breaking: change Model parameter order to make model_data optional#1579

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Merged
laurenyu merged 1 commit into from
Jun 11, 2020
Merged

breaking: change Model parameter order to make model_data optional #1579

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2 changes: 1 addition & 1 deletion src/sagemaker/amazon/factorization_machines.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
super(FactorizationMachinesModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=FactorizationMachinesPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/ipinsights.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
)

super(IPInsightsModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=IPInsightsPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/kmeans.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
super(KMeansModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=KMeansPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/knn.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
)
super(KNNModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=KNNPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/lda.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
)
super(LDAModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=LDAPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/linear_learner.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
super(LinearLearnerModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=LinearLearnerPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/ntm.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
)
super(NTMModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=NTMPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/object2vec.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
)
super(Object2VecModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=RealTimePredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/pca.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
super(PCAModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=PCAPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/amazon/randomcutforest.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
)
super(RandomCutForestModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=RandomCutForestPredictor,
sagemaker_session=sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/estimator.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
kwargs["enable_network_isolation"] = self.enable_network_isolation()

return Model(
self.model_data,
image or self.train_image(),
self.model_data,
role,
vpc_config=self.get_vpc_config(vpc_config_override),
sagemaker_session=self.sagemaker_session,
Expand Down
10 changes: 6 additions & 4 deletions src/sagemaker/model.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -60,8 +60,8 @@ class Model(object):

def __init__(
self,
model_data,
image,
model_data=None,
role=None,
predictor_cls=None,
env=None,
Expand All@@ -74,9 +74,9 @@ def __init__(
"""Initialize an SageMaker ``Model``.

Args:
model_data (str): The S3 location of a SageMaker model data
``.tar.gz`` file.
image (str): A Docker image URI.
model_data (str): The S3 location of a SageMaker model data
``.tar.gz`` file (default: None).
role (str): An AWS IAM role (either name or full ARN). The Amazon
SageMaker training jobs and APIs that create Amazon SageMaker
endpoints use this role to access training data and model
Expand DownExpand Up@@ -361,6 +361,8 @@ def compile(
)
if job_name is None:
raise ValueError("You must provide a compilation job name")
if self.model_data is None:
raise ValueError("You must provide an S3 path to the compressed model artifacts.")

framework = framework.upper()
framework_version = self._get_framework_version() or framework_version
Expand DownExpand Up@@ -778,8 +780,8 @@ def __init__(
:class:`~sagemaker.model.Model`.
"""
super(FrameworkModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=predictor_cls,
env=env,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/multidatamodel.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -103,8 +103,8 @@ def __init__(
# Set the ``Model`` parameters if the model parameter is not specified
if not self.model:
super(MultiDataModel, self).__init__(
self.model_data_prefix,
image,
self.model_data_prefix,
role,
name=self.name,
sagemaker_session=self.sagemaker_session,
Expand Down
2 changes: 1 addition & 1 deletion src/sagemaker/sparkml/model.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
region_name = (sagemaker_session or Session()).boto_region_name
image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
super(SparkMLModel, self).__init__(
model_data,
image,
model_data,
role,
predictor_cls=SparkMLPredictor,
sagemaker_session=sagemaker_session,
Expand Down
26 changes: 13 additions & 13 deletions tests/unit/sagemaker/model/test_deploy.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
prepare_container_def.return_value = container_def

model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)

name_from_image.assert_called_with(MODEL_IMAGE)
Expand All@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
@patch("sagemaker.production_variant")
def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
)

production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
Expand DownExpand Up@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_endpoint_name(sagemaker_session):
model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)

endpoint_name = "blah"
model.deploy(
Expand All@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
@patch("sagemaker.model.Model._create_sagemaker_model")
def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
)

tags = [{"Key": "ModelName", "Value": "TestModel"}]
Expand All@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_kms_key(production_variant, sagemaker_session):
model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
)

key = "some-key-arn"
Expand All@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_async(production_variant, sagemaker_session):
model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
)

model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
Expand All@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_data_capture_config(production_variant, sagemaker_session):
model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
)

data_capture_config = Mock()
Expand All@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
@patch("sagemaker.local.LocalSession")
def test_deploy_creates_correct_session(local_session, session):
# We expect a LocalSession when deploying to instance_type = 'local'
model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
assert model.sagemaker_session == local_session.return_value

