Arguments of the submit script
- 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': 'dptechnology/dpgen2:latest'}
argument path:default_step_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:default_step_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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
, optionalargument path:default_step_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|float
, 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:
NoneType
|int
, optional, default:None
argument path:default_step_config/parallelism
The parallelism for the step
- 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
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
- 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
- prep_train_config:
- type:
dict
, optional, default:{'template_config': {'image': 'dptechnology/dpgen2:latest', '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_train_config
Configuration for prepare train
- template_config:
- type:
dict
, optional, default:{'image': 'dptechnology/dpgen2:latest'}
argument path:step_configs/prep_train_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/prep_train_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, optional, default:None
argument path:step_configs/prep_train_config/template_config/timeout
The time limit of the OP. Unit is second.
- retry_on_transient_error:
- type:
NoneType
|int
, optional, default:None
argument path:step_configs/prep_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/prep_train_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
- envs:
- type:
dict
|NoneType
, optional, default:None
argument path:step_configs/prep_train_config/template_config/envs
The environmental variables.
- template_slice_config:
- type:
dict
, optionalargument path:step_configs/prep_train_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, optional, default:None
argument path:step_configs/prep_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:
NoneType
|int
, optional, default:None
argument path:step_configs/prep_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/prep_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:
NoneType
|int
, optional, default:None
argument path:step_configs/prep_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:
NoneType
|float
, optional, default:None
argument path:step_configs/prep_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:
NoneType
|int
, optional, default:None
argument path:step_configs/prep_train_config/parallelism
The parallelism for the step
- executor:
- type:
dict
|NoneType
, optional, default:None
argument path:step_configs/prep_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/prep_train_config/executor/type
possible choices:dispatcher
The type of the executor.
When type is set to
dispatcher
:
- run_train_config:
- type:
dict
, optional, default:{'template_config': {'image': 'dptechnology/dpgen2:latest', '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': 'dptechnology/dpgen2:latest'}
argument path:step_configs/run_train_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/run_train_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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
, optionalargument path:step_configs/run_train_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|float
, 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:
NoneType
|int
, 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': 'dptechnology/dpgen2:latest', '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': 'dptechnology/dpgen2:latest'}
argument path:step_configs/prep_explore_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/prep_explore_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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
, optionalargument path:step_configs/prep_explore_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|float
, 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:
NoneType
|int
, 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': 'dptechnology/dpgen2:latest', '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': 'dptechnology/dpgen2:latest'}
argument path:step_configs/run_explore_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/run_explore_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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
, optionalargument path:step_configs/run_explore_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|float
, 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:
NoneType
|int
, 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': 'dptechnology/dpgen2:latest', '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': 'dptechnology/dpgen2:latest'}
argument path:step_configs/prep_fp_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/prep_fp_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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
, optionalargument path:step_configs/prep_fp_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|float
, 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:
NoneType
|int
, 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': 'dptechnology/dpgen2:latest', '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': 'dptechnology/dpgen2:latest'}
argument path:step_configs/run_fp_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/run_fp_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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
, optionalargument path:step_configs/run_fp_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|float
, 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:
NoneType
|int
, 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': 'dptechnology/dpgen2:latest', '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': 'dptechnology/dpgen2:latest'}
argument path:step_configs/select_confs_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/select_confs_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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
, optionalargument path:step_configs/select_confs_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|float
, 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:
NoneType
|int
, 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': 'dptechnology/dpgen2:latest', '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': 'dptechnology/dpgen2:latest'}
argument path:step_configs/collect_data_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/collect_data_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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
, optionalargument path:step_configs/collect_data_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|int
, 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:
NoneType
|float
, 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:
NoneType
|int
, 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
:
- cl_step_config:
- type:
dict
, optional, default:{'template_config': {'image': 'dptechnology/dpgen2:latest', '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/cl_step_config
Configuration for the concurrent learning step
- template_config:
- type:
dict
, optional, default:{'image': 'dptechnology/dpgen2:latest'}
argument path:step_configs/cl_step_config/template_config
The configs passed to the PythonOPTemplate.
- image:
- type:
str
, optional, default:dptechnology/dpgen2:latest
argument path:step_configs/cl_step_config/template_config/image
The image to run the step.
