From 6e2a55cf84fe67f1583cb76d8308a6bca023b3df Mon Sep 17 00:00:00 2001 From: Gautam Kumar Date: Wed, 20 May 2020 12:36:20 -0700 Subject: [PATCH] Changing the default volume size to 30 (#3792) --- .../hyperparameter_tuning/src/hyperparameter_tuning.py | 2 +- components/aws/sagemaker/train/src/train.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/components/aws/sagemaker/hyperparameter_tuning/src/hyperparameter_tuning.py b/components/aws/sagemaker/hyperparameter_tuning/src/hyperparameter_tuning.py index df44a4098a3..517acaf32d1 100644 --- 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 index 2e01aaffb78..f60fefac056 100644 --- 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.')