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hello, I finished the first run impl of the tzrec based rec training. thanks for the help along the way. one thing I don't understand is, I tried to use embedding group to manage the tables, and I tried build the features as IdFeature and lookupfeature, but no matter how I configure my embedding_dim in the config, the table dimension will be 4 when I check the sharding plan. is this correct? please see my embedding config below.
feature_configs = []
# Initialize RawFeature instances for each feature
for emb_bag_name, emb_bag_config in embedding_bag_configs.items():
feature_names = emb_bag_config.feature_names
for feature_name in feature_names:
feature_configs.append(
feature_pb2.FeatureConfig(
lookup_feature=feature_pb2.LookupFeature(
feature_name=feature_name,
embedding_name=emb_bag_config.name,
embedding_dim=128,
pooling='mean',
num_buckets=emb_bag_config.num_embeddings
)
)
)
# Create features
features = create_features(feature_configs)
# Define feature groups
feature_groups = [
model_pb2.FeatureGroupConfig(
group_name="wide",
feature_names=embedding_features,
group_type=model_pb2.FeatureGroupType.WIDE,
),
]
# Initialize EmbeddingGroup
embedding_group = EmbeddingGroup(features, feature_groups, device=device)
return embedding_group, embedding_features
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