# huggingface/knockknock

🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

Repository: https://github.com/huggingface/knockknock
Canonical: https://ross.abutalabs.com/products/knockknock
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, natural-language-processing, nlproc, nlp, computer-vision, cv, python, python36, deep-learning, neural-networks, train
Last push: 2023-06-23T10:52:46+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2723, "days_push": 1167, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2828, forks 225 (observed 2026-08-28T04:07:24.232853+00:00)

## What it is
A small Python library that sends notifications when machine learning training completes or crashes, requiring only a decorator on your main function. It supports twelve notification channels including email, Slack, Telegram, Discord, and Microsoft Teams.

## Use cases
- get notified when my model training finishes
- alert me if training crashes mid-run
- send slack notification when python script ends
- notify training completion on telegram
- monitor long-running deep learning jobs
- add completion alerts to training script with minimal code

## When to choose
- you train ML models and want near-zero-effort completion or failure notifications
- you want a decorator-based integration with many messaging platforms

## When to avoid
- you need full ML experiment tracking or metrics dashboards
- you need actively developed tooling - the project sees only maintenance releases

## Facets
- artifact type: library
- maturity: maintenance
- function: monitoring, alerting
- domain: machine-learning, deep-learning, developer-tools
- platform: python, cross-platform
- tags: training-notifications, decorator, slack, telegram, discord, email-notifications, notification

## Member repositories
- huggingface/knockknock (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:24.232853+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 2026-08-30T07:38:12.071874+00:00, confidence not recorded.
  - readme: https://github.com/huggingface/knockknock (fetched 2026-08-28T04:07:24.232853+00:00, sha b3d8cb453ce0)
  - registry_pypi: https://pypi.org/pypi/knockknock/json (fetched 2026-08-29T09:53:26.566988+00:00, sha 09e942467696)
- Data as of 2026-08-30T08:39:29.467469+00:00.