Setup

1. Make sure you have Miniconda installed

   1. Conda is a package manager that sandboxes your project’s dependencies in a virtual environment
   2. Miniconda contains Conda and its dependencies with no extra packages by default (as opposed to Anaconda, which installs some extra packages)

2. cd into src, run conda env create -f environment.yml

   1. This creates a Conda environment called squad

3. Run source activate squad

   1. This activates the squad environment
   2. Do this each time you want to write/test your code

4. Run python setup.py

   1. This downloads SQuAD 2.0 training and dev sets, as well as the GloVe 300-dimensional word vectors (840B)
   2. This also pre-processes the dataset for efficient data loading
   3. For a MacBook Pro on the Stanford network, setup.py takes around 30 minutes total

5. Browse the code in train.py 0. Prior to running the train.py script, please type in your console export CUDA_VISIBLE_DEVICES=0 and check that only one GPU is visible: echo $CUDA_VISIBLE_DEVICES. This is required as multi-GPU creates issues with the code.

   1. The train.py script is the entry point for training a model. It reads command-line arguments, loads the SQuAD dataset, and trains a model.
   2. You may find it helpful to browse the arguments provided by the starter code. Either look directly at the parser.add_argument lines in the source code, or run python train.py -h.
   3. To train the baseline, use python train.py --model bidaf --name baseline --num_workers 0
   4. To train the bidafextra model, use python train.py --model bidafextra --name bidafextra --num_workers 0
   5. To train the FusionNet model, use python train.py --model fusionnet --name fusionnet --num_workers 0

6. To generate the file required to be uploaded to the leaderboard, it is necessary to use the following command: python test.py --split dev --name fusionnet --num_workers=0 --batch_size=64 --model fusionnet --load_path ./save/train/fusionnet-03/best.pth.tar