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

    num_grad_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 batch type. 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.yml
      type: dict
      schema:
        model:
          type: dict
        train:
          type: dict
        data:
          type: dict