https://github.com/awsdataarchitect/sagemaker-examples-ip-exhaustion
Demo samples for SageMaker hands-on running on AWS CloudShell environment
https://github.com/awsdataarchitect/sagemaker-examples-ip-exhaustion
Last synced: over 1 year ago
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Demo samples for SageMaker hands-on running on AWS CloudShell environment
- Host: GitHub
- URL: https://github.com/awsdataarchitect/sagemaker-examples-ip-exhaustion
- Owner: awsdataarchitect
- Created: 2024-08-02T20:10:23.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-14T04:44:08.000Z (almost 2 years ago)
- Last Synced: 2024-08-14T05:48:01.100Z (almost 2 years ago)
- Language: Python
- Homepage:
- Size: 72.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Executing SageMaker Jobs from AWS CloudShell
Step-by-step guide to running SageMaker jobs using AWS CloudShell.
Follow these instructions to ensure that your setup works correctly and that SageMaker jobs run as expected.
## AWS Environment Setup
Before running the tests, ensure that you have completed the following:
1. **AWS CloudShell:** Open AWS CloudShell from the AWS Management Console.
2. **SageMaker Execution Role:** Create a SageMaker execution role using the `sagemaker-execution-role-template.yaml` template.
3. **Repository Cloned:** Clone this GitHub repository containing the SageMaker scripts.
4. **Check Installed Packages:** Confirm that the required Python packages are installed:
`pip show sagemaker scikit-learn matplotlib`
* [Refer to blog post for step by step guide](https://vivek-aws.medium.com/4-ways-to-get-hands-on-with-sagemaker-for-free-41ff9bee0d54).
* [SageMaker Example using SageMaker Model Registry for model deployment and batch transform](https://vivek-aws.medium.com/using-aws-cloudshell-for-automating-xgboost-model-deployment-and-batch-transform-with-aws-sagemaker-2adedc4d2b02).
# Using SageMaker Studio to manage the Model Registry and Training Jobs
Creating Studio User and Domain using AWS-CDK and Leveraging the Model Registry
* [Maximizing ML Efficiency with SageMaker Studio](https://medium.com/@vivek-aws/maximizing-ml-efficiency-with-sagemaker-studio-a55030da2a45).