# We expect a real Session when deploying to instance_type != local/local_gpu
model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
model.deploy(
endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
)
assert model.sagemaker_session == session.return_value


def test_deploy_no_role(sagemaker_session):
model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)

with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
Expand All@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_predictor_cls(production_variant, sagemaker_session):
model = Model(
MODEL_DATA,
MODEL_IMAGE,
MODEL_DATA,
role=ROLE,
name=MODEL_NAME,
predictor_cls=sagemaker.predictor.RealTimePredictor,
Expand All@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):


def test_deploy_update_endpoint(sagemaker_session):
model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
model.deploy(
instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
)
Expand DownExpand Up@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
kms_key = "foo"
data_capture_config = Mock()

model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
model.deploy(
instance_type=INSTANCE_TYPE,
initial_instance_count=INSTANCE_COUNT,
Expand Down
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breaking: change Model parameter order to make model_data optional#1579

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 Merged

laurenyu
merged 1 commit into

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Jun 11, 2020
 Merged


breaking: change Model parameter order to make model_data optional
#1579
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/factorization_machines.py

View file


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Original file line numberDiff line numberDiff line change
Expand Up@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(FactorizationMachinesModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=FactorizationMachinesPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ipinsights.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 )

 super(IPInsightsModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=IPInsightsPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/kmeans.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(KMeansModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KMeansPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/knn.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
 )
 super(KNNModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KNNPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/lda.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
 )
 super(LDAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LDAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/linear_learner.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(LinearLearnerModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LinearLearnerPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ntm.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
 )
 super(NTMModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=NTMPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/object2vec.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
 )
 super(Object2VecModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RealTimePredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/pca.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(PCAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=PCAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/randomcutforest.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
 )
 super(RandomCutForestModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RandomCutForestPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
 kwargs["enable_network_isolation"] = self.enable_network_isolation()

 return Model(
 self.model_data,
 image or self.train_image(),
 self.model_data,
 role,
 vpc_config=self.get_vpc_config(vpc_config_override),
 sagemaker_session=self.sagemaker_session,
Expand Down

10 changes: 6 additions & 4 deletions
src/sagemaker/model.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,8 +60,8 @@ class Model(object):

 def __init__(
 self,
 model_data,
 image,
 model_data=None,
 role=None,
 predictor_cls=None,
 env=None,
Expand All@@ -74,9 +74,9 @@ def __init__(
 """Initialize an SageMaker ``Model``.

 Args:
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file.
 image (str): A Docker image URI.
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file (default: None).
 role (str): An AWS IAM role (either name or full ARN). The Amazon
 SageMaker training jobs and APIs that create Amazon SageMaker
 endpoints use this role to access training data and model
Expand DownExpand Up@@ -361,6 +361,8 @@ def compile(
 )
 if job_name is None:
 raise ValueError("You must provide a compilation job name")
 if self.model_data is None:
 raise ValueError("You must provide an S3 path to the compressed model artifacts.")

 framework = framework.upper()
 framework_version = self._get_framework_version() or framework_version
Expand DownExpand Up@@ -778,8 +780,8 @@ def __init__(
 :class:`~sagemaker.model.Model`.
 """
 super(FrameworkModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=predictor_cls,
 env=env,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/multidatamodel.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -103,8 +103,8 @@ def __init__(
 # Set the ``Model`` parameters if the model parameter is not specified
 if not self.model:
 super(MultiDataModel, self).__init__(
 self.model_data_prefix,
 image,
 self.model_data_prefix,
 role,
 name=self.name,
 sagemaker_session=self.sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/sparkml/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
 region_name = (sagemaker_session or Session()).boto_region_name
 image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
 super(SparkMLModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=SparkMLPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

26 changes: 13 additions & 13 deletions
tests/unit/sagemaker/model/test_deploy.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
 container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
 prepare_container_def.return_value = container_def

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)

 name_from_image.assert_called_with(MODEL_IMAGE)
Expand All@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
@patch("sagemaker.production_variant")
def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
Expand DownExpand Up@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_endpoint_name(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)

 endpoint_name = "blah"
 model.deploy(
Expand All@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
@patch("sagemaker.model.Model._create_sagemaker_model")
def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 tags = [{"Key": "ModelName", "Value": "TestModel"}]
Expand All@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_kms_key(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 key = "some-key-arn"
Expand All@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_async(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
Expand All@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_data_capture_config(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 data_capture_config = Mock()
Expand All@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
@patch("sagemaker.local.LocalSession")
def test_deploy_creates_correct_session(local_session, session):
 # We expect a LocalSession when deploying to instance_type = 'local'
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
 assert model.sagemaker_session == local_session.return_value

 # We expect a real Session when deploying to instance_type != local/local_gpu
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(
 endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
 )
 assert model.sagemaker_session == session.return_value


def test_deploy_no_role(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)

 with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
Expand All@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_predictor_cls(production_variant, sagemaker_session):
 model = Model(
 MODEL_DATA,
 MODEL_IMAGE,
 MODEL_DATA,
 role=ROLE,
 name=MODEL_NAME,
 predictor_cls=sagemaker.predictor.RealTimePredictor,
Expand All@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):


def test_deploy_update_endpoint(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
 )
Expand DownExpand Up@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
 kms_key = "foo"
 data_capture_config = Mock()

