generate_trajectories

# @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: "generate_trajectory"

task_name: null

tags: ["dev"]

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

paths:
  generated_trajectory_fp: ${paths.time_output_dir}/generated_trajectory_${model.generate_id}.parquet
  predict_fp: ${paths.time_output_dir}/predictions_${model.generate_id}.parquet

seed: ${model.generate_id}
actual_future_name: null