You can call "train_net.py" with args that can be configured.
Make sure to define 'dataset_path' in train_net.py
You can write new models and place them in "models/". Make sure to update loading "get_network_model" function in helper_funcs, and other dependencies.
On one A100, we can train a BLT RNN with a 10 timestep unroll and a batch size of 1024. It takes ~3hrs for 60 epochs. The final timestep accuracy is 50% on the testplus set of miniecoset-100.
Just a minor issue to bring to your attention in using variable names, while running pre-commit checks Flake8 complaint about 'E741 ambiguous variable name 'l'' coming from two instances in the model definition :