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE,
 initial_instance_count=INSTANCE_COUNT,
Expand Down
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 Merged

laurenyu
merged 1 commit into

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Jun 11, 2020
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breaking: change Model parameter order to make model_data optional
#1579
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/factorization_machines.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(FactorizationMachinesModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=FactorizationMachinesPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ipinsights.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 )

 super(IPInsightsModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=IPInsightsPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/kmeans.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(KMeansModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KMeansPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/knn.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
 )
 super(KNNModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KNNPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/lda.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
 )
 super(LDAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LDAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/linear_learner.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(LinearLearnerModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LinearLearnerPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ntm.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
 )
 super(NTMModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=NTMPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/object2vec.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
 )
 super(Object2VecModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RealTimePredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/pca.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(PCAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=PCAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/randomcutforest.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
 )
 super(RandomCutForestModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RandomCutForestPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
 kwargs["enable_network_isolation"] = self.enable_network_isolation()

 return Model(
 self.model_data,
 image or self.train_image(),
 self.model_data,
 role,
 vpc_config=self.get_vpc_config(vpc_config_override),
 sagemaker_session=self.sagemaker_session,
Expand Down

10 changes: 6 additions & 4 deletions
src/sagemaker/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -60,8 +60,8 @@ class Model(object):

 def __init__(
 self,
 model_data,
 image,
 model_data=None,
 role=None,
 predictor_cls=None,
 env=None,
Expand All@@ -74,9 +74,9 @@ def __init__(
 """Initialize an SageMaker ``Model``.

 Args:
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file.
 image (str): A Docker image URI.
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file (default: None).
 role (str): An AWS IAM role (either name or full ARN). The Amazon
 SageMaker training jobs and APIs that create Amazon SageMaker
 endpoints use this role to access training data and model
Expand DownExpand Up@@ -361,6 +361,8 @@ def compile(
 )
 if job_name is None:
 raise ValueError("You must provide a compilation job name")
 if self.model_data is None:
 raise ValueError("You must provide an S3 path to the compressed model artifacts.")

 framework = framework.upper()
 framework_version = self._get_framework_version() or framework_version
Expand DownExpand Up@@ -778,8 +780,8 @@ def __init__(
 :class:`~sagemaker.model.Model`.
 """
 super(FrameworkModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=predictor_cls,
 env=env,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/multidatamodel.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -103,8 +103,8 @@ def __init__(
 # Set the ``Model`` parameters if the model parameter is not specified
 if not self.model:
 super(MultiDataModel, self).__init__(
 self.model_data_prefix,
 image,
 self.model_data_prefix,
 role,
 name=self.name,
 sagemaker_session=self.sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/sparkml/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
 region_name = (sagemaker_session or Session()).boto_region_name
 image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
 super(SparkMLModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=SparkMLPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

26 changes: 13 additions & 13 deletions
tests/unit/sagemaker/model/test_deploy.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
 container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
 prepare_container_def.return_value = container_def

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)

 name_from_image.assert_called_with(MODEL_IMAGE)
Expand All@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
@patch("sagemaker.production_variant")
def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
Expand DownExpand Up@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_endpoint_name(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)

 endpoint_name = "blah"
 model.deploy(
Expand All@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
@patch("sagemaker.model.Model._create_sagemaker_model")
def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 tags = [{"Key": "ModelName", "Value": "TestModel"}]
Expand All@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_kms_key(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 key = "some-key-arn"
Expand All@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_async(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
Expand All@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_data_capture_config(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 data_capture_config = Mock()
Expand All@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
@patch("sagemaker.local.LocalSession")
def test_deploy_creates_correct_session(local_session, session):
 # We expect a LocalSession when deploying to instance_type = 'local'
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
 assert model.sagemaker_session == local_session.return_value

 # We expect a real Session when deploying to instance_type != local/local_gpu
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(
 endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
 )
 assert model.sagemaker_session == session.return_value


def test_deploy_no_role(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)

 with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
Expand All@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_predictor_cls(production_variant, sagemaker_session):
 model = Model(
 MODEL_DATA,
 MODEL_IMAGE,
 MODEL_DATA,
 role=ROLE,
 name=MODEL_NAME,
 predictor_cls=sagemaker.predictor.RealTimePredictor,
Expand All@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):


def test_deploy_update_endpoint(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
 )
Expand DownExpand Up@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
 kms_key = "foo"
 data_capture_config = Mock()

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE,
 initial_instance_count=INSTANCE_COUNT,
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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breaking: change Model parameter order to make model_data optional#1579

