Arguments of the input script

Host and nodes

name:

type: str, optional, default: pfd

argument path: name

The workflow name, ‘pfd’ for default

dflow_config:

type: dict | NoneType, optional, default: None

argument path: dflow_config

The configuration passed to dflow

dflow_s3_config:

type: dict | NoneType, optional, default: None

argument path: dflow_s3_config

The S3 configuration passed to dflow

default_step_config:

type: dict, optional, default: {}

argument path: default_step_config

The default step configuration.

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: default_step_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: default_step_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: default_step_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: default_step_config/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: default_step_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: default_step_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: default_step_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: default_step_config/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: int | NoneType, optional, default: None

argument path: default_step_config/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: default_step_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: default_step_config/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: float | NoneType, optional, default: None

argument path: default_step_config/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: int | NoneType, optional, default: None

argument path: default_step_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: default_step_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: default_step_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

parallelism:

type: int | NoneType, optional, default: None

argument path: parallelism

The parallelism for the workflow. Accept an int that stands for the maximum number of running pods for the workflow. None for default

bohrium_config:

type: dict | NoneType, optional, default: None

argument path: bohrium_config

Configurations for the Bohrium platform.

username:

type: str

argument path: bohrium_config/username

The username of the Bohrium platform

password:

type: str, optional

argument path: bohrium_config/password

The password of the Bohrium platform

project_id:

type: int

argument path: bohrium_config/project_id

The project ID of the Bohrium platform

ticket:

type: str, optional

argument path: bohrium_config/ticket

host:

type: str, optional, default: https://workflows.deepmodeling.com

argument path: bohrium_config/host

The host name of the Bohrium platform. Will overwrite dflow_config[‘host’]

k8s_api_server:

type: str, optional, default: https://workflows.deepmodeling.com

argument path: bohrium_config/k8s_api_server

The k8s server of the Bohrium platform. Will overwrite dflow_config[‘k8s_api_server’]

repo_key:

type: str, optional, default: oss-bohrium

argument path: bohrium_config/repo_key

The repo key of the Bohrium platform. Will overwrite dflow_s3_config[‘repo_key’]

storage_client:

type: str, optional, default: dflow.plugins.bohrium.TiefblueClient

argument path: bohrium_config/storage_client

The storage client of the Bohrium platform. Will overwrite dflow_s3_config[‘storage_client’]

step_configs:

type: dict, optional, default: {}

argument path: step_configs

Configurations for executing dflow steps

run_train_config:

type: dict, optional, default: {'template_config': {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}

argument path: step_configs/run_train_config

Configuration for run train

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: step_configs/run_train_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: step_configs/run_train_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: step_configs/run_train_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: step_configs/run_train_config/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: step_configs/run_train_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: step_configs/run_train_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: step_configs/run_train_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: step_configs/run_train_config/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: int | NoneType, optional, default: None

argument path: step_configs/run_train_config/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: step_configs/run_train_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: step_configs/run_train_config/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: float | NoneType, optional, default: None

argument path: step_configs/run_train_config/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: int | NoneType, optional, default: None

argument path: step_configs/run_train_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: step_configs/run_train_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: step_configs/run_train_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

prep_explore_config:

type: dict, optional, default: {'template_config': {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}

argument path: step_configs/prep_explore_config

Configuration for prepare exploration

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: step_configs/prep_explore_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: step_configs/prep_explore_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/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: step_configs/prep_explore_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: step_configs/prep_explore_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/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: int | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/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: step_configs/prep_explore_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/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: float | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/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: int | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: step_configs/prep_explore_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: step_configs/prep_explore_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

run_explore_config:

type: dict, optional, default: {'template_config': {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}

argument path: step_configs/run_explore_config

Configuration for run exploration

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: step_configs/run_explore_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: step_configs/run_explore_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: step_configs/run_explore_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: step_configs/run_explore_config/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: step_configs/run_explore_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: step_configs/run_explore_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: step_configs/run_explore_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: step_configs/run_explore_config/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: int | NoneType, optional, default: None

