ML-Template

This is a template for Machine Learning projects.

How to use

To instantiate your project using this template, on the template's GitHub page, click Use this template, then Create a new repository.

Otheriwse, if you already have a local git repository, run

cd <local-git-repo>
git remote add ml-template https://github.com/ignasa007/ML-Template.git
git fetch ml-template
git merge --allow-unrelated-histories ml-template/main
git remote remove ml-template

Directory Structure

• assets - plots generated from different experiments.
• config - configuration files for different datasets and models.
• data - raw datasets store.
• data_classes - Python classes to handle different datasets, and make them suitable for training.
• model_classes - Python classes to handle different models.
• results - results of the different runs.
  
  • directory structure - <dataset> -> <model> -> <run-date> -> logs and <data-split>_results
• utils - utility functions for running the transformer experiments.
• main.py - main file for training the models.
• inference.py - main file for testing the models.

Setup

conda create --name <env-name> --file requirements.txt python=3.8
conda activate <env-name>

Execution

To run the transformer experiments, execute

python3 -B main.py \
    --dataset <dataset> \
    --model <model>

You can also override default configurations using the command line.

For inference, execute

python3 -B inference.py \
    --dataset <dataset> \
    --model <model> \
    --weights <path-to-weights>

Note: Make sure to set the device index to None if you do not wish to use the GPU.