|---| | [ View source on GitHub](https://github.com/tensorflow/models/blob/v2.20.0/official/core/config_definitions.py#L210-L285) | Configuration for trainer. Inherits From: [`Config`](https://www.tensorflow.org/api_docs/python/tfm/hyperparams/Config), [`ParamsDict`](https://www.tensorflow.org/api_docs/python/tfm/hyperparams/ParamsDict) #### View aliases **Main aliases** [`tfm.core.config_definitions.TrainerConfig`](https://www.tensorflow.org/api_docs/python/tfm/core/base_trainer/TrainerConfig)
tfm.core.base_trainer.TrainerConfig( default_params: dataclasses.InitVar[Optional[Mapping[str, Any]]] = None, restrictions: dataclasses.InitVar[Optional[List[str]]] = None, optimizer_config: https://www.tensorflow.org/api_docs/python/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 )


| ## Args || |---|---| | `default_params` | a Python dict or another ParamsDict object including the default parameters to initialize. | | `restrictions` | a list of strings, which define a list of restrictions to ensure the consistency of different parameters internally. Each restriction string is defined as a binary relation with a set of operators, including {'==', '!=', '\<', '\<=', '\>', '\>='}. |



| ## Attributes || |---|---| | `optimizer_config` | optimizer config, it includes optimizer, learning rate, and warmup schedule configs. | | `train_tf_while_loop` | whether or not to use tf while loop. | | `train_tf_function` | whether or not to use tf_function for training loop. | | `eval_tf_function` | whether or not to use tf_function for eval. | | `eval_tf_while_loop` | whether or not to use tf while loop for eval. | | `allow_tpu_summary` | Whether to allow summary happen inside the XLA program runs on TPU through automatic outside compilation. | | `steps_per_loop` | number of steps per loop to report training metrics. This can also be used to reduce host worker communication in a TPU setup. | | `summary_interval` | number of steps between each summary. | | `checkpoint_interval` | number of steps between checkpoints. | | `max_to_keep` | max checkpoints to keep. | | `continuous_eval_timeout` | maximum number of seconds to wait between checkpoints, if set to None, continuous eval will wait indefinitely. This is only used continuous_train_and_eval and continuous_eval modes. Default value is 1 hrs. | | `train_steps` | number of train steps. | | `validation_steps` | number of eval steps. If -1, the entire eval dataset is used. | | `validation_interval` | number of training steps to run between evaluations. | | `best_checkpoint_export_subdir` | if set, the trainer will keep track of the best evaluation metric, and export the corresponding best checkpoint under `model_dir/best_checkpoint_export_subdir`. Note that this only works if mode contains eval (such as `train_and_eval`, `continuous_eval`, and `continuous_train_and_eval`). | | `best_checkpoint_eval_metric` | for exporting the best checkpoint, which evaluation metric the trainer should monitor. This can be any evaluation metric appears on tensorboard. | | `best_checkpoint_metric_comp` | for exporting the best checkpoint, how the trainer should compare the evaluation metrics. This can be either `higher` (higher the better) or `lower` (lower the better). | | `validation_summary_subdir` | A 'str', sub directory for saving eval summary. | | `preemption_on_demand_checkpoint` | whether or not to save on-demand checkpoints after a preemption. | | `BUILDER` |

| | `default_params` | Dataclass field | | `restrictions` | Dataclass field | | `loss_upper_bound` | Dataclass field | | `recovery_begin_steps` | Dataclass field | | `recovery_max_trials` | Dataclass field |
## Methods ### `as_dict` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L315-L324) as_dict() Returns a dict representation of params_dict.ParamsDict. For the nested params_dict.ParamsDict, a nested dict will be returned. ### `from_args` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L349-L358) @classmethod from_args( *args, **kwargs ) Builds a config from the given list of arguments. ### `from_json` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L344-L347) @classmethod from_json( file_path: str ) Wrapper for `from_yaml`. ### `from_yaml` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L335-L342) @classmethod from_yaml( file_path: str ) ### `get` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L142-L144) get( key, value=None ) Accesses through built-in dictionary get method. ### `lock` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L204-L206) lock() Makes the ParamsDict immutable. ### `override` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L165-L181) 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` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/base_config.py#L326-L333) replace( **kwargs ) Overrides/returns a unlocked copy with the current config unchanged. ### `validate` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L222-L329) 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__` [View source](https://github.com/tensorflow/models/blob/v2.20.0/official/modeling/hyperparams/params_dict.py#L138-L140) __contains__( key ) Implements the membership test operator. ### `__eq__` __eq__( other ) Return self==value.


| ## Class Variables || |---|---| | IMMUTABLE_TYPES | `(, , , , )` | | RESERVED_ATTR | `['_locked', '_restrictions']` | | SEQUENCE_TYPES | `(, )` | | allow_tpu_summary | `False` | | best_checkpoint_eval_metric | `''` | | best_checkpoint_export_subdir | `''` | | best_checkpoint_metric_comp | `'higher'` | | checkpoint_interval | `1000` | | continuous_eval_timeout | `3600` | | default_params | `None` | | eval_tf_function | `True` | | eval_tf_while_loop | `False` | | loss_upper_bound | `1000000.0` | | max_to_keep | `5` | | preemption_on_demand_checkpoint | `True` | | recovery_begin_steps | `0` | | recovery_max_trials | `0` | | restrictions | `None` | | steps_per_loop | `1000` | | summary_interval | `1000` | | train_steps | `0` | | train_tf_function | `True` | | train_tf_while_loop | `True` | | validation_interval | `1000` | | validation_steps | `-1` | | validation_summary_subdir | `'validation'` |