argument path: step_configs/run_explore_config/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: step_configs/run_explore_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: step_configs/run_explore_config/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: float | NoneType, optional, default: None

argument path: step_configs/run_explore_config/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: int | NoneType, optional, default: None

argument path: step_configs/run_explore_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: step_configs/run_explore_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: step_configs/run_explore_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

prep_fp_config:

type: dict, optional, default: {'template_config': {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}

argument path: step_configs/prep_fp_config

Configuration for prepare fp

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: step_configs/prep_fp_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: step_configs/prep_fp_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/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: step_configs/prep_fp_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: step_configs/prep_fp_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/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: int | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/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: step_configs/prep_fp_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/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: float | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/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: int | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: step_configs/prep_fp_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: step_configs/prep_fp_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

run_fp_config:

type: dict, optional, default: {'template_config': {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}

argument path: step_configs/run_fp_config

Configuration for run fp

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: step_configs/run_fp_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: step_configs/run_fp_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: step_configs/run_fp_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: step_configs/run_fp_config/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: step_configs/run_fp_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: step_configs/run_fp_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: step_configs/run_fp_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: step_configs/run_fp_config/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: int | NoneType, optional, default: None

argument path: step_configs/run_fp_config/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: step_configs/run_fp_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: step_configs/run_fp_config/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: float | NoneType, optional, default: None

argument path: step_configs/run_fp_config/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: int | NoneType, optional, default: None

argument path: step_configs/run_fp_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: step_configs/run_fp_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: step_configs/run_fp_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

select_confs_config:

type: dict, optional, default: {'template_config': {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}

argument path: step_configs/select_confs_config

Configuration for the select confs

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: step_configs/select_confs_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: step_configs/select_confs_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: step_configs/select_confs_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: step_configs/select_confs_config/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: step_configs/select_confs_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: step_configs/select_confs_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: step_configs/select_confs_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: step_configs/select_confs_config/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: int | NoneType, optional, default: None

argument path: step_configs/select_confs_config/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: step_configs/select_confs_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: step_configs/select_confs_config/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: float | NoneType, optional, default: None

argument path: step_configs/select_confs_config/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: int | NoneType, optional, default: None

argument path: step_configs/select_confs_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: step_configs/select_confs_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: step_configs/select_confs_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

collect_data_config:

type: dict, optional, default: {'template_config': {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}

argument path: step_configs/collect_data_config

Configuration for the collect data

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: step_configs/collect_data_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: step_configs/collect_data_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: step_configs/collect_data_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: step_configs/collect_data_config/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: step_configs/collect_data_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: step_configs/collect_data_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: step_configs/collect_data_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: step_configs/collect_data_config/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: int | NoneType, optional, default: None

argument path: step_configs/collect_data_config/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: step_configs/collect_data_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: step_configs/collect_data_config/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: float | NoneType, optional, default: None

argument path: step_configs/collect_data_config/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: int | NoneType, optional, default: None

argument path: step_configs/collect_data_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: step_configs/collect_data_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: step_configs/collect_data_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

evaluate_config:

type: dict, optional, default: {'template_config': {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1', 'timeout': None, 'retry_on_transient_error': None, 'timeout_as_transient_error': False, 'envs': None}, 'continue_on_failed': False, 'continue_on_num_success': None, 'continue_on_success_ratio': None, 'parallelism': None, 'executor': None}

argument path: step_configs/evaluate_config

Configuration for model evaluation

template_config:

type: dict, optional, default: {'image': 'registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1'}

argument path: step_configs/evaluate_config/template_config

The configs passed to the PythonOPTemplate.

image:

type: str, optional, default: registry.dp.tech/dptech/deepmd-kit:v3.0.0a1-2024Q1

argument path: step_configs/evaluate_config/template_config/image

The image to run the step.

