DPGEN2 configurations

Op configs

RunDPTrain

command:

type: str, optional, default: dp

argument path: command

The command for DP, ‘dp’ for default

impl:

type: str, optional, default: tensorflow, alias: backend

argument path: impl

The implementation/backend of DP. It can be ‘tensorflow’ or ‘pytorch’. ‘tensorflow’ for default.

init_model_policy:

type: str, optional, default: no

argument path: init_model_policy

The policy of init-model training. It can be

• ‘no’: No init-model training. Traing from scratch.

• ‘yes’: Do init-model training.

• ‘old_data_larger_than:XXX’: Do init-model if the training data size of the previous model is larger than XXX. XXX is an int number.

init_model_old_ratio:

type: float, optional, default: 0.9

argument path: init_model_old_ratio

The frequency ratio of old data over new data

init_model_numb_steps:

type: int, optional, default: 400000, alias: init_model_stop_batch

argument path: init_model_numb_steps

The number of training steps when init-model

init_model_start_lr:

type: float, optional, default: 0.0001

argument path: init_model_start_lr

The start learning rate when init-model

init_model_start_pref_e:

type: float, optional, default: 0.1

argument path: init_model_start_pref_e

The start energy prefactor in loss when init-model

init_model_start_pref_f:

type: float, optional, default: 100

argument path: init_model_start_pref_f

The start force prefactor in loss when init-model

init_model_start_pref_v:

type: float, optional, default: 0.0

argument path: init_model_start_pref_v

The start virial prefactor in loss when init-model

init_model_with_finetune:

type: bool, optional, default: False

argument path: init_model_with_finetune

Use finetune for init model

finetune_args:

type: str, optional, default: (empty string)

argument path: finetune_args

Extra arguments for finetuning

multitask:

type: bool, optional, default: False

argument path: multitask

Do multitask training

head:

type: str | NoneType, optional, default: None

argument path: head

Head to use in the multitask training

train_args:

type: str, optional, default: (empty string)

argument path: train_args

Extra arguments for dp train

RunLmp

command:

type: str, optional, default: lmp

argument path: command

The command of LAMMPS

teacher_model_path:

type: str | BinaryFileInput | NoneType, optional, default: None

argument path: teacher_model_path

The teacher model in Knowledge Distillation

shuffle_models:

type: bool, optional, default: False

argument path: shuffle_models

Randomly pick a model from the group of models to drive theexploration MD simulation

head:

type: str | NoneType, optional, default: None

argument path: head

Select a head from multitask

use_ele_temp:

type: int, optional, default: 0

argument path: use_ele_temp

Whether to use electronic temperature, 0 for no, 1 for frame temperature, and 2 for atomic temperature

model_frozen_head:

type: str | NoneType, optional, default: None

argument path: model_frozen_head

Select a head from multitask

use_hdf5:

type: bool, optional, default: False

argument path: use_hdf5

Use HDF5 to store trajs and model_devis

RunVasp

Alloy configs

Task group configs

Step configs

template_config:

type: dict, optional, default: {'image': 'dptechnology/dpgen2:latest'}

argument path: template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: dptechnology/dpgen2:latest

argument path: template_config/image

The image to run the step.

timeout:

type: NoneType | int, optional, default: None

argument path: template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: NoneType | int, optional, default: None

argument path: template_config/retry_on_transient_error

The number of retry times if a TransientError is raised.

timeout_as_transient_error:

type: bool, optional, default: False

argument path: template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: template_slice_config

The configs passed to the Slices.

group_size:

type: NoneType | int, optional, default: None

argument path: template_slice_config/group_size

The number of tasks running on a single node. It is efficient for a large number of short tasks.

pool_size:

type: NoneType | int, optional, default: None

argument path: template_slice_config/pool_size

The number of tasks running at the same time on one node.

continue_on_failed:

type: bool, optional, default: False

argument path: continue_on_failed

If continue the the step is failed (FatalError, TransientError, A certain number of retrial is reached…).

continue_on_num_success:

type: NoneType | int, optional, default: None

argument path: continue_on_num_success

Only in the sliced OP case. Continue the workflow if a certain number of the sliced jobs are successful.

continue_on_success_ratio:

type: NoneType | float, optional, default: None

argument path: continue_on_success_ratio

Only in the sliced OP case. Continue the workflow if a certain ratio of the sliced jobs are successful.

parallelism:

type: NoneType | int, optional, default: None

argument path: parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: executor

The executor of the step.

Depending on the value of type, different sub args are accepted.

type:

type: str (flag key)

argument path: executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher: