def get_existing_monitor_pipeline(workflow_arn):
"""
Create a dummpy implementation of get existing data pipeline
"""
data_pipeline = Workflow(
name="data_pipeline_name",
definition=Chain([]),
role="workflow_execution_role",
)
return data_pipeline.attach(workflow_arn)
def get_existing_training_pipeline(workflow_arn):
"""
Create a dummpy implementation of get existing training pipeline
"""
training_pipeline = Workflow(
name="training_pipeline_name",
definition=Chain([]),
role="workflow_execution_role",
)
return training_pipeline.attach(workflow_arn)
def get_existing_inference_pipeline(workflow_arn):
"""
Create a dummy implementation to get existing training pipeline
TODO: This could be a good PR for the SDK.
"""
inference_pipeline = Workflow(
name="inference_pipeline_name",
definition=Chain([]),
role="workflow_execution_role",
)
return inference_pipeline.attach(workflow_arn)
def test_attach_existing_workflow(client):
workflow = Workflow.attach(state_machine_arn, client)
assert workflow.name == state_machine_name
assert workflow.role == role_arn
assert workflow.state_machine_arn == state_machine_arn
model_name=job_name,
initial_instance_count=1,
instance_type='ml.m5.large')
endpoint_step = steps.EndpointStep(
"Create or Update Endpoint",
endpoint_name=execution_input['EndpointName'],
endpoint_config_name=job_name,
update=update_endpoint)
workflow_definition = steps.Chain(
[training_step, model_step, endpoint_config_step, endpoint_step])
# Update the workflow that is already created
workflow = Workflow.attach(workflow_arn)
workflow.update(definition=workflow_definition)
print('Workflow updated: {}'.format(workflow_arn))
# Sleep for 5 seconds then execute after this is applied
time.sleep(5)
execution = workflow.execute(inputs=execution_params)
stepfunction_arn = execution.execution_arn
print('Workflow exectuted: {}'.format(stepfunction_arn))
# Export environment variables
if not os.path.exists('cloud_formation'):
os.makedirs('cloud_formation')
workflow_definition = steps.Chain([
etl_step,
training_step,
model_step,
lambda_step,
check_accuracy_step
])
# This can be used to create a brand new workflow
try:
# This is used to update the existing workflow.
# That way you can still see all the step function run history
# You could alternatively delete and recreate the workflow
state_machine_arn = 'arn:aws:states:ap-southeast-2:' + account_id + ':stateMachine:' + workflow_name
workflow = Workflow.attach(state_machine_arn=state_machine_arn)
workflow.update(
definition = workflow_definition,
role=workflow_execution_role
)
except:
workflow = Workflow(
name=workflow_name,
definition=workflow_definition,
role=workflow_execution_role,
execution_input=execution_input
)
workflow.create()
# Documentation states the following:
def execute(state_machine_arn: str, execution_input: Union[dict, None]):
"""execute statemachine based on the name."""
workflow = Workflow.attach(state_machine_arn)
return workflow.execute(inputs=execution_input)
def _execute(state_machine_arn: str):
"""execute statemachine based on the name."""
workflow = Workflow.attach(state_machine_arn)
return workflow.execute()
