deepmd

Warning

This trainer requires an extra package dpdata. Use conda install dpdata -c deepmodeling to install it.

gdp converts structures into the deepmd format stored in two folders train and valid based on dataset and writes a training configuration deepmd.json. The training will be performed by dp train deepmd.json.

Some parameters in the deepmd.json will be filled automatically by gdp. training.training_data and training.validation_data will be the folder paths generated by gdp. 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.

See DEEPMD doc for more info about configuration parameters. Example Configuration:

dataset:
  name: xyz
  dataset_path: ./dataset
  train_ratio: 0.9
  batchsize: 16
  random_seed: 1112
trainer:
  name: deepmd
  config: ./dpconfig.json
  type_list: ["H", "O"]
  train_epochs: 10
  random_seed: 1112
init_model: ../model.ckpt

Note

Deepmd Trainer in gdp supports a init_model keyword that allows one to initialise model parameters from a previous checkpoint. This is useful when training models iteratively in an active learning loop.