# cxxnet

move forward to https://github.com/dmlc/mxnet

Repository: https://github.com/dmlc/cxxnet
Canonical: https://ross.abutalabs.com/products/cxxnet
Language: C++
License: NOASSERTION
License Family: other
Archived: true
Last push: 2015-09-29T03:57:26+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4584, "days_push": 3991, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1024, forks 406 (observed 2026-08-28T04:03:16.472837+00:00)

## What it is
ps-lite is a lightweight, efficient C++ implementation of the parameter server framework for distributed machine learning. It exposes simple Push/Pull/Wait APIs for key-value communication between worker and server nodes, with zero-copy transfers and user-defined server-side handles.

## Use cases
- synchronize model parameters across distributed training workers
- build distributed deep learning systems like MXNet or BytePS
- run distributed sparse logistic regression or factorization machines
- push gradients to server nodes and pull updated weights
- compress communication with user-defined filters
- implement custom server-side update logic for KV stores

## When to choose
- you need a minimal, embeddable parameter server layer in C++ for distributed ML
- you are building or extending distributed training frameworks that communicate via key-value push/pull
- you need high-performance, zero-copy asynchronous communication between workers and servers

## When to avoid
- you want a full-featured distributed training framework rather than a communication layer
- your project is Python-only and you prefer higher-level APIs like those in MXNet or PyTorch DDP
- you need actively developed features; the project is largely in maintenance mode

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, llm-training, networking, concurrency
- domain: machine-learning, deep-learning, microservices, large-language-models
- platform: cpp, cross-platform
- tags: parameter-server, distributed-training, kvstore, zero-copy, async-communication, dmlc, linux, macos

## Member repositories
- dmlc/cxxnet (main) score 10
- dmlc/ps-lite (infra) score 64
- dmlc/mshadow (backend) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:16.472837+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:29:42.659594+00:00, confidence not recorded.
  - readme: https://github.com/dmlc/cxxnet (fetched 2026-08-28T04:03:16.472837+00:00, sha 1ccbcf4ab3a1)
- Data as of 2026-08-30T08:39:29.467469+00:00.