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change: enable wrong-import-position pylint check #907

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Merged
merged 8 commits into from
Jul 9, 2019
Merged

change: enable wrong-import-position pylint check #907

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bc57c5e
change: enable wrong-import-position pylint check
knakad Jul 5, 2019
08c3722
Merge branch 'master' into 20190705-lint-tighten-import-order
knakad Jul 5, 2019
20197b3
change: updating import pattern for sagemaker.tensorflow
knakad Jul 8, 2019
414a468
Merge branch 'master' into 20190705-lint-tighten-import-order
knakad Jul 8, 2019
9249318
Merge branch 'master' into 20190705-lint-tighten-import-order
knakad Jul 9, 2019
ff57e34
fix: fixing integration tests
knakad Jul 9, 2019
4a19235
change: reformatting
knakad Jul 9, 2019
fded477
Merge branch 'master' into 20190705-lint-tighten-import-order
knakad Jul 9, 2019
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1 change: 0 additions & 1 deletion .pylintrc
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Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,6 @@ 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
Expand Down
3 changes: 2 additions & 1 deletion src/sagemaker/amazon/record_pb2.py
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11 changes: 5 additions & 6 deletions src/sagemaker/cli/tensorflow.py
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Expand Up @@ -12,6 +12,9 @@
# 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


Expand All @@ -30,9 +33,7 @@ def __init__(self, args):
self.evaluation_steps = args.evaluation_steps

def create_estimator(self):
from sagemaker.tensorflow import TensorFlow

return TensorFlow(
return estimator.TensorFlow(
training_steps=self.training_steps,
evaluation_steps=self.evaluation_steps,
py_version=self.python,
Expand All @@ -47,9 +48,7 @@ def create_estimator(self):

class TensorFlowHostCommand(HostCommand):
def create_model(self, model_url):
from sagemaker.tensorflow.model import TensorFlowModel

return TensorFlowModel(
return model.TensorFlowModel(
model_data=model_url,
role=self.role_name,
entry_point=self.script,
Expand Down
6 changes: 4 additions & 2 deletions src/sagemaker/tensorflow/__init__.py
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Expand Up @@ -19,5 +19,7 @@
# 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.estimator import TensorFlow # noqa: E402, F401
from sagemaker.tensorflow.model import TensorFlowModel, TensorFlowPredictor # noqa: E402, F401
from sagemaker.tensorflow import ( # noqa: E402,F401 # pylint: disable=wrong-import-position
estimator,
)
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why not:

from sagemaker.tensorflow import model, estimator

?

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That'll work as well, but shouldn't we aim to be more restrictive in our imports?

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As discussed, per the google python style guide, we should aim to import modules, not classes. Good callout!
Also per the google style guide, I went ahead and made the change in two lines.

from sagemaker.tensorflow import model # noqa: E402,F401 # pylint: disable=wrong-import-position
12 changes: 6 additions & 6 deletions tests/component/test_tf_estimator.py
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Expand Up @@ -14,7 +14,7 @@

import pytest
from mock import Mock
from sagemaker.tensorflow import TensorFlow
from sagemaker.tensorflow import estimator


SCRIPT = "resnet_cifar_10.py"
Expand Down Expand Up @@ -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):
estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point=SCRIPT,
source_dir=SOURCE_DIR,
role=ROLE,
Expand All @@ -64,13 +64,13 @@ def test_deploy(sagemaker_session, tf_version):
base_job_name="test-cifar",
)

estimator.fit("s3://mybucket/train")
print("job succeeded: {}".format(estimator.latest_training_job.name))
tensorflow_estimator.fit("s3://mybucket/train")
print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))

estimator.deploy(initial_instance_count=1, instance_type=INSTANCE_TYPE_CPU)
tensorflow_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(
estimator._current_job_name,
tensorflow_estimator._current_job_name,
ROLE,
{
"Environment": {
Expand Down
12 changes: 6 additions & 6 deletions tests/integ/test_horovod.py
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Expand Up @@ -22,7 +22,7 @@

import sagemaker.utils
import tests.integ as integ
from sagemaker.tensorflow import TensorFlow
from sagemaker.tensorflow import estimator
from tests.integ import test_region, timeout, HOSTING_NO_P3_REGIONS