- timeout:
- type:
NoneType
|int
, optional, default:None
argument path:step_configs/cl_step_config/template_config/timeout
The time limit of the OP. Unit is second.
- retry_on_transient_error:
- type:
NoneType
|int
, optional, default:None
argument path:step_configs/cl_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:step_configs/cl_step_config/template_config/timeout_as_transient_error
Treat the timeout as TransientError.
- envs:
- type:
dict
|NoneType
, optional, default:None
argument path:step_configs/cl_step_config/template_config/envs
The environmental variables.
- template_slice_config:
- type:
dict
, optionalargument path:step_configs/cl_step_config/template_slice_config
The configs passed to the Slices.
- group_size:
- type:
NoneType
|int
, optional, default:None
argument path:step_configs/cl_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:
NoneType
|int
, optional, default:None
argument path:step_configs/cl_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:step_configs/cl_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:
NoneType
|int
, optional, default:None
argument path:step_configs/cl_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:
NoneType
|float
, optional, default:None
argument path:step_configs/cl_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:
NoneType
|int
, optional, default:None
argument path:step_configs/cl_step_config/parallelism
The parallelism for the step
- executor:
- type:
dict
|NoneType
, optional, default:None
argument path:step_configs/cl_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:step_configs/cl_step_config/executor/type
possible choices:dispatcher
The type of the executor.
When type is set to
dispatcher
:
- upload_python_packages:
- type:
str
|list
|NoneType
, optional, default:None
, alias: upload_python_packageargument path:upload_python_packages
Upload python package, for debug purpose
- inputs:
- type:
dict
argument path:inputs
The input parameter and artifacts for dpgen2
- type_map:
- type:
list
argument path:inputs/type_map
The type map. e.g. [“Al”, “Mg”]. Al and Mg will have type 0 and 1, respectively.
- mass_map:
- type:
list
argument path:inputs/mass_map
The mass map. e.g. [27., 24.]. Al and Mg will be set with mass 27. and 24. amu, respectively.
- init_data_prefix:
- type:
str
|NoneType
, optional, default:None
argument path:inputs/init_data_prefix
The prefix of initial data systems
- mixed_type:
- type:
bool
, optional, default:False
argument path:inputs/mixed_type
Use deepmd/npy/mixed format for storing training data.
- do_finetune:
- type:
bool
, optional, default:False
argument path:inputs/do_finetune
- Finetune the pretrained model before the first iteration. If it is set to True, then an additional step, finetune-step,
which is based on a branch of “PrepRunDPTrain,” will be added before the dpgen_step. In the finetune-step, the internal flag finetune_mode is set to “finetune,” which means SuperOP “PrepRunDPTrain” is now used as the “Finetune.” In this step, we finetune the pretrained model in the train step and modify the template after training. After that, in the normal dpgen-step, the flag do_finetune is set as “train-init,” which means we use –init-frz-model to train based on models from the previous iteration. The “do_finetune” flag is set to False by default, while the internal flag finetune_mode is set to “no,” which means anything related to finetuning will not be done.
- init_data_sys:
- type:
str
|list
argument path:inputs/init_data_sys
The inital data systems
- train:
- type:
dict
argument path:train
The configuration for training
Depending on the value of type, different sub args are accepted.
- type:
When type is set to
dp
:- config:
- type:
dict
, optional, default:{'init_model_policy': 'no', 'init_model_old_ratio': 0.9, 'init_model_numb_steps': 400000, 'init_model_start_lr': 0.0001, 'init_model_start_pref_e': 0.1, 'init_model_start_pref_f': 100, 'init_model_start_pref_v': 0.0}
argument path:train[dp]/config
Number of models trained for evaluating the model deviation
- init_model_policy:
- type:
str
, optional, default:no
argument path:train[dp]/config/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:train[dp]/config/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_batchargument path:train[dp]/config/init_model_numb_steps
The number of training steps when init-model
- init_model_start_lr:
- type:
float
, optional, default:0.0001
argument path:train[dp]/config/init_model_start_lr
The start learning rate when init-model
- init_model_start_pref_e:
- type:
float
, optional, default:0.1
argument path:train[dp]/config/init_model_start_pref_e
The start energy prefactor in loss when init-model
- init_model_start_pref_f:
- type:
int
|float
, optional, default:100
argument path:train[dp]/config/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:train[dp]/config/init_model_start_pref_v
The start virial prefactor in loss when init-model
- numb_models:
- type:
int
, optional, default:4
argument path:train[dp]/numb_models
Number of models trained for evaluating the model deviation
- template_script:
- type:
str
|list
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.
