| [DEFAULTS] | |
| #name of the run | |
| name = stable_audio_tools | |
| # name of the project | |
| project = None | |
| # the batch size | |
| batch_size = 4 | |
| # If `true`, attempts to resume training from latest checkpoint. | |
| # In this case, each run must have unique config filename. | |
| recover = false | |
| # Save top K model checkpoints during training. | |
| save_top_k = -1 | |
| # number of nodes to use for training | |
| num_nodes = 1 | |
| # Multi-GPU strategy for PyTorch Lightning | |
| strategy = "auto" | |
| # Precision to use for training | |
| precision = "16-mixed" | |
| # number of CPU workers for the DataLoader | |
| num_workers = 6 | |
| # the random seed | |
| seed = 42 | |
| # Batches for gradient accumulation | |
| accum_batches = 1 | |
| # Number of steps between checkpoints | |
| checkpoint_every = 10000 | |
| # Number of steps between validation runs | |
| val_every = -1 | |
| # trainer checkpoint file to restart training from | |
| ckpt_path = '' | |
| # model checkpoint file to start a new training run from | |
| pretrained_ckpt_path = '' | |
| # Checkpoint path for the pretransform model if needed | |
| pretransform_ckpt_path = '' | |
| # configuration model specifying model hyperparameters | |
| model_config = '' | |
| # configuration for datasets | |
| dataset_config = '' | |
| # configuration for validation datasets | |
| val_dataset_config = '' | |
| # directory to save the checkpoints in | |
| save_dir = '' | |
| # gradient_clip_val passed into PyTorch Lightning Trainer | |
| gradient_clip_val = 0.0 | |
| # remove the weight norm from the pretransform model | |
| remove_pretransform_weight_norm = '' | |
| # Logger type to use | |
| logger = 'wandb' | |