[英]How to autoscale a SKLearn job on sagemaker
I want to launch a SKLearn job using sagemaker
.我想使用
sagemaker
启动 SKLearn 作业。 The way I do this is as follows:我这样做的方式如下:
from sagemaker.sklearn.estimator import SKLearn
FRAMEWORK_VERSION = '0.23-1'
script_path = 'main.py'
sklearn = SKLearn(
entry_point=os.path.join(script_path),
framework_version=FRAMEWORK_VERSION,
instance_type='ml.m5.2xlarge',
source_dir='src',
output_path='my/output/path',
)
I am not sure if the instance_type that I have chosen is enough (in terms of memory etc) for my application though.我不确定我选择的 instance_type 是否足够(就 memory 等而言)我的应用程序。
Is there a way to "let sagemaker" decide on the instance type?有没有办法“让 sagemaker”决定实例类型?
Or, is there a way to choose an instance_type and if along the way it is about to run out of memory, the sagemaker to automatically scale up?或者,有没有办法选择一个instance_type,如果沿途快要用完memory,sagemaker会自动扩容?
Automatic scale-up feature for Training doesn't exist in SageMaker at this time.目前,SageMaker 中不存在用于训练的自动扩展功能。
On a separate note, for selecting the right instance type for inference, we have an instance recommender service ( https://docs.aws.amazon.com/sagemaker/latest/dg/inference-recommender.html ).另外,为了选择正确的推理实例类型,我们有一个实例推荐服务 ( https://docs.aws.amazon.com/sagemaker/latest/dg/inference-recommender.html )。
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