AWS: example using sagemaker jupyter notebook for a simple training

AWS
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This is a example procedure for a simple deeplearning case using jupyter notebook on AWS sagemaker. I followed a tutorial shown in a github repository [1]

Summary of the procedure

  • Here deep learning case used is for a bank data with personal information.
  • included components in the AWS sagemaker is the sagemaker jupyter notebook instance creation, EC2 instance use for training and s3 bucket object creation for traing/test input data and output model.
  • input data loading and deep learning model build

Sagemaker jupyter notebook instance creation
A AWS sagemaker jupyter notebook instance is created with ml.t3.medium instance

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S3 bucket creation for data
S3 bucket object is created for training and test input and model output data. Here
boto3 module to access s3
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Data loading
panda module is used to parse the training and test data and
sagemaker.input module is used to load the data into the model.

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Model building
sagemaker xgboost model library is used to build the model and
ml.m5.2xlarge compute instance is also used for the training.
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The code used in this example is shown the ref. [3]

Reference

[1] sagemaker introduction: https://gmoein.github.io/files/Amazon%20SageMaker.pdf
[2] sagemaker xgbost documentation: Use the XGBoost algorithm with Amazon SageMaker - Amazon SageMaker
[3] github: GitHub - dchoi/sagemakerModelPractice