/grokking

Implementation of OpenAI's 'Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets' paper.

Primary LanguageJupyter NotebookMIT LicenseMIT

Grokking

An implementation of the OpenAI 'Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets' paper in PyTorch.

Installation

  • Clone the repo and cd into it: 
    git clone https://github.com/danielmamay/grokking.git
cd grokking
  • Use Python 3.9 or later: 
    conda create -n grokking python=3.9
conda activate grokking
pip install -r requirements.txt

Usage

The project uses Weights & Biases to keep track of experiments. Run wandb login to use the online dashboard, or wandb offline to store the data on your local machine.

  • To run a single experiment using the CLI:

    wandb login
python grokking/cli.py

  • To run a grid search using W&B Sweeps:

    wandb sweep sweep.yaml
wandb agent {entity}/grokking/{sweep_id}

References

Code:

Paper: