neural-modular

Info

Simple codebase for flexible neural net training.

Allows for seamless exchange of models, dataset, and optimizers.

Uses hydra for config-building and logging.

Option to enable wandb for run-tracking and cloud-storage.

Run python main.py to train your model.

Setup

Run

conda env update -f envs/environment.yaml
cona activate neural

to install and activate the conda environent.

Understanding the Code

• main.py is the main entry point

• conf/config.yaml is the default config in standard Hydra syntax:

  • by running python main.py +experiments=blabla you can overwrite and extend the config by whatever you put in experiments/blabla.yaml.
  • alternatively you can run python main.py +new=arg to add new to the config, or python main.py new=arg to overwrite key new

• using the config, we then instantiate a dataset from neural.datasets and a model from neural.models

• model and dataset are then given to the trainer neural.train.Trainer which further instantiates optimizers, schedulers, and the losses

• we then train the model to convergence and checkpoint the final model

• see neural.utils.restore for how to restore a model/trainer instance