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laurenyu
merged 1 commit into

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Jun 11, 2020
 Merged


breaking: change Model parameter order to make model_data optional
#1579
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/factorization_machines.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(FactorizationMachinesModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=FactorizationMachinesPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ipinsights.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 )

 super(IPInsightsModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=IPInsightsPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/kmeans.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(KMeansModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KMeansPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/knn.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
 )
 super(KNNModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KNNPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/lda.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
 )
 super(LDAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LDAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/linear_learner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(LinearLearnerModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LinearLearnerPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ntm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
 )
 super(NTMModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=NTMPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/object2vec.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
 )
 super(Object2VecModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RealTimePredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/pca.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(PCAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=PCAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/randomcutforest.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
 )
 super(RandomCutForestModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RandomCutForestPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/estimator.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
 kwargs["enable_network_isolation"] = self.enable_network_isolation()

 return Model(
 self.model_data,
 image or self.train_image(),
 self.model_data,
 role,
 vpc_config=self.get_vpc_config(vpc_config_override),
 sagemaker_session=self.sagemaker_session,
Expand Down

10 changes: 6 additions & 4 deletions
src/sagemaker/model.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,8 +60,8 @@ class Model(object):

 def __init__(
 self,
 model_data,
 image,
 model_data=None,
 role=None,
 predictor_cls=None,
 env=None,
Expand All@@ -74,9 +74,9 @@ def __init__(
 """Initialize an SageMaker ``Model``.

 Args:
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file.
 image (str): A Docker image URI.
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file (default: None).
 role (str): An AWS IAM role (either name or full ARN). The Amazon
 SageMaker training jobs and APIs that create Amazon SageMaker
 endpoints use this role to access training data and model
Expand DownExpand Up@@ -361,6 +361,8 @@ def compile(
 )
 if job_name is None:
 raise ValueError("You must provide a compilation job name")
 if self.model_data is None:
 raise ValueError("You must provide an S3 path to the compressed model artifacts.")

 framework = framework.upper()
 framework_version = self._get_framework_version() or framework_version
Expand DownExpand Up@@ -778,8 +780,8 @@ def __init__(
 :class:`~sagemaker.model.Model`.
 """
 super(FrameworkModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=predictor_cls,
 env=env,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/multidatamodel.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -103,8 +103,8 @@ def __init__(
 # Set the ``Model`` parameters if the model parameter is not specified
 if not self.model:
 super(MultiDataModel, self).__init__(
 self.model_data_prefix,
 image,
 self.model_data_prefix,
 role,
 name=self.name,
 sagemaker_session=self.sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/sparkml/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
 region_name = (sagemaker_session or Session()).boto_region_name
 image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
 super(SparkMLModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=SparkMLPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

26 changes: 13 additions & 13 deletions
tests/unit/sagemaker/model/test_deploy.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
 container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
 prepare_container_def.return_value = container_def

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)

 name_from_image.assert_called_with(MODEL_IMAGE)
Expand All@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
@patch("sagemaker.production_variant")
def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
Expand DownExpand Up@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_endpoint_name(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)

 endpoint_name = "blah"
 model.deploy(
Expand All@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
@patch("sagemaker.model.Model._create_sagemaker_model")
def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 tags = [{"Key": "ModelName", "Value": "TestModel"}]
Expand All@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_kms_key(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 key = "some-key-arn"
Expand All@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_async(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
Expand All@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_data_capture_config(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 data_capture_config = Mock()
Expand All@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
@patch("sagemaker.local.LocalSession")
def test_deploy_creates_correct_session(local_session, session):
 # We expect a LocalSession when deploying to instance_type = 'local'
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
 assert model.sagemaker_session == local_session.return_value

 # We expect a real Session when deploying to instance_type != local/local_gpu
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(
 endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
 )
 assert model.sagemaker_session == session.return_value


def test_deploy_no_role(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)

 with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
Expand All@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_predictor_cls(production_variant, sagemaker_session):
 model = Model(
 MODEL_DATA,
 MODEL_IMAGE,
 MODEL_DATA,
 role=ROLE,
 name=MODEL_NAME,
 predictor_cls=sagemaker.predictor.RealTimePredictor,
Expand All@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):


def test_deploy_update_endpoint(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
 )
Expand DownExpand Up@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
 kms_key = "foo"
 data_capture_config = Mock()

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE,
 initial_instance_count=INSTANCE_COUNT,
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" + '

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breaking: change Model parameter order to make model_data optional#1579

