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config.yaml
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config.yaml
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# @package _global_
# specify here default training configuration
defaults:
- _self_
- trainer: default.yaml
- model: mnist_model.yaml
- datamodule: mnist_datamodule.yaml
- callbacks: default.yaml
- logger: null # set logger here or use command line (e.g. `python run.py logger=wandb`)
- mode: default.yaml
- 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
# evaluate on test set, using best model weights achieved during training
# lightning chooses best weights based on metric specified in checkpoint callback
test_after_training: True
# seed for random number generators in pytorch, numpy and python.random
seed: null
# name of the run is accessed by loggers
# should be used along with experiment mode
name: null