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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import
import os
import sagemaker
import tests.integ
import tests.integ.timeout
from sagemaker.model_monitor import DataCaptureConfig, NetworkConfig
from sagemaker.tensorflow.model import TensorFlowModel
from sagemaker.utils import unique_name_from_base
from tests.integ.retry import retries
ROLE = "SageMakerRole"
SKLEARN_FRAMEWORK = "scikit-learn"
INSTANCE_COUNT = 1
INSTANCE_TYPE = "ml.m5.xlarge"
VOLUME_SIZE_IN_GB = 20
MAX_RUNTIME_IN_SECONDS = 2 * 60 * 60
ENVIRONMENT = {"env_key_1": "env_value_1"}
TAGS = [{"Key": "tag_key_1", "Value": "tag_value_1"}]
NETWORK_CONFIG = NetworkConfig(enable_network_isolation=True)
CUSTOM_SAMPLING_PERCENTAGE = 10
CUSTOM_CAPTURE_OPTIONS = ["REQUEST"]
CUSTOM_CSV_CONTENT_TYPES = ["text/csvtype1", "text/csvtype2"]
CUSTOM_JSON_CONTENT_TYPES = ["application/jsontype1", "application/jsontype2"]
def test_enabling_data_capture_on_endpoint_shows_correct_data_capture_status(
sagemaker_session, tensorflow_inference_latest_version
):
endpoint_name = unique_name_from_base("sagemaker-tensorflow-serving")
model_data = sagemaker_session.upload_data(
path=os.path.join(tests.integ.DATA_DIR, "tensorflow-serving-test-model.tar.gz"),
key_prefix="tensorflow-serving/models",
)
with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = TensorFlowModel(
model_data=model_data,
role=ROLE,
framework_version=tensorflow_inference_latest_version,
sagemaker_session=sagemaker_session,
)
predictor = model.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
endpoint_name=endpoint_name,
)
endpoint_desc = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
endpoint_config_desc = sagemaker_session.sagemaker_client.describe_endpoint_config(
EndpointConfigName=endpoint_desc["EndpointConfigName"]
)
assert endpoint_config_desc.get("DataCaptureConfig") is None
predictor.enable_data_capture()
# Wait for endpoint to finish updating
# Endpoint update takes ~7min. 25 retries * 60s sleeps = 25min timeout
for _ in retries(
max_retry_count=25,
exception_message_prefix="Waiting for 'InService' endpoint status",
seconds_to_sleep=60,
):
new_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
if new_endpoint["EndpointStatus"] == "InService":
break
endpoint_desc = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
endpoint_config_desc = sagemaker_session.sagemaker_client.describe_endpoint_config(
EndpointConfigName=endpoint_desc["EndpointConfigName"]
)
assert endpoint_config_desc["DataCaptureConfig"]["EnableCapture"]
def test_disabling_data_capture_on_endpoint_shows_correct_data_capture_status(
sagemaker_session, tensorflow_inference_latest_version
):
endpoint_name = unique_name_from_base("sagemaker-tensorflow-serving")
model_data = sagemaker_session.upload_data(
path=os.path.join(tests.integ.DATA_DIR, "tensorflow-serving-test-model.tar.gz"),
key_prefix="tensorflow-serving/models",
)
with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = TensorFlowModel(
model_data=model_data,
role=ROLE,
framework_version=tensorflow_inference_latest_version,
sagemaker_session=sagemaker_session,
)
destination_s3_uri = os.path.join(
"s3://", sagemaker_session.default_bucket(), endpoint_name, "custom"
)
predictor = model.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
endpoint_name=endpoint_name,
data_capture_config=DataCaptureConfig(
enable_capture=True,
sampling_percentage=CUSTOM_SAMPLING_PERCENTAGE,
destination_s3_uri=destination_s3_uri,
capture_options=CUSTOM_CAPTURE_OPTIONS,
csv_content_types=CUSTOM_CSV_CONTENT_TYPES,
json_content_types=CUSTOM_JSON_CONTENT_TYPES,
sagemaker_session=sagemaker_session,
),
)
endpoint_desc = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
endpoint_config_desc = sagemaker_session.sagemaker_client.describe_endpoint_config(
EndpointConfigName=endpoint_desc["EndpointConfigName"]
