def train(obj, input_s3_dir, output_s3_dir, hyperparams_file, ec2_type,
volume_size, time_out, aws_tags, iam_role_arn, external_id,
base_job_name, job_name, metric_names):
"""
Command to train ML model(s) on SageMaker
"""
logger.info(ASCII_LOGO)
logger.info("Started training on SageMaker...\n")
try:
s3_model_location = api_cloud.train(
dir=_config().sagify_module_dir,
input_s3_dir=input_s3_dir,
output_s3_dir=output_s3_dir,
hyperparams_file=hyperparams_file,
ec2_type=ec2_type,
volume_size=volume_size,
time_out=time_out,
docker_tag=obj['docker_tag'],
tags=aws_tags,
aws_role=iam_role_arn,
external_id=external_id,
base_job_name=base_job_name,
job_name=job_name,
metric_names=[_val.strip() for _val in metric_names.split(',')]
if metric_names else None)
logger.info("Training on SageMaker succeeded")
logger.info("Model S3 location: {}".format(s3_model_location))
except ValueError as e:
logger.info("{}".format(e))
sys.exit(-1)
def train(obj, dir, job_name, input_s3_dir, output_s3_dir, hyperparams_file,
ec2_type, volume_size, time_out, aws_tags):
"""
Command to train ML model(s) on SageMaker
"""
logger.info(ASCII_LOGO)
logger.info("Started training on SageMaker...\n")
try:
s3_model_location = api_cloud.train(dir=dir,
job_name=job_name,
input_s3_dir=input_s3_dir,
output_s3_dir=output_s3_dir,
hyperparams_file=hyperparams_file,
ec2_type=ec2_type,
volume_size=volume_size,
time_out=time_out,
docker_tag=obj['docker_tag'],
tags=aws_tags)
logger.info("Training on SageMaker succeeded")
logger.info("Model S3 location: {}".format(s3_model_location))
except ValueError as e:
logger.info("{}".format(e))
sys.exit(-1)
