azure-ml-problem-sets/shrike-examples

Getting started with the shrike problems

Prerequisites

• Provision a public AML workspace (you can find instructions here).
  • Note that you do NOT want a Heron a.k.a. eyes-off a.k.a. compliant workspace. This introductory problem set is fairly general and does not touch on compliance-specific notions, so using a Heron workspace would make things more complicated.
• Create a dataset of type FileDataset (as opposed to TabularDataset).
  • This can be done following these instructions. You can create a dataset from a local csv on your machine, this iris.csv file for instance (just download it first, from the link above, then upload it to your workspace as you create the dataset).

Setup

• Clone the current repository and set shrike-examples as your working directory.
• Set up and activate a new Conda environment: conda create --name shrike-examples-env python=3.7 -y, conda activate shrike-examples-env.
• Install the shrike dependencies: pip install -r requirements.txt

List of problems

The list of problems is given here in the shrike-problems directory, and guidance for individual problems can be found in the problems subdirectory.

To solve the problems, just follow the Guidance section in each problem description, and modify the appropriate files as indicated. You can look for the # To-Do comment string to locate the parts of the files that need modifying.