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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
from sagemaker import FactorizationMachines, FactorizationMachinesModel
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_factorization_machines(sagemaker_session, cpu_instance_type, training_set):
job_name = unique_name_from_base("fm")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
fm = FactorizationMachines(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_factors=10,
predictor_type="regressor",
epochs=2,
clip_gradient=1e2,
eps=0.001,
rescale_grad=1.0 / 100,
sagemaker_session=sagemaker_session,
)
# training labels must be 'float32'
fm.fit(
fm.record_set(training_set[0][:200], training_set[1][:200].astype("float32")),
job_name=job_name,
)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
model = FactorizationMachinesModel(
fm.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session
)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(training_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_async_factorization_machines(sagemaker_session, cpu_instance_type, training_set):
job_name = unique_name_from_base("fm")
with timeout(minutes=5):
fm = FactorizationMachines(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_factors=10,
predictor_type="regressor",
epochs=2,
clip_gradient=1e2,
eps=0.001,
rescale_grad=1.0 / 100,
sagemaker_session=sagemaker_session,
)
# training labels must be 'float32'
fm.fit(
fm.record_set(training_set[0][:200], training_set[1][:200].astype("float32")),
job_name=job_name,
wait=False,
)
print("Detached from training job. Will re-attach in 20 seconds")
time.sleep(20)
print("attaching now...")
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
estimator = FactorizationMachines.attach(
training_job_name=job_name, sagemaker_session=sagemaker_session
)
model = FactorizationMachinesModel(
estimator.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session
)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(training_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_factorization_machines_serverless_inference(
sagemaker_session, cpu_instance_type, training_set
):
job_name = unique_name_from_base("fm-serverless")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
fm = FactorizationMachines(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
num_factors=10,
predictor_type="regressor",
epochs=2,
clip_gradient=1e2,
eps=0.001,
rescale_grad=1.0 / 100,
sagemaker_session=sagemaker_session,
)
# training labels must be 'float32'
fm.fit(
fm.record_set(training_set[0][:200], training_set[1][:200].astype("float32")),
job_name=job_name,
)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
model = FactorizationMachinesModel(
fm.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session
)
predictor = model.deploy(
serverless_inference_config=ServerlessInferenceConfig(), endpoint_name=job_name
)
result = predictor.predict(training_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
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