mapping_networks.config.TrainerConfig

class mapping_networks.config.TrainerConfig(*, max_epochs: int = 100, optimizer: str = 'adam', learning_rate: float = 0.001, weight_decay: float = 0.0, scheduler: str | None = None, scheduler_kwargs: dict[str, ~typing.Any]=<factory>, gradient_clip_norm: float | None = None, gradient_clip_value: float | None = None, accumulation_steps: int = 1, amp_enabled: bool = False, device: str = 'auto', seed: int | None = None)

Configuration for MappingTrainer.

Covers optimizer, scheduler, gradient management, AMP, device selection, and reproducibility.

Example:

config = TrainerConfig(
    max_epochs=50,
    learning_rate=3e-4,
    gradient_clip_norm=1.0,
    amp_enabled=True,
)

__init__(**data: Any) → None

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema(by_alias, ref_template, ...)	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
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)
validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
max_epochs
optimizer
learning_rate
weight_decay
scheduler
scheduler_kwargs
gradient_clip_norm
gradient_clip_value
accumulation_steps
amp_enabled
device
seed