pytorch biggraph

We have hosted the application pytorch biggraph in order to run this application in our online workstations with Wine or directly.

Quick description about pytorch biggraph:

PyTorch-BigGraph (PBG) is a system for learning embeddings on massive graphs�think billions of nodes and edges�using partitioning and distributed training to keep memory and compute tractable. It shards entities into partitions and buckets edges so that each training pass only touches a small slice of parameters, which drastically reduces peak RAM and enables horizontal scaling across machines. PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions, negative sampling strategies, and typed entities, making it suitable for link prediction and retrieval. Its training loop is built for throughput: asynchronous I/O, memory-mapped tensors, and lock-free updates keep GPUs and CPUs fed even at extreme scale. The toolkit includes evaluation metrics and export tools so learned embeddings can be used in downstream nearest-neighbor search, recommendation, or analytics. In practice, PBG�s design lets practitioners train high-quality graph embeddings.

Features:

• Partitioned training for billion-scale graphs
• Multi-relation scoring for knowledge graph link prediction
• Efficient negative sampling and edge bucketing
• Export and evaluation utilities for ANN and downstream tasks
• Asynchronous I/O with memory-mapped tensors
• Distributed, multi-machine training with simple orchestration

Programming Language: Python.
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