# alibaba/x-deeplearning

An industrial deep learning framework for high-dimension sparse data

Repository: https://github.com/alibaba/x-deeplearning
Canonical: https://ross.abutalabs.com/products/x-deeplearning
Language: PureBasic
License: Apache-2.0
License Family: permissive
Last push: 2024-09-25T14:42:15+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2821, "days_push": 707, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4304, forks 1025 (observed 2026-08-28T04:08:41.712643+00:00)

## What it is
X-DeepLearning (XDL) is an industrial deep learning framework from Alibaba optimized for high-dimension sparse data scenarios such as advertising, recommendation, and search. It includes a training engine, the Blaze inference engine, and a deep tree matching (TDM) serving engine.

## Use cases
- train deep learning models on high-dimensional sparse features
- build CTR prediction models for advertising
- streaming training with feature admission and eviction
- serve recommendation recall models with deep tree matching
- scale parameter servers for large sparse embeddings

## When to choose
- you train models on very large sparse feature spaces like ads or recommendations
- you need parameter server optimizations and streaming training features
- you want Alibaba's proven industrial deep learning stack

## When to avoid
- you need a general-purpose deep learning framework for dense data like images or text
- you want a framework with a large active community and frequent updates
- you need first-class GPU ecosystem support or modern tooling

## Facets
- artifact type: framework
- maturity: maintenance
- function: deep-learning, machine-learning, llm-training
- domain: deep-learning, machine-learning
- platform: cpp, python
- tags: sparse-data, recommendation-systems, parameter-server, streaming-training, industrial-ml, advertising, recommendation, linux

## Member repositories
- alibaba/x-deeplearning (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:41.712643+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 2026-08-29T18:21:49.507229+00:00, confidence not recorded.
  - readme: https://github.com/alibaba/x-deeplearning (fetched 2026-08-28T04:08:41.712643+00:00, sha ec43fe64f996)
- Data as of 2026-08-30T08:39:29.467469+00:00.