# D-X-Y/Awesome-AutoDL

Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)

Repository: https://github.com/D-X-Y/Awesome-AutoDL
Canonical: https://ross.abutalabs.com/products/awesome-autodl
Language: Python
License: MIT
License Family: permissive
Topics: neural-architecture-search, nas, automl, deep-learning, awesome, autodl, hyper-parameter-optimization
Last push: 2022-09-26T01:35:49+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2879, "days_push": 1438, "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 2342, forks 318 (observed 2026-08-28T04:06:39.111136+00:00)

## What it is
A curated awesome-list of automated deep learning (AutoDL) resources, covering neural architecture search, hyperparameter optimization, libraries, benchmarks, blogs, and surveys. It also includes an in-depth analysis of the field and a companion PyPI package for paper analysis.

## Use cases
- find papers on neural architecture search
- learn about automated deep learning
- discover NAS benchmark datasets
- compare AutoML libraries like NNI and AutoGluon
- research hyperparameter optimization methods
- find surveys on AutoDL

## When to choose
- you need a starting point for researching NAS or AutoML
- you want a curated bibliography of AutoDL papers by venue and year
- you are looking for NAS benchmarks and libraries

## When to avoid
- you need a runnable AutoML tool rather than a resource list
- you need actively maintained NAS benchmarks (last release 2022)
- you want production AutoML functionality out of the box

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, developer-tools
- domain: deep-learning, machine-learning, awesome-lists, tutorials
- platform: python, cross-platform
- tags: awesome-list, neural-architecture-search, nas, automl, hyperparameter-optimization, curated-resources, papers

## Member repositories
- D-X-Y/Awesome-AutoDL (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:39.111136+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 2026-08-30T02:37:16.468890+00:00, confidence not recorded.
  - readme: https://github.com/D-X-Y/Awesome-AutoDL (fetched 2026-08-28T04:06:39.111136+00:00, sha a2af8bad7b63)
  - registry_pypi: https://pypi.org/pypi/awesome-autodl/json (fetched 2026-08-29T10:17:46.715040+00:00, sha b50175adf6b8)
- Data as of 2026-08-30T08:39:29.467469+00:00.