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 Merged

laurenyu
merged 1 commit into

from

Jun 11, 2020
 Merged


breaking: change Model parameter order to make model_data optional
#1579
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/factorization_machines.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(FactorizationMachinesModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=FactorizationMachinesPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ipinsights.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 )

 super(IPInsightsModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=IPInsightsPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/kmeans.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(KMeansModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KMeansPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/knn.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
 )
 super(KNNModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KNNPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/lda.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
 )
 super(LDAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LDAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/linear_learner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(LinearLearnerModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LinearLearnerPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ntm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
 )
 super(NTMModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=NTMPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/object2vec.py

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Expand Up@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
 )
 super(Object2VecModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RealTimePredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/pca.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(PCAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=PCAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/randomcutforest.py

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Expand Up@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
 )
 super(RandomCutForestModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RandomCutForestPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
 kwargs["enable_network_isolation"] = self.enable_network_isolation()

 return Model(
 self.model_data,
 image or self.train_image(),
 self.model_data,
 role,
 vpc_config=self.get_vpc_config(vpc_config_override),
 sagemaker_session=self.sagemaker_session,
Expand Down

10 changes: 6 additions & 4 deletions
src/sagemaker/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -60,8 +60,8 @@ class Model(object):

 def __init__(
 self,
 model_data,
 image,
 model_data=None,
 role=None,
 predictor_cls=None,
 env=None,
Expand All@@ -74,9 +74,9 @@ def __init__(
 """Initialize an SageMaker ``Model``.

 Args:
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file.
 image (str): A Docker image URI.
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file (default: None).
 role (str): An AWS IAM role (either name or full ARN). The Amazon
 SageMaker training jobs and APIs that create Amazon SageMaker
 endpoints use this role to access training data and model
Expand DownExpand Up@@ -361,6 +361,8 @@ def compile(
 )
 if job_name is None:
 raise ValueError("You must provide a compilation job name")
 if self.model_data is None:
 raise ValueError("You must provide an S3 path to the compressed model artifacts.")

 framework = framework.upper()
 framework_version = self._get_framework_version() or framework_version
Expand DownExpand Up@@ -778,8 +780,8 @@ def __init__(
 :class:`~sagemaker.model.Model`.
 """
 super(FrameworkModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=predictor_cls,
 env=env,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/multidatamodel.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -103,8 +103,8 @@ def __init__(
 # Set the ``Model`` parameters if the model parameter is not specified
 if not self.model:
 super(MultiDataModel, self).__init__(
 self.model_data_prefix,
 image,
 self.model_data_prefix,
 role,
 name=self.name,
 sagemaker_session=self.sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/sparkml/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
 region_name = (sagemaker_session or Session()).boto_region_name
 image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
 super(SparkMLModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=SparkMLPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

26 changes: 13 additions & 13 deletions
tests/unit/sagemaker/model/test_deploy.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
 container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
 prepare_container_def.return_value = container_def

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)

 name_from_image.assert_called_with(MODEL_IMAGE)
Expand All@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
@patch("sagemaker.production_variant")
def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
Expand DownExpand Up@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_endpoint_name(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)

 endpoint_name = "blah"
 model.deploy(
Expand All@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
@patch("sagemaker.model.Model._create_sagemaker_model")
def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 tags = [{"Key": "ModelName", "Value": "TestModel"}]
Expand All@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_kms_key(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 key = "some-key-arn"
Expand All@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_async(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
Expand All@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_data_capture_config(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 data_capture_config = Mock()
Expand All@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
@patch("sagemaker.local.LocalSession")
def test_deploy_creates_correct_session(local_session, session):
 # We expect a LocalSession when deploying to instance_type = 'local'
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
 assert model.sagemaker_session == local_session.return_value

 # We expect a real Session when deploying to instance_type != local/local_gpu
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(
 endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
 )
 assert model.sagemaker_session == session.return_value


def test_deploy_no_role(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)

 with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
Expand All@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_predictor_cls(production_variant, sagemaker_session):
 model = Model(
 MODEL_DATA,
 MODEL_IMAGE,
 MODEL_DATA,
 role=ROLE,
 name=MODEL_NAME,
 predictor_cls=sagemaker.predictor.RealTimePredictor,
Expand All@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):


def test_deploy_update_endpoint(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
 )
Expand DownExpand Up@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
 kms_key = "foo"
 data_capture_config = Mock()

