videomae_pre_training.yaml

# @package _global_

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

defaults:
  - override /data: bundestag_slr_pretrain
  - override /model: videomae_pretraining_module
  - override /callbacks: default
  - override /trainer: gpu

# all parameters below will be merged with parameters from default configurations set above
# this allows you to overwrite only specified parameters

tags: ["videomae", "bundestag_slr", "pre_training"]

seed: 12345

trainer:
  min_epochs: 10
  max_epochs: 40
  precision: 16-mixed

  gradient_clip_val: 0.5
  gradient_clip_algorithm: value

  devices: 1

data:
  batch_size: 4

paths:
  log_dir: /data/1braach/logs/

logger:
  wandb:
    tags: ${tags}
    group: "sign_language"

callbacks:
  model_checkpoint:
    dirpath: ${paths.output_dir}/checkpoints
    filename: "videomae_{epoch:03d}_{step:07d}"
    mode: "min"
    save_top_k: 5
    save_last: True
    auto_insert_metric_name: True
    train_time_interval:
      _target_: datetime.timedelta
      minutes: 30

  early_stopping: null