Homework 2

Table of contents

  1. Starter Code and Assignment File
  2. Preparation: Setting up AWS
  3. Submission

This homework is due on Tuesday, October 24th at 2 PM.

Starter Code and Assignment File

The starter code for this homework is available here.
The assignment file is available here.
The latex template for this assignment is available here.

Preparation: Setting up AWS

To complete this homework, you will need access to GPU resources for training models.

The TAs have compiled step-by-step setup instructions with screenshots here: AWS setup guide

Note: Question 1, Question 2.1-4 and Question 3.1 can be done without GPU access. We suggest that you start the assignment early while waiting for AWS credits.

Submission

For homework 2, we will be using Gradescope. Please use the link from the Canvas coursepage.
Two assignments have been created on Gradescope: Homework 2 - PDF and Homework 2 - Code and Checkpoints.

Homework 2 - PDF will be manually graded by the instructors.
Please submit your a pdf file [andrew-id].pdf with answers to the written questions, and annotate the locations of each question’s solution in your PDF.

Homework 2 - Code and Checkpoints contains an autograder that will grade your code with the same unit tests provided in the homework.
We will also be downloading your model checkpoint model.pt and configuration config.yaml for perplexity testing.
The results of these tests will not be made visible until grades are released.
Please submit the following files, with these exact filenames:

  • model.py
  • train.py
  • generate.py
  • classify.py
  • utils.py
  • model.pt (weights from your trained model in Question 2.6.)
  • config.yaml (configuration file for your trained model in Question 2.6.)

Please use this script to check that your submission format is correct.

Note: Since many students are having trouble with submitting their model checkpoints on Gradescope with the 100MB size limit, there is a Canvas assignment for this: Homework 2 - Model Checkpoint.
If your model checkpoint exceeds 100MB, please submit your model.pt and config.yaml file on Canvas.
If your model checkpoint is below 100MB, you can still submit model.pt and config.yaml on Gradescope.