timeout:

type: int | NoneType, optional, default: None

argument path: step_configs/evaluate_config/template_config/timeout

The time limit of the OP. Unit is second.

retry_on_transient_error:

type: int | NoneType, optional, default: None

argument path: step_configs/evaluate_config/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: step_configs/evaluate_config/template_config/timeout_as_transient_error

Treat the timeout as TransientError.

envs:

type: dict | NoneType, optional, default: None

argument path: step_configs/evaluate_config/template_config/envs

The environmental variables.

template_slice_config:

type: dict, optional

argument path: step_configs/evaluate_config/template_slice_config

The configs passed to the Slices.

group_size:

type: int | NoneType, optional, default: None

argument path: step_configs/evaluate_config/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: int | NoneType, optional, default: None

argument path: step_configs/evaluate_config/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: step_configs/evaluate_config/continue_on_failed

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

continue_on_num_success:

type: int | NoneType, optional, default: None

argument path: step_configs/evaluate_config/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: float | NoneType, optional, default: None

argument path: step_configs/evaluate_config/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: int | NoneType, optional, default: None

argument path: step_configs/evaluate_config/parallelism

The parallelism for the step

executor:

type: dict | NoneType, optional, default: None

argument path: step_configs/evaluate_config/executor

The executor of the step.

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

type:

type: str (flag key)

argument path: step_configs/evaluate_config/executor/type

possible choices: dispatcher

The type of the executor.

When type is set to dispatcher:

upload_python_packages:

type: NoneType | str | typing.List[str], optional, default: None, alias: upload_python_package

argument path: upload_python_packages

Upload python package, for debug purpose

Task definition

type:

type: str

argument path: type

Task type, finetune or dist

max_iter:

type: int, optional, default: 1

argument path: max_iter

Maximum number of iterations

init_fp:

type: bool, optional, default: False

argument path: init_fp

Initialize fine-tuning

init_train:

type: bool, optional, default: False

argument path: init_train

Initialize training

Input definition

init_confs:

type: dict

argument path: init_confs

The initial configurations for exploration

prefix:

type: NoneType | str, optional, default: None

argument path: init_confs/prefix

fmt:

type: str, optional, default: extxyz

argument path: init_confs/fmt

ASE compatible format of input structure files

confs_paths:

type: NoneType | str | typing.List[str], optional, default: None, alias: files

argument path: init_confs/confs_paths

confs_uri:

type: NoneType | str | typing.List[str], optional, default: None

argument path: init_confs/confs_uri

init_fp_confs:

type: dict, optional, default: {}

argument path: init_fp_confs

The configurations for initial first-principles calculations

prefix:

type: NoneType | str, optional, default: None

argument path: init_fp_confs/prefix

fmt:

type: str, optional, default: extxyz

argument path: init_fp_confs/fmt

ASE compatible format of input structure files

confs_paths:

type: NoneType | str | typing.List[str], optional, default: None, alias: files

argument path: init_fp_confs/confs_paths

confs_uri:

type: NoneType | str | typing.List[str], optional, default: None

argument path: init_fp_confs/confs_uri

init_data_prefix:

type: NoneType | str, optional, default: None

argument path: init_data_prefix

The prefix of initial data systems

init_data_sys:

type: NoneType | str | typing.List[str], optional, default: None

argument path: init_data_sys

The inital data systems

init_data_uri:

type: NoneType | str, optional, default: None

argument path: init_data_uri

The URI of initial data

base_model_path:

type: NoneType | str | typing.List[str], optional, default: None, aliases: teacher_model_path, pretrain_model_path, teacher_models_paths

argument path: base_model_path

Path to the base model.In finetune task, this is the path to the pretrained model.In distillation task, this is the path to the teacher model.

base_model_uri:

type: NoneType | str, optional, default: None, aliases: teacher_model_uri, pretrain_model_uri

argument path: base_model_uri

URI of the base model.

