clika_resume

clika_resume(

• clika_state_path: Union[str, Path]
• init_training_dataset_fn: Callable[[], Any]
• init_evaluation_dataset_fn: Optional[Callable[[], Any]]
• optimizer: Optional[object] =  None
• training_losses: dict
• training_metrics: Optional[dict] =  None
• evaluation_losses: Optional[dict] =  None
• evaluation_metrics: Optional[dict] =  None
• callbacks: Optional[List[BaseCallback]] =  None
• new_settings: Optional[Settings] =  None
• resume_start_epoch: Optional[int] =  None
• **kwargs: dict

) ‑> str

Resumes a Compression Job from Given path. This can be used to fine-tune the model using more Data or just resume a run that had to be stopped. This can be used to finetune on more Data or just resume a run that had to be stopped.

Parameters:

• clika_state_path (Union[str, Path]) - Path to the CLIKA state directory or file
• init_training_dataset_fn (Callable[[], Any]) - Pointer to a function that initializes the Dataloaders
• init_evaluation_dataset_fn (Optional[Callable[[], Any]]) - Pointer to a function that initializes the Dataloaders
• optimizer (torch.optim.Optimizer) - Optimizer object. In PyTorch it's an object from: torch.optim... When Resuming, 'optimizer' can be set to None unless it is a custom Optimizer.
• training_losses (Dict[str, Callable]) - Dictionary mapping between a Name to a Loss Object used for Training. Can be a function that accepts (preds, target) or a Metric that implements an interface like torchmetrics.
• training_metrics (Dict[str, Callable]) - Dictionary mapping between a Name to a Metric Object used for Training. Can be a function that accepts (preds, target) or a Metric that implements an interface like torchmetrics.
• evaluation_losses (Dict[str, Callable]) - Dictionary mapping between a Name to a Loss Object used for Evaluation. Can be a function that accepts (preds, target) or a Metric that implements an interface like torchmetrics.
• evaluation_metrics (Dict[str, Callable]) - Dictionary mapping between a Name to a Metric Object used for Evaluation. Can be a function that accepts (preds, target) or a Metric that implements an interface like torchmetrics.
• callbacks (List[BaseCallback]) - Callbacks that will be called during compression to update the status of it.
• multi_gpu (bool) - whether or not to do Multi-GPU compression
• new_settings (Settings) - object to use for the Compression Run. TrainingSettings and DeploymentSettings will be updated with this.
• resume_start_epoch (Optional[int]) - Will override the Epoch present in the CLIKA State directory at Path.
• kwargs (Optional[dict]) - Unsupported for now.

Returns:

• str: Path to latest CLIKA State path