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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 time
import pytest
import sagemaker.amazon.pca
from sagemaker.serverless import ServerlessInferenceConfig
from sagemaker.utils import unique_name_from_base
from tests.integ import datasets, TRAINING_DEFAULT_TIMEOUT_MINUTES
from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
@pytest.fixture
def training_set():
return datasets.one_p_mnist()
def test_pca(sagemaker_session, cpu_instance_type, training_set):
job_name = unique_name_from_base("pca")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
pca = sagemaker.amazon.pca.PCA(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_components=48,
sagemaker_session=sagemaker_session,
enable_network_isolation=True,
)
pca.algorithm_mode = "randomized"
pca.subtract_mean = True
pca.extra_components = 5
pca.fit(pca.record_set(training_set[0][:100]), job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
pca_model = sagemaker.amazon.pca.PCAModel(
model_data=pca.model_data,
role="SageMakerRole",
sagemaker_session=sagemaker_session,
enable_network_isolation=True,
)
predictor = pca_model.deploy(
initial_instance_count=1, instance_type=cpu_instance_type, endpoint_name=job_name
)
result = predictor.predict(training_set[0][:5])
assert len(result) == 5
for record in result:
assert record.label["projection"] is not None
def test_async_pca(sagemaker_session, cpu_instance_type, training_set):
job_name = unique_name_from_base("pca")
with timeout(minutes=5):
pca = sagemaker.amazon.pca.PCA(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_components=48,
sagemaker_session=sagemaker_session,
base_job_name="test-pca",
)
pca.algorithm_mode = "randomized"
pca.subtract_mean = True
pca.extra_components = 5
pca.fit(pca.record_set(training_set[0][:100]), wait=False, job_name=job_name)
print("Detached from training job. Will re-attach in 20 seconds")
time.sleep(20)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
estimator = sagemaker.amazon.pca.PCA.attach(
training_job_name=job_name, sagemaker_session=sagemaker_session
)
model = sagemaker.amazon.pca.PCAModel(
estimator.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session
)
predictor = model.deploy(
initial_instance_count=1, instance_type=cpu_instance_type, endpoint_name=job_name
)
result = predictor.predict(training_set[0][:5])
assert len(result) == 5
for record in result:
assert record.label["projection"] is not None
def test_pca_serverless_inference(sagemaker_session, cpu_instance_type, training_set):
job_name = unique_name_from_base("pca-serverless")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
pca = sagemaker.amazon.pca.PCA(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_components=48,
sagemaker_session=sagemaker_session,
enable_network_isolation=True,
)
pca.algorithm_mode = "randomized"
pca.subtract_mean = True
pca.extra_components = 5
pca.fit(pca.record_set(training_set[0][:100]), job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
pca_model = sagemaker.amazon.pca.PCAModel(
model_data=pca.model_data,
role="SageMakerRole",
sagemaker_session=sagemaker_session,
)
predictor = pca_model.deploy(
serverless_inference_config=ServerlessInferenceConfig(), endpoint_name=job_name
)
result = predictor.predict(training_set[0][:5])
assert len(result) == 5
for record in result:
assert record.label["projection"] is not None
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