- init_models_paths:
- type:
list
|NoneType
, optional, default:None
, alias: training_iter0_model_pathargument path:train[dp]/init_models_paths
the paths to initial models
When type is set to
dp-dist
:- config:
- type:
dict
, optional, default:{'init_model_policy': 'no', 'init_model_old_ratio': 0.9, 'init_model_numb_steps': 400000, 'init_model_start_lr': 0.0001, 'init_model_start_pref_e': 0.1, 'init_model_start_pref_f': 100, 'init_model_start_pref_v': 0.0}
argument path:train[dp-dist]/config
Configuration of training
- init_model_policy:
- type:
str
, optional, default:no
argument path:train[dp-dist]/config/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:train[dp-dist]/config/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_batchargument path:train[dp-dist]/config/init_model_numb_steps
The number of training steps when init-model
- init_model_start_lr:
- type:
float
, optional, default:0.0001
argument path:train[dp-dist]/config/init_model_start_lr
The start learning rate when init-model
- init_model_start_pref_e:
- type:
float
, optional, default:0.1
argument path:train[dp-dist]/config/init_model_start_pref_e
The start energy prefactor in loss when init-model
- init_model_start_pref_f:
- type:
int
|float
, optional, default:100
argument path:train[dp-dist]/config/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:train[dp-dist]/config/init_model_start_pref_v
The start virial prefactor in loss when init-model
- template_script:
- type:
str
|list
argument path:train[dp-dist]/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.
- student_model_path:
- type:
str
argument path:train[dp-dist]/student_model_path
The path of student model
- explore:
- type:
dict
argument path:explore
The configuration for exploration
Depending on the value of type, different sub args are accepted.
- type:
the type of the exploration
When type is set to
lmp
:- config:
- type:
dict
, optional, default:{'command': 'lmp', 'teacher_model_path': None, 'shuffle_models': False}
argument path:explore[lmp]/config
Configuration of lmp exploration
- command:
- type:
str
, optional, default:lmp
argument path:explore[lmp]/config/command
The command of LAMMPS
- teacher_model_path:
- type:
str
|BinaryFileInput
|NoneType
, optional, default:None
argument path:explore[lmp]/config/teacher_model_path
The teacher model in Knowledge Distillation
- shuffle_models:
- type:
bool
, optional, default:False
argument path:explore[lmp]/config/shuffle_models
Randomly pick a model from the group of models to drive theexploration MD simulation
- max_numb_iter:
- type:
int
, optional, default:10
argument path:explore[lmp]/max_numb_iter
Maximum number of iterations per stage
- fatal_at_max:
- type:
bool
, optional, default:True
argument path:explore[lmp]/fatal_at_max
Fatal when the number of iteration per stage reaches the max_numb_iter
- output_nopbc:
- type:
bool
, optional, default:False
argument path:explore[lmp]/output_nopbc
Remove pbc of the output configurations
- convergence:
- type:
list
|dict
argument path:explore[lmp]/convergence
The method of convergence check.
Depending on the value of type, different sub args are accepted.
