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Input config for training.

Inherits From: DataConfig, Config, ParamsDict

weights Sampling weights for each corresponding input_path. If used, then input_path must be a config with matching keys.

default_params Dataclass field
restrictions Dataclass field
input_path Dataclass field
tfds_name Dataclass field
tfds_split Dataclass field
global_batch_size Dataclass field
is_training Dataclass field
drop_remainder Dataclass field
shuffle_buffer_size Dataclass field
cache Dataclass field
cycle_length Dataclass field
block_length Dataclass field
deterministic Dataclass field
sharding Dataclass field
enable_tf_data_service Dataclass field
tf_data_service_address Dataclass field
tf_data_service_job_name Dataclass field
tfds_data_dir Dataclass field
tfds_as_supervised Dataclass field
tfds_skip_decoding_feature Dataclass field
enable_shared_tf_data_service_between_parallel_trainers Dataclass field
apply_tf_data_service_before_batching Dataclass field
trainer_id Dataclass field
seed Dataclass field
prefetch_buffer_size Dataclass field
dtype Dataclass field
decoder Dataclass field
parser Dataclass field
file_type Dataclass field



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Returns a dict representation of params_dict.ParamsDict.

For the nested params_dict.ParamsDict, a nested dict will be returned.


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Builds a config from the given list of arguments.


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Wrapper for from_yaml.


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Accesses through built-in dictionary get method.


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Makes the ParamsDict immutable.


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Override the ParamsDict with a set of given params.

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.


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Overrides/returns a unlocked copy with the current config unchanged.


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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 specfiies 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

  a1: 1
  a2: 2
    bb1: 10
    bb2: 20
    a1: 1
    a3: 3

one can define two restrictions like this ['a.a1 == b.ccc.a1', 'a.a2 <=']

What it enforces are

  • a.a1 = 1 == b.ccc.a1 = 1
  • a.a2 = 2 <= = 20

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.


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Implements the membership test operator.


IMMUTABLE_TYPES (<class 'str'>, <class 'int'>, <class 'float'>, <class 'bool'>, <class 'NoneType'>)
RESERVED_ATTR ['_locked', '_restrictions']
SEQUENCE_TYPES (<class 'list'>, <class 'tuple'>)
apply_tf_data_service_before_batching False
block_length 1
cache False
cycle_length None
decoder Instance of
default_params None
deterministic None
drop_remainder True
dtype 'bfloat16'
enable_shared_tf_data_service_between_parallel_trainers False
enable_tf_data_service False
file_type 'tfrecord'
global_batch_size 0
input_path ''
is_training False
parser Instance of
prefetch_buffer_size None
restrictions None
seed None
sharding True
shuffle_buffer_size 10000
tf_data_service_address None
tf_data_service_job_name None
tfds_as_supervised False
tfds_data_dir ''
tfds_name ''
tfds_skip_decoding_feature ''
tfds_split ''
trainer_id None
weights None