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63 lines (48 loc) · 2.06 KB
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# @package _global_
# specify here default training configuration
defaults:
- trainer: default_trainer.yaml
- model: resnet_model.yaml
- datamodule: pnas_datamodule.yaml # mnist_datamodule.yaml
- callbacks: wandb_callbacks.yaml # default_callbacks.yaml # set this to null if you don't want to use callbacks
- logger: many_loggers.yaml # null e.g. `python run.py logger=wandb`
- wandb: init.yaml
- artifacts: input_artifacts.yaml
- tuner: default_tuner.yaml
# - experiment: null
# - hparams_search: null
# - 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
# learn more here: 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/
root_dir: ${oc.env:WANDB_CACHE_DIR}
results_dir: '${model.model_dir}/results'
# use `python run.py debug=true` for easy debugging!
# this will run 1 train, val and test loop with only 1 batch
# equivalent to running `python run.py trainer.fast_dev_run=true`
# (this is placed here just for easier access from command line)
debug: False
# pretty print config at the start of the run using Rich library
print_config: True
# disable python warnings if they annoy you
disable_warnings: True
hydra:
# output paths for hydra logs
run:
dir: logs/runs/${now:%Y-%m-%d}/${now:%H-%M-%S}
sweep:
dir: logs/multiruns/${now:%Y-%m-%d_%H-%M-%S}
subdir: ${hydra.job.num}
job:
# you can set here environment variables that are universal for all users
# for system specific variables (like data paths) it's better to use .env file!
env_set:
# currently there are some issues with running sweeps alongside wandb
# https://github.com/wandb/client/issues/1314
# this env var fixes that
WANDB_START_METHOD: thread