- type:
- type:
str
(flag key)argument path:explore[lmp]/convergence/type
the type of the convergence check
When type is set to
fixed-levels
:- level_f_lo:
- type:
float
argument path:explore[lmp]/convergence[fixed-levels]/level_f_lo
The lower trust level of force model deviation
- level_f_hi:
- type:
float
argument path:explore[lmp]/convergence[fixed-levels]/level_f_hi
The higher trust level of force model deviation
- level_v_lo:
- type:
NoneType
|float
, optional, default:None
argument path:explore[lmp]/convergence[fixed-levels]/level_v_lo
The lower trust level of virial model deviation
- level_v_hi:
- type:
NoneType
|float
, optional, default:None
argument path:explore[lmp]/convergence[fixed-levels]/level_v_hi
The higher trust level of virial model deviation
- conv_accuracy:
- type:
float
, optional, default:0.9
argument path:explore[lmp]/convergence[fixed-levels]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to
fixed-levels-max-select
:- level_f_lo:
- type:
float
argument path:explore[lmp]/convergence[fixed-levels-max-select]/level_f_lo
The lower trust level of force model deviation
- level_f_hi:
- type:
float
argument path:explore[lmp]/convergence[fixed-levels-max-select]/level_f_hi
The higher trust level of force model deviation
- level_v_lo:
- type:
NoneType
|float
, optional, default:None
argument path:explore[lmp]/convergence[fixed-levels-max-select]/level_v_lo
The lower trust level of virial model deviation
- level_v_hi:
- type:
NoneType
|float
, optional, default:None
argument path:explore[lmp]/convergence[fixed-levels-max-select]/level_v_hi
The higher trust level of virial model deviation
- conv_accuracy:
- type:
float
, optional, default:0.9
argument path:explore[lmp]/convergence[fixed-levels-max-select]/conv_accuracy
If the ratio of accurate frames is larger than this value, the stage is converged
When type is set to
adaptive-lower
:- level_f_hi:
- type:
float
, optional, default:0.5
argument path:explore[lmp]/convergence[adaptive-lower]/level_f_hi
The higher trust level of force model deviation
- numb_candi_f:
- type:
int
, optional, default:200
argument path:explore[lmp]/convergence[adaptive-lower]/numb_candi_f
The number of force frames that has a model deviation lower than level_f_hi treated as candidate.
- rate_candi_f:
- type:
float
, optional, default:0.01
argument path:explore[lmp]/convergence[adaptive-lower]/rate_candi_f
The ratio of force frames that has a model deviation lower than level_f_hi treated as candidate.
- level_v_hi:
- type:
NoneType
|float
, optional, default:None
argument path:explore[lmp]/convergence[adaptive-lower]/level_v_hi
The higher trust level of virial model deviation
- numb_candi_v:
- type:
int
, optional, default:0
argument path:explore[lmp]/convergence[adaptive-lower]/numb_candi_v
The number of virial frames that has a model deviation lower than level_v_hi treated as candidate.
- rate_candi_v:
- type:
float
, optional, default:0.0
argument path:explore[lmp]/convergence[adaptive-lower]/rate_candi_v
The ratio of virial frames that has a model deviation lower than level_v_hi treated as candidate.
- n_checked_steps:
- type:
int
, optional, default:2
argument path:explore[lmp]/convergence[adaptive-lower]/n_checked_steps
The number of steps to check the convergence.
- conv_tolerance:
- type:
float
, optional, default:0.05
argument path:explore[lmp]/convergence[adaptive-lower]/conv_tolerance
The convergence tolerance.
- candi_sel_prob:
- type:
str
, optional, default:uniform
argument path:explore[lmp]/convergence[adaptive-lower]/candi_sel_prob
The method for selecting candidates. It can be ‘uniform’: all candidates are of the same probability. ‘inv_pop_f’ or ‘inv_pop_f:nhist’: the probability is inversely propotional to the population of a histogram between leven_f_lo and level_f_hi. The number of bins in the histogram is set by nhist, which should be an integer. The default is 10.
- configuration_prefix:
- type:
str
|NoneType
, optional, default:None
argument path:explore[lmp]/configuration_prefix
The path prefix of lmp initial configurations
- configurations:
- type:
list
, alias: configurationargument path:explore[lmp]/configurations
A list of initial configurations.
This argument takes a list with each element containing the following:
Depending on the value of type, different sub args are accepted.
- type:
When type is set to
alloy
:- numb_confs:
- type:
int
, optional, default:1
argument path:explore[lmp]/configurations[alloy]/numb_confs
The number of configurations to generate
- lattice:
- type:
list
|tuple
argument path:explore[lmp]/configurations[alloy]/lattice
The lattice. Should be a list providing [ “lattice_type”, lattice_const ], or a list providing [ “/path/to/dpdata/system”, “fmt” ]. The two styles are distinguished by the type of the second element.
