# bytedance/monolith

A Lightweight Recommendation System

Repository: https://github.com/bytedance/monolith
Canonical: https://ross.abutalabs.com/products/bytedance-monolith
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2025-10-13T02:39:09+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 46, release rhythm 35, longevity 100
- inputs: {"age_days": 1434, "days_push": 325, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, 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 9298, forks 719 (observed 2026-08-28T04:10:30.428685+00:00)

## What it is
Monolith is a deep learning framework built on TensorFlow for large-scale recommendation modeling. It provides collisionless embedding tables for unique id feature representations and supports real-time training to capture emerging user interests.

## Use cases
- build large-scale recommendation systems
- train models with collisionless embedding tables
- run real-time training for recommendation models
- serve recommendation models in production
- distributed async training of deep learning models

## When to choose
- you need a specialized framework for large-scale recommendation modeling
- you want collisionless embedding tables for id features
- you need real-time training to capture latest user interests
- you are building on TensorFlow infrastructure

## When to avoid
- you need a general-purpose deep learning framework outside recommendations
- you require compilation on platforms other than Linux
- you want a lightweight solution without Bazel build complexity

## Facets
- artifact type: framework
- maturity: active
- function: deep-learning, machine-learning, llm-training
- domain: machine-learning, deep-learning
- platform: python
- tags: recommendation-system, tensorflow, embedding-tables, real-time-training, collaborative-filtering, distributed-training, recommendation-systems, linux

## Member repositories
- bytedance/monolith (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:30.428685+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 2026-08-29T17:22:31.724247+00:00, confidence not recorded.
  - readme: https://github.com/bytedance/monolith (fetched 2026-08-28T04:10:30.428685+00:00, sha f6f773da27da)
- Data as of 2026-08-30T08:39:29.467469+00:00.