inference/data/train_config.yaml

defaults:
  - base_config
  - _self_

devices: [0,1,2,3]
epochs: 500
batch_size: 8

monitor_metric: "val_loss"
monitor_metric_mood: "val_loss_mood"
monitor_metric_va: "val_loss_va"

checkpoint:
  monitor: "${monitor_metric}"
  filename: "{epoch:02d}-{${monitor_metric}:.4f}"
  save_top_k: -1
  mode: "min"
  auto_insert_metric_name: False
  save_last: True

checkpoint_mood:
  monitor: "${monitor_metric_mood}"
  filename: "mood-{epoch:02d}-{${monitor_metric_mood}:.4f}"
  save_top_k: -1
  mode: "min"
  auto_insert_metric_name: False
  save_last: True

checkpoint_va:
  monitor: "${monitor_metric_va}"
  filename: "va-{epoch:02d}-{${monitor_metric_va}:.4f}"
  save_top_k: 5
  mode: "min"
  auto_insert_metric_name: False
  save_last: True

earlystopping:
  monitor: "${monitor_metric_mood}"
  patience: 10
  min_delta: 0.0001
  mode: "min"

trainer:
  devices: ${devices}
  max_epochs: ${epochs}
  accelerator: 'gpu'







# strategy: 'ddp_find_unused_parameters_true'
# optimizer:
#   _target_: torch.optim.AdamW
#   _partial_: true
#   lr: 1e-4
#   weight_decay: 0.01
# scheduler:
#   _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
#   _partial_: true
#   cooldown: 5
#   mode: max
#   factor: 0.2
#   patience: 10
#   min_lr: 1.6e-7
# monitor_metric: "val_loss"
# # val_loss
# # val_loss_mood
# # val_loss_va
# checkpoint:
#   monitor: "${monitor_metric}"
#   filename: "{epoch:02d}-{${monitor_metric}:.4f}"
#   save_top_k: 2
#   mode: "min"
#   auto_insert_metric_name: False
#   save_last: True
# checkpoint:
#   monitor: "val_loss_mood"
#   filename: "{epoch:02d}-{val_loss_mood:.4f}"
#   save_top_k: 2
#   mode: "min"
#   auto_insert_metric_name: False
#   save_last: True
# earlystopping:
#   monitor: 'val_loss_mood'
#   patience: 10
#   min_delta: 0.0001
#   mode: "min"
# datasets:
#   - jamendo
#   - dmdd