config.yaml

# @package _global_

# specify here default training configuration
defaults:
  - _self_ # see https://hydra.cc/docs/tutorials/basic/your_first_app/defaults/#composition-order-of-primary-config
  - hydra: local
  - paths: default
  - logger: default
  - trainer: single_gpu
  - callbacks: default
  - analysis: failure_detection
  - dataset: null
  - datamodule: null
  - backbone: null
  - segmentation: null
  - csf_pixel: null
  - csf_image: null
  - csf_aggregation: null
  # debugging config (enable through command line, e.g. `python train.py debug=default)
  - debug: 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

# I currently use loguru
loguru:
  level: INFO
  file: main.log # relative to hydra:run.dir

# seed for random number generators in pytorch, numpy and python.random
seed: 32586152

test:
  last_ckpt: true
  # this applies only to the image CSF; for segmentation, we always use the last checkpoint

resume_from_checkpoint:
  path: null
  load_expt_config: false

# Sometimes this causes errors on my workstation. See this discussion:
# https://github.com/pytorch/pytorch/issues/973#issuecomment-459398189
# Unfortunately, I wasn't able to find the cause of the issue, so
# the workaround is to set this to `file_system` instead.
mp_sharing_strategy: file_descriptor

expt_group: default
expt_name: ???