testbed/aws__sagemaker-python-sdk/tests/integ/test_ipinsights.py

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# 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