EmbCacheEmbeddingBagCollection

torch.optim.Adagrad: Adagrad optimizer.
torch.optim.Adam: Adam optimizer.
torch.optim.SGD: SGD optimizer.

Creates a single-server table object with pooling, hash mapping, and multi-level cache.

class EmbCacheEmbeddingBagCollection:
     def __init__(**kwargs):

Parameter	Data Type	Required/Optional	Description
tables	List[EmbCacheEmbeddingBagConfig\|EmbeddingConfig]	Mandatory	List of sparse table configuration files. The list length range is [1, 10000].
world_size	int	Mandatory	Size of world_size for distributed training. The value range is [1, 10000].
batch_size	int	Mandatory	Batch size. The value range is [1, 102400].
multi_hot_sizes	List[int]	Mandatory	List of multi-hot encoding sizes of each feature. The length of this parameter list must be the same as that of the tables list. The value range is [1, 10000]. The value range of the multi-hot encoding size in the list is [1, 102400].
is_weighted	bool	Optional	Only the default value False is supported.
need_accumulate_offset	bool	Optional	Whether to accumulate offsets. The default value is True.
device	torch.device	Optional	The default value is CPU. The compute device is the same as that of HashEmbeddingBagCollection.
embedding_optimizer_cls	Type[torch.optim.Optimizer]	Optional	Embedding optimizer type. The default value is torch.optim.Adagrad. The options are as follows: torch.optim.Adagrad: Adagrad optimizer. torch.optim.Adam: Adam optimizer. torch.optim.SGD: SGD optimizer.

Parent topic: Table Creation APIs