def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting Training Job to SageMaker...')
job_name = _utils.create_training_job(client, vars(args))
def signal_term_handler(signalNumber, frame):
_utils.stop_training_job(client, job_name)
logging.info(f"Training Job: {job_name} request submitted to Stop")
signal.signal(signal.SIGTERM, signal_term_handler)
logging.info('Job request submitted. Waiting for completion...')
try:
_utils.wait_for_training_job(client, job_name)
_utils.wait_for_debug_rules(client, job_name)
except:
raise
finally:
cw_client = _utils.get_cloudwatch_client(args.region)
_utils.print_logs_for_job(cw_client, '/aws/sagemaker/TrainingJobs', job_name)
image = _utils.get_image_from_job(client, job_name)
model_artifact_url = _utils.get_model_artifacts_from_job(client, job_name)
logging.info('Get model artifacts %s from training job %s.', model_artifact_url, job_name)
_utils.write_output(args.model_artifact_url_output_path, model_artifact_url)
_utils.write_output(args.job_name_output_path, job_name)
_utils.write_output(args.training_image_output_path, image)
logging.info('Job completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region)
logging.info(
'Submitting HyperParameter Tuning Job request to SageMaker...')
hpo_job_name = _utils.create_hyperparameter_tuning_job(client, vars(args))
logging.info(
'HyperParameter Tuning Job request submitted. Waiting for completion...'
)
_utils.wait_for_hyperparameter_training_job(client, hpo_job_name)
best_job, best_hyperparameters = _utils.get_best_training_job_and_hyperparameters(
client, hpo_job_name)
model_artifact_url = _utils.get_model_artifacts_from_job(client, best_job)
image = _utils.get_image_from_job(client, best_job)
logging.info('HyperParameter Tuning Job completed.')
with open('/tmp/hpo_job_name.txt', 'w') as f:
f.write(hpo_job_name)
with open('/tmp/best_job_name.txt', 'w') as f:
f.write(best_job)
with open('/tmp/best_hyperparameters.txt', 'w') as f:
f.write(json.dumps(best_hyperparameters))
with open('/tmp/model_artifact_url.txt', 'w') as f:
f.write(model_artifact_url)
with open('/tmp/training_image.txt', 'w') as f:
f.write(image)
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting Training Job to SageMaker...')
job_name = _utils.create_training_job(client, vars(args))
logging.info('Job request submitted. Waiting for completion...')
_utils.wait_for_training_job(client, job_name)
image = _utils.get_image_from_job(client, job_name)
model_artifact_url = _utils.get_model_artifacts_from_job(client, job_name)
logging.info('Get model artifacts %s from training job %s.',
model_artifact_url, job_name)
with open('/tmp/model_artifact_url.txt', 'w') as f:
f.write(model_artifact_url)
with open('/tmp/job_name.txt', 'w') as f:
f.write(job_name)
with open('/tmp/training_image.txt', 'w') as f:
f.write(image)
logging.info('Job completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region)
logging.info(
'Submitting HyperParameter Tuning Job request to SageMaker...')
hpo_job_name = _utils.create_hyperparameter_tuning_job(client, vars(args))
logging.info(
'HyperParameter Tuning Job request submitted. Waiting for completion...'
)
_utils.wait_for_hyperparameter_training_job(client, hpo_job_name)
best_job, best_hyperparameters = _utils.get_best_training_job_and_hyperparameters(
client, hpo_job_name)
model_artifact_url = _utils.get_model_artifacts_from_job(client, best_job)
image = _utils.get_image_from_job(client, best_job)
logging.info('HyperParameter Tuning Job completed.')
_utils.write_output(args.hpo_job_name_output_path, hpo_job_name)
_utils.write_output(args.model_artifact_url_output_path,
model_artifact_url)
_utils.write_output(args.best_job_name_output_path, best_job)
_utils.write_output(args.best_hyperparameters_output_path,
best_hyperparameters,
json_encode=True)
_utils.write_output(args.training_image_output_path, image)
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region,
args.endpoint_url,
assume_role_arn=args.assume_role)
logging.info('Submitting Batch Transformation request to SageMaker...')
batch_job_name = _utils.create_transform_job(client, vars(args))
def signal_term_handler(signalNumber, frame):
_utils.stop_transform_job(client, batch_job_name)
logging.info(
f"Transform job: {batch_job_name} request submitted to Stop")
signal.signal(signal.SIGTERM, signal_term_handler)
logging.info('Batch Job request submitted. Waiting for completion...')
try:
_utils.wait_for_transform_job(client, batch_job_name)
except:
raise
finally:
cw_client = _utils.get_cloudwatch_client(
args.region, assume_role_arn=args.assume_role)
_utils.print_logs_for_job(cw_client, '/aws/sagemaker/TransformJobs',
batch_job_name)
_utils.write_output(args.output_location_output_path, args.output_location)
logging.info('Batch Transformation creation completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting Processing Job to SageMaker...')
