Revert "change: enable wrong-import-position pylint check"#916

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mvsusp merged 1 commit into from
Jul 9, 2019
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Revert "change: enable wrong-import-position pylint check" #916

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1 change: 1 addition & 0 deletions .pylintrc
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand Down
3 changes: 1 addition & 2 deletions src/sagemaker/amazon/record_pb2.py
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11 changes: 6 additions & 5 deletions src/sagemaker/cli/tensorflow.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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


Expand All@@ -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,
Expand All@@ -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,
Expand Down
6 changes: 2 additions & 4 deletions src/sagemaker/tensorflow/__init__.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
12 changes: 6 additions & 6 deletions tests/component/test_tf_estimator.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand DownExpand 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):
tensorflow_estimator = estimator.TensorFlow(
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",
)

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": {
Expand Down
12 changes: 6 additions & 6 deletions tests/integ/test_horovod.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
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")
tensorflow_estimator = estimator.TensorFlow(
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):
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
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")
tensorflow_estimator = estimator.TensorFlow(
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):
tensorflow_estimator.fit(job_name=job_name)
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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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
Expand DownExpand 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")

tensorflow_estimator = estimator.TensorFlow(
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 = 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
)

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
)

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
)

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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

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
)

Expand All@@ -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
Expand Down
16 changes: 7 additions & 9 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 estimator
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand DownExpand Up@@ -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",
Expand All@@ -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

Expand Down
14 changes: 6 additions & 8 deletions tests/integ/test_tf_keras.py
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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
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):
tensorflow_estimator = estimator.TensorFlow(
estimator = TensorFlow(
entry_point="keras_cnn_cifar_10.py",
source_dir=script_path,
role="SageMakerRole",
Expand All@@ -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)
Expand Down
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Revert "change: enable wrong-import-position pylint check"#916

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 Merged

mvsusp
merged 1 commit into

from

Jul 9, 2019
 Merged


Revert "change: enable wrong-import-position pylint check"
#916
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1 change: 1 addition & 0 deletions
.pylintrc

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand Down

3 changes: 1 addition & 2 deletions
src/sagemaker/amazon/record_pb2.py

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11 changes: 6 additions & 5 deletions
src/sagemaker/cli/tensorflow.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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


Expand All@@ -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,
Expand All@@ -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,
Expand Down

6 changes: 2 additions & 4 deletions
src/sagemaker/tensorflow/__init__.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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

12 changes: 6 additions & 6 deletions
tests/component/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand DownExpand 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):
tensorflow_estimator = estimator.TensorFlow(
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",
 )

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": {
Expand Down

12 changes: 6 additions & 6 deletions
tests/integ/test_horovod.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
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")
tensorflow_estimator = estimator.TensorFlow(
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):
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
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")
tensorflow_estimator = estimator.TensorFlow(
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):
tensorflow_estimator.fit(job_name=job_name)
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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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
Expand DownExpand 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")

tensorflow_estimator = estimator.TensorFlow(
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 = 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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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

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
 )

Expand All@@ -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
Expand Down

16 changes: 7 additions & 9 deletions
tests/integ/test_tf_cifar.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand DownExpand Up@@ -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",
Expand All@@ -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

Expand Down

14 changes: 6 additions & 8 deletions
tests/integ/test_tf_keras.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
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):
tensorflow_estimator = estimator.TensorFlow(
estimator = TensorFlow(
 entry_point="keras_cnn_cifar_10.py",
 source_dir=script_path,
 role="SageMakerRole",
Expand All@@ -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)
Expand Down
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Revert "change: enable wrong-import-position pylint check"#916

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 Merged

mvsusp
merged 1 commit into

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Jul 9, 2019
 Merged


Revert "change: enable wrong-import-position pylint check"
#916
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1 change: 1 addition & 0 deletions
.pylintrc

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand Down

3 changes: 1 addition & 2 deletions
src/sagemaker/amazon/record_pb2.py

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11 changes: 6 additions & 5 deletions
src/sagemaker/cli/tensorflow.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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


Expand All@@ -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,
Expand All@@ -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,
Expand Down

6 changes: 2 additions & 4 deletions
src/sagemaker/tensorflow/__init__.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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

12 changes: 6 additions & 6 deletions
tests/component/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand DownExpand 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):
tensorflow_estimator = estimator.TensorFlow(
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",
 )

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": {
Expand Down

12 changes: 6 additions & 6 deletions
tests/integ/test_horovod.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
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")
tensorflow_estimator = estimator.TensorFlow(
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):
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
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")
tensorflow_estimator = estimator.TensorFlow(
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):
tensorflow_estimator.fit(job_name=job_name)
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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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
Expand DownExpand 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")

tensorflow_estimator = estimator.TensorFlow(
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 = 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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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

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
 )

