Schema for Fine-tuning

The schema to configure the input file for fine-turning.

Schema:

### Trainning
train:                              ### ANCHOR: Trainning ML model
    type: dict
    required: True
    schema:
        num_models:                 # Number of models to train. Default is 1
            type: integer
        init_data_paths:            # List of paths to initial data.
            type: list
            required: True

        trainset_ratio:             # Ratio of training set. Default is 0.9
            type: float
        validset_ratio:             # Ratio of validation set. Default is 0.1
            type: float
        num_cores_buildgraph:       # number of cores for building graph data
            type: integer

        init_checkpoints:           # list of checkpoint files, each for each model
            type: list

        max_num_updates:            # Maximum number of updates to guess num_epochs. Default is None
            type: integer

        distributed:
            type: dict
            schema:
                distributed_backend: # choices: 'mpi' or 'nccl'  'gloo'
                    type: string
                cluster_type:       # choices: 'slurm' or 'sge'
                    type: string
                gpu_per_node:       # only need in sge. Default is 1
                    type: integer

        mlp_engine:                 # ML engine. Default is 'sevenn'. Choices: 'sevenn'
            type: string
        sevenn_args:                ### See: https://github.com/MDIL-SNU/SevenNet/blob/main/example_inputs/training/input_full.yaml
            type: dict
            schema:
                model:
                    type: dict
                train:
                    type: dict
                data:
                    type: dict