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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
from sagemaker import IPInsights, IPInsightsModel
from sagemaker.predictor import Predictor
from sagemaker.serverless import ServerlessInferenceConfig
from sagemaker.utils import unique_name_from_base
from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
from tests.integ.record_set import prepare_record_set_from_local_files
from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
FEATURE_DIM = None
def test_ipinsights(sagemaker_session, cpu_instance_type):
job_name = unique_name_from_base("ipinsights")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "ipinsights")
data_filename = "train.csv"
with open(os.path.join(data_path, data_filename), "rb") as f:
num_records = len(f.readlines())
ipinsights = IPInsights(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_entity_vectors=10,
vector_dim=100,
sagemaker_session=sagemaker_session,
)
record_set = prepare_record_set_from_local_files(
data_path, ipinsights.data_location, num_records, FEATURE_DIM, sagemaker_session
)
ipinsights.fit(records=record_set, job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
model = IPInsightsModel(
ipinsights.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session
)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
assert isinstance(predictor, Predictor)
predict_input = [["user_1", "1.1.1.1"]]
result = predictor.predict(predict_input)
assert len(result["predictions"]) == 1
assert 0 > result["predictions"][0]["dot_product"] > -1 # We expect ~ -0.22
def test_ipinsights_serverless_inference(sagemaker_session, cpu_instance_type):
job_name = unique_name_from_base("ipinsights-serverless")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "ipinsights")
data_filename = "train.csv"
with open(os.path.join(data_path, data_filename), "rb") as f:
num_records = len(f.readlines())
ipinsights = IPInsights(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_entity_vectors=10,
vector_dim=100,
sagemaker_session=sagemaker_session,
)
record_set = prepare_record_set_from_local_files(
data_path, ipinsights.data_location, num_records, FEATURE_DIM, sagemaker_session
)
ipinsights.fit(records=record_set, job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
model = IPInsightsModel(
ipinsights.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session
)
predictor = model.deploy(
serverless_inference_config=ServerlessInferenceConfig(memory_size_in_mb=6144),
endpoint_name=job_name,
)
assert isinstance(predictor, Predictor)
predict_input = [["user_1", "1.1.1.1"]]
result = predictor.predict(predict_input)
assert len(result["predictions"]) == 1
assert 0 > result["predictions"][0]["dot_product"] > -1 # We expect ~ -0.22
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