Exploration

exploration:

type: dict, alias: explore

argument path: exploration

The configuration for exploration

test_set_config:

type: dict, optional, default: {'test_size': 0.1}, alias: test_set

argument path: exploration/test_set_config

Set the portion of test set. Only available for dist

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

type:

type: str (flag key)

argument path: exploration/type

possible choices: ase, calypso, calypso:merge

The type of the exploration

• ase: Exploration by ASE

• calypso: Exploration by Calypso

• calypso:merge: Exploration by Calypso

When type is set to ase:

Exploration by ASE

config:

type: dict, optional, default: {}

argument path: exploration[ase]/config

Configuration of ase exploration

calculator:

type: str, alias: calc

argument path: exploration[ase]/config/calculator

The type of calculator to use, e.g., ‘mace’, ‘mattersim’.

stages:

type: typing.List[typing.List[dict]]

argument path: exploration[ase]/stages

Exploration stages.The definition of exploration stages of type List[List[ExplorationTaskGroup]. The outer list provides the enumeration of the exploration stages. Then each stage is defined by a list of exploration task groups. Each task group is described in the task group definition

When type is set to calypso:

Exploration by Calypso

config:

type: dict

argument path: exploration[calypso]/config

Configuration of ase exploration

calculator:

type: str, alias: calc

argument path: exploration[calypso]/config/calculator

The type of calculator to use, e.g., ‘mace’, ‘mattersim’.

run_calypso_command:

type: str, optional, default: calypso.x

argument path: exploration[calypso]/config/run_calypso_command

command of running calypso.

stages:

type: typing.List[typing.List[dict]]

argument path: exploration[calypso]/stages

Exploration stages.The definition of exploration stages of type List[List[ExplorationTaskGroup]. The outer list provides the enumeration of the exploration stages. Then each stage is defined by a list of exploration task groups. Each task group is described in the task group definition

When type is set to calypso:merge:

Exploration by Calypso

config:

type: dict

argument path: exploration[calypso:merge]/config

Configuration of ase exploration

calculator:

type: str, alias: calc

argument path: exploration[calypso:merge]/config/calculator

The type of calculator to use, e.g., ‘mace’, ‘mattersim’.

run_calypso_command:

type: str, optional, default: calypso.x

argument path: exploration[calypso:merge]/config/run_calypso_command

command of running calypso.

stages:

type: typing.List[typing.List[dict]]

argument path: exploration[calypso:merge]/stages

Exploration stages.The definition of exploration stages of type List[List[ExplorationTaskGroup]. The outer list provides the enumeration of the exploration stages. Then each stage is defined by a list of exploration task groups. Each task group is described in the task group definition

Explore task group definition

ASE task group

temps:

type: list

argument path: temps

List of temperatures for MD simulation.

press:

type: NoneType | list, optional, default: None

argument path: press

List of pressures for MD simulation (optional).

ens:

type: str, optional, default: npt

argument path: ens

Ensemble type (e.g., ‘npt’, ‘nvt’).

dt:

type: float, optional, default: 2

argument path: dt

MD time step (fs or ps).

nsteps:

type: int, optional, default: 1000

argument path: nsteps

Number of MD steps.

trj_freq:

type: int, optional, default: 100

argument path: trj_freq

Trajectory output frequency.

tau_t:

type: float, optional, default: 100

argument path: tau_t

Thermostat time constant.

tau_p:

type: float, optional, default: 500

argument path: tau_p

Barostat time constant.

no_pbc:

type: bool, optional, default: False

argument path: no_pbc

Disable periodic boundary conditions.

