quaterion.train.cache package

Submodules

• quaterion.train.cache.cache_config module
• quaterion.train.cache.cache_encoder module
• quaterion.train.cache.cache_model module
• quaterion.train.cache.cache_train_collator module
• quaterion.train.cache.in_memory_cache_encoder module

Module contents

class CacheConfig(cache_type: ~typing.Optional[~quaterion.train.cache.cache_config.CacheType] = CacheType.AUTO, mapping: ~typing.Dict[str, ~quaterion.train.cache.cache_config.CacheType] = <factory>, key_extractors: ~typing.Union[KeyExtractorType, ~typing.Dict[str, KeyExtractorType]] = <factory>, batch_size: ~typing.Optional[int] = 32, num_workers: ~typing.Optional[int] = None, save_dir: ~typing.Optional[str] = None)

Bases: object

Determine cache settings.

This class should be passed to configure_caches()

batch_size: Optional[int] = 32

Batch size to be used in CacheDataLoader during caching process. It does not affect others training stages.

cache_type: Optional[CacheType] = 'auto'

Cache type used for cacheable encoders not set in mapping

key_extractors: Union[KeyExtractorType, Dict[str, KeyExtractorType]]

Mapping of encoders to key extractor functions required to cache non-hashable objects.

mapping: Dict[str, CacheType]

Mapping of encoder_name to CacheType

num_workers: Optional[int] = None

Num of workers to be used in CacheDataLoader during caching process. It does not affect others training stages.

save_dir: Optional[str] = None

If provided, cache fill be saved to the given directory and re-used between launches

class CacheType(value)

Bases: str, Enum

Available tensor devices to be used for caching.

AUTO = 'auto'

Use CUDA if it is available, else use CPU.

CPU = 'cpu'

Tensors device is CPU.

GPU = 'gpu'

Tensors device is GPU.

NONE = 'none'

Disable cache