aws / aws/amazon-sagemaker-examples

User-friendly Documentation on Sagemaker Instances and Service Limit Increase

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Jupyter Notebook
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Description

Lately running into too many Sagemaker issues. Is there any unambiguous documentation on Sagemakers Instances? I could glean the following from different sources:

1. Sagemaker Instances, Sagemaker being a managed service, have nothing to do with EC2 instances.
2. Unlike EC2 console, Sagemaker console has no option to view limits or increase limits. One has to go directly to the support page and select instance limit increase and pick Sagemaker as the service. But must one run into Resource Limit Exceeded errors before to find out instance limits?

3. Sagemaler notebook instances are different from the training, deployment instances.

4. Not every algorithm in Sagemaker supports every(training/deployment) instance type. It doesn't make sense for XGboost to be GPU accelerated.

I wonder if all of this is documented anywhere at all, in simple language. It'd be immensely helpful to have it included in a one-page document that shown up prominently to users of Sagemaker, at least new ones.

Contributor guide

Open the contributing guide

Research direction

Review the existing SageMaker examples and documentation to determine where a prominent one-page guide could fit. Cover the distinctions and limit-increase process described in the issue, and consider the work complete when new users can understand instance types, service limits, and algorithm support without encountering an error first.

Written by the indexing model from the issue text.

Assessment

Tech stack
aws
Domain
cloud, documentation, machine-learning
Issue type
Documentation
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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