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE,
 initial_instance_count=INSTANCE_COUNT,
Expand Down
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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");
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var __m = "*";
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 Merged

laurenyu
merged 1 commit into

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Jun 11, 2020
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breaking: change Model parameter order to make model_data optional
#1579
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/factorization_machines.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(FactorizationMachinesModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=FactorizationMachinesPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ipinsights.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 )

 super(IPInsightsModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=IPInsightsPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/kmeans.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(KMeansModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KMeansPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/knn.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
 )
 super(KNNModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KNNPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/lda.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
 )
 super(LDAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LDAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/linear_learner.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(LinearLearnerModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LinearLearnerPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ntm.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
 )
 super(NTMModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=NTMPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/object2vec.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
 )
 super(Object2VecModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RealTimePredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/pca.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(PCAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=PCAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/randomcutforest.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
 )
 super(RandomCutForestModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RandomCutForestPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
 kwargs["enable_network_isolation"] = self.enable_network_isolation()

 return Model(
 self.model_data,
 image or self.train_image(),
 self.model_data,
 role,
 vpc_config=self.get_vpc_config(vpc_config_override),
 sagemaker_session=self.sagemaker_session,
Expand Down

10 changes: 6 additions & 4 deletions
src/sagemaker/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -60,8 +60,8 @@ class Model(object):

 def __init__(
 self,
 model_data,
 image,
 model_data=None,
 role=None,
 predictor_cls=None,
 env=None,
Expand All@@ -74,9 +74,9 @@ def __init__(
 """Initialize an SageMaker ``Model``.

 Args:
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file.
 image (str): A Docker image URI.
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file (default: None).
 role (str): An AWS IAM role (either name or full ARN). The Amazon
 SageMaker training jobs and APIs that create Amazon SageMaker
 endpoints use this role to access training data and model
Expand DownExpand Up@@ -361,6 +361,8 @@ def compile(
 )
 if job_name is None:
 raise ValueError("You must provide a compilation job name")
 if self.model_data is None:
 raise ValueError("You must provide an S3 path to the compressed model artifacts.")

 framework = framework.upper()
 framework_version = self._get_framework_version() or framework_version
Expand DownExpand Up@@ -778,8 +780,8 @@ def __init__(
 :class:`~sagemaker.model.Model`.
 """
 super(FrameworkModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=predictor_cls,
 env=env,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/multidatamodel.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -103,8 +103,8 @@ def __init__(
 # Set the ``Model`` parameters if the model parameter is not specified
 if not self.model:
 super(MultiDataModel, self).__init__(
 self.model_data_prefix,
 image,
 self.model_data_prefix,
 role,
 name=self.name,
 sagemaker_session=self.sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/sparkml/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
 region_name = (sagemaker_session or Session()).boto_region_name
 image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
 super(SparkMLModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=SparkMLPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

26 changes: 13 additions & 13 deletions
tests/unit/sagemaker/model/test_deploy.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
 container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
 prepare_container_def.return_value = container_def

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)

 name_from_image.assert_called_with(MODEL_IMAGE)
Expand All@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
@patch("sagemaker.production_variant")
def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
Expand DownExpand Up@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_endpoint_name(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)

 endpoint_name = "blah"
 model.deploy(
Expand All@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
@patch("sagemaker.model.Model._create_sagemaker_model")
def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 tags = [{"Key": "ModelName", "Value": "TestModel"}]
Expand All@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_kms_key(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 key = "some-key-arn"
Expand All@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_async(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
Expand All@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_data_capture_config(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 data_capture_config = Mock()
Expand All@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
@patch("sagemaker.local.LocalSession")
def test_deploy_creates_correct_session(local_session, session):
 # We expect a LocalSession when deploying to instance_type = 'local'
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
 assert model.sagemaker_session == local_session.return_value

 # We expect a real Session when deploying to instance_type != local/local_gpu
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(
 endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
 )
 assert model.sagemaker_session == session.return_value


def test_deploy_no_role(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)

 with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
Expand All@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_predictor_cls(production_variant, sagemaker_session):
 model = Model(
 MODEL_DATA,
 MODEL_IMAGE,
 MODEL_DATA,
 role=ROLE,
 name=MODEL_NAME,
 predictor_cls=sagemaker.predictor.RealTimePredictor,
Expand All@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):


def test_deploy_update_endpoint(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
 )
Expand DownExpand Up@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
 kms_key = "foo"
 data_capture_config = Mock()

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE,
 initial_instance_count=INSTANCE_COUNT,
Expand Down
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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breaking: change Model parameter order to make model_data optional#1579

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laurenyu
merged 1 commit into

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Jun 11, 2020
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breaking: change Model parameter order to make model_data optional
#1579
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/factorization_machines.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(FactorizationMachinesModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=FactorizationMachinesPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ipinsights.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 )

 super(IPInsightsModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=IPInsightsPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/kmeans.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(KMeansModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KMeansPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/knn.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
 )
 super(KNNModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KNNPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/lda.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
 )
 super(LDAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LDAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/linear_learner.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(LinearLearnerModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LinearLearnerPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ntm.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
 )
 super(NTMModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=NTMPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/object2vec.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
 )
 super(Object2VecModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RealTimePredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/pca.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(PCAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=PCAPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/randomcutforest.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
 )
 super(RandomCutForestModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RandomCutForestPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/estimator.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
 kwargs["enable_network_isolation"] = self.enable_network_isolation()

 return Model(
 self.model_data,
 image or self.train_image(),
 self.model_data,
 role,
 vpc_config=self.get_vpc_config(vpc_config_override),
 sagemaker_session=self.sagemaker_session,
Expand Down

