primeqa.ir.dense.dpr_top.torch_util.hypers_base.HypersBase#

class primeqa.ir.dense.dpr_top.torch_util.hypers_base.HypersBase#

Bases: object

This should be the base hyperparameters class, others should extend this.

Methods

`fill_from_args`	Fill this hyperparameter object from the command line.
`fill_from_config`	Fill this hyperparameter object from the config.
`from_dict`
`jsonl_instances`	Provides json objects for one pass over 'filename'.
`set_gradient_accumulation_steps`	when searching for bsize in hyperparameter tuning we need to update the gradient accumulation steps to stay within GPU memory constraints :return:
`set_seed`
`to_dict`

fill_from_args()#: Fill this hyperparameter object from the command line. :return:

fill_from_config(config)#: Fill this hyperparameter object from the config. :return:

jsonl_instances(filename: str, *, rand: Optional[random.Random], filter_out: Optional[Callable[[Dict], bool]] = None)#: Provides json objects for one pass over ‘filename’. Only instances for our global rank are returned :param filename: the file or directory of the jsonl dataset :param rand: the random.Random, should be seeded from hypers.seed, None if no shuffling :param filter_out: json objects are passed to this function, True means the instance is excluded :return: this is a generator, it yields json objects

set_gradient_accumulation_steps()#: when searching for bsize in hyperparameter tuning we need to update the gradient accumulation steps to stay within GPU memory constraints :return: