Changing the default volume size to 30 (#3792)

kubeflow · May 20, 2020 · 6e2a55c · 6e2a55c
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commit 6e2a55c
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Showing 2 changed files with 2 additions and 2 deletions.
diff --git a/components/aws/sagemaker/hyperparameter_tuning/src/hyperparameter_tuning.py b/components/aws/sagemaker/hyperparameter_tuning/src/hyperparameter_tuning.py
@@ -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)

diff --git a/components/aws/sagemaker/train/src/train.py b/components/aws/sagemaker/train/src/train.py
@@ -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.')