Revert "change: enable wrong-import-position pylint check"

.pylintrc

@@ -100,6 +100,7 @@ disable=
     simplifiable-if-expression, # TODO: Simplify expressions
     too-many-public-methods, # TODO: Resolve
     ungrouped-imports, # TODO: Group imports
+    wrong-import-position, # TODO: Correct import positions
     consider-using-ternary, # TODO: Consider ternary expressions
     chained-comparison, # TODO: Simplify chained comparison between operands
     simplifiable-if-statement, # TODO: Simplify ifs

src/sagemaker/cli/tensorflow.py

@@ -12,9 +12,6 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
-from sagemaker.tensorflow import estimator
-from sagemaker.tensorflow import model
-
 from sagemaker.cli.common import HostCommand, TrainCommand
 
 
@@ -33,7 +30,9 @@ def __init__(self, args):
         self.evaluation_steps = args.evaluation_steps
 
     def create_estimator(self):
-        return estimator.TensorFlow(
+        from sagemaker.tensorflow import TensorFlow
+
+        return TensorFlow(
             training_steps=self.training_steps,
             evaluation_steps=self.evaluation_steps,
             py_version=self.python,
@@ -48,7 +47,9 @@ def create_estimator(self):
 
 class TensorFlowHostCommand(HostCommand):
     def create_model(self, model_url):
-        return model.TensorFlowModel(
+        from sagemaker.tensorflow.model import TensorFlowModel
+
+        return TensorFlowModel(
             model_data=model_url,
             role=self.role_name,
             entry_point=self.script,

src/sagemaker/tensorflow/__init__.py

@@ -19,7 +19,5 @@
 # classes for tensorflow serving. Currently tensorflow_serving_api can only be pip-installed for python 2.
 sys.path.append(os.path.dirname(__file__))
 
-from sagemaker.tensorflow import (  # noqa: E402,F401  # pylint: disable=wrong-import-position
-    estimator,
-)
-from sagemaker.tensorflow import model  # noqa: E402,F401  # pylint: disable=wrong-import-position
+from sagemaker.tensorflow.estimator import TensorFlow  # noqa: E402, F401
+from sagemaker.tensorflow.model import TensorFlowModel, TensorFlowPredictor  # noqa: E402, F401

tests/component/test_tf_estimator.py

@@ -14,7 +14,7 @@
 
 import pytest
 from mock import Mock
-from sagemaker.tensorflow import estimator
+from sagemaker.tensorflow import TensorFlow
 
 
 SCRIPT = "resnet_cifar_10.py"
@@ -53,7 +53,7 @@ def sagemaker_session():
 
 # Test that we pass all necessary fields from estimator to the session when we call deploy
 def test_deploy(sagemaker_session, tf_version):
-    tensorflow_estimator = estimator.TensorFlow(
+    estimator = TensorFlow(
         entry_point=SCRIPT,
         source_dir=SOURCE_DIR,
         role=ROLE,
@@ -64,13 +64,13 @@ def test_deploy(sagemaker_session, tf_version):
         base_job_name="test-cifar",
     )
 
-    tensorflow_estimator.fit("s3://mybucket/train")
-    print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))
+    estimator.fit("s3://mybucket/train")
+    print("job succeeded: {}".format(estimator.latest_training_job.name))
 
-    tensorflow_estimator.deploy(initial_instance_count=1, instance_type=INSTANCE_TYPE_CPU)
+    estimator.deploy(initial_instance_count=1, instance_type=INSTANCE_TYPE_CPU)
     image = IMAGE_URI_FORMAT_STRING.format(REGION, CPU_IMAGE_NAME, tf_version, "cpu", "py2")
     sagemaker_session.create_model.assert_called_with(
-        tensorflow_estimator._current_job_name,
+        estimator._current_job_name,
         ROLE,
         {
             "Environment": {