job_name = _utils.create_processing_job(client, vars(args))
logging.info('Job request submitted. Waiting for completion...')
try:
_utils.wait_for_processing_job(client, job_name)
except:
raise
finally:
cw_client = _utils.get_cloudwatch_client(args.region)
_utils.print_logs_for_job(cw_client, '/aws/sagemaker/ProcessingJobs',
job_name)
outputs = _utils.get_processing_job_outputs(client, job_name)
with open('/tmp/job_name.txt', 'w') as f:
f.write(job_name)
with open('/tmp/output_artifacts.txt', 'w') as f:
f.write(json.dumps(outputs))
logging.info('Job completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url, assume_role_arn=args.assume_role)
endpoint_name = None
old_endpoint_config_name = None
if (args.update_endpoint and _utils.endpoint_name_exists(client, args.endpoint_name)):
## Get the old endpoint config to cleanup later
old_endpoint_config_name = _utils.get_endpoint_config(client, args.endpoint_name)
logging.info('Submitting Update Endpoint request to SageMaker...')
endpoint_name = _utils.update_deployed_model(client, vars(args))
else:
logging.info('Submitting Create Endpoint request to SageMaker...')
endpoint_name = _utils.deploy_model(client, vars(args))
logging.info('Endpoint creation/update request submitted. Waiting for completion...')
_utils.wait_for_endpoint_creation(client, endpoint_name)
## If updating existing endpoint, cleanup old endpoint config
if old_endpoint_config_name:
logging.info("Deleting old endpoint config: " + old_endpoint_config_name)
if _utils.delete_endpoint_config(client, old_endpoint_config_name):
logging.info("Deleted old endpoint config: " + old_endpoint_config_name)
else:
logging.info("Unable to delete old endpoint config: " + old_endpoint_config_name)
_utils.write_output(args.endpoint_name_output_path, endpoint_name)
logging.info('Endpoint creation/update completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting a create workteam request to SageMaker...')
workteam_arn = _utils.create_workteam(client, vars(args))
logging.info('Workteam created.')
_utils.write_output(args.workteam_arn_output_path, workteam_arn)
def main(argv=None):
parser = create_parser()
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting model creation request to SageMaker...')
_utils.create_model(client, vars(args))
logging.info('Model creation completed.')
with open('/tmp/model_name.txt', 'w') as f:
f.write(args.model_name)
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting model creation request to SageMaker...')
_utils.create_model(client, vars(args))
logging.info('Model creation completed.')
_utils.write_output(args.model_name_output_path, args.model_name)
def test_assumed_sagemaker_client(self):
_utils.get_boto3_session = MagicMock()
mock_sm_client = MagicMock()
# Mock the client("SageMaker", ...) return value
_utils.get_boto3_session.return_value.client.return_value = mock_sm_client
client = _utils.get_sagemaker_client("us-east-1", assume_role_arn="abc123")
assert client == mock_sm_client
_utils.get_boto3_session.assert_called_once_with("us-east-1", "abc123")
_utils.get_boto3_session.return_value.client.assert_called_once_with("sagemaker", endpoint_url=None, config=ANY)
def main(argv=None):
parser = create_parser()
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting a create workteam request to SageMaker...')
workteam_arn = _utils.create_workteam(client, vars(args))
logging.info('Workteam created.')
with open('/tmp/workteam_arn.txt', 'w') as f:
f.write(workteam_arn)
def main(argv=None):
parser = create_parser()
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting Batch Transformation request to SageMaker...')
batch_job_name = _utils.create_transform_job(client, vars(args))
logging.info('Batch Job request submitted. Waiting for completion...')
_utils.wait_for_transform_job(client, batch_job_name)
Path(args.output_location_file).parent.mkdir(parents=True, exist_ok=True)
Path(args.output_location_file).write_text(unicode(args.output_location))
logging.info('Batch Transformation creation completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting Endpoint request to SageMaker...')
endpoint_name = _utils.deploy_model(client, vars(args))
logging.info('Endpoint creation request submitted. Waiting for completion...')
_utils.wait_for_endpoint_creation(client, endpoint_name)
with open('/tmp/endpoint_name.txt', 'w') as f:
f.write(endpoint_name)
logging.info('Endpoint creation completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region,
args.endpoint_url,
assume_role_arn=args.assume_role)
logging.info('Submitting Endpoint request to SageMaker...')
endpoint_name = _utils.deploy_model(client, vars(args))
logging.info(
'Endpoint creation request submitted. Waiting for completion...')
