predict

# @package _global_

defaults:
  - _self_
  - data: pytorch_dataset # choose datamodule with `test_dataloader()` for evaluation
  - model: supervised
  - logger: null
  - trainer: default
  - paths: default
  - extras: default
  - hydra: default
  # experiment configs allow for version control of specific hyperparameters
  # e.g. best hyperparameters for given model and datamodule
  - experiment: 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

  # debugging config (enable through command line, e.g. `python train.py debug=default)
  - debug: null


name: "predict"

task_name: null

tags: ["dev"]

# passing checkpoint path is necessary for evaluation
ckpt_path: ???

seed: null
paths:
  predict_fp: ${paths.time_output_dir}/predict.parquet