horovod_dir = os.path.join(os.path.dirname(__file__), "..", "data", "horovod")
Expand All @@ -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")
estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point=os.path.join(horovod_dir, "test_hvd_basic.py"),
role="SageMakerRole",
train_instance_count=2,
Expand All @@ -60,10 +60,10 @@ def test_horovod(sagemaker_session, instance_type, tmpdir):
)

with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
estimator.fit(job_name=job_name)
tensorflow_estimator.fit(job_name=job_name)

tmp = str(tmpdir)
extract_files_from_s3(estimator.model_data, tmp)
extract_files_from_s3(tensorflow_estimator.model_data, tmp)

for rank in range(2):
assert read_json("rank-%s" % rank, tmp)["rank"] == rank
Expand All @@ -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")
estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point=os.path.join(horovod_dir, "test_hvd_basic.py"),
role="SageMakerRole",
train_instance_count=2,
Expand All @@ -88,7 +88,7 @@ def test_horovod_local_mode(sagemaker_local_session, instances, processes, tmpdi
)

with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
estimator.fit(job_name=job_name)
tensorflow_estimator.fit(job_name=job_name)

tmp = str(tmpdir)
extract_files(output_path.replace("file://", ""), tmp)
Expand Down
52 changes: 26 additions & 26 deletions tests/integ/test_local_mode.py
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Expand Up @@ -27,7 +27,7 @@

from sagemaker.local import LocalSession, LocalSagemakerRuntimeClient, LocalSagemakerClient
from sagemaker.mxnet import MXNet
from sagemaker.tensorflow import TensorFlow
from sagemaker.tensorflow import estimator

# 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")
Expand Down Expand Up @@ -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")

estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point=script_path,
role="SageMakerRole",
framework_version="1.12",
Expand All @@ -103,16 +103,16 @@ def test_tf_local_mode(sagemaker_local_session):
sagemaker_session=sagemaker_local_session,
)

inputs = estimator.sagemaker_session.upload_data(
inputs = tensorflow_estimator.sagemaker_session.upload_data(
path=DATA_PATH, key_prefix="integ-test-data/tf_iris"
)
estimator.fit(inputs)
print("job succeeded: {}".format(estimator.latest_training_job.name))
tensorflow_estimator.fit(inputs)
print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))

endpoint_name = estimator.latest_training_job.name
endpoint_name = tensorflow_estimator.latest_training_job.name
with lock.lock(LOCK_PATH):
try:
json_predictor = estimator.deploy(
json_predictor = tensorflow_estimator.deploy(
initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
)

Expand All @@ -124,7 +124,7 @@ def test_tf_local_mode(sagemaker_local_session):

assert dict_result == list_result
finally:
estimator.delete_endpoint()
tensorflow_estimator.delete_endpoint()


@pytest.mark.local_mode
Expand All @@ -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")

estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point=script_path,
role="SageMakerRole",
framework_version="1.12",
Expand All @@ -147,14 +147,14 @@ def test_tf_distributed_local_mode(sagemaker_local_session):
)

inputs = "file://" + DATA_PATH
estimator.fit(inputs)
print("job succeeded: {}".format(estimator.latest_training_job.name))
tensorflow_estimator.fit(inputs)
print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))

endpoint_name = estimator.latest_training_job.name
endpoint_name = tensorflow_estimator.latest_training_job.name

with lock.lock(LOCK_PATH):
try:
json_predictor = estimator.deploy(
json_predictor = tensorflow_estimator.deploy(
initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
)

Expand All @@ -166,7 +166,7 @@ def test_tf_distributed_local_mode(sagemaker_local_session):

assert dict_result == list_result
finally:
estimator.delete_endpoint()
tensorflow_estimator.delete_endpoint()


@pytest.mark.local_mode
Expand All @@ -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")

estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point=script_path,
role="SageMakerRole",
framework_version="1.12",
Expand All @@ -189,13 +189,13 @@ def test_tf_local_data(sagemaker_local_session):
)

inputs = "file://" + DATA_PATH
estimator.fit(inputs)
print("job succeeded: {}".format(estimator.latest_training_job.name))
tensorflow_estimator.fit(inputs)
print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))

endpoint_name = estimator.latest_training_job.name
endpoint_name = tensorflow_estimator.latest_training_job.name
with lock.lock(LOCK_PATH):
try:
json_predictor = estimator.deploy(
json_predictor = tensorflow_estimator.deploy(
initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
)

