🎉 Initial Release

RecIS (Recommendation Intelligence System) v1.0.0 is now officially released! This is a unified architecture deep learning framework designed specifically for ultra-large-scale sparse models, built on the PyTorch open-source ecosystem. It has been widely used in Alibaba advertising, recommendation, searching and other scenarios.

✨ New Features

Core Architecture

• ColumnIO: Data Reading
  • Supports distributed sharded data reading
  • Completes simple feature pre-computation during the reading phase
  • Assembles samples into Torch Tensors and provides data prefetching functionality
• Feature Engine: Feature Processing
  • Provides feature engineering and feature transformation processing capabilities, including Hash / Mod / Bucketize, etc.
  • Supports automatic operator fusion optimization strategies
• Embedding Engine: Embedding Management and Computing
  • Provides conflict-free, scalable KV storage embedding tables
  • Provides multi-table fusion optimization capabilities for better memory access performance
  • Supports feature elimination and admission strategies
• Saver: Parameter Saving and Loading
  • Provides sparse parameter storage and delivery capabilities in SafeTensors standard format
• Pipelines: Training Process Orchestration
  • Connects the above components and encapsulates training processes
  • Supports complex training workflows such as multi-stage (training/testing interleaved) and multi-objective computation

🛠️ Installation & Compatibility

System Requirements

• Python: 3.10+
• PyTorch: 2.4+
• CUDA: 12.4

Installation Methods

• Docker Installation: Pre-built Docker images for PyTorch 2.4.0/2.5.1/2.6.0
• Source Installation: Complete build system with CMake and setuptools

Dependencies

• torch>=2.4
• accelerate==0.29.2
• simple-parsing
• pyarrow (for ORC support)

📚 Documentation

• Complete English and Chinese documentation
• Quick start tutorials with CTR model examples
• Comprehensive API reference
• Installation guides for different environments
• FAQ and troubleshooting guides

📦 Package Structure

• Core Library: recis/ - Main framework code
• C++ Extensions: csrc/ - High-performance C++ implementations
• Documentation: docs/ - Comprehensive documentation in RST format
• Examples: examples/ - Practical usage examples
• Tools: tools/ - Data conversion and utility tools
• Tests: tests/ - Comprehensive test suite

🚀 Key Optimizations

Efficient Dynamic Embedding

The RecIS framework implements efficient dynamic embedding (HashTable) through a two-level storage architecture:

• IDMap: Serves as first-level storage, using feature ID as key and Offset as value
• EmbeddingBlocks:
  • Serves as second-level storage, continuous sharded memory blocks for storing embedding parameters and optimizer states
  • Supports dynamic sharding with flexible expansion capabilities
• Flexible Hardware Adaptation Strategy: Supports both GPU and CPU placement for IDMap and EmbeddingBlocks

Distributed Optimization

• Parameter Aggregation and Sharding:
  • During model creation phase, merges parameter tables with identical properties (dimensions, initializers, etc.) into one logical table
  • Parameters are evenly distributed across compute nodes
• Request Merging and Splitting:
  • During forward computation, merges requests for parameter tables with identical properties and computes sharding information with deduplication
  • Obtains embedding vectors from various compute nodes through All-to-All collective communication

Efficient Hardware Resource Utilization

• GPU Concurrency Optimization:
  • Supports feature processing operator fusion optimization, significantly reducing operator count and launch overhead
• Parameter Table Fusion Optimization:
  • Supports merging parameter tables with identical properties, reducing feature lookup frequency, significantly decreasing operator count, and improving memory space utilization efficiency
• Operator Implementation Optimization:
  • Operator implementations use vectorized memory access to improve memory utilization
  • Optimizes reduction operators through warp-level merging, reducing atomic operations and improving memory access utilization

🤝 Community & Support

• Open source under Apache 2.0 license
• Issue tracking and community support
• Active development by XDL Team

For detailed usage instructions, please refer to our documentation and quick start guide.