CALYPSO task group

numb_of_species:

type: int

argument path: numb_of_species

Number of different atomic species in the system.

name_of_atoms:

type: str | list

argument path: name_of_atoms

List of atomic symbols (e.g., [‘Li’, ‘O’]) or nested lists for random selection.

numb_of_atoms:

type: list

argument path: numb_of_atoms

List of number of atoms for each species.

distance_of_ions:

type: dict | list | NoneType, optional, default: None

argument path: distance_of_ions

Distance matrix between ions or dict of covalent radii adjustments. If None, uses default covalent radii.

atomic_number:

type: NoneType | list, optional, default: None

argument path: atomic_number

List of atomic numbers for each species. Auto-generated if None.

pop_size:

type: int, optional, default: 30

argument path: pop_size

Population size for CALYPSO genetic algorithm.

max_step:

type: int, optional, default: 5

argument path: max_step

Maximum number of CALYPSO evolution steps.

system_name:

type: str, optional, default: CALYPSO

argument path: system_name

Name of the system for CALYPSO.

numb_of_formula:

type: list, optional, default: [1, 1]

argument path: numb_of_formula

Number of formula units range [min, max].

pressure:

type: float, optional, default: 0.0

argument path: pressure

External pressure for ASE optimization (GPa).

fmax:

type: float, optional, default: 0.01

argument path: fmax

Maximum force criterion for structure optimization (eV/Å).

volume:

type: float, optional, default: 0

argument path: volume

Volume constraint for structure generation. 0 means no constraint.

ialgo:

type: int, optional, default: 2

argument path: ialgo

CALYPSO algorithm type (1 or 2).

pso_ratio:

type: float, optional, default: 0.6

argument path: pso_ratio

PSO (Particle Swarm Optimization) ratio.

icode:

type: int, optional, default: 15

argument path: icode

Local optimization code (e.g., 1=VASP, 2=SIESTA, 15=ASE).

numb_of_lbest:

type: int, optional, default: 4

argument path: numb_of_lbest

Number of local best structures to keep.

numb_of_local_optim:

type: int, optional, default: 4

argument path: numb_of_local_optim

Number of structures to perform local optimization.

command:

type: str, optional, default: sh submit.sh

argument path: command

Command to execute for structure optimization.

max_time:

type: int, optional, default: 9000

argument path: max_time

Maximum time allowed for each optimization (seconds).

gen_type:

type: int, optional, default: 1

argument path: gen_type

Structure generation type.

pick_up:

type: bool, optional, default: False

argument path: pick_up

Whether to pick up from previous run.

pick_step:

type: int, optional, default: 1

argument path: pick_step

Step number to pick up from.

parallel:

type: bool, optional, default: False

argument path: parallel

Enable parallel CALYPSO execution.

split:

type: bool, optional, default: True

argument path: split

Split CALYPSO tasks.

spec_space_group:

type: list, optional, default: [2, 230]

argument path: spec_space_group

Specified space group range [min, max].

vsc:

type: bool, optional, default: False

argument path: vsc

Variable stoichiometry composition.

ctrl_range:

type: list, optional, default: [[1, 10]]

argument path: ctrl_range

Control range for variable composition [[min, max]].

max_numb_atoms:

type: int, optional, default: 100

argument path: max_numb_atoms

Maximum number of atoms in generated structures.

opt_step:

type: int, optional, default: 1000

argument path: opt_step

Maximum optimization steps for ASE.

ens:

type: str, optional, default: lbfgs

argument path: ens

Optimizer/ensemble for ASE (e.g., ‘lbfgs’, ‘bfgs’, ‘fire’).

