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Increase the integration test timeout. #18

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Merged
merged 1 commit into from
Jan 13, 2020
Merged

Increase the integration test timeout. #18

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2 changes: 1 addition & 1 deletion tests/integ/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,4 +15,4 @@
import os

DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "data")
DEFAULT_TIMEOUT_MINUTES = 10
DEFAULT_TIMEOUT_MINUTES = 25
3 changes: 1 addition & 2 deletions tests/integ/test_inference_pipeline.py
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Expand Up @@ -35,7 +35,6 @@
# Constants
BASE_NAME = 'inference-pipeline-integtest'
COMPRESSED_NPY_DATA = 'mnist.npy.gz'
CREATE_ENDPOINT_TIMEOUT_MINUTES = 20

# Fixtures
@pytest.fixture(scope="module")
Expand Down Expand Up @@ -88,7 +87,7 @@ def test_inference_pipeline_framework(
inputs):
bucket_name = sagemaker_session.default_bucket()
unique_name = '{}-{}'.format(BASE_NAME, datetime.now().strftime('%Y%m%d%H%M%S'))
with timeout(minutes=CREATE_ENDPOINT_TIMEOUT_MINUTES):
with timeout(minutes=DEFAULT_TIMEOUT_MINUTES):
pipeline = InferencePipeline(
preprocessor=sklearn_preprocessor,
estimator=sklearn_estimator,
Expand Down
5 changes: 2 additions & 3 deletions tests/integ/test_sagemaker_steps.py
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Expand Up @@ -45,7 +45,6 @@
INSTANCE_COUNT = 1
INSTANCE_TYPE = "ml.m5.large"

CREATE_ENDPOINT_TIMEOUT_MINUTES = 20

@pytest.fixture(scope="module")
def trained_estimator(pca_estimator_fixture, record_set_fixture):
Expand Down Expand Up @@ -224,7 +223,7 @@ def test_create_endpoint_step(trained_estimator, record_set_fixture, sfn_client,
endpoint_step = EndpointStep('create_endpoint_step', endpoint_name=endpoint_name, endpoint_config_name=model.name)
workflow_graph = Chain([endpoint_step])

with timeout(minutes=CREATE_ENDPOINT_TIMEOUT_MINUTES):
with timeout(minutes=DEFAULT_TIMEOUT_MINUTES):
# Create workflow and check definition
workflow = create_workflow_and_check_definition(
workflow_graph=workflow_graph,
Expand Down Expand Up @@ -279,7 +278,7 @@ def test_tuning_step(sfn_client, record_set_for_hyperparameter_tuning, sagemaker
tuning_step = TuningStep('Tuning', tuner=tuner, job_name=job_name, data=record_set_for_hyperparameter_tuning)
workflow_graph = Chain([tuning_step])

with timeout(minutes=CREATE_ENDPOINT_TIMEOUT_MINUTES):
with timeout(minutes=DEFAULT_TIMEOUT_MINUTES):
# Create workflow and check definition
workflow = create_workflow_and_check_definition(
workflow_graph=workflow_graph,
Expand Down
3 changes: 1 addition & 2 deletions tests/integ/test_training_pipeline_estimators.py
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Expand Up @@ -42,7 +42,6 @@

# Constants
BASE_NAME = 'training-pipeline-integtest'
CREATE_ENDPOINT_TIMEOUT_MINUTES = 20


# Fixtures
Expand Down Expand Up @@ -80,7 +79,7 @@ def test_pca_estimator(sfn_client, sagemaker_session, sagemaker_role_arn, sfn_ro
unique_name = '{}-{}'.format(BASE_NAME, datetime.now().strftime('%Y%m%d%H%M%S'))
hyperparams = pca_estimator.hyperparameters()

with timeout(minutes=CREATE_ENDPOINT_TIMEOUT_MINUTES):
with timeout(minutes=DEFAULT_TIMEOUT_MINUTES):
tp = TrainingPipeline(
estimator=pca_estimator,
role=sfn_role_arn,
Expand Down
8 changes: 3 additions & 5 deletions tests/integ/test_training_pipeline_framework_estimator.py
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Expand Up @@ -16,7 +16,7 @@
import sagemaker
import os

from tests.integ import DATA_DIR
from tests.integ import DATA_DIR, DEFAULT_TIMEOUT_MINUTES
from tests.integ.timeout import timeout
from stepfunctions.template import TrainingPipeline
from sagemaker.pytorch import PyTorch
Expand All @@ -29,8 +29,6 @@
get_resource_name_from_arn
)

PIPELINE_TIMEOUT_LIMIT = 20

@pytest.fixture(scope="module")
def torch_estimator(sagemaker_role_arn):
script_path = os.path.join(DATA_DIR, "pytorch_mnist", "mnist.py")
Expand Down Expand Up @@ -88,7 +86,7 @@ def _pipeline_teardown(sfn_client, sagemaker_session, endpoint_name, pipeline):


def test_torch_training_pipeline(sfn_client, sagemaker_client, torch_estimator, sagemaker_session, sfn_role_arn):
with timeout(minutes=PIPELINE_TIMEOUT_LIMIT):
with timeout(minutes=DEFAULT_TIMEOUT_MINUTES):
# upload input data
data_path = os.path.join(DATA_DIR, "pytorch_mnist")
inputs = sagemaker_session.upload_data(
Expand Down Expand Up @@ -123,7 +121,7 @@ def test_torch_training_pipeline(sfn_client, sagemaker_client, torch_estimator,


def test_sklearn_training_pipeline(sfn_client, sagemaker_client, sklearn_estimator, sagemaker_session, sfn_role_arn):
with timeout(minutes=PIPELINE_TIMEOUT_LIMIT):
with timeout(minutes=DEFAULT_TIMEOUT_MINUTES):
# upload input data
data_path = os.path.join(DATA_DIR, "sklearn_mnist")
inputs = sagemaker_session.upload_data(
Expand Down