10 changes: 6 additions & 4 deletions
src/sagemaker/model.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -60,8 +60,8 @@ class Model(object):

 def __init__(
 self,
 model_data,
 image,
 model_data=None,
 role=None,
 predictor_cls=None,
 env=None,
Expand All@@ -74,9 +74,9 @@ def __init__(
 """Initialize an SageMaker ``Model``.

 Args:
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file.
 image (str): A Docker image URI.
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file (default: None).
 role (str): An AWS IAM role (either name or full ARN). The Amazon
 SageMaker training jobs and APIs that create Amazon SageMaker
 endpoints use this role to access training data and model
Expand DownExpand Up@@ -361,6 +361,8 @@ def compile(
 )
 if job_name is None:
 raise ValueError("You must provide a compilation job name")
 if self.model_data is None:
 raise ValueError("You must provide an S3 path to the compressed model artifacts.")

 framework = framework.upper()
 framework_version = self._get_framework_version() or framework_version
Expand DownExpand Up@@ -778,8 +780,8 @@ def __init__(
 :class:`~sagemaker.model.Model`.
 """
 super(FrameworkModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=predictor_cls,
 env=env,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/multidatamodel.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -103,8 +103,8 @@ def __init__(
 # Set the ``Model`` parameters if the model parameter is not specified
 if not self.model:
 super(MultiDataModel, self).__init__(
 self.model_data_prefix,
 image,
 self.model_data_prefix,
 role,
 name=self.name,
 sagemaker_session=self.sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/sparkml/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
 region_name = (sagemaker_session or Session()).boto_region_name
 image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
 super(SparkMLModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=SparkMLPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

26 changes: 13 additions & 13 deletions
tests/unit/sagemaker/model/test_deploy.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
 container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
 prepare_container_def.return_value = container_def

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)

 name_from_image.assert_called_with(MODEL_IMAGE)
Expand All@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
@patch("sagemaker.production_variant")
def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
Expand DownExpand Up@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_endpoint_name(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)

 endpoint_name = "blah"
 model.deploy(
Expand All@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
@patch("sagemaker.model.Model._create_sagemaker_model")
def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 tags = [{"Key": "ModelName", "Value": "TestModel"}]
Expand All@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_kms_key(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 key = "some-key-arn"
Expand All@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_async(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
Expand All@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_data_capture_config(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 data_capture_config = Mock()
Expand All@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
@patch("sagemaker.local.LocalSession")
def test_deploy_creates_correct_session(local_session, session):
 # We expect a LocalSession when deploying to instance_type = 'local'
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
 assert model.sagemaker_session == local_session.return_value

 # We expect a real Session when deploying to instance_type != local/local_gpu
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(
 endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
 )
 assert model.sagemaker_session == session.return_value


def test_deploy_no_role(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)

 with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
Expand All@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_predictor_cls(production_variant, sagemaker_session):
 model = Model(
 MODEL_DATA,
 MODEL_IMAGE,
 MODEL_DATA,
 role=ROLE,
 name=MODEL_NAME,
 predictor_cls=sagemaker.predictor.RealTimePredictor,
Expand All@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):


def test_deploy_update_endpoint(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
 )
Expand DownExpand Up@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
 kms_key = "foo"
 data_capture_config = Mock()

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE,
 initial_instance_count=INSTANCE_COUNT,
Expand Down
Loading

, '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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breaking: change Model parameter order to make model_data optional#1579

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 Merged

laurenyu
merged 1 commit into

from

Jun 11, 2020
 Merged


breaking: change Model parameter order to make model_data optional
#1579
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/factorization_machines.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(FactorizationMachinesModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=FactorizationMachinesPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ipinsights.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 )

 super(IPInsightsModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=IPInsightsPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/kmeans.py

View file


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Original file line numberDiff line numberDiff line change
Expand Up@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(KMeansModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KMeansPredictor,
 sagemaker_session=sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/knn.py

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Expand Up@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
 )
 super(KNNModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=KNNPredictor,
 sagemaker_session=sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/lda.py

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Expand Up@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
 )
 super(LDAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LDAPredictor,
 sagemaker_session=sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/linear_learner.py

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Expand Up@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(LinearLearnerModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=LinearLearnerPredictor,
 sagemaker_session=sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/ntm.py