Frame selection

test_size:

type: float, optional, default: 0.1

argument path: test_size

The number of data frames split from training data as test set.If test_size<1, it is the portion of test set. If test_size>=1,it is the number of frames in the test set.

frame_filter:

type: dict | typing.List[dict], optional, default: []

argument path: frame_filter

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

type:

type: str (flag key)

argument path: frame_filter/type

possible choices: distance, box_skewness, box_length

the type of the frame selector

• distance: The parameters of atom distance filter

• box_skewness: The parameters of box skewness filter

• box_length: The parameters of box length filter

When type is set to distance:

The parameters of atom distance filter

custom_safe_dist:

type: dict, optional, default: {}

argument path: frame_filter[distance]/custom_safe_dist

Custom safe distance (in unit of bohr) for each element

safe_dist_ratio:

type: float, optional, default: 1.0

argument path: frame_filter[distance]/safe_dist_ratio

The ratio multiplied to the safe distance

When type is set to box_skewness:

The parameters of box skewness filter

theta:

type: float, optional, default: 60.0

argument path: frame_filter[box_skewness]/theta

The threshold for angles between the edges of the cell. If all angles are larger than this value the check is passed

When type is set to box_length:

The parameters of box length filter

length_ratio:

type: float, optional, default: 5.0

argument path: frame_filter[box_length]/length_ratio

The threshold for the length ratio between the edges of the cell. If all length ratios are smaller than this value the check is passed

h_filter:

type: dict | NoneType, optional, default: None

argument path: h_filter

Select configurations based on entropy contribution

k:

type: int, optional, default: 32

argument path: h_filter/k

Number of nearest neighbors to consider

cutoff:

type: float, optional, default: 5.0

argument path: h_filter/cutoff

Cutoff distance (in unit of angstrom)

batch_size:

type: int, optional, default: 1000

argument path: h_filter/batch_size

Batch size for calculating the similarity matrix

h:

type: float, optional, default: 0.015

argument path: h_filter/h

Bandwidth of the Gaussian kernel (in unit of angstrom).It controls the level of ‘similarity’ between two configurations

chunk_size:

type: int, optional, default: 10

argument path: h_filter/chunk_size

The chunk size of adding new configurations.

Labeling

fp:

type: dict, optional

argument path: fp

The configuration for FP

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

type:

type: str (flag key)

argument path: fp/type

possible choices: vasp, ase, foo

Tpyes of first-principles calculators

When type is set to vasp:

inputs_config:

type: dict

argument path: fp[vasp]/inputs_config

Configuration for preparing vasp inputs

incar:

type: str

argument path: fp[vasp]/inputs_config/incar

The path to the template incar file

pp_files:

type: dict

argument path: fp[vasp]/inputs_config/pp_files

The pseudopotential files set by a dict, e.g. {“Al” : “path/to/the/al/pp/file”, “Mg” : “path/to/the/mg/pp/file”}

kspacing:

type: float

argument path: fp[vasp]/inputs_config/kspacing

The spacing of k-point sampling. ksapcing will overwrite the incar template

kgamma:

type: bool, optional, default: True

argument path: fp[vasp]/inputs_config/kgamma

If the k-mesh includes the gamma point. kgamma will overwrite the incar template

run_config:

type: dict

argument path: fp[vasp]/run_config

Configuration for running vasp tasks

command:

type: str, optional, default: vasp

argument path: fp[vasp]/run_config/command

The command of VASP

out:

type: str, optional, default: structures.extxyz

argument path: fp[vasp]/run_config/out

The output dir name of labeled data. In deepmd/npy format provided by dpdata.

log:

type: str, optional, default: fp.log

argument path: fp[vasp]/run_config/log

The log file name of VASP

extra_output_files:

type: typing.List, optional, default: []

argument path: fp[vasp]/extra_output_files

Extra output file names, support wildcards

When type is set to ase:

inputs_config:

type: dict

argument path: fp[ase]/inputs_config

Configuration for preparing vasp inputs

run_config:

type: dict

argument path: fp[ase]/run_config

Configuration for running vasp tasks

model_style:

type: str, optional, default: dp, aliases: style, model

argument path: fp[ase]/run_config/model_style

Distillation model style, e.g., ‘mace’, ‘mattersim’.

model_args:

type: typing.Dict, optional, default: {}

argument path: fp[ase]/run_config/model_args

Model arguments, e.g., {‘device’:’cuda’}, etc.

