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Configuration for trainer.
Inherits From: Config
, ParamsDict
tfm.core.base_trainer.TrainerConfig(
default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None,
restrictions: dataclasses.InitVar[Optional[List[str]]] = None,
optimizer_config: tfm.optimization.OptimizationConfig
= dataclasses.field(default_factory=OptimizationConfig),
train_tf_while_loop: bool = True,
train_tf_function: bool = True,
eval_tf_function: bool = True,
eval_tf_while_loop: bool = False,
allow_tpu_summary: bool = False,
steps_per_loop: int = 1000,
summary_interval: int = 1000,
checkpoint_interval: int = 1000,
max_to_keep: int = 5,
continuous_eval_timeout: int = (60 * 60),
train_steps: int = 0,
validation_steps: int = -1,
validation_interval: int = 1000,
best_checkpoint_export_subdir: str = '',
best_checkpoint_eval_metric: str = '',
best_checkpoint_metric_comp: str = 'higher',
loss_upper_bound: float = 1000000.0,
recovery_begin_steps: int = 0,
recovery_max_trials: int = 0,
validation_summary_subdir: str = 'validation',
preemption_on_demand_checkpoint: bool = True
)
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 | |
---|---|
|
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
)