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Expand Up@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
 )
 super(NTMModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=NTMPredictor,
 sagemaker_session=sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/object2vec.py

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Expand Up@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
 )
 super(Object2VecModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RealTimePredictor,
 sagemaker_session=sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/pca.py

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Expand Up@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
 image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
 super(PCAModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=PCAPredictor,
 sagemaker_session=sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/amazon/randomcutforest.py

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Expand Up@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
 registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
 )
 super(RandomCutForestModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=RandomCutForestPredictor,
 sagemaker_session=sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/estimator.py

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Expand Up@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
 kwargs["enable_network_isolation"] = self.enable_network_isolation()

 return Model(
 self.model_data,
 image or self.train_image(),
 self.model_data,
 role,
 vpc_config=self.get_vpc_config(vpc_config_override),
 sagemaker_session=self.sagemaker_session,
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10 changes: 6 additions & 4 deletions
src/sagemaker/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -60,8 +60,8 @@ class Model(object):

 def __init__(
 self,
 model_data,
 image,
 model_data=None,
 role=None,
 predictor_cls=None,
 env=None,
Expand All@@ -74,9 +74,9 @@ def __init__(
 """Initialize an SageMaker ``Model``.

 Args:
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file.
 image (str): A Docker image URI.
 model_data (str): The S3 location of a SageMaker model data
 ``.tar.gz`` file (default: None).
 role (str): An AWS IAM role (either name or full ARN). The Amazon
 SageMaker training jobs and APIs that create Amazon SageMaker
 endpoints use this role to access training data and model
Expand DownExpand Up@@ -361,6 +361,8 @@ def compile(
 )
 if job_name is None:
 raise ValueError("You must provide a compilation job name")
 if self.model_data is None:
 raise ValueError("You must provide an S3 path to the compressed model artifacts.")

 framework = framework.upper()
 framework_version = self._get_framework_version() or framework_version
Expand DownExpand Up@@ -778,8 +780,8 @@ def __init__(
 :class:`~sagemaker.model.Model`.
 """
 super(FrameworkModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=predictor_cls,
 env=env,
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2 changes: 1 addition & 1 deletion
src/sagemaker/multidatamodel.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -103,8 +103,8 @@ def __init__(
 # Set the ``Model`` parameters if the model parameter is not specified
 if not self.model:
 super(MultiDataModel, self).__init__(
 self.model_data_prefix,
 image,
 self.model_data_prefix,
 role,
 name=self.name,
 sagemaker_session=self.sagemaker_session,
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2 changes: 1 addition & 1 deletion
src/sagemaker/sparkml/model.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
 region_name = (sagemaker_session or Session()).boto_region_name
 image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
 super(SparkMLModel, self).__init__(
 model_data,
 image,
 model_data,
 role,
 predictor_cls=SparkMLPredictor,
 sagemaker_session=sagemaker_session,
Expand Down

26 changes: 13 additions & 13 deletions
tests/unit/sagemaker/model/test_deploy.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
 container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
 prepare_container_def.return_value = container_def

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)

 name_from_image.assert_called_with(MODEL_IMAGE)
Expand All@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
@patch("sagemaker.production_variant")
def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
Expand DownExpand Up@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_endpoint_name(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)

 endpoint_name = "blah"
 model.deploy(
Expand All@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
@patch("sagemaker.model.Model._create_sagemaker_model")
def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 tags = [{"Key": "ModelName", "Value": "TestModel"}]
Expand All@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_kms_key(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 key = "some-key-arn"
Expand All@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_async(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
Expand All@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_data_capture_config(production_variant, sagemaker_session):
 model = Model(
MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
 )

 data_capture_config = Mock()
Expand All@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
@patch("sagemaker.local.LocalSession")
def test_deploy_creates_correct_session(local_session, session):
 # We expect a LocalSession when deploying to instance_type = 'local'
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
 assert model.sagemaker_session == local_session.return_value

 # We expect a real Session when deploying to instance_type != local/local_gpu
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
 model.deploy(
 endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
 )
 assert model.sagemaker_session == session.return_value


def test_deploy_no_role(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)

 with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
 model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
Expand All@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
def test_deploy_predictor_cls(production_variant, sagemaker_session):
 model = Model(
 MODEL_DATA,
 MODEL_IMAGE,
 MODEL_DATA,
 role=ROLE,
 name=MODEL_NAME,
 predictor_cls=sagemaker.predictor.RealTimePredictor,
Expand All@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):


def test_deploy_update_endpoint(sagemaker_session):
 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
 )
Expand DownExpand Up@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
 kms_key = "foo"
 data_capture_config = Mock()

 model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
 model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 model.deploy(
 instance_type=INSTANCE_TYPE,
 initial_instance_count=INSTANCE_COUNT,
Expand Down
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