Training with Hydra Configuration

You may find Hydra configurations under configs/ divided into categories (model, env, train, experiment, etc.).

Usage

Train model with default configuration (AM on TSP environment):

python run.py

Change experiment

Train model with chosen experiment configuration from configs/experiment/ (e.g. tsp/am, and environment with 42 cities)

python run.py experiment=tsp/am env.num_loc=42

Disable logging

python run.py experiment=test/am logger=none '~callbacks.learning_rate_monitor'

Note that ~ is used to disable a callback that would need a logger.

Create a sweep over hyperparameters

We can use -m for multirun:

python run.py -m experiment=tsp/am  train.optimizer.lr=1e-3,1e-4,1e-5