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)
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, 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 = argparse.ArgumentParser(description='SageMaker Hyperparameter Tuning Job')
# Set required=True only if parameter is required in the request and there is no default value for it, where default values are based on default values in SageMaker UI
parser.add_argument('--region', type=str, required=True, help='The region where the cluster launches.')
parser.add_argument('--job_name', type=str, required=True, help='The name of the tuning job. Must be unique within the same AWS account and AWS region.')
parser.add_argument('--role', type=str, required=True, help='The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf.')
parser.add_argument('--image', type=str, required=False, help='The registry path of the Docker image that contains the training algorithm.')
parser.add_argument('--algorithm_name', type=str, required=False, help='The name of the resource algorithm to use for the hyperparameter tuning job.')
parser.add_argument('--training_input_mode', choices=['File', 'Pipe'], required=True, help='The input mode that the algorithm supports. File or Pipe.', default='File')
parser.add_argument('--metric_definitions', type=json.loads, required=False, help='The dictionary of name-regex pairs specify the metrics that the algorithm emits.', default='{}')
parser.add_argument('--strategy', choices=['Bayesian', 'Random'], required=False, help='How hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches.', default='Bayesian')
parser.add_argument('--metric_name', type=str, required=True, help='The name of the metric to use for the objective metric.')
parser.add_argument('--metric_type', choices=['Maximize', 'Minimize'], required=True, help='Whether to minimize or maximize the objective metric.')
parser.add_argument('--early_stopping_type', choices=['Off', 'Auto'], required=False, help='Whether to minimize or maximize the objective metric.', default='Off')
parser.add_argument('--static_parameters', type=json.loads, required=False, help='The values of hyperparameters that do not change for the tuning job.', default='{}')
parser.add_argument('--integer_parameters', type=json.loads, required=False, help='The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that you want to search.', default='[]')
parser.add_argument('--continuous_parameters', type=json.loads, required=False, help='The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that you want to search.', default='[]')
parser.add_argument('--categorical_parameters', type=json.loads, required=False, help='The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that you want to search.', default='[]')
parser.add_argument('--channels', type=json.loads, required=True, help='A list of dicts specifying the input channels. Must have at least one.', default='[{}]')
parser.add_argument('--output_location', type=str, required=True, help='The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job.')
parser.add_argument('--output_encryption_key', type=str, required=False, help='The AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts.', default='')
parser.add_argument('--instance_type', choices=['ml.m4.xlarge', 'ml.m4.2xlarge', 'ml.m4.4xlarge', 'ml.m4.10xlarge', 'ml.m4.16xlarge', 'ml.m5.large', 'ml.m5.xlarge', 'ml.m5.2xlarge', 'ml.m5.4xlarge',
'ml.m5.12xlarge', 'ml.m5.24xlarge', 'ml.c4.xlarge', 'ml.c4.2xlarge', 'ml.c4.4xlarge', 'ml.c4.8xlarge', 'ml.p2.xlarge', 'ml.p2.8xlarge', 'ml.p2.16xlarge', 'ml.p3.2xlarge', 'ml.p3.8xlarge', 'ml.p3.16xlarge',
'ml.c5.xlarge', 'ml.c5.2xlarge', 'ml.c5.4xlarge', 'ml.c5.9xlarge', 'ml.c5.18xlarge'], required=False, help='The ML compute instance type.', default='ml.m4.xlarge')
parser.add_argument('--instance_count', type=str_to_int, required=False, help='The number of ML compute instances to use in each training job.', default=1)
parser.add_argument('--volume_size', type=str_to_int, required=False, help='The size of the ML storage volume that you want to provision.', default=1)
parser.add_argument('--max_num_jobs', type=str_to_int, required=True, help='The maximum number of training jobs that a hyperparameter tuning job can launch.')
parser.add_argument('--max_parallel_jobs', type=str_to_int, required=True, help='The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.')
parser.add_argument('--max_run_time', type=str_to_int, required=False, help='The maximum run time in seconds per training job.', default=86400)
parser.add_argument('--resource_encryption_key', type=str, required=False, help='The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s).', default='')
parser.add_argument('--vpc_security_group_ids', type=str, required=False, help='The VPC security group IDs, in the form sg-xxxxxxxx.')
parser.add_argument('--vpc_subnets', type=str, required=False, help='The ID of the subnets in the VPC to which you want to connect your hpo job.')
parser.add_argument('--network_isolation', type=str_to_bool, required=False, help='Isolates the training container.', default=True)
parser.add_argument('--traffic_encryption', type=str_to_bool, required=False, help='Encrypts all communications between ML compute instances in distributed training.', default=False)
parser.add_argument('--warm_start_type', choices=['IdenticalDataAndAlgorithm', 'TransferLearning', ''], required=False, help='Specifies either "IdenticalDataAndAlgorithm" or "TransferLearning"')
parser.add_argument('--parent_hpo_jobs', type=str, required=False, help='List of previously completed or stopped hyperparameter tuning jobs to be used as a starting point.', default='')
parser.add_argument('--tags', type=json.loads, required=False, help='An array of key-value pairs, to categorize AWS resources.', default='{}')
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_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)
logging.info('HyperParameter Tuning Job completed.')
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)
def test_get_model_artifacts_from_job(self):
mock_client = MagicMock()
mock_client.describe_training_job.return_value = {
"ModelArtifacts": {
"S3ModelArtifacts": "s3://path/"
}
}
self.assertEqual(
_utils.get_model_artifacts_from_job(mock_client, 'training-job'),
's3://path/')
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 = argparse.ArgumentParser(description='SageMaker Training Job')
parser.add_argument('--region',
type=str,
help='The region where the training job launches.')
parser.add_argument(
'--image',
type=str,
help=
'The registry path of the Docker image that contains the training algorithm.'
