| # @package _global_ | |
| # specify here default training configuration | |
| defaults: | |
| - _self_ | |
| - trainer: default | |
| - optimizer: adamw | |
| - scheduler: null | |
| - task: sequence-model | |
| - model: null | |
| - datamodule: null | |
| - callbacks: default # set this to null if you don't want to use callbacks | |
| - metrics: null | |
| - logger: null # set logger here or use command line (e.g. `python run.py logger=wandb`) | |
| - mode: default | |
| - experiment: null | |
| - hparams_search: null | |
| # enable color logging | |
| - override hydra/hydra_logging: colorlog | |
| - override hydra/job_logging: colorlog | |
| # path to original working directory | |
| # hydra hijacks working directory by changing it to the current log directory, | |
| # so it's useful to have this path as a special variable | |
| # https://hydra.cc/docs/next/tutorials/basic/running_your_app/working_directory | |
| work_dir: ${hydra:runtime.cwd} | |
| # path to folder with data | |
| data_dir: ${work_dir}/data/ | |
| # pretty print config at the start of the run using Rich library | |
| print_config: True | |
| # disable python warnings if they annoy you | |
| ignore_warnings: True | |
| # check performance on test set, using the best model achieved during training | |
| # lightning chooses best model based on metric specified in checkpoint callback | |
| test_after_training: True | |
| resume: False | |
| # seed for random number generators in pytorch, numpy and python.random | |
| seed: null | |
| # name of the run, accessed by loggers | |
| name: null | |