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TEDBench
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miae-s

Graph Machine Learning
PyTorch
tedbench
protein
structure
fold-classification
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miae-s
File size: 1,473 Bytes
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{
  "seed": 42,
  "debug": false,
  "wandb": true,
  "mask_ratio": 0.9,
  "noise": 0.0,
  "datamodule": {
    "batch_size": 32,
    "pin_memory": true,
    "num_workers": 12,
    "_target_": "tedbench.data.LightningStructureDataset",
    "root": "./datasets/ted",
    "dataset_name": "afdb_stream"
  },
  "trainer": {
    "_target_": "pytorch_lightning.Trainer",
    "accelerator": "auto",
    "max_steps": 100000,
    "strategy": "auto",
    "devices": "auto",
    "default_root_dir": "${logs.path}",
    "num_sanity_val_steps": 0,
    "val_check_interval": 1000,
    "check_val_every_n_epoch": null,
    "accumulate_grad_batches": 4,
    "num_nodes": 4
  },
  "train": {
    "optimizer": {
      "_target_": "torch.optim.AdamW",
      "lr": 0.0024,
      "weight_decay": 0.05,
      "betas": [
        0.9,
        0.95
      ]
    },
    "lr_scheduler": {
      "_target_": "tedbench.lr_schedulers.get_cosine_schedule_with_warmup",
      "warmup_steps": 5000,
      "max_steps": "${eval:0.99 * ${trainer.max_steps}}",
      "min_factor": 0.1
    },
    "ckpt_path": null,
    "compile": false
  },
  "model": {
    "_target_": "tedbench.model.miae_model",
    "name": "miae_s",
    "use_seq_input": false,
    "masking_strategy": "fixed",
    "use_inverse_folding_loss": true
  },
  "logs": {
    "prefix": "logs/pretrain/${datamodule.dataset_name}/${seed}",
    "path": "${logs.prefix}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S}"
  },
  "mode": {},
  "_model_class": "miae"
}