def test_terminate_in_asynchronous_mode_returns_before_transform_job_termination_completes(
pretrained_model, sagemaker_client):
transform_job_termination_latency = 10
get_sagemaker_backend(
sagemaker_client.meta.region_name).set_transform_job_update_latency(
transform_job_termination_latency)
job_name = "test-job"
termination_start_time = time.time()
mfs.deploy_transform_job(
job_name=job_name,
model_uri=pretrained_model.model_uri,
s3_input_data_type="Some Data Type",
s3_input_uri="Some Input Uri",
content_type="Some Content Type",
s3_output_path="Some Output Path",
archive=True,
synchronous=False,
)
mfs.terminate_transform_job(job_name=job_name,
archive=True,
synchronous=False)
termination_end_time = time.time()
assert (termination_end_time -
termination_start_time) < transform_job_termination_latency
transform_job_description = sagemaker_client.describe_transform_job(
TransformJobName=job_name)
assert transform_job_description[
"TransformJobStatus"] == TransformJob.STATUS_STOPPING
def test_attempting_to_terminate_in_asynchronous_mode_without_archiving_throws_exception(
pretrained_model, ):
job_name = "test-job"
mfs.deploy_transform_job(
job_name=job_name,
model_uri=pretrained_model.model_uri,
s3_input_data_type="Some Data Type",
s3_input_uri="Some Input Uri",
content_type="Some Content Type",
s3_output_path="Some Output Path",
)
with pytest.raises(MlflowException) as exc:
mfs.terminate_transform_job(
job_name=job_name,
archive=False,
synchronous=False,
)
assert "Resources must be archived" in exc.value.message
assert exc.value.error_code == ErrorCode.Name(INVALID_PARAMETER_VALUE)