alpa-projects/alpa

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

3178 stars · 361 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Alpa is a Python system for training and serving large-scale neural networks by automatically parallelizing single-device code across distributed clusters using data, operator, and pipeline parallelism. It is built on JAX, XLA, and Ray, and is now unmaintained as a research artifact with its core algorithm merged into XLA.

Use cases

• train multi-billion parameter models on a distributed cluster
• automatically parallelize single-device JAX training code
• serve large language models like OPT-175B across multiple devices
• run pipeline and operator parallelism without manual sharding
• scale deep learning training linearly on clusters

When to choose

• you need automatic parallelization for JAX-based large model training and can work with a research artifact
• you want to study or extend auto-sharding and auto-parallelization algorithms
• you need to serve very large transformer models with a Hugging Face-style interface

When to avoid

• you need actively maintained software with bug fixes and support
• you use PyTorch or non-JAX frameworks
• you want production distributed training - use the auto-sharding code merged into XLA instead

Facets

library · maturity abandoned

machine-learning llm-training llm-inference compiler deep-learning machine-learning large-language-models microservices python cloud auto-parallelization jax distributed-training model-parallelism pipeline-parallelism research-artifact linux gpu

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