tfm.core.config_definitions.TaskConfig | TensorFlow v2.16.1 (original) (raw)
tfm.core.config_definitions.TaskConfig
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Config passed to task.
Inherits From: Config, ParamsDict
tfm.core.config_definitions.TaskConfig(
default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
init_checkpoint: str = '',
model: Optional[tfm.hyperparams.Config] = None,
train_data: tfm.core.config_definitions.DataConfig = dataclasses.field(default_factory=DataConfig),
validation_data: tfm.core.config_definitions.DataConfig = dataclasses.field(default_factory=DataConfig),
name: Optional[str] = None,
differential_privacy_config: Optional[tfm.core.base_task.DifferentialPrivacyConfig] = None,
allow_image_summary: bool = False
)
Attributes | |
---|---|
BUILDER | |
default_params | Dataclass field |
restrictions | Dataclass field |
init_checkpoint | Dataclass field |
model | Dataclass field |
train_data | Dataclass field |
validation_data | Dataclass field |
name | Dataclass field |
differential_privacy_config | Dataclass field |
allow_image_summary | Dataclass field |
Methods
as_dict
as_dict()
Returns a dict representation of params_dict.ParamsDict.
For the nested params_dict.ParamsDict, a nested dict will be returned.
from_args
@classmethod
from_args( *args, **kwargs )
Builds a config from the given list of arguments.
from_json
@classmethod
from_json( file_path: str )
Wrapper for from_yaml
.
from_yaml
@classmethod
from_yaml( file_path: str )
get
get(
key, value=None
)
Accesses through built-in dictionary get method.
lock
lock()
Makes the ParamsDict immutable.
override
override(
override_params, is_strict=True
)
Override the ParamsDict with a set of given params.
Args | |
---|---|
override_params | a dict or a ParamsDict specifying the parameters to be overridden. |
is_strict | a boolean specifying whether override is strict or not. If True, keys in override_params must be present in the ParamsDict. If False, keys in override_params can be different from what is currently defined in the ParamsDict. In this case, the ParamsDict will be extended to include the new keys. |
replace
replace(
**kwargs
)
Overrides/returns a unlocked copy with the current config unchanged.
validate
validate()
Validate the parameters consistency based on the restrictions.
This method validates the internal consistency using the pre-defined list of restrictions. A restriction is defined as a string which specifies a binary operation. The supported binary operations are {'==', '!=', '<', '<=', '>', '>='}. Note that the meaning of these operators are consistent with the underlying Python immplementation. Users should make sure the define restrictions on their type make sense.
For example, for a ParamsDict like the following
a:
a1: 1
a2: 2
b:
bb:
bb1: 10
bb2: 20
ccc:
a1: 1
a3: 3
one can define two restrictions like this ['a.a1 == b.ccc.a1', 'a.a2 <= b.bb.bb2']
What it enforces are |
---|
a.a1 = 1 == b.ccc.a1 = 1 a.a2 = 2 <= b.bb.bb2 = 20 |
Raises | |
---|---|
KeyError | if any of the following happens (1) any of parameters in any of restrictions is not defined in ParamsDict, (2) any inconsistency violating the restriction is found. |
ValueError | if the restriction defined in the string is not supported. |
__contains__
__contains__(
key
)
Implements the membership test operator.
__eq__
__eq__(
other
)
Class Variables | |
---|---|
IMMUTABLE_TYPES | (<class 'str'>, <class 'int'>, <class 'float'>, <class 'bool'>, <class 'NoneType'>) |
RESERVED_ATTR | ['_locked', '_restrictions'] |
SEQUENCE_TYPES | (<class 'list'>, <class 'tuple'>) |
allow_image_summary | False |
default_params | None |
differential_privacy_config | None |
init_checkpoint | '' |
model | None |
name | None |
restrictions | None |