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Changing the default volume size to 30 #3792

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May 20, 2020
Merged

Changing the default volume size to 30 #3792

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2 changes: 1 addition & 1 deletion components/aws/sagemaker/hyperparameter_tuning/src/hyperparameter_tuning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ def create_parser():
'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, required=False, help='The ML compute instance type.', default='ml.m4.xlarge')
parser.add_argument('--instance_count', type=int, required=False, help='The number of ML compute instances to use in each training job.', default=1)
parser.add_argument('--volume_size', type=int, required=False, help='The size of the ML storage volume that you want to provision.', default=1)
parser.add_argument('--volume_size', type=int, required=False, help='The size of the ML storage volume that you want to provision.', default=30)
parser.add_argument('--max_num_jobs', type=int, required=True, help='The maximum number of training jobs that a hyperparameter tuning job can launch.')
parser.add_argument('--max_parallel_jobs', type=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=int, required=False, help='The maximum run time in seconds per training job.', default=86400)
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2 changes: 1 addition & 1 deletion components/aws/sagemaker/train/src/train.py
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Expand Up @@ -32,7 +32,7 @@ def create_parser():
'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, help='The ML compute instance type.', default='ml.m4.xlarge')
parser.add_argument('--instance_count', required=True, type=int, help='The registry path of the Docker image that contains the training algorithm.', default=1)
parser.add_argument('--volume_size', type=int, required=True, help='The size of the ML storage volume that you want to provision.', default=1)
parser.add_argument('--volume_size', type=int, required=True, help='The size of the ML storage volume that you want to provision.', default=30)
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('--max_run_time', type=int, required=True, help='The maximum run time in seconds for the training job.', default=86400)
parser.add_argument('--model_artifact_path', type=str, required=True, help='Identifies the S3 path where you want Amazon SageMaker to store the model artifacts.')
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