)
assert endpoint_config_desc["DataCaptureConfig"]["EnableCapture"]
assert (
endpoint_config_desc["DataCaptureConfig"]["InitialSamplingPercentage"]
== CUSTOM_SAMPLING_PERCENTAGE
)
assert endpoint_config_desc["DataCaptureConfig"]["CaptureOptions"] == [
{"CaptureMode": "Input"}
]
assert (
endpoint_config_desc["DataCaptureConfig"]["CaptureContentTypeHeader"]["CsvContentTypes"]
== CUSTOM_CSV_CONTENT_TYPES
)
assert (
endpoint_config_desc["DataCaptureConfig"]["CaptureContentTypeHeader"][
"JsonContentTypes"
]
== CUSTOM_JSON_CONTENT_TYPES
)
predictor.disable_data_capture()
# Wait for endpoint to finish updating
# Endpoint update takes ~7min. 25 retries * 60s sleeps = 25min timeout
for _ in retries(
max_retry_count=25,
exception_message_prefix="Waiting for 'InService' endpoint status",
seconds_to_sleep=60,
):
new_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
if new_endpoint["EndpointStatus"] == "InService":
break
endpoint_desc = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
endpoint_config_desc = sagemaker_session.sagemaker_client.describe_endpoint_config(
EndpointConfigName=endpoint_desc["EndpointConfigName"]
)
assert not endpoint_config_desc["DataCaptureConfig"]["EnableCapture"]
def test_updating_data_capture_on_endpoint_shows_correct_data_capture_status(
sagemaker_session, tensorflow_inference_latest_version
):
endpoint_name = sagemaker.utils.unique_name_from_base("sagemaker-tensorflow-serving")
model_data = sagemaker_session.upload_data(
path=os.path.join(tests.integ.DATA_DIR, "tensorflow-serving-test-model.tar.gz"),
key_prefix="tensorflow-serving/models",
)
with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = TensorFlowModel(
model_data=model_data,
role=ROLE,
framework_version=tensorflow_inference_latest_version,
sagemaker_session=sagemaker_session,
)
destination_s3_uri = os.path.join(
"s3://", sagemaker_session.default_bucket(), endpoint_name, "custom"
)
predictor = model.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
endpoint_name=endpoint_name,
)
endpoint_desc = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
endpoint_config_desc = sagemaker_session.sagemaker_client.describe_endpoint_config(
EndpointConfigName=endpoint_desc["EndpointConfigName"]
)
assert endpoint_config_desc.get("DataCaptureConfig") is None
predictor.update_data_capture_config(
data_capture_config=DataCaptureConfig(
enable_capture=True,
sampling_percentage=CUSTOM_SAMPLING_PERCENTAGE,
destination_s3_uri=destination_s3_uri,
capture_options=CUSTOM_CAPTURE_OPTIONS,
csv_content_types=CUSTOM_CSV_CONTENT_TYPES,
json_content_types=CUSTOM_JSON_CONTENT_TYPES,
sagemaker_session=sagemaker_session,
)
)
# Wait for endpoint to finish updating
# Endpoint update takes ~7min. 25 retries * 60s sleeps = 25min timeout
for _ in retries(
max_retry_count=25,
exception_message_prefix="Waiting for 'InService' endpoint status",
seconds_to_sleep=60,
):
new_endpoint = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
if new_endpoint["EndpointStatus"] == "InService":
break
endpoint_desc = sagemaker_session.sagemaker_client.describe_endpoint(
EndpointName=predictor.endpoint_name
)
endpoint_config_desc = sagemaker_session.sagemaker_client.describe_endpoint_config(
EndpointConfigName=endpoint_desc["EndpointConfigName"]
)
assert endpoint_config_desc["DataCaptureConfig"]["EnableCapture"]
assert (
endpoint_config_desc["DataCaptureConfig"]["InitialSamplingPercentage"]
== CUSTOM_SAMPLING_PERCENTAGE
)
assert endpoint_config_desc["DataCaptureConfig"]["CaptureOptions"] == [
{"CaptureMode": "Input"}
]
assert (
endpoint_config_desc["DataCaptureConfig"]["CaptureContentTypeHeader"]["CsvContentTypes"]
== CUSTOM_CSV_CONTENT_TYPES
)
assert (
endpoint_config_desc["DataCaptureConfig"]["CaptureContentTypeHeader"][
"JsonContentTypes"
]
== CUSTOM_JSON_CONTENT_TYPES
)
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