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A logical, reasonably standardized, but flexible project structure for conducting ml research 🍪

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This is an evolving repo optimized for machine-learning projects aimed at designing a new algorithm. They require sweeping over different hyperparameters, comparing to baselines, and iteratively refining an algorithm. Based of cookiecutter-data-science.

Organization

  • project_name: should be renamed, contains main code for modeling (e.g. model architecture)
  • experiments: code for runnning experiments (e.g. loading data, training models, evaluating models)
  • scripts: scripts for hyperparameter sweeps (python scripts that launch jobs in experiments folder with different hyperparams)
  • notebooks: jupyter notebooks for analyzing results and making figures
  • tests: unit tests

Setup

  • first, rename project_name to your project name and modify setup.py accordingly
  • clone and run pip install -e ., resulting in a package named project_name that can be imported
    • see setup.py for dependencies, not all are required
  • example run: run python scripts/01_train_basic_models.py (which calls experiments/01_train_model.py then view the results in notebooks/01_model_results.ipynb
  • keep tests upated and run using pytest

Features

  • scripts sweep over hyperparameters using easy-to-specify python code
  • experiments automatically cache runs that have already completed
    • caching uses the (non-default) arguments in the argparse namespace
  • notebooks can easily evaluate results aggregated over multiple experiments using pandas

Guidelines

  • See some useful packages here
  • Avoid notebooks whenever possible (ideally, only for analyzing results, making figures)
  • Paths should be specified relative to a file's location (e.g. os.path.join(os.path.dirname(__file__), 'data'))
  • Naming variables: use the main thing first followed by the modifiers (e.g. X_train, acc_test)
    • binary arguments should start with the word "use" (e.g. --use_caching) and take values 0 or 1
  • Use logging instead of print
  • Use argparse and sweep over hyperparams using python scripts (or custom things, like amulet)
    • Note, arguments get passed as strings so shouldn't pass args that aren't primitives or a list of primitives (more complex structures should be handled in the experiments code)
  • Each run should save a single pickle file of its results
  • All experiments that depend on each other should run end-to-end with one script (caching things along the way)
  • Keep updated requirements in setup.py
  • Follow sklearn apis whenever possible
  • Use Huggingface whenever possible, then pytorch