Expand All @@ -207,7 +207,7 @@ def test_tf_local_data(sagemaker_local_session):

assert dict_result == list_result
finally:
estimator.delete_endpoint()
tensorflow_estimator.delete_endpoint()


@pytest.mark.local_mode
Expand All @@ -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")

estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point=script_path,
role="SageMakerRole",
framework_version="1.12",
Expand All @@ -231,13 +231,13 @@ def test_tf_local_data_local_script():

inputs = "file://" + DATA_PATH

estimator.fit(inputs)
print("job succeeded: {}".format(estimator.latest_training_job.name))
tensorflow_estimator.fit(inputs)
print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))

endpoint_name = estimator.latest_training_job.name
endpoint_name = tensorflow_estimator.latest_training_job.name
with lock.lock(LOCK_PATH):
try:
json_predictor = estimator.deploy(
json_predictor = tensorflow_estimator.deploy(
initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
)

Expand All @@ -249,7 +249,7 @@ def test_tf_local_data_local_script():

assert dict_result == list_result
finally:
estimator.delete_endpoint()
tensorflow_estimator.delete_endpoint()


@pytest.mark.local_mode
Expand Down
16 changes: 9 additions & 7 deletions tests/integ/test_tf_cifar.py
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Expand Up @@ -21,7 +21,7 @@
import tests.integ
from tests.integ.timeout import timeout_and_delete_endpoint_by_name, timeout

from sagemaker.tensorflow import TensorFlow
from sagemaker.tensorflow import estimator
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand Down Expand Up @@ -50,7 +50,7 @@ def test_cifar(sagemaker_session):

dataset_path = os.path.join(tests.integ.DATA_DIR, "cifar_10", "data")

estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point="resnet_cifar_10.py",
source_dir=script_path,
role="SageMakerRole",
Expand All @@ -64,17 +64,19 @@ def test_cifar(sagemaker_session):
base_job_name="test-cifar",
)

inputs = estimator.sagemaker_session.upload_data(
inputs = tensorflow_estimator.sagemaker_session.upload_data(
path=dataset_path, key_prefix="data/cifar10"
)
job_name = unique_name_from_base("test-tf-cifar")

estimator.fit(inputs, logs=False, job_name=job_name)
print("job succeeded: {}".format(estimator.latest_training_job.name))
tensorflow_estimator.fit(inputs, logs=False, job_name=job_name)
print("job succeeded: {}".format(tensorflow_estimator.latest_training_job.name))

endpoint_name = estimator.latest_training_job.name
endpoint_name = tensorflow_estimator.latest_training_job.name
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
predictor = estimator.deploy(initial_instance_count=1, instance_type="ml.p2.xlarge")
predictor = tensorflow_estimator.deploy(
initial_instance_count=1, instance_type="ml.p2.xlarge"
)
predictor.serializer = PickleSerializer()
predictor.content_type = PICKLE_CONTENT_TYPE

Expand Down
14 changes: 8 additions & 6 deletions tests/integ/test_tf_keras.py
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Expand Up @@ -20,7 +20,7 @@
import tests.integ
from tests.integ.timeout import timeout_and_delete_endpoint_by_name, timeout

from sagemaker.tensorflow import TensorFlow
from sagemaker.tensorflow import estimator
from sagemaker.utils import unique_name_from_base


Expand All @@ -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):
estimator = TensorFlow(
tensorflow_estimator = estimator.TensorFlow(
entry_point="keras_cnn_cifar_10.py",
source_dir=script_path,
role="SageMakerRole",
Expand All @@ -51,16 +51,18 @@ def test_keras(sagemaker_session):
train_max_run=45 * 60,
)

inputs = estimator.sagemaker_session.upload_data(
inputs = tensorflow_estimator.sagemaker_session.upload_data(
path=dataset_path, key_prefix="data/cifar10"
)
job_name = unique_name_from_base("test-tf-keras")

estimator.fit(inputs, job_name=job_name)
tensorflow_estimator.fit(inputs, job_name=job_name)

endpoint_name = estimator.latest_training_job.name
endpoint_name = tensorflow_estimator.latest_training_job.name
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
predictor = estimator.deploy(initial_instance_count=1, instance_type="ml.p2.xlarge")
predictor = tensorflow_estimator.deploy(
initial_instance_count=1, instance_type="ml.p2.xlarge"
)

data = np.random.randn(32, 32, 3)
predict_response = predictor.predict(data)
Expand Down
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