Trainers

Related Commands

# - train a mode in the current directory
$ gdp train ./config.yaml

# - explore configuration space defined by `config.yaml`
#   training outputs will be written to the `m0` folder
#   a log file will be written to `m0/gdp.out` as well
$ gdp -d m0 train ./config.yaml

Configuration File

The config.yaml requires two sections dataset and trainer to define a training process as

dataset:
  name: xyz
  dataset_path: ./dataset
  train_ratio: 0.9
  batchsize: 16
  random_seed: 1112
trainer:
  name: deepmd
  command: dp
  freeze_command: dp
  config: ./dpconfig.json
  type_list: ["H", "O"]
  train_epochs: 100
  random_seed: 1112

In the dataset section, a dataset is defined. gdp will load the structures in the dataset and convert to the proper format required by the trainer.

• name: Dataset format. (Only xyz is supported now.)

• dataset_path: Dataset filepath.

• train_ratio: Train-valid-split ratio.

• batchsize: Training batchsize.

• random_seed: Random seed that affects how to split structures into train and test sets.

A typical xyz dataset looks like

$ tree ./dataset
./dataset/
└── water-H128O64
    ├── init_aimd.xyz
    └── iter_dpmd.xyz

In the trainer section, a trainer is defined. The parameters related to the model architecture is defined in config, which may be different by models. gdp will automatically update some parameters in the config, which include the training dataset section and training epochs.

For example, if one is training deepmd, training.training_data and training.validation_data in the ./dpconfig.json can be left empty. gdp will convert dataset into deepmd-format and update the file path. Moreover, deepmd uses numb_steps instead of epochs. gdp will compute the number of batches based on the input dataset and multiply it with train_epochs to give the value of numb_steps.

• name: Trainer target.

• commad: Command to train.

• freeze_command: Command to freeze/deploy the trained model.

• config: Model architecture configuration.

• type_list: Type list of the model.

• train_epochs: Number of training epochs.

• random_seed: Random number generator to generate random numbers in the training.

List of Trainers

Read notes for the training of a specific MLIP formulation.

• deepmd
• mace

Use Scheduler

If the training is too time-consuming, one can use gdp session to access a workflow that defines a training operation. See instructions in the Session section.