Topics / #distributed-computing

Distributed-computing projects on GitHub

Frameworks for distributed training and enterprise distributed systems.

As of , the most-starred Distributed-computing projects on GitHub are ray (44.1K stars), ColossalAI (41.4K stars) and hadoop (15.7K stars). GitHub.Locker tracks 12 Distributed-computing repositories with 177.6K stars combined. The fastest-growing on October 16 was ray, with +12 stars in a day.

12Projects
177.6KStars in total
+30Stars in a day
Browse in the catalog →github.com/topics/distributed-computing ↗

Hot right now

Most stars gained in a day · Oct 16

+12

ray

ray-project/ray · ★ 44.1K

Ray is an open-source framework for scaling AI and Python applications: a core distributed runtime plus a set of AI libraries that simplify ML compute.

+4

petals

bigscience-workshop/petals · ★ 10.6K

Petals lets you run big open LLMs at home, BitTorrent-style: you join a public swarm that serves models like Llama 3.1 (405B), Mixtral (8x22B), Falcon (40B+) and BLOOM (176B) for inference and fine-tuning right from your desktop or Google Colab, up to 10x faster than offloading.

+3

ColossalAI

hpcaitech/ColossalAI · ★ 41.4K

Colossal-AI is an open-source system for training and running large AI models more cheaply, faster and with less hardware, by providing a toolbox of parallelism and memory-optimization techniques.

+3

beta9

beam-cloud/beta9 · ★ 1.8K

An open-source runtime for serverless AI workloads — it runs Python code in autoscaling GPU containers that scale to zero. It's also the engine powering Beam, a fully managed cloud.

The all-time top

12 projects · by stars

  1. #1 · ray-project/ray

    ray

    Ray is an open-source framework for scaling AI and Python applications: a core distributed runtime plus a set of AI libraries that simplify ML compute.

    44.1K
  2. #2 · hpcaitech/ColossalAI

    ColossalAI

    Colossal-AI is an open-source system for training and running large AI models more cheaply, faster and with less hardware, by providing a toolbox of parallelism and memory-optimization techniques.

    41.4K
  3. #3 · apache/hadoop

    hadoop

    Apache Hadoop is the open-source big data platform for reliable, distributed storage and processing of very large data sets across clusters of machines.

    15.7K
  1. 4

    Qix

    15.2Kty4z2008/Qix

    Qix is a curated collection of learning resources covering Machine Learning, Deep Learning, PostgreSQL, Distributed Systems, Node.js and Golang, maintained by one author sharing notes and translations.

  2. 5

    dask

    13.9Kdask/dask · Python

    Dask is a flexible parallel computing library for analytics — it runs Python data workloads as scheduled tasks so they can use many cores or machines instead of a single process.

  3. 6

    akka-core

    13.3Kakka/akka-core · Scala

    Akka is a platform for building highly responsive, resilient and scalable distributed services; this repo holds its core libraries. It is used for transactional, analytical, agentic-AI, digital-twin, IoT and edge-to-cloud systems.

  4. 7

    petals

    10.6Kbigscience-workshop/petals · Python

    Petals lets you run big open LLMs at home, BitTorrent-style: you join a public swarm that serves models like Llama 3.1 (405B), Mixtral (8x22B), Falcon (40B+) and BLOOM (176B) for inference and fine-tuning right from your desktop or Google Colab, up to 10x faster than offloading.

  5. 8

    DeepEP

    10.3Kdeepseek-ai/DeepEP · Cuda

    DeepEP is a high-performance communication library for MoE (mixture-of-experts) models, providing fast all-to-all GPU kernels for expert-parallel dispatch and combine operations used in ML training and inference.

  6. 9

    PySyft

    10KOpenMined/PySyft · Python

    PySyft (the 'syft' package) lets you perform data science on data that stays on someone else's server, so researchers can get answers from data without needing a copy or direct access to it.

  7. 10

    beta9

    1.8Kbeam-cloud/beta9 · Go

    An open-source runtime for serverless AI workloads — it runs Python code in autoscaling GPU containers that scale to zero. It's also the engine powering Beam, a fully managed cloud.

  8. 11

    enterprise-system-design

    642DrHazemAli/enterprise-system-design

    This is a source-grounded engineering course and architectural reference for designing enterprise systems that survive real traffic, partial failure, security review, and changing requirements, covering distributed systems, AI workloads, cybersecurity, reliability, cloud, HPC, and edge infrastructure built on Azure.

  9. 12

    pooled

    589Nehanth/pooled · JavaScript

    SwarmLLM pools the WebGPU power of several devices open in browser tabs to run large open models that no single device could hold on its own, splitting the model into layer slices and passing a small activation vector between devices over WebRTC.

Often tagged together