def main():
sagemaker_session = sagemaker.Session()
stepfunctions.set_stream_logger(level=logging.INFO)
bucket = 's3://pixiv-image-backet'
sagemaker_execution_role = 'arn:aws:iam::829044821271:role/service-role/AmazonSageMaker-ExecutionRole-20200412T194702'
workflow_execution_role = 'arn:aws:iam::829044821271:role/StepFunctionsWorkflowExecutionRole'
estimator1 = PyTorch(entry_point='train.py',
source_dir='projection_discriminator',
role=sagemaker_execution_role,
framework_version='1.4.0',
train_instance_count=2,
train_instance_type='ml.m5.2xlarge',
hyperparameters={
'train_epoch': 1,
})
estimator2 = PyTorch(entry_point='train.py',
source_dir='wgan_gp',
role=sagemaker_execution_role,
framework_version='1.4.0',
train_instance_count=2,
train_instance_type='ml.m5.2xlarge',
hyperparameters={
'train_epoch': 1,
})
training_step1 = steps.TrainingStep(state_id='Train Step1',
estimator=estimator1,
data={
'training': bucket,
},
job_name='PD-Train-{0}'.format(
uuid.uuid4()))
training_step2 = steps.TrainingStep(state_id='Train Step2',
estimator=estimator2,
data={
'training': bucket,
},
job_name='PD-Train-{0}'.format(
uuid.uuid4()))
parallel_state = steps.Parallel(state_id='Parallel', )
parallel_state.add_branch(training_step1)
parallel_state.add_branch(training_step2)
workflow_definition = steps.Chain([parallel_state])
workflow = Workflow(
name='MyTraining-{0}'.format(uuid.uuid4()),
definition=workflow_definition,
role=workflow_execution_role,
)
workflow.create()
workflow.execute()
def workflow(client):
workflow = Workflow(name=state_machine_name,
definition=definition,
role=role_arn,
client=client)
workflow.create()
return workflow
def main():
stepfunctions.set_stream_logger(level=logging.INFO)
workflow_execution_role = 'arn:aws:iam::829044821271:role/StepFunctionsWorkflowExecutionRole'
# Load job name
with open('./stepfunctions_name.json', 'r') as f:
stepfunctions_name = json.load(f)
with open('./face_clip/aws_batch/batch_names.json', 'r') as f:
face_clip_name = json.load(f)
with open('./tag_extraction/aws_batch/batch_names.json', 'r') as f:
tag_extraction_name = json.load(f)
# Define steps
face_clip_step = steps.BatchSubmitJobStep(
state_id = 'Face Clip Step',
parameters={
'JobDefinition': face_clip_name['jobDefinition'],
'JobName': face_clip_name['job'],
'JobQueue': face_clip_name['jobQueue']
}
)
tag_extraction_step = steps.BatchSubmitJobStep(
state_id = 'Tag Extraction Step',
parameters={
'JobDefinition': tag_extraction_name['jobDefinition'],
'JobName': tag_extraction_name['job'],
'JobQueue': tag_extraction_name['jobQueue']
}
)
# Define workflow
chain_list = [face_clip_step, tag_extraction_step]
workflow_definition = steps.Chain(chain_list)
workflow = Workflow(
name=stepfunctions_name['workflow'],
definition=workflow_definition,
role=workflow_execution_role,
)
# workflow
workflow.create()
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
def create_workflow_and_check_definition(workflow_graph, workflow_name,
sfn_client, sfn_role_arn):
# Create workflow
workflow = Workflow(name=workflow_name,
definition=workflow_graph,
role=sfn_role_arn,
client=sfn_client)
state_machine_arn = workflow.create()
# Check workflow definition
state_machine_desc = sfn_client.describe_state_machine(
stateMachineArn=state_machine_arn)
assert workflow.definition.to_dict() == json.loads(
state_machine_desc.get('definition'))
return 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:
# Updates an existing state machine by modifying its definition and/or role. Executions started immediately after calling this method may use the previous definition and role.
import time
time.sleep(60) # wait 60 secs to allow the update of the workflow to complete. The method is not syncronous!