extra_output_files:

type: typing.List, optional, default: []

argument path: fp[ase]/extra_output_files

Extra output file names, support wildcards

When type is set to foo:

inputs_config:

type: dict

argument path: fp[foo]/inputs_config

Configuration for preparing vasp inputs

run_config:

type: dict

argument path: fp[foo]/run_config

Configuration for running vasp tasks

command:

type: str, optional, default: foo

argument path: fp[foo]/run_config/command

Command to run the foo task.

extra_output_files:

type: typing.List, optional, default: []

argument path: fp[foo]/extra_output_files

Extra output file names, support wildcards

Model training

train:

type: dict

argument path: train

The configuration for training

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

type:

type: str (flag key)

argument path: train/type

possible choices: dp, mattersim

the type of the training model

When type is set to dp:

config:

type: dict, optional, default: {'command': 'dp', 'impl': 'tensorflow', 'finetune_args': '', 'multitask': False, 'head': None, 'train_args': '', 'finetune_mode': False, 'mixed_type': False}

argument path: train[dp]/config

Configuration of training

command:

type: str, optional, default: dp

argument path: train[dp]/config/command

The command for DP, ‘dp’ for default

impl:

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

argument path: train[dp]/config/impl

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

finetune_args:

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

argument path: train[dp]/config/finetune_args

Extra arguments for finetuning

multitask:

type: bool, optional, default: False

argument path: train[dp]/config/multitask

Do multitask training

head:

type: NoneType | str, optional, default: None

argument path: train[dp]/config/head

Head to use in the multitask training

train_args:

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

argument path: train[dp]/config/train_args

Extra arguments for dp train

finetune_mode:

type: bool, optional, default: False

argument path: train[dp]/config/finetune_mode

Whether to run in finetune mode

mixed_type:

type: bool, optional, default: False

argument path: train[dp]/config/mixed_type

Whether to use mixed type system for training

template_script:

type: dict | str | typing.List[str], optional, default: {}

argument path: train[dp]/template_script

File names of the template training script. It can be a List[str], the length of which is the same as numb_models. Each template script in the list is used to train a model. Can be a str, the models share the same template training script.

optional_files:

type: NoneType | list, optional, default: None

argument path: train[dp]/optional_files

Optional files for training

When type is set to mattersim:

config:

type: dict, optional, default: {'run_name': 'train', 'save_path': './', 'save_checkpoint': True, 'ckpt_interval': 10, 'device': 'cuda', 'cutoff': 5.0, 'threebody_cutoff': 4.0, 'epochs': 10, 'batch_size': 8, 'lr': 0.0002, 'step_size': 10, 'include_forces': True, 'include_stresses': False, 'force_loss_ratio': 1.0, 'stress_loss_ratio': 0.1, 'early_stop_patience': 10, 'seed': 42, 're_normalize': False, 'scale_key': 'per_species_forces_rms', 'shift_key': 'per_species_energy_mean_linear_reg', 'init_scale': None, 'init_shift': None, 'trainable_scale': False, 'trainable_shift': False}

argument path: train[mattersim]/config

Configuration of training

run_name:

type: str, optional, default: train

argument path: train[mattersim]/config/run_name

Name of the run.

save_path:

type: str, optional, default: ./

argument path: train[mattersim]/config/save_path

Path to save the model.

save_checkpoint:

type: bool, optional, default: True

argument path: train[mattersim]/config/save_checkpoint

Save checkpoint during training.

ckpt_interval:

type: int, optional, default: 10

argument path: train[mattersim]/config/ckpt_interval

Save checkpoint every ckpt_interval epochs.

device:

type: str, optional, default: cuda

argument path: train[mattersim]/config/device

Device to use for training (e.g., ‘cuda’ or ‘cpu’).

cutoff:

type: float, optional, default: 5.0

argument path: train[mattersim]/config/cutoff

Cutoff radius for two-body interactions.