- replicate:
- type:
list
|NoneType
, optional, default:None
argument path:explore[lmp]/configurations[alloy]/replicate
The number of replicates in each direction
- concentration:
- type:
list
|NoneType
, optional, default:None
argument path:explore[lmp]/configurations[alloy]/concentration
The concentration of each element. If None all elements have the same concentration
- cell_pert_frac:
- type:
float
, optional, default:0.0
argument path:explore[lmp]/configurations[alloy]/cell_pert_frac
The faction of cell perturbation
- atom_pert_dist:
- type:
float
, optional, default:0.0
argument path:explore[lmp]/configurations[alloy]/atom_pert_dist
The distance of atomic position perturbation
When type is set to
file
:- files:
- type:
str
|list
argument path:explore[lmp]/configurations[file]/files
The paths to the configuration files. widecards are supported.
- prefix:
- type:
str
|NoneType
, optional, default:None
argument path:explore[lmp]/configurations[file]/prefix
The prefix of file paths.
- fmt:
- type:
str
, optional, default:auto
argument path:explore[lmp]/configurations[file]/fmt
The format (dpdata accepted formats) of the files.
- remove_pbc:
- type:
bool
, optional, default:False
argument path:explore[lmp]/configurations[file]/remove_pbc
The remove the pbc of the data. shift the coords to the center of box so it can be used with lammps.
- stages:
- type:
list
argument path:explore[lmp]/stages
A list of exploration stages.
- fp:
- type:
dict
argument path:fp
The configuration for FP
Depending on the value of type, different sub args are accepted.
- type:
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:data
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
- task_max:
- type:
int
, optional, default:10
argument path:fp[vasp]/task_max
Maximum number of vasp tasks for each iteration
When type is set to
gaussian
:- inputs_config:
- type:
dict
argument path:fp[gaussian]/inputs_config
Configuration for preparing vasp inputs
- keywords:
- type:
str
|list
argument path:fp[gaussian]/inputs_config/keywords
Gaussian keywords, e.g. force b3lyp/6-31g**. If a list, run multiple steps.
- multiplicity:
- type:
str
|int
, optional, default:auto
argument path:fp[gaussian]/inputs_config/multiplicity
spin multiplicity state. It can be a number. If auto, multiplicity will be detected automatically, with the following rules:
fragment_guesses=True multiplicity will +1 for each radical, and +2 for each oxygen molecule
fragment_guesses=False multiplicity will be 1 or 2, but +2 for each oxygen molecule.
- charge:
- type:
int
, optional, default:0
argument path:fp[gaussian]/inputs_config/charge
molecule charge. Only used when charge is not provided by the system
- basis_set:
- type:
str
, optionalargument path:fp[gaussian]/inputs_config/basis_set
custom basis set
- keywords_high_multiplicity:
- type:
str
, optionalargument path:fp[gaussian]/inputs_config/keywords_high_multiplicity
keywords for points with multiple raicals. multiplicity should be auto. If not set, fallback to normal keywords
- fragment_guesses:
- type:
bool
, optional, default:False
argument path:fp[gaussian]/inputs_config/fragment_guesses
initial guess generated from fragment guesses. If True, multiplicity should be auto
- nproc:
- type:
int
, optional, default:1
argument path:fp[gaussian]/inputs_config/nproc
Number of CPUs to use
- run_config:
- type:
dict
argument path:fp[gaussian]/run_config
Configuration for running vasp tasks
- command:
- type:
str
, optional, default:g16
argument path:fp[gaussian]/run_config/command
The command of Gaussian
- out:
- type:
str
, optional, default:data
argument path:fp[gaussian]/run_config/out
The output dir name of labeled data. In deepmd/npy format provided by dpdata.
- task_max:
- type:
int
, optional, default:10
argument path:fp[gaussian]/task_max
Maximum number of vasp tasks for each iteration
When type is set to
deepmd
:- inputs_config:
- type:
dict
argument path:fp[deepmd]/inputs_config
Configuration for preparing vasp inputs
- run_config:
- type:
dict
argument path:fp[deepmd]/run_config
Configuration for running vasp tasks
- teacher_model_path:
- type:
str
|BinaryFileInput
argument path:fp[deepmd]/run_config/teacher_model_path
The path of teacher model, which can be loaded by deepmd.infer.DeepPot
- out:
- type:
str
, optional, default:data
argument path:fp[deepmd]/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[deepmd]/run_config/log
The log file name of dp
- task_max:
- type:
int
, optional, default:10
argument path:fp[deepmd]/task_max
Maximum number of vasp tasks for each iteration
- name:
- type:
str
, optional, default:dpgen
argument path:name
The workflow name, ‘dpgen’ for default