# Finally, run the workflow!
execution = workflow.execute(
inputs={
'TrainingJobName': training_job_name, # Each Sagemaker Job requires a unique name,
'ModelName': model_name # Each Model requires a unique name,
}
)
def setup_workflow(project, purpose, workflow_execution_role, script_dir,
ecr_repository):
""" to setup all needed for a step function with sagemaker.
arg:
project: project name under sagemaker
purpose: subproject
workflow_execution_role: arn to execute step functions
script_dir: processing file name, like a .py file
ecr_repository: ecr repository name
return:
workflow: a stepfunctions.workflow.Workflow instance
example:
PROJECT = '[dpt-proj-2022]'
PURPOSE = '[processing]'
WORKFLOW_EXECUTION_ROLE = "arn:aws-cn:iam::[*********]:role/[**************]"
SCRIPT_DIR = "[processing].py"
ECR_REPOSITORY = '[ecr-2022]'
"""
# SageMaker Session setup
# ========================================================================================
# SageMaker Session
# ====================================
account_id = boto3.client('sts').get_caller_identity().get('Account')
role = sagemaker.get_execution_role()
# Storage
# ====================================
session = sagemaker.Session()
region = session.boto_region_name
s3_output = session.default_bucket()
# Code storage
# ==================
s3_prefix = '{}/{}'.format(project, purpose)
s3_prefix_code = '{}/code'.format(s3_prefix)
s3CodePath = 's3://{}/{}/code'.format(s3_output, s3_prefix)
## preprocess & prediction
script_list = [script_dir]
for script in script_list:
session.upload_data(script,
bucket=session.default_bucket(),
key_prefix=s3_prefix_code)
# ECR environment
# ====================================
uri_suffix = 'amazonaws.com.cn'
tag = ':latest'
ecr_repository_uri = '{}.dkr.ecr.{}.{}/{}'.format(account_id, region,
uri_suffix,
ecr_repository + tag)
# SageMaker Experiments setup
# ========================================================================================
experiment = Experiment.create(
experiment_name="{}-{}".format(project, int(time.time())),
description="machine learning project",
sagemaker_boto_client=boto3.client('sagemaker'))
print(experiment)
execution_input = ExecutionInput(schema={
"ProcessingJobName": str,
"ResultPath": str,
})
# setup script processor
script_processor = ScriptProcessor(command=['python3'],
image_uri=ecr_repository_uri,
role=role,
instance_count=1,
instance_type='ml.m5.4xlarge')
# Step
# ========================================================================================
optimizing_step = steps.ProcessingStep(
"Processing Step",
processor=script_processor,
job_name=execution_input["ProcessingJobName"],
inputs=[
ProcessingInput(source=s3CodePath,
destination='/opt/ml/processing/input/code',
input_name='code')
],
outputs=[
ProcessingOutput(output_name=purpose,
destination=execution_input["ResultPath"],
source='/opt/ml/processing/{}'.format(purpose))
],
container_entrypoint=[
"python3", "/opt/ml/processing/input/code/" + script_dir
],
)
# Fail Sate
# ========================================================================================
failed_state = steps.states.Fail("Processing Workflow failed",
cause="SageMakerProcessingJobFailed")
catch_state_processing = steps.states.Catch(
error_equals=["States.TaskFailed"], next_step=failed_state)
# Create Workflow
# ========================================================================================
optimizing_step.add_catch(catch_state_processing)
workflow_name = workflow_name = "workflow-{}-{}".format(project,
purpose).upper()
workflow_graph = steps.Chain([optimizing_step])
workflow = Workflow(name=workflow_name,
definition=workflow_graph,
role=workflow_execution_role)
workflow.create()
return workflow
ChoiceRule.StringEquals(variable=check_autopilot_job_status.output()
['Payload']['AutopilotJobStatus'],
value='Completed'),
next_step=model_and_endpoint_step)
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',
# COMMAND ----------
Dataset_workflow = Workflow(
name="Dataset-workflow",
definition=Dataset_workflow_definition,
role=workflow_execution_role
)
# COMMAND ----------
Dataset_workflow.render_graph()
# COMMAND ----------
DatasetWorkflowArn = Dataset_workflow.create()
# COMMAND ----------
# MAGIC %md
# MAGIC ### DatasetImportWorkflow
# COMMAND ----------
DatasetImport_workflow_definition=Chain([lambda_state_createdataset,
wait_state_dataset,
lambda_state_datasetimportjob,
wait_state_datasetimportjob,
lambda_state_datasetimportjob_status,
datasetimportjob_choice_state
])
