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train.yaml
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train.yaml
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# @package _global_
# specify here default training configuration
defaults:
- _self_
- datamodule: mnist.yaml
- model: mnist.yaml
- callbacks: default.yaml
- logger: null # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
- trainer: default.yaml
- log_dir: default.yaml
# experiment configs allow for version control of specific configurations
# e.g. best hyperparameters for each combination of model and datamodule
- experiment: null
# debugging config (enable through command line, e.g. `python train.py debug=default)
- debug: null
# config for hyperparameter optimization
- hparams_search: null
# optional local config for machine/user specific settings
# it's optional since it doesn't need to exist and is excluded from version control
- optional local: default.yaml
# 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 new log directory
# https://hydra.cc/docs/next/tutorials/basic/running_your_app/working_directory
original_work_dir: ${hydra:runtime.cwd}
# path to folder with data
data_dir: ${original_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
# set False to skip model training
train: True
# evaluate on test set, using best model weights achieved during training
# lightning chooses best weights based on the metric specified in checkpoint callback
test: True
# seed for random number generators in pytorch, numpy and python.random
seed: null
# default name for the experiment, determines logging folder path
# (you can overwrite this name in experiment configs)
name: "default"