hanxiao/bert-as-service
Bert As Service
This project is a high-performance BERT embedding service and inference server designed to map text sequences into fixed-length numerical vectors. It functions as a machine learning microservice and distributed model server that decouples request handling from heavy computation.
The system utilizes a ZeroMQ messaging infrastructure to provide low-latency communication between distributed clients and the inference server. It incorporates server-side batch processing and GPU workload scaling to maximize hardware utilization and manage high request volumes.
The platform supports semantic search infrastructure by generating cross-modal embeddings for both text and images within a shared vector space. This enables cross-modal search, content relevance ranking, and the re-ranking of results based on the semantic alignment between visual content and text descriptions.
The service can be deployed as an elastic microservice accessible via gRPC, HTTP, or WebSocket protocols, featuring non-blocking duplex streaming for handling large datasets.
Features
- Model Serving & Deployment - Functions as a high-performance infrastructure for hosting and serving trained ML models for real-time inference.
- Multi-Modal Search Engines - Enables retrieval of matching images for text queries and vice versa by calculating mathematical distance in a shared vector space.
- Cross-Modal Representations - Transforms images and text into a shared latent space to enable cross-modal vectorization.
- Transformer Embedding Extraction - Uses deep bidirectional encoder representations to map text sequences into fixed-length dense vector embeddings.
- Image-Text Match Ranking - Scores the likelihood of matches between images and multiple sentences to re-rank search results for higher accuracy.
- Joint Embedding Spaces - Implements a shared mathematical space where text and image modalities are mapped for direct semantic comparison.
- BERT Embedding Servers - Operates as a high-performance server mapping text sequences to vectors using BERT models.
- Distributed Model Servers - Implements a distributed model server that decouples request handling from heavy GPU computation.
- Inference Optimizations - Optimizes inference by utilizing server-side batching and asynchronous messaging to maximize GPU throughput.
- Multi-modal Embedding Generation - Converts text and images into a unified high-dimensional vector space for cross-modal retrieval.
- Retrieval Re-ranking - Improves retrieval accuracy by adjusting search result ordering based on joint likelihood scores between images and text.
- Text Embedding Generators - Generates fixed-length vector representations of variable-length text for semantic similarity and clustering.
- Vector Embeddings - Converts multimodal inputs into numerical vector embeddings for fast mathematical comparison.
- Inference Batching - Groups individual requests into optimized batches to maximize GPU throughput during inference.
- Embedding Generation - Transforms text and image inputs into high-dimensional representations using pre-trained neural models.
- AI-Based Relevance Ranking - Calculates similarity scores between images and text using learned embeddings to determine semantic relevance.
- Model Inference Microservices - Provides a scalable microservice architecture to expose neural models via gRPC, HTTP, and WebSockets.
- ML Model Microservices - Deploys neural models as scalable network services to provide real-time inference capabilities.
- Decoupled Request Handling - Decouples network request handling from heavy model computation to manage simultaneous inference calls.
- Microservice Architectures - Employs a scalable microservice architecture to decouple request handling from heavy neural model computation.
- Single-GPU Scaling - Distributes multiple models across a single GPU using automatic load balancing to maximize hardware utilization.
- Multi-Protocol Interfaces - Supports exposing neural models via gRPC, HTTP, and WebSocket protocols for optimized and secure network traffic.
- Inference Scaling - Implements an elastic microservice architecture that automatically adjusts capacity to manage high traffic volumes for model inference.
- Semantic Vector Search - Provides the infrastructure for generating vector embeddings to enable semantic search and similarity analysis.
- Visual Content Analysis - Ranks text descriptions against images to determine visual features, object counts, and spatial relationships.
- Asynchronous Message Passing - Utilizes ZeroMQ for high-performance asynchronous message passing between distributed clients and the server.
- Low Latency Messaging - Provides a low-latency messaging infrastructure using ZeroMQ to coordinate distributed requests.
- Remote Procedure Calls - Exposes complex machine learning models as network-accessible services using remote procedure call patterns for distributed clients.
- Non-Blocking Duplex Streaming - Features non-blocking duplex streaming to efficiently handle long-running tasks and large datasets.
- Duplex Request-Response Streaming - Features non-blocking duplex streaming for requests and responses to handle large datasets efficiently.
- Concurrency Models - Provides a thread-safe concurrency model that separates network request handling from model inference to support multiple simultaneous calls.
- Model Utilities - Service for mapping sentences to fixed-length vectors using BERT.
- Natural Language Processing - Mapping sentences to vectors using BERT.
