Angel-ML/angel

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

6787 stars · 1589 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Angel is a high-performance distributed parameter server for large-scale machine learning and graph computing, developed by Tencent and Peking University. It partitions model parameters across parameter-server nodes and integrates with Spark via Spark on Angel, running on Yarn clusters.

Use cases

• train large-scale machine learning models on a Spark cluster
• run distributed parameter server for high-dimensional models
• perform online learning with streaming data on Spark
• run graph computing algorithms at scale
• accelerate Spark ML workloads with PS Service
• train models on Yarn with fault-tolerant parameter synchronization

When to choose

• you need a parameter server for very high-dimensional models on Hadoop/Yarn
• you want to boost Spark MLlib with distributed model storage and updates
• you run large-scale graph algorithms alongside machine learning pipelines
• you need a battle-tested system tuned for industrial big data workloads

When to avoid

• you need deep learning training on GPUs rather than CPU-based parameter server workloads
• your project uses Python-native ecosystems like PyTorch or TensorFlow distributed training
• you want a lightweight single-node ML library
• you cannot deploy on Yarn or a JVM-based cluster environment

Facets

library · maturity maintenance

machine-learning llm-training streaming machine-learning big-data microservices graph-processing jvm parameter-server spark-on-angel yarn high-dimensional-models online-learning graph-computing tencent linux docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Angel-ML/angel")