tests/integ/test_horovod.py

@@ -22,7 +22,7 @@
 
 import sagemaker.utils
 import tests.integ as integ
-from sagemaker.tensorflow import estimator
+from sagemaker.tensorflow import TensorFlow
 from tests.integ import test_region, timeout, HOSTING_NO_P3_REGIONS
 
 horovod_dir = os.path.join(os.path.dirname(__file__), "..", "data", "horovod")
@@ -47,7 +47,7 @@ def instance_type(request):
 @pytest.mark.canary_quick
 def test_horovod(sagemaker_session, instance_type, tmpdir):
     job_name = sagemaker.utils.unique_name_from_base("tf-horovod")
-    tensorflow_estimator = estimator.TensorFlow(
+    estimator = TensorFlow(
         entry_point=os.path.join(horovod_dir, "test_hvd_basic.py"),
         role="SageMakerRole",
         train_instance_count=2,
@@ -60,10 +60,10 @@ def test_horovod(sagemaker_session, instance_type, tmpdir):
     )
 
     with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
-        tensorflow_estimator.fit(job_name=job_name)
+        estimator.fit(job_name=job_name)
 
         tmp = str(tmpdir)
-        extract_files_from_s3(tensorflow_estimator.model_data, tmp)
+        extract_files_from_s3(estimator.model_data, tmp)
 
         for rank in range(2):
             assert read_json("rank-%s" % rank, tmp)["rank"] == rank
@@ -74,7 +74,7 @@ def test_horovod(sagemaker_session, instance_type, tmpdir):
 def test_horovod_local_mode(sagemaker_local_session, instances, processes, tmpdir):
     output_path = "file://%s" % tmpdir
     job_name = sagemaker.utils.unique_name_from_base("tf-horovod")
-    tensorflow_estimator = estimator.TensorFlow(
+    estimator = TensorFlow(
         entry_point=os.path.join(horovod_dir, "test_hvd_basic.py"),
         role="SageMakerRole",
         train_instance_count=2,
@@ -88,7 +88,7 @@ def test_horovod_local_mode(sagemaker_local_session, instances, processes, tmpdi
     )
 
     with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
-        tensorflow_estimator.fit(job_name=job_name)
+        estimator.fit(job_name=job_name)
 
         tmp = str(tmpdir)
         extract_files(output_path.replace("file://", ""), tmp)

tests/integ/test_local_mode.py

@@ -27,7 +27,7 @@
 
 from sagemaker.local import LocalSession, LocalSagemakerRuntimeClient, LocalSagemakerClient
 from sagemaker.mxnet import MXNet
-from sagemaker.tensorflow import estimator
+from sagemaker.tensorflow import TensorFlow
 
 # endpoint tests all use the same port, so we use this lock to prevent concurrent execution
 LOCK_PATH = os.path.join(tempfile.gettempdir(), "sagemaker_test_local_mode_lock")
@@ -90,7 +90,7 @@ def test_tf_local_mode(sagemaker_local_session):
     with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
         script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")
 
-        tensorflow_estimator = estimator.TensorFlow(
+        estimator = TensorFlow(
             entry_point=script_path,
             role="SageMakerRole",
             framework_version="1.12",
@@ -103,16 +103,16 @@ def test_tf_local_mode(sagemaker_local_session):
             sagemaker_session=sagemaker_local_session,
         )
 
-        inputs = tensorflow_estimator.sagemaker_session.upload_data(
+        inputs = estimator.sagemaker_session.upload_data(
             path=DATA_PATH, key_prefix="integ-test-data/tf_iris"
         )
-        tensorflow_estimator.fit(inputs)
-        print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))
+        estimator.fit(inputs)
+        print("job succeeded: {}".format(estimator.latest_training_job.name))
 
-    endpoint_name = tensorflow_estimator.latest_training_job.name
+    endpoint_name = estimator.latest_training_job.name
     with lock.lock(LOCK_PATH):
         try:
-            json_predictor = tensorflow_estimator.deploy(
+            json_predictor = estimator.deploy(
                 initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
             )
 
