Qihoo360/XLearning-XDML
🏗️ FrameworkQihoo360
A high-performance distributed machine learning framework designed for large-scale data processing on Hadoop and Spark ecosystems.
XLearning-XDML is an advanced distributed machine learning platform engineered to address the challenges of training models on massive, multi-terabyte datasets. At its core, the framework employs a parameter server architecture, which is essential for synchronizing model weights across distributed nodes. It is deeply integrated with the Apache Hadoop ecosystem, allowing it to utilize existing data infrastructure while providing native support for Spark and Kudu for data ingestion and storage.
The framework is written in Scala, ensuring high performance and type safety. Key features include fault-tolerant task scheduling, efficient communication protocols for gradient updates, and flexible support for various machine learning algorithms. By abstracting the complexities of distributed computing, XLearning-XDML allows data scientists and engineers to focus on model architecture rather than infrastructure management. It is particularly well-suited for organizations that require a scalable, stable, and high-throughput environment for training deep learning or traditional ML models in production settings.
💡Highlights
- ├─Parameter server architecture
- ├─Native Hadoop and Spark integration
- └─High-throughput distributed training
🎯For
- ├─Data Engineers
- └─Machine Learning Researchers