File size: 4,280 Bytes
476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | # 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
|