def test_training_step(pca_estimator_fixture, record_set_fixture, sfn_client,
sfn_role_arn):
# Build workflow definition
job_name = generate_job_name()
training_step = TrainingStep('create_training_job_step',
estimator=pca_estimator_fixture,
job_name=job_name,
data=record_set_fixture,
mini_batch_size=200)
training_step.add_retry(SAGEMAKER_RETRY_STRATEGY)
workflow_graph = Chain([training_step])
with timeout(minutes=DEFAULT_TIMEOUT_MINUTES):
# Create workflow and check definition
workflow = create_workflow_and_check_definition(
workflow_graph=workflow_graph,
workflow_name=unique_name_from_base(
"integ-test-training-step-workflow"),
sfn_client=sfn_client,
sfn_role_arn=sfn_role_arn)
# Execute workflow
execution = workflow.execute()
execution_output = execution.get_output(wait=True)
# Check workflow output
assert execution_output.get("TrainingJobStatus") == "Completed"
# Cleanup
state_machine_delete_wait(sfn_client, workflow.state_machine_arn)
def test_training_step_with_placeholders(pca_estimator_fixture,
record_set_fixture, sfn_client,
sfn_role_arn):
execution_input = ExecutionInput(
schema={
'JobName': str,
'HyperParameters': str,
'InstanceCount': int,
'InstanceType': str,
'MaxRun': int
})
parameters = {
'HyperParameters': execution_input['HyperParameters'],
'ResourceConfig': {
'InstanceCount': execution_input['InstanceCount'],
'InstanceType': execution_input['InstanceType']
},
'StoppingCondition': {
'MaxRuntimeInSeconds': execution_input['MaxRun']
}
}
training_step = TrainingStep('create_training_job_step',
estimator=pca_estimator_fixture,
job_name=execution_input['JobName'],
data=record_set_fixture,
mini_batch_size=200,
parameters=parameters)
training_step.add_retry(SAGEMAKER_RETRY_STRATEGY)
workflow_graph = Chain([training_step])
with timeout(minutes=DEFAULT_TIMEOUT_MINUTES):
# Create workflow and check definition
workflow = create_workflow_and_check_definition(
workflow_graph=workflow_graph,
workflow_name=unique_name_from_base(
"integ-test-training-step-workflow"),
sfn_client=sfn_client,
sfn_role_arn=sfn_role_arn)
inputs = {
'JobName': generate_job_name(),
'HyperParameters': {
"num_components": "48",
"feature_dim": "784",
"mini_batch_size": "250"
},
'InstanceCount': INSTANCE_COUNT,
'InstanceType': INSTANCE_TYPE,
'MaxRun': 100000
}
# Execute workflow
execution = workflow.execute(inputs=inputs)
execution_output = execution.get_output(wait=True)
# Check workflow output
assert execution_output.get("TrainingJobStatus") == "Completed"
# Cleanup
state_machine_delete_wait(sfn_client, workflow.state_machine_arn)