c1228.yaml

# @package _global_

# to execute this experiment run:
# python train.py experiment=c1228

defaults:
  - override /callbacks: default.yaml
  - override /datamodule: cmapss.yaml
  - override /model: default.yaml
  - override /trainer: default.yaml

# all parameters below will be merged with parameters from default
# configurations set above this allows you to overwrite only specified parameters
tags: ["optuna_sweep"]

seed: 42

datamodule:
  batch_size: 64

model:
  _target_: src.modules.pl_module.PLModule
  optimizer:
    _target_: torch.optim.Adam
    _partial_: true
    lr: 0.008940400954986757
    weight_decay: 0.009799143120957261
  scheduler:
    _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
    _partial_: true
    mode: min
    factor: 0.1
    patience: 10
  net:
    _target_: src.models.model.CNNLSTM
    conv_out: 32
    lstm_hidden: 50

callbacks:
  model_checkpoint:
    _target_: pytorch_lightning.callbacks.ModelCheckpoint
    dirpath: ${paths.output_dir}/checkpoints
    filename: epoch_{epoch:03d}
    monitor: val/rmse
    verbose: false
    save_last: true
    save_top_k: 1
    mode: min
    auto_insert_metric_name: false
    save_weights_only: false
    every_n_train_steps: null
    train_time_interval: null
    every_n_epochs: null
    save_on_train_epoch_end: null
  early_stopping:
    _target_: pytorch_lightning.callbacks.EarlyStopping
    monitor: val/rmse_best
    min_delta: 0.1
    patience: 3
    verbose: true
    mode: min
    strict: true
    check_finite: true
    stopping_threshold: null
    divergence_threshold: null
    check_on_train_epoch_end: null
logger:
  wandb:
    _target_: pytorch_lightning.loggers.wandb.WandbLogger
    save_dir: ${paths.output_dir}
    offline: false
    id: null
    anonymous: null
    project: lightning-hydra-template
    log_model: true
    prefix: rmse
    group: fd1
    tags:
      - experiment
    job_type: train
trainer:
  _target_: pytorch_lightning.Trainer
  default_root_dir: ${paths.output_dir}
  min_epochs: 1
  max_epochs: 10
  accelerator: cpu
  devices: 1
  check_val_every_n_epoch: 1
  deterministic: true
extras:
  print_config: true