_utils.wait_for_endpoint_creation(client, endpoint_name)
_utils.write_output(args.endpoint_name_output_path, endpoint_name)
logging.info('Endpoint creation completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting Ground Truth Job request to SageMaker...')
_utils.create_labeling_job(client, vars(args))
logging.info('Ground Truth labeling job request submitted. Waiting for completion...')
_utils.wait_for_labeling_job(client, args.job_name)
output_manifest, active_learning_model_arn = _utils.get_labeling_job_outputs(client, args.job_name, args.enable_auto_labeling)
logging.info('Ground Truth Labeling Job completed.')
with open('/tmp/output_manifest_location.txt', 'w') as f:
f.write(output_manifest)
with open('/tmp/active_learning_model_arn.txt', 'w') as f:
f.write(active_learning_model_arn)
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region,
args.endpoint_url,
assume_role_arn=args.assume_role)
logging.info('Submitting Processing Job to SageMaker...')
job_name = _utils.create_processing_job(client, vars(args))
def signal_term_handler(signalNumber, frame):
logging.info(f"Stopping Processing Job: {job_name}")
_utils.stop_processing_job(client, job_name)
logging.info(f"Processing Job: {job_name} request submitted to Stop")
signal.signal(signal.SIGTERM, signal_term_handler)
logging.info('Job request submitted. Waiting for completion...')
try:
_utils.wait_for_processing_job(client, job_name)
except:
raise
finally:
cw_client = _utils.get_cloudwatch_client(
args.region, assume_role_arn=args.assume_role)
_utils.print_logs_for_job(cw_client, '/aws/sagemaker/ProcessingJobs',
job_name)
outputs = _utils.get_processing_job_outputs(client, job_name)
_utils.write_output(args.job_name_output_path, job_name)
_utils.write_output(args.output_artifacts_output_path,
outputs,
json_encode=True)
logging.info('Job completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url)
logging.info('Submitting Batch Transformation request to SageMaker...')
batch_job_name = _utils.create_transform_job(client, vars(args))
logging.info('Batch Job request submitted. Waiting for completion...')
try:
_utils.wait_for_transform_job(client, batch_job_name)
except:
raise
finally:
cw_client = _utils.get_cloudwatch_client(args.region)
_utils.print_logs_for_job(cw_client, '/aws/sagemaker/TransformJobs',
batch_job_name)
_utils.write_output(args.output_location_output_path, args.output_location)
logging.info('Batch Transformation creation completed.')
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region,
args.endpoint_url,
assume_role_arn=args.assume_role)
logging.info(
'Submitting HyperParameter Tuning Job request to SageMaker...')
hpo_job_name = _utils.create_hyperparameter_tuning_job(client, vars(args))
def signal_term_handler(signalNumber, frame):
_utils.stop_hyperparameter_tuning_job(client, hpo_job_name)
logging.info(
f"HyperParameter Tuning Job: {hpo_job_name} request submitted to Stop"
)
signal.signal(signal.SIGTERM, signal_term_handler)
logging.info(
'HyperParameter Tuning Job request submitted. Waiting for completion...'
)
_utils.wait_for_hyperparameter_training_job(client, hpo_job_name)
best_job, best_hyperparameters = _utils.get_best_training_job_and_hyperparameters(
client, hpo_job_name)
model_artifact_url = _utils.get_model_artifacts_from_job(client, best_job)
image = _utils.get_image_from_job(client, best_job)
logging.info('HyperParameter Tuning Job completed.')
_utils.write_output(args.hpo_job_name_output_path, hpo_job_name)
_utils.write_output(args.model_artifact_url_output_path,
model_artifact_url)
_utils.write_output(args.best_job_name_output_path, best_job)
_utils.write_output(args.best_hyperparameters_output_path,
best_hyperparameters,
json_encode=True)
_utils.write_output(args.training_image_output_path, image)
def main(argv=None):
parser = create_parser()
args = parser.parse_args(argv)
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_sagemaker_client(args.region, args.endpoint_url, assume_role_arn=args.assume_role)
logging.info('Submitting Ground Truth Job request to SageMaker...')
_utils.create_labeling_job(client, vars(args))
def signal_term_handler(signalNumber, frame):
_utils.stop_labeling_job(client, args.job_name)
logging.info(f"Ground Truth labeling job: {args.job_name} request submitted to Stop")
signal.signal(signal.SIGTERM, signal_term_handler)
logging.info('Ground Truth labeling job request submitted. Waiting for completion...')
_utils.wait_for_labeling_job(client, args.job_name)
output_manifest, active_learning_model_arn = _utils.get_labeling_job_outputs(client, args.job_name, args.enable_auto_labeling)
logging.info('Ground Truth Labeling Job completed.')
_utils.write_output(args.output_manifest_location_output_path, output_manifest)
_utils.write_output(args.active_learning_model_arn_output_path, active_learning_model_arn)
