# lightning.pytorch==2.4.0 seed_everything: 42 trainer: accelerator: auto strategy: class_path: lightning.pytorch.strategies.DDPStrategy init_args: accelerator: null parallel_devices: null cluster_environment: null checkpoint_io: null precision_plugin: null ddp_comm_state: null ddp_comm_hook: null ddp_comm_wrapper: null model_averaging_period: null process_group_backend: null timeout: 0:30:00 start_method: popen output_device: null dim: 0 broadcast_buffers: true process_group: null bucket_cap_mb: 25 find_unused_parameters: false check_reduction: false gradient_as_bucket_view: false static_graph: false delay_all_reduce_named_params: null param_to_hook_all_reduce: null mixed_precision: null device_mesh: null devices: auto num_nodes: 8 precision: 32 logger: class_path: lightning.pytorch.loggers.WandbLogger init_args: name: 7B_splice_reconstruction save_dir: logs version: null offline: false dir: null id: null anonymous: null project: GBFT_DNAFM_GUE log_model: false experiment: null prefix: '' checkpoint_name: null job_type: null config: null entity: null reinit: null tags: null group: null notes: null magic: null config_exclude_keys: null config_include_keys: null mode: null allow_val_change: null resume: null force: null tensorboard: null sync_tensorboard: null monitor_gym: null save_code: true settings: null callbacks: - class_path: lightning.pytorch.callbacks.LearningRateMonitor init_args: logging_interval: step log_momentum: false log_weight_decay: false - class_path: lightning.pytorch.callbacks.ModelCheckpoint # save ckpt at the end of each epoch, and save the best val_mcc ckpt init_args: dirpath: null filename: epoch_{epoch}-val_mcc:{val_mcc:.3f} monitor: val_mcc verbose: false save_last: true save_top_k: 1 save_weights_only: false mode: max auto_insert_metric_name: true every_n_train_steps: null train_time_interval: null every_n_epochs: 1 save_on_train_epoch_end: null enable_version_counter: true - class_path: lightning.pytorch.callbacks.early_stopping.EarlyStopping dict_kwargs: monitor: val_mcc mode: max patience: 10 fast_dev_run: false max_epochs: 20 min_epochs: null max_steps: -1 min_steps: null max_time: null limit_train_batches: null limit_val_batches: null limit_test_batches: null limit_predict_batches: null overfit_batches: 0.0 val_check_interval: null check_val_every_n_epoch: 1 num_sanity_val_steps: null log_every_n_steps: 50 enable_checkpointing: null enable_progress_bar: null enable_model_summary: null accumulate_grad_batches: 1 gradient_clip_val: 1 gradient_clip_algorithm: null deterministic: null benchmark: null inference_mode: true use_distributed_sampler: true profiler: class_path: lightning.pytorch.profilers.PyTorchProfiler init_args: dirpath: null filename: null group_by_input_shapes: false emit_nvtx: false export_to_chrome: true row_limit: 20 sort_by_key: null record_module_names: true table_kwargs: null record_shapes: false dict_kwargs: profile_memory: true detect_anomaly: false barebones: false plugins: null sync_batchnorm: false reload_dataloaders_every_n_epochs: 0 default_root_dir: logs model: class_path: genbio_finetune.tasks.SequenceClassification init_args: backbone: class_path: genbio_finetune.models.dnafm init_args: from_scratch: false use_peft: true save_peft_only: true lora_r: 16 lora_alpha: 32 lora_dropout: 0.1 config_overwrites: null model_init_args: null max_length: 402 adapter: class_path: genbio_finetune.models.MLPPoolAdapter init_args: pooling: mean_pooling hidden_sizes: - 128 bias: true dropout: 0.1 dropout_in_middle: false n_classes: 3 optimizer: class_path: torch.optim.AdamW init_args: lr: 0.0005 betas: - 0.9 - 0.95 eps: 1.0e-08 weight_decay: 0.1 amsgrad: false maximize: false foreach: null capturable: false differentiable: false fused: null lr_scheduler: class_path: genbio_finetune.lr_schedulers.CosineWithWarmup init_args: warmup_ratio: 0.1 use_legacy_adapter: false strict_loading: true reset_optimizer_states: false data: class_path: genbio_finetune.data.GUEClassification init_args: hf_name: leannmlindsey/GUE task: splice_reconstructed x_col: sequence y_col: label train_split_name: train test_split_name: test valid_split_name: null valid_split_size: 0.1 batch_size: 4 shuffle: true sampler: null num_workers: 0 pin_memory: true persistent_workers: false ckpt_path: null