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Practical Worksheet 8

Version: 1.0 Date: 27/9/2021 Author: Camilo Pestana

Learning Objectives

  1. Download data to be explored and then upload to S3
  2. Create a Hyperparameter Tuning Job using Amazon SageMaker

Technologies Covered

Ubuntu AWS Amazon SageMaker S3 boto3 Python

Background

The aim of this lab is to write a program that will:

  1. Use jupyter notebooks and pandas to explore a dataset.
  2. Use boto3 and sagemaker to create training and hyperparameter optimization jobs, two important steps in every machine learning project.
  3. After a job is learned SageMaker allows to deploy models in EC2 instances. However, this is out of the scope for this lab.

Set Up Python Environment

This lab is best done using a Python virtual environment for your packages, or within your own VM. If you are using a virtual environment, remember to activate it prior to installing packages.

To run the SageMaker commands contained within the notebook the following packages are required:

  • sagemaker
  • pandas
  • ipykernel

This can be installed using pip:

pip install sagemaker pandas ipykernel

or

pip3 install sagemaker pandas ipykernel

depending on your Python installation.

Run Hyperparameter Tuning Jobs

The steps for this lab are stored in the notebook here If you have installed the required packages as above you can skip the first installation step in the notebook.

Remember to add your student ID to the session preparation cell as well as creating a bucket for the data to be loaded in to. This bucket cannot be restricted to only accessible for yourself as per a previous lab.

The notebook contains all of the instructions required. Please do not edit any of the machine learning code as it has been specifically designed to work on the resources available whilst minimising costs. You should only have to edit resource names to complete this lab.

The tuning will normally take between 2-4 minutes, review the output and the best hyperparameters to ensure that the job was completed successfully.

Lab Assessment: This semester all labs will be assessed as "Lab notes". You should follow all steps in each lab and include your own comments. In addition, include screenshots showing the output for every commandline instruction that you execute in the terminal and any other relevant screenshots that demonstrate you followed the steps from the corresponding lab. Please also include any linux or python script that you create and the corresponding output you get when executed. Please submit a single PDF file. The formatting is up to you but a well organised structure of your notes is appreciated.