mmlearn.conf.MMLearnConf

class MMLearnConf(defaults=<factory>, experiment_name='???', job_type=JobType.train, seed=None, datasets=<factory>, dataloader=<factory>, task='???', trainer=<factory>, tags=<factory>, resume_from_checkpoint=None, strict_loading=True, torch_compile_kwargs=<factory>, hydra=<factory>)

Bases: object

Top-level configuration for mmlearn experiments.

Methods

Attributes

dataloader: DataLoaderConf

Configuration for the dataloaders.

datasets: DatasetConf

Configuration for the datasets.

defaults: list[Any]

experiment_name: str = '???'

Name of the experiment. This must be specified for any experiment to run.

hydra: HydraConf

Hydra configuration.

job_type: JobType = 'train'

Type of the job.

resume_from_checkpoint: Optional[Path] = None

Path to the checkpoint to resume training from.

seed: Optional[int] = None

Seed for the random number generators. This is set for Python, Numpy and PyTorch, including the workers in PyTorch Dataloaders.

strict_loading: bool = True

Whether to strictly enforce loading of model weights i.e. strict=True in load_from_checkpoint().

tags: Optional[list[str]]

Tags for the experiment. This is useful for wandb logging.

task: Any = '???'

Configuration for the task. This is required to run any experiment.

torch_compile_kwargs: dict[str, Any]

Configuration for torch.compile. These are essentially the same as the arguments for torch.compile().

trainer: Any

Configuration for the trainer. The options here are the same as in Trainer