Plato is a high-performance distributed graph computation and machine learning framework designed for large-scale applications, particularly within WeChat. It aims to provide efficient processing of graph data for researchers and engineers working with massive datasets.

How It Works

Plato leverages a distributed architecture for parallel processing of graph data. It builds upon foundational work from Gemini and KnightKing, incorporating design principles from Gemini's dual-mode engine and KnightKing's walk engine. This approach allows for efficient execution of graph algorithms, including PageRank and random walks, in a distributed environment.

Quick Start & Requirements

• Install dependencies: sudo ./docker/install-dependencies.sh
• Build static libraries: ./3rdtools.sh distclean && ./3rdtools.sh install
• Build Plato: ./build.sh
• Run locally: ./scripts/run_pagerank_local.sh
• Run in production: Requires an MPI-compatible cluster (e.g., using Hydra) and HDFS access. A sample submit script is provided.
• Tested on x86_64 cluster with CentOS 7.0.

Highlighted Details

• High-performance distributed graph computation.
• Supports graph machine learning tasks.
• Influenced by Gemini and KnightKing projects for core engine design.
• Includes algorithms like PageRank and random walks.

Maintenance & Community

The project is developed by Tencent. Contact is available via plato@tencent.com.

Licensing & Compatibility

The README does not explicitly state the license. Given its origin at Tencent, users should verify licensing for commercial or closed-source integration.

Limitations & Caveats

The framework was developed and tested on CentOS 7.0, and while theoretically portable, other Linux distributions may require adjustments. Production deployment necessitates specific cluster and HDFS configurations.