Expand All@@ -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
Expand Down

16 changes: 7 additions & 9 deletions
tests/integ/test_tf_cifar.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand DownExpand Up@@ -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",
Expand All@@ -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

Expand Down

14 changes: 6 additions & 8 deletions
tests/integ/test_tf_keras.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
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):
tensorflow_estimator = estimator.TensorFlow(
estimator = TensorFlow(
 entry_point="keras_cnn_cifar_10.py",
 source_dir=script_path,
 role="SageMakerRole",
Expand All@@ -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)
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Revert "change: enable wrong-import-position pylint check"#916

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 Merged

mvsusp
merged 1 commit into

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Jul 9, 2019
 Merged


Revert "change: enable wrong-import-position pylint check"
#916
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1 change: 1 addition & 0 deletions
.pylintrc

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand Down

3 changes: 1 addition & 2 deletions
src/sagemaker/amazon/record_pb2.py

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11 changes: 6 additions & 5 deletions
src/sagemaker/cli/tensorflow.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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


Expand All@@ -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,
Expand All@@ -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,
Expand Down

6 changes: 2 additions & 4 deletions
src/sagemaker/tensorflow/__init__.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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

12 changes: 6 additions & 6 deletions
tests/component/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand DownExpand 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):
tensorflow_estimator = estimator.TensorFlow(
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",
 )

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": {
Expand Down

12 changes: 6 additions & 6 deletions
tests/integ/test_horovod.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
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")
tensorflow_estimator = estimator.TensorFlow(
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):
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
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")
tensorflow_estimator = estimator.TensorFlow(
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):
tensorflow_estimator.fit(job_name=job_name)
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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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
Expand DownExpand 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")

tensorflow_estimator = estimator.TensorFlow(
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 = 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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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

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
 )

Expand All@@ -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
Expand Down

16 changes: 7 additions & 9 deletions
tests/integ/test_tf_cifar.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand DownExpand Up@@ -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",
Expand All@@ -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

Expand Down

14 changes: 6 additions & 8 deletions
tests/integ/test_tf_keras.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
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):
tensorflow_estimator = estimator.TensorFlow(
estimator = TensorFlow(
 entry_point="keras_cnn_cifar_10.py",
 source_dir=script_path,
 role="SageMakerRole",
Expand All@@ -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)
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Revert "change: enable wrong-import-position pylint check"#916

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 Merged

mvsusp
merged 1 commit into

from

Jul 9, 2019
 Merged


Revert "change: enable wrong-import-position pylint check"
#916
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1 change: 1 addition & 0 deletions
.pylintrc

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand Down

3 changes: 1 addition & 2 deletions
src/sagemaker/amazon/record_pb2.py

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11 changes: 6 additions & 5 deletions
src/sagemaker/cli/tensorflow.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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


Expand All@@ -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,
Expand All@@ -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,
Expand Down

6 changes: 2 additions & 4 deletions
src/sagemaker/tensorflow/__init__.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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

12 changes: 6 additions & 6 deletions
tests/component/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand DownExpand 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):
tensorflow_estimator = estimator.TensorFlow(
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",
 )

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": {
Expand Down

12 changes: 6 additions & 6 deletions
tests/integ/test_horovod.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
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")
tensorflow_estimator = estimator.TensorFlow(
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):
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
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")
tensorflow_estimator = estimator.TensorFlow(
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):
tensorflow_estimator.fit(job_name=job_name)
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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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
Expand DownExpand 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")

tensorflow_estimator = estimator.TensorFlow(
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 = 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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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

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
 )

Expand All@@ -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
Expand Down

16 changes: 7 additions & 9 deletions
tests/integ/test_tf_cifar.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand DownExpand Up@@ -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",
Expand All@@ -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

Expand Down

14 changes: 6 additions & 8 deletions
tests/integ/test_tf_keras.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
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):
tensorflow_estimator = estimator.TensorFlow(
estimator = TensorFlow(
 entry_point="keras_cnn_cifar_10.py",
 source_dir=script_path,
 role="SageMakerRole",
Expand All@@ -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)
Expand Down
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Revert "change: enable wrong-import-position pylint check"#916

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 Merged

mvsusp
merged 1 commit into

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Jul 9, 2019
 Merged


Revert "change: enable wrong-import-position pylint check"
#916
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1 change: 1 addition & 0 deletions
.pylintrc

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand Down

3 changes: 1 addition & 2 deletions
src/sagemaker/amazon/record_pb2.py

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11 changes: 6 additions & 5 deletions
src/sagemaker/cli/tensorflow.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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


Expand All@@ -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,
Expand All@@ -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,
Expand Down

6 changes: 2 additions & 4 deletions
src/sagemaker/tensorflow/__init__.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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

12 changes: 6 additions & 6 deletions
tests/component/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand DownExpand 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):
tensorflow_estimator = estimator.TensorFlow(
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",
 )

