Explore our collection of open-source projects designed to advance AI technology and make it accessible to everyone.
A high-performance Python library for sparse parameter server using RocksDB, written in C++. Designed for efficient embedding operations in large-scale machine learning systems.
# Python usage example
import damo_embedding as de
# Initialize embedding
emb = de.Embedding(
dim=128, path="/tmp/db"
)
# Set values
emb.set("key1", [1.0] * 128)
emb.set("key2", [2.0] * 128)
# Get values
val1 = emb.get("key1")
val2 = emb.get("key2")Feature processing module in C++ with Python bindings, designed for both model training and inference scenarios. Offers consistent feature processing across different programming languages.
Example implementation and usage demonstrations of the Luban feature processing library. Includes practical examples and integration patterns.
Torch model inference engine optimized for production environments. Built with damo-embedding integration for efficient model serving.
Low-cost data storage and retrieval tool based on SQLite, etcd, and S3/OSS. Designed for efficient handling of large-scale feature data.
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