jade.models.submitter_params.SubmitterParams

class jade.models.submitter_params.SubmitterParams(*, generate_reports: bool = True, hpc_config: HpcConfig, max_nodes: int | None = None, num_processes: int | None = None, per_node_batch_size: int = 500, node_setup_script: str | None = None, node_shutdown_script: str | None = None, poll_interval: int = 10, resource_monitor_interval: int | None = 10, resource_monitor_type: ResourceMonitorType = ResourceMonitorType.AGGREGATION, resource_monitor_stats: ResourceMonitorStats = ResourceMonitorStats(cpu=True, disk=False, memory=True, network=False, process=False, include_child_processes=True, recurse_child_processes=False), try_add_blocked_jobs: bool = True, time_based_batching: bool = False, dry_run: bool = False, verbose: bool = False, singularity_params: SingularityParams | None = None, distributed_submitter: bool = True)[source]

Bases: JadeBaseModel

Defines the submitter options selected by the user.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Methods

construct([_fields_set])

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.

copy(*[, include, exclude, update, deep])

Duplicate a model, optionally choose which fields to include, exclude and change.

dict(*args, **kwargs)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

from_orm(obj)

get_wall_time()

Return the wall time from the HPC parameters.

json(*[, include, exclude, by_alias, ...])

Generate a JSON representation of the model, include and exclude arguments as per dict().

load(path)

Load a model from a file.

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

update_forward_refs(**localns)

Try to update ForwardRefs on fields based on this Model, globalns and localns.

validate(value)

Attributes

generate_reports

hpc_config

max_nodes

num_parallel_processes_per_node

per_node_batch_size

node_setup_script

node_shutdown_script

poll_interval

resource_monitor_interval

resource_monitor_type

resource_monitor_stats

try_add_blocked_jobs

time_based_batching

dry_run

verbose

singularity_params

distributed_submitter

get_wall_time()[source]

Return the wall time from the HPC parameters.

Return type:

timedelta

dict(*args, **kwargs)[source]

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod construct(_fields_set: SetStr | None = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: DictStrAny | None = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns:

new model instance

json(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → str

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod load(path: Path)

Load a model from a file.

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.