ignore", category=FutureWarning)
 
-import numpy as np
-import pandas as pd
 import pickle
+from collections import Counter
 from io import StringIO
-from timeit import default_timer as timer
 from itertools import islice
-from collections import Counter
+from timeit import default_timer as timer
+
+import numpy as np
+import pandas as pd
 
 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 ast
+import glob
+import json
 import logging
-import warnings
 import os
-import json
-import glob
+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 @@
 
 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 @@
 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

@@ -1,20 +1,21 @@
-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 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.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
+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 @@ def binary_mask_to_rle(cls, binary_mask):
 # 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))