From 33906622dd14d19efcb67c1395543cc3f13207e5 Mon Sep 17 00:00:00 2001 From: Gautam Kumar Date: Sun, 26 Apr 2020 17:17:28 -0700 Subject: [PATCH] Fixing volume size default value from 1 to 30 (#3598) --- components/aws/sagemaker/hyperparameter_tuning/README.md | 2 +- components/aws/sagemaker/hyperparameter_tuning/component.yaml | 2 +- components/aws/sagemaker/train/README.md | 4 ++-- components/aws/sagemaker/train/component.yaml | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/components/aws/sagemaker/hyperparameter_tuning/README.md b/components/aws/sagemaker/hyperparameter_tuning/README.md index 1437702f628d..9a6a0090063a 100644 --- a/components/aws/sagemaker/hyperparameter_tuning/README.md +++ b/components/aws/sagemaker/hyperparameter_tuning/README.md @@ -30,7 +30,7 @@ output_location | The Amazon S3 path where you want Amazon SageMaker to store th output_encryption_key | The AWS KMS key that Amazon SageMaker uses to encrypt the model artifacts | Yes | Yes | String | | | instance_type | The ML compute instance type | Yes | No | String | 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 | ml.m4.xlarge | instance_count | The number of ML compute instances to use in each training job | Yes | Yes | Int | ≥ 1 | 1 | -volume_size | The size of the ML storage volume that you want to provision in GB | Yes | Yes | Int | ≥ 1 | 1 | +volume_size | The size of the ML storage volume that you want to provision in GB | Yes | Yes | Int | ≥ 1 | 30 | max_num_jobs | The maximum number of training jobs that a hyperparameter tuning job can launch | No | No | Int | [1, 500] | | max_parallel_jobs | The maximum number of concurrent training jobs that a hyperparameter tuning job can launch | No | No | Int | [1, 10] | | max_run_time | The maximum run time in seconds per training job | Yes | Yes | Int | ≤ 432000 (5 days) | 86400 (1 day) | diff --git a/components/aws/sagemaker/hyperparameter_tuning/component.yaml b/components/aws/sagemaker/hyperparameter_tuning/component.yaml index 1e5b315e2581..7d34164a6a91 100644 --- a/components/aws/sagemaker/hyperparameter_tuning/component.yaml +++ b/components/aws/sagemaker/hyperparameter_tuning/component.yaml @@ -58,7 +58,7 @@ inputs: default: '1' - name: volume_size description: 'The size of the ML storage volume that you want to provision.' - default: '1' + default: '30' - name: max_num_jobs description: 'The maximum number of training jobs that a hyperparameter tuning job can launch.' - name: max_parallel_jobs diff --git a/components/aws/sagemaker/train/README.md b/components/aws/sagemaker/train/README.md index 14cdaf22db66..5b9e68eeac0e 100644 --- a/components/aws/sagemaker/train/README.md +++ b/components/aws/sagemaker/train/README.md @@ -23,7 +23,7 @@ hyperparameters | Hyperparameters for the selected algorithm | No | Dict | [Dep channels | A list of dicts specifying the input channels (at least one); refer to [documentation](https://github.com/awsdocs/amazon-sagemaker-developer-guide/blob/master/doc_source/API_Channel.md) for parameters | No | No | List of Dicts | | | instance_type | The ML compute instance type | Yes | No | String | 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 | ml.m4.xlarge | instance_count | The number of ML compute instances to use in each training job | Yes | Int | ≥ 1 | 1 | -volume_size | The size of the ML storage volume that you want to provision in GB | Yes | Int | ≥ 1 | 1 | +volume_size | The size of the ML storage volume that you want to provision in GB | Yes | Int | ≥ 1 | 30 | resource_encryption_key | The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) | Yes | String | | | max_run_time | The maximum run time in seconds per training job | Yes | Int | ≤ 432000 (5 days) | 86400 (1 day) | model_artifact_path | | No | String | | | @@ -45,4 +45,4 @@ Stores the Model in the s3 bucket you specified Simple example pipeline with only Train component : [simple_train_pipeline](https://github.com/kubeflow/pipelines/tree/documents/samples/contrib/aws-samples/simple_train_pipeline) # Resources -* [Using Amazon built-in algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html) \ No newline at end of file +* [Using Amazon built-in algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html) diff --git a/components/aws/sagemaker/train/component.yaml b/components/aws/sagemaker/train/component.yaml index b326a22965b8..e26f82d57338 100644 --- a/components/aws/sagemaker/train/component.yaml +++ b/components/aws/sagemaker/train/component.yaml @@ -34,7 +34,7 @@ inputs: default: '1' - name: volume_size description: 'The size of the ML storage volume that you want to provision.' - default: '1' + default: '30' - name: resource_encryption_key description: 'The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s).' default: ''