aicomp 0.1.1

AI Competition - Multi-task learning for mathematics misconception detection

DataScience Project Template

A template with src and tests already prepared, based on Python 3.11.9 and Torch 2.3.1 (same as on GPUHub).

Structure

• notebooks Jupyter Notebooks go in here, try to use notebooks only for analysis and prototyping, the real training should be done via scripts
• scripts Python/Bash/PowerShell scripts go in here, that can be downloading the dataset, transforming the data, training a model etc.
• src The source code (heart) of your project
  • datasets: Write your torch.utils.data.Dataset in here
  • evaluation: Write tasks used to evaluate your model(s) in this module
  • models: Custom model implementation go in here
  • optimizers: Custom optimizers are written in here
  • schedulers: Custom implementation of schedulers
  • trainers: Your model trainer(s) live here
  • transforms: If you need specific transformations you will create them in here
  • utils: Utility functions and modules

TODOs

1. Set up your dev environment

pip install -r requirements.txt
# Install pre-commit hook
pre-commit install

To run all the linters on all files:

pre-commit run --all-files

2. Change project name and description in pyproject.toml

[project]
name = "Datascience Project Template"
description = "Template for an AICOMP DataScience Project"
version = "0.1.0"
authors = [
    {name = "Pascal Baumann", email = "[e-mail]"},
]

3. Add your requirements to requirements.txt
4. Create some code =)
5. Add a PyTest configuration in PyCharm

Code and test conventions

• black for code style
• isort for import sorting
• darglint for docstring checking
• docstring style: sphinx
• pytest for running tests
• nbclean cleans up your Jupyter notebooks before committing
• main/master branch is protected and needs merge request with approval

Running on the GPU and logging to W&B

Due to us now having an enterprise license on W&B we also have our service bot which can log runs to the appropriate team.