def test_workflow_update_when_statemachinearn_is_none(client):
workflow = Workflow(name=state_machine_name,
definition=definition,
role=role_arn,
client=client)
new_definition = steps.Pass('HelloWorld')
with pytest.raises(WorkflowNotFound):
workflow.update(definition=new_definition)
def create_sfn_workflow(params, steps):
sfn_workflow_name = params['sfn-workflow-name']
workflow_execution_role = params['sfn-role-arn']
workflow_graph = Chain(steps)
branching_workflow = Workflow(
name=sfn_workflow_name,
definition=workflow_graph,
role=workflow_execution_role,
)
branching_workflow.create()
branching_workflow.update(workflow_graph)
time.sleep(5)
return branching_workflow
workflow_definition = Chain([
create_autopilot_job_step, check_autopilot_job_status,
check_job_wait_state, check_job_choice
])
autopilot_ml_workflow = Workflow(name="AutopilotStateMachineWorkflow",
definition=workflow_definition,
role=utils.get_workflow_role())
try:
state_machine_arn = autopilot_ml_workflow.create()
except sfn_client.exceptions.StateMachineAlreadyExists as e:
print(e.message)
else:
print("Updating workflow definition")
state_machine_arn = autopilot_ml_workflow.update(workflow_definition)
utils.save_state_machine_arn(state_machine_arn)
timestamp_suffix = strftime('%d-%H-%M-%S', gmtime())
# Uncomment below when you're ready to execute workflow on deployment
# autopilot_ml_workflow.execute(
# inputs={
# 'AutoMLJobName': f'autopilot-workflow-job-{timestamp_suffix}',
# 'ModelName': f'autopilot-workflow-{timestamp_suffix}-model',
# 'EndpointConfigName': f'autopilot-workflow-{timestamp_suffix}-endpoint-config',
# 'EndpointName': f'autopilot-workflow-{timestamp_suffix}-endpoint',
# 'S3InputData': '',
# 'TargetColumnName': '',
# 'S3OutputData': '',
