testbed/aws__sagemaker-python-sdk/tests/integ/test_lda.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

import numpy as np

import pytest
import tests.integ
from sagemaker import LDA, LDAModel
from sagemaker.amazon.common import read_records
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.timeout import timeout, timeout_and_delete_endpoint_by_name
from tests.integ.record_set import prepare_record_set_from_local_files


@pytest.mark.slow_test
@pytest.mark.skipif(
    tests.integ.test_region() in tests.integ.NO_LDA_REGIONS,
    reason="LDA image is not supported in certain regions",
)
def test_lda(sagemaker_session, cpu_instance_type):
    job_name = unique_name_from_base("lda")

    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
        data_path = os.path.join(DATA_DIR, "lda")
        data_filename = "nips-train_1.pbr"

        with open(os.path.join(data_path, data_filename), "rb") as f:
            all_records = read_records(f)

        # all records must be same
        feature_num = int(all_records[0].features["values"].float32_tensor.shape[0])

        lda = LDA(
            role="SageMakerRole",
            instance_type=cpu_instance_type,
            num_topics=10,
            sagemaker_session=sagemaker_session,
        )

        record_set = prepare_record_set_from_local_files(
            data_path, lda.data_location, len(all_records), feature_num, sagemaker_session
        )
        lda.fit(records=record_set, mini_batch_size=100, job_name=job_name)

    with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
        model = LDAModel(lda.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session)
        predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)

        predict_input = np.random.rand(1, feature_num)
        result = predictor.predict(predict_input)

        assert len(result) == 1
        for record in result:
            assert record.label["topic_mixture"] is not None


@pytest.mark.slow_test
@pytest.mark.skipif(
    tests.integ.test_region() in tests.integ.NO_LDA_REGIONS,
    reason="LDA image is not supported in certain regions",
)
def test_lda_serverless_inference(sagemaker_session, cpu_instance_type):
    job_name = unique_name_from_base("lda-serverless")

    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
        data_path = os.path.join(DATA_DIR, "lda")
        data_filename = "nips-train_1.pbr"

        with open(os.path.join(data_path, data_filename), "rb") as f:
            all_records = read_records(f)

        # all records must be same
        feature_num = int(all_records[0].features["values"].float32_tensor.shape[0])

        lda = LDA(
            role="SageMakerRole",
            instance_type=cpu_instance_type,
            num_topics=10,
            sagemaker_session=sagemaker_session,
        )

        record_set = prepare_record_set_from_local_files(
            data_path, lda.data_location, len(all_records), feature_num, sagemaker_session
        )
        lda.fit(records=record_set, mini_batch_size=100, job_name=job_name)

    with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
        model = LDAModel(lda.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session)
        predictor = model.deploy(
            serverless_inference_config=ServerlessInferenceConfig(), endpoint_name=job_name
        )

        predict_input = np.random.rand(1, feature_num)
        result = predictor.predict(predict_input)

        assert len(result) == 1
        for record in result:
            assert record.label["topic_mixture"] is not None