)
parser.add_argument('--instance_type',
type=str,
help='The ML compute instance type.')
parser.add_argument(
'--instance_count',
type=int,
help=
'The registry path of the Docker image that contains the training algorithm.'
)
parser.add_argument(
'--volume_size',
type=int,
help='The size of the ML storage volume that you want to provision.')
parser.add_argument(
'--dataset_path',
type=str,
help=
'The S3 location of the data source that is associated with a channel.'
)
parser.add_argument(
'--model_artifact_path',
type=str,
help=
'Identifies the S3 path where you want Amazon SageMaker to store the model artifacts.'
)
parser.add_argument(
'--role',
type=str,
help=
'The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf.'
)
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_client(args.region)
logging.info('Submitting Training Job to SageMaker...')
job_name = _utils.create_training_job(client, args.image,
args.instance_type,
args.instance_count,
args.volume_size, args.dataset_path,
args.model_artifact_path, args.role)
logging.info('Job request submitted. Waiting for completion...')
_utils.wait_for_training_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)
logging.info('Job completed.')
def main(argv=None):
parser = argparse.ArgumentParser(description='SageMaker Training Job')
parser.add_argument('--region',
type=str.strip,
required=True,
help='The region where the training job launches.')
parser.add_argument('--job_name',
type=str.strip,
required=False,
help='The name of the training job.',
default='')
parser.add_argument(
'--role',
type=str.strip,
required=True,
help=
'The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf.'
)
parser.add_argument(
'--image',
type=str.strip,
required=True,
help=
'The registry path of the Docker image that contains the training algorithm.',
default='')
parser.add_argument(
'--algorithm_name',
type=str.strip,
required=False,
help='The name of the resource algorithm to use for the training job.',
default='')
parser.add_argument(
'--metric_definitions',
type=_utils.str_to_json_dict,
required=False,
help=
'The dictionary of name-regex pairs specify the metrics that the algorithm emits.',
default='{}')
parser.add_argument(
'--training_input_mode',
choices=['File', 'Pipe'],
type=str.strip,
help='The input mode that the algorithm supports. File or Pipe.',
default='File')
parser.add_argument(
'--hyperparameters',
type=_utils.str_to_json_dict,
help='Dictionary of hyperparameters for the the algorithm.',
default='{}')
parser.add_argument(
'--channels',
type=_utils.str_to_json_list,
required=True,
help=
'A list of dicts specifying the input channels. Must have at least one.'
)
parser.add_argument(
'--instance_type',
required=True,
choices=[
'ml.m4.xlarge', 'ml.m4.2xlarge', 'ml.m4.4xlarge', 'ml.m4.10xlarge',
'ml.m4.16xlarge', 'ml.m5.large', 'ml.m5.xlarge', 'ml.m5.2xlarge',
'ml.m5.4xlarge', 'ml.m5.12xlarge', 'ml.m5.24xlarge',
'ml.c4.xlarge', 'ml.c4.2xlarge', 'ml.c4.4xlarge', 'ml.c4.8xlarge',
'ml.p2.xlarge', 'ml.p2.8xlarge', 'ml.p2.16xlarge', 'ml.p3.2xlarge',
'ml.p3.8xlarge', 'ml.p3.16xlarge', 'ml.c5.xlarge', 'ml.c5.2xlarge',
'ml.c5.4xlarge', 'ml.c5.9xlarge', 'ml.c5.18xlarge'
],
type=str.strip,
help='The ML compute instance type.',
default='ml.m4.xlarge')
parser.add_argument(
'--instance_count',
required=True,
type=_utils.str_to_int,
help=
'The registry path of the Docker image that contains the training algorithm.',
default=1)
parser.add_argument(
'--volume_size',
type=_utils.str_to_int,
required=True,
help='The size of the ML storage volume that you want to provision.',
default=1)
parser.add_argument(
'--resource_encryption_key',
type=str.strip,
required=False,
help=
'The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s).',
default='')
parser.add_argument(
'--max_run_time',
type=_utils.str_to_int,
required=True,
help='The maximum run time in seconds for the training job.',
default=86400)
parser.add_argument(
'--model_artifact_path',
type=str.strip,
required=True,
help=
'Identifies the S3 path where you want Amazon SageMaker to store the model artifacts.'
)
parser.add_argument(
'--output_encryption_key',
type=str.strip,
required=False,
help=
'The AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts.',
default='')
parser.add_argument(
'--vpc_security_group_ids',
type=str.strip,
required=False,
help='The VPC security group IDs, in the form sg-xxxxxxxx.')
parser.add_argument(
'--vpc_subnets',
type=str.strip,
required=False,
help=
'The ID of the subnets in the VPC to which you want to connect your hpo job.'
)
parser.add_argument('--network_isolation',
type=_utils.str_to_bool,
required=False,
help='Isolates the training container.',
default=True)
parser.add_argument(
'--traffic_encryption',
type=_utils.str_to_bool,
required=False,
help=
'Encrypts all communications between ML compute instances in distributed training.',
default=False)
parser.add_argument(
'--tags',
type=_utils.str_to_json_dict,
required=False,
help='An array of key-value pairs, to categorize AWS resources.',
default='{}')
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
client = _utils.get_client(args.region)
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.')