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": {
Expand Down

12 changes: 6 additions & 6 deletions
tests/integ/test_horovod.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
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")
tensorflow_estimator = estimator.TensorFlow(
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):
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
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")
tensorflow_estimator = estimator.TensorFlow(
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):
tensorflow_estimator.fit(job_name=job_name)
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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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
Expand DownExpand 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")

tensorflow_estimator = estimator.TensorFlow(
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 = 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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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

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
 )

Expand All@@ -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
Expand Down

16 changes: 7 additions & 9 deletions
tests/integ/test_tf_cifar.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand DownExpand Up@@ -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",
Expand All@@ -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

Expand Down

14 changes: 6 additions & 8 deletions
tests/integ/test_tf_keras.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
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):
tensorflow_estimator = estimator.TensorFlow(
estimator = TensorFlow(
 entry_point="keras_cnn_cifar_10.py",
 source_dir=script_path,
 role="SageMakerRole",
Expand All@@ -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)
Expand Down
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Revert "change: enable wrong-import-position pylint check"#916

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 Merged

mvsusp
merged 1 commit into

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Jul 9, 2019
 Merged


Revert "change: enable wrong-import-position pylint check"
#916
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1 change: 1 addition & 0 deletions
.pylintrc

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand Down

3 changes: 1 addition & 2 deletions
src/sagemaker/amazon/record_pb2.py

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11 changes: 6 additions & 5 deletions
src/sagemaker/cli/tensorflow.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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


Expand All@@ -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,
Expand All@@ -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,
Expand Down

6 changes: 2 additions & 4 deletions
src/sagemaker/tensorflow/__init__.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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

12 changes: 6 additions & 6 deletions
tests/component/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand DownExpand 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):
tensorflow_estimator = estimator.TensorFlow(
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",
 )

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": {
Expand Down

12 changes: 6 additions & 6 deletions
tests/integ/test_horovod.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
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")
tensorflow_estimator = estimator.TensorFlow(
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):
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
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")
tensorflow_estimator = estimator.TensorFlow(
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):
tensorflow_estimator.fit(job_name=job_name)
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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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
Expand DownExpand 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")

tensorflow_estimator = estimator.TensorFlow(
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 = 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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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

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
 )

Expand All@@ -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
Expand Down

16 changes: 7 additions & 9 deletions
tests/integ/test_tf_cifar.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand DownExpand Up@@ -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",
Expand All@@ -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

Expand Down

14 changes: 6 additions & 8 deletions
tests/integ/test_tf_keras.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
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):
tensorflow_estimator = estimator.TensorFlow(
estimator = TensorFlow(
 entry_point="keras_cnn_cifar_10.py",
 source_dir=script_path,
 role="SageMakerRole",
Expand All@@ -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)
Expand Down
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Revert "change: enable wrong-import-position pylint check"#916

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 Merged

mvsusp
merged 1 commit into

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Jul 9, 2019
 Merged


Revert "change: enable wrong-import-position pylint check"
#916
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1 change: 1 addition & 0 deletions
.pylintrc

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand Down

3 changes: 1 addition & 2 deletions
src/sagemaker/amazon/record_pb2.py

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11 changes: 6 additions & 5 deletions
src/sagemaker/cli/tensorflow.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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


Expand All@@ -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,
Expand All@@ -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,
Expand Down

6 changes: 2 additions & 4 deletions
src/sagemaker/tensorflow/__init__.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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

12 changes: 6 additions & 6 deletions
tests/component/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand DownExpand 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):
tensorflow_estimator = estimator.TensorFlow(
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",
 )

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": {
Expand Down

12 changes: 6 additions & 6 deletions
tests/integ/test_horovod.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
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")
tensorflow_estimator = estimator.TensorFlow(
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):
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
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")
tensorflow_estimator = estimator.TensorFlow(
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):
tensorflow_estimator.fit(job_name=job_name)
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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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")
Expand DownExpand 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")

tensorflow_estimator = estimator.TensorFlow(
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 = 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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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
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
 )

Expand All@@ -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
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")

tensorflow_estimator = estimator.TensorFlow(
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

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
 )

Expand All@@ -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
Expand Down

16 changes: 7 additions & 9 deletions
tests/integ/test_tf_cifar.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

PICKLE_CONTENT_TYPE = "application/python-pickle"
Expand DownExpand Up@@ -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",
Expand All@@ -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

Expand Down

14 changes: 6 additions & 8 deletions
tests/integ/test_tf_keras.py

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Original file line numberDiff line numberDiff line change
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 estimator
from sagemaker.tensorflow import TensorFlow
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):
tensorflow_estimator = estimator.TensorFlow(
estimator = TensorFlow(
 entry_point="keras_cnn_cifar_10.py",
 source_dir=script_path,
 role="SageMakerRole",
Expand All@@ -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)
Expand Down
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