threebody_cutoff:

type: float, optional, default: 4.0

argument path: train[mattersim]/config/threebody_cutoff

Cutoff radius for three-body interactions.

epochs:

type: int, optional, default: 10

argument path: train[mattersim]/config/epochs

Number of training epochs.

batch_size:

type: int, optional, default: 8

argument path: train[mattersim]/config/batch_size

Batch size for training.

lr:

type: float, optional, default: 0.0002

argument path: train[mattersim]/config/lr

Learning rate.

step_size:

type: int, optional, default: 10

argument path: train[mattersim]/config/step_size

Step epoch for learning rate scheduler.

include_forces:

type: bool, optional, default: True

argument path: train[mattersim]/config/include_forces

Include forces in training.

include_stresses:

type: bool, optional, default: False

argument path: train[mattersim]/config/include_stresses

Include stresses in training.

force_loss_ratio:

type: float, optional, default: 1.0

argument path: train[mattersim]/config/force_loss_ratio

Weight for force loss.

stress_loss_ratio:

type: float, optional, default: 0.1

argument path: train[mattersim]/config/stress_loss_ratio

Weight for stress loss.

early_stop_patience:

type: int, optional, default: 10

argument path: train[mattersim]/config/early_stop_patience

Patience for early stopping.

seed:

type: int, optional, default: 42

argument path: train[mattersim]/config/seed

Random seed for reproducibility.

re_normalize:

type: bool, optional, default: False

argument path: train[mattersim]/config/re_normalize

Re-normalize energy and forces.

scale_key:

type: str, optional, default: per_species_forces_rms

argument path: train[mattersim]/config/scale_key

Key for scaling forces.

shift_key:

type: str, optional, default: per_species_energy_mean_linear_reg

argument path: train[mattersim]/config/shift_key

Key for shifting energy.

init_scale:

type: float | NoneType, optional, default: None

argument path: train[mattersim]/config/init_scale

Initial scale value.

init_shift:

type: float | NoneType, optional, default: None

argument path: train[mattersim]/config/init_shift

Initial shift value.

trainable_scale:

type: bool, optional, default: False

argument path: train[mattersim]/config/trainable_scale

Allow scale to be trainable.

trainable_shift:

type: bool, optional, default: False

argument path: train[mattersim]/config/trainable_shift

Allow shift to be trainable.

template_script:

type: dict | str | typing.List[str], optional, default: {}

argument path: train[mattersim]/template_script

File names of the template training script. It can be a List[str], the length of which is the same as numb_models. Each template script in the list is used to train a model. Can be a str, the models share the same template training script.

optional_files:

type: NoneType | list, optional, default: None

argument path: train[mattersim]/optional_files

Optional files for training

Evaluate

max_sel:

type: int, optional, default: 50

argument path: max_sel

Maximum number of selected configurations

model:

type: str, optional, default: dp

argument path: model

The model type used in the evaluation. It should be consistent with the model type used in training.

converge:

type: dict, optional, default: {}

argument path: converge

The method of convergence check.

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

type:

type: str (flag key)

argument path: converge/type

possible choices: force_rmse, force_rmse_idv, energy_rmse

the type of the condidate selection and convergence check method.

• force_rmse: Converge by RMSE of atomic forces

• force_rmse_idv: Converge by RMSE of atomic forces

• energy_rmse: Converge by RMSE of energy per atom

When type is set to force_rmse:

Converge by RMSE of atomic forces

RMSE:

type: float, optional, default: 0.01

argument path: converge[force_rmse]/RMSE

When type is set to force_rmse_idv:

Converge by RMSE of atomic forces

RMSE:

type: float, optional, default: 0.01

argument path: converge[force_rmse_idv]/RMSE

When type is set to energy_rmse:

Converge by RMSE of energy per atom

RMSE:

type: float, optional, default: 0.01

argument path: converge[energy_rmse]/RMSE