@@ -124,7 +124,7 @@ def test_tf_local_mode(sagemaker_local_session):
 
             assert dict_result == list_result
         finally:
-            tensorflow_estimator.delete_endpoint()
+            estimator.delete_endpoint()
 
 
 @pytest.mark.local_mode
@@ -133,7 +133,7 @@ def test_tf_distributed_local_mode(sagemaker_local_session):
     with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
         script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")
 
-        tensorflow_estimator = estimator.TensorFlow(
+        estimator = TensorFlow(
             entry_point=script_path,
             role="SageMakerRole",
             framework_version="1.12",
@@ -147,14 +147,14 @@ def test_tf_distributed_local_mode(sagemaker_local_session):
         )
 
         inputs = "file://" + DATA_PATH
-        tensorflow_estimator.fit(inputs)
-        print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))
+        estimator.fit(inputs)
+        print("job succeeded: {}".format(estimator.latest_training_job.name))
 
-    endpoint_name = tensorflow_estimator.latest_training_job.name
+    endpoint_name = estimator.latest_training_job.name
 
     with lock.lock(LOCK_PATH):
         try:
-            json_predictor = tensorflow_estimator.deploy(
+            json_predictor = estimator.deploy(
                 initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
             )
 
@@ -166,7 +166,7 @@ def test_tf_distributed_local_mode(sagemaker_local_session):
 
             assert dict_result == list_result
         finally:
-            tensorflow_estimator.delete_endpoint()
+            estimator.delete_endpoint()
 
 
 @pytest.mark.local_mode
@@ -175,7 +175,7 @@ def test_tf_local_data(sagemaker_local_session):
     with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
         script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")
 
-        tensorflow_estimator = estimator.TensorFlow(
+        estimator = TensorFlow(
             entry_point=script_path,
             role="SageMakerRole",
             framework_version="1.12",
@@ -189,13 +189,13 @@ def test_tf_local_data(sagemaker_local_session):
         )
 
         inputs = "file://" + DATA_PATH
-        tensorflow_estimator.fit(inputs)
-        print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))
+        estimator.fit(inputs)
+        print("job succeeded: {}".format(estimator.latest_training_job.name))
 
-    endpoint_name = tensorflow_estimator.latest_training_job.name
+    endpoint_name = estimator.latest_training_job.name
     with lock.lock(LOCK_PATH):
         try:
-            json_predictor = tensorflow_estimator.deploy(
+            json_predictor = estimator.deploy(
                 initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
             )
 
@@ -207,7 +207,7 @@ def test_tf_local_data(sagemaker_local_session):
 
             assert dict_result == list_result
         finally:
-            tensorflow_estimator.delete_endpoint()
+            estimator.delete_endpoint()
 
 
 @pytest.mark.local_mode
@@ -216,7 +216,7 @@ def test_tf_local_data_local_script():
     with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
         script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")
 
-        tensorflow_estimator = estimator.TensorFlow(
+        estimator = TensorFlow(
             entry_point=script_path,
             role="SageMakerRole",
             framework_version="1.12",
@@ -231,13 +231,13 @@ def test_tf_local_data_local_script():
 
         inputs = "file://" + DATA_PATH
 
-        tensorflow_estimator.fit(inputs)
-        print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))
+        estimator.fit(inputs)
+        print("job succeeded: {}".format(estimator.latest_training_job.name))
 
-    endpoint_name = tensorflow_estimator.latest_training_job.name
+    endpoint_name = estimator.latest_training_job.name
     with lock.lock(LOCK_PATH):
         try:
-            json_predictor = tensorflow_estimator.deploy(
+            json_predictor = estimator.deploy(
                 initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
             )
 
