cerebras.modelzoo.data.nlp.gpt.config.InferenceDataProcessorConfig#
- class cerebras.modelzoo.data.nlp.gpt.config.InferenceDataProcessorConfig(*args, **kwargs)[source]#
- data_processor#
- num_workers = 0#
- classmethod check_for_deprecated_fields()#
classmethod(function) -> method
Convert a function to be a class method.
A class method receives the class as implicit first argument, just like an instance method receives the instance. To declare a class method, use this idiom:
- class C:
@classmethod def f(cls, arg1, arg2, …):
…
It can be called either on the class (e.g. C.f()) or on an instance (e.g. C().f()). The instance is ignored except for its class. If a class method is called for a derived class, the derived class object is passed as the implied first argument.
Class methods are different than C++ or Java static methods. If you want those, see the staticmethod builtin.
- check_literal_discriminator_field(data)#
- copy(*, validate=True, **kwargs)#
- discriminator = 'data_processor'#
- property discriminator_value#
- classmethod get_orig_class()#
- get_orig_class_args(**kwargs)#
- model_copy(**kwargs)#
- model_post_init(context)#
- post_init(context)#
- prefetch_factor = 10#
- persistent_workers = False#
- drop_last = True#
similar to the PyTorch drop_last setting except that samples that when set to True, samples that would have been dropped at the end of one epoch are yielded at the start of the next epoch so that there is no data loss. This is necessary for a data ordering that is independent of the distributed setup being used.