task:
  name: classification_small
  output_dir_name: classification_small_cpr_paper
  label_smoothing: 0.1
  train_transforms:
    trivial_augment:
      use: false
optimizer:
  - name: adamw_baseline
    learning_rate: [1.e-1, 1.e-2, 1.e-3, 1.e-4]
    # learning_rate: [1.e-1, 3.16e-2, 1.e-2, 3.16e-3, 1.e-3]  # finer grid
    weight_decay: [1, 1.e-1, 1.e-2, 1.e-3, 1.e-4, 0]
    warmup_factor: 0.025
    eta_min_factor: 0.1
  - name: adamcpr_fast
    learning_rate: [1.e-1, 1.e-2, 1.e-3, 1.e-4]
    # learning_rate: [1.e-1, 3.16e-2, 1.e-2, 3.16e-3, 1.e-3]  # finer grid
    kappa_init_param: [0.5, 1, 2, 4, 8, 16, 32]
    warmup_factor: 0.025
    eta_min_factor: 0.1
engine:
  seed: [1, 2, 3]
  # data_dir: ./data
  # output_dir: ./experiments
  plot: false
  silent: true
  sbatch_script_template: baselines/sbatch_template.sh  # adapt the template to your needs
  run_scheduler: slurm_array
  sbatch_time_factor: 1.8  # increase this for slower machine
  sbatch_args:
    partition: single  # adapt to your cluster
evaluation:
  output_types: [pdf]
  plot:
    x_axis:
      - optimizer.kappa_init_param
      - optimizer.weight_decay