@@ -249,7 +249,7 @@ def test_tf_local_data_local_script():
 
             assert dict_result == list_result
         finally:
-            tensorflow_estimator.delete_endpoint()
+            estimator.delete_endpoint()
 
 
 @pytest.mark.local_mode

tests/integ/test_tf_cifar.py

@@ -21,7 +21,7 @@
 import tests.integ
 from tests.integ.timeout import timeout_and_delete_endpoint_by_name, timeout
 
-from sagemaker.tensorflow import estimator
+from sagemaker.tensorflow import TensorFlow
 from sagemaker.utils import unique_name_from_base
 
 PICKLE_CONTENT_TYPE = "application/python-pickle"
@@ -50,7 +50,7 @@ def test_cifar(sagemaker_session):
 
         dataset_path = os.path.join(tests.integ.DATA_DIR, "cifar_10", "data")
 
-        tensorflow_estimator = estimator.TensorFlow(
+        estimator = TensorFlow(
             entry_point="resnet_cifar_10.py",
             source_dir=script_path,
             role="SageMakerRole",
@@ -64,19 +64,17 @@ def test_cifar(sagemaker_session):
             base_job_name="test-cifar",
         )
 
-        inputs = tensorflow_estimator.sagemaker_session.upload_data(
+        inputs = estimator.sagemaker_session.upload_data(
             path=dataset_path, key_prefix="data/cifar10"
         )
         job_name = unique_name_from_base("test-tf-cifar")
 
-        tensorflow_estimator.fit(inputs, logs=False, job_name=job_name)
-        print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))
+        estimator.fit(inputs, logs=False, job_name=job_name)
+        print("job succeeded: {}".format(estimator.latest_training_job.name))
 
-    endpoint_name = tensorflow_estimator.latest_training_job.name
+    endpoint_name = estimator.latest_training_job.name
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
-        predictor = tensorflow_estimator.deploy(
-            initial_instance_count=1, instance_type="ml.p2.xlarge"
-        )
+        predictor = estimator.deploy(initial_instance_count=1, instance_type="ml.p2.xlarge")
         predictor.serializer = PickleSerializer()
         predictor.content_type = PICKLE_CONTENT_TYPE
 

tests/integ/test_tf_keras.py

@@ -20,7 +20,7 @@
 import tests.integ
 from tests.integ.timeout import timeout_and_delete_endpoint_by_name, timeout
 
-from sagemaker.tensorflow import estimator
+from sagemaker.tensorflow import TensorFlow
 from sagemaker.utils import unique_name_from_base
 
 
@@ -37,7 +37,7 @@ def test_keras(sagemaker_session):
     dataset_path = os.path.join(tests.integ.DATA_DIR, "cifar_10", "data")
 
     with timeout(minutes=45):
-        tensorflow_estimator = estimator.TensorFlow(
+        estimator = TensorFlow(
             entry_point="keras_cnn_cifar_10.py",
             source_dir=script_path,
             role="SageMakerRole",
@@ -51,18 +51,16 @@ def test_keras(sagemaker_session):
             train_max_run=45 * 60,
         )
 
-        inputs = tensorflow_estimator.sagemaker_session.upload_data(
+        inputs = estimator.sagemaker_session.upload_data(
             path=dataset_path, key_prefix="data/cifar10"
         )
         job_name = unique_name_from_base("test-tf-keras")
 
-        tensorflow_estimator.fit(inputs, job_name=job_name)
+        estimator.fit(inputs, job_name=job_name)
 
-    endpoint_name = tensorflow_estimator.latest_training_job.name
+    endpoint_name = estimator.latest_training_job.name
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
-        predictor = tensorflow_estimator.deploy(
-            initial_instance_count=1, instance_type="ml.p2.xlarge"
-        )
+        predictor = estimator.deploy(initial_instance_count=1, instance_type="ml.p2.xlarge")
 
         data = np.random.randn(32, 32, 3)
         predict_response = predictor.predict(data)