Home/Compare/nas-env vs autokeras

Comparison

nas-env vs autokeras

Verdict

Pick nas-env if nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license; pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.

Markdown twin · nas-env alternatives · autokeras alternatives

GraphCanon updated 2w

nas-env logo

nas-env

gomerudo/nas-env

31pushed May 4, 2020
vs
autokeras logo

autokeras

keras-team/autokeras

9.3kpushed Nov 25, 2025

Trust & integrity

Signalnas-envautokeras
Maintenance
Dormant (2282d since push)
As of 2w · github_public_v1
Slowing (251d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Personal account
As of 2w · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

nas-env
Simple OpenAI Gym environment for Neural Architecture Search (NAS)
autokeras
AutoML library for deep learning

Stars

nas-env
31
autokeras
9.3k

Forks

nas-env
3
autokeras
1.4k

Open issues

nas-env
0
autokeras
161

Language

nas-env
Python
autokeras
Python

Adopt for

nas-env
nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license.
autokeras
AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.

Persona

nas-env
-
autokeras
-

Runtime

nas-env
-
autokeras
-

License

nas-env
MIT
autokeras
Apache-2.0

Last pushed

nas-env
May 4, 2020
autokeras
Nov 25, 2025

Categories

nas-env
Model Training
autokeras
Developer Tools, Model Training

Trust and health

Maintenance

nas-env
Dormant (18%)
autokeras
Slowing (36%)

Days since push

nas-env
2282d
autokeras
251d

Open issues (now)

nas-env
0
autokeras
161

Owner type

nas-env
User
autokeras
Organization

Full report

autokeras
Trust report

Shared compatibility

  • Python · nas-env: Python runtime · autokeras: Python runtime

Choose nas-env if…

  • License: nas-env is MIT, autokeras is Apache-2.0.
  • Tags unique to nas-env: openai-gym, python, reinforcement-learning.
  • When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym

When NOT to use nas-env

  • If you require a fully documented package as documentation for nas-env remains under development
  • During production phases when stability is crucial because nas-env is still undergoing architectural changes

Choose autokeras if…

  • License: autokeras is Apache-2.0, nas-env is MIT.
  • Tags unique to autokeras: autodl, automl, deep-learning, keras.
  • Also covers Developer Tools.
  • When your project involves deep learning tasks requiring minimal manual intervention in designing models.

When NOT to use autokeras

  • When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible.
  • If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: nas-env 31 · autokeras 9.3k (synced Aug 4, 2026).

Common questions

What is the difference between nas-env and autokeras?
nas-env: Simple OpenAI Gym environment for Neural Architecture Search (NAS). autokeras: AutoML library for deep learning. See the comparison table for live GitHub stats and shared categories.
When should I choose nas-env over autokeras?
Choose nas-env over autokeras when License: nas-env is MIT, autokeras is Apache-2.0; Tags unique to nas-env: openai-gym, python, reinforcement-learning; When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym.
When should I choose autokeras over nas-env?
Choose autokeras over nas-env when License: autokeras is Apache-2.0, nas-env is MIT; Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.
When should I avoid nas-env?
If you require a fully documented package as documentation for nas-env remains under development During production phases when stability is crucial because nas-env is still undergoing architectural changes
When should I avoid autokeras?
When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
Is nas-env or autokeras more popular on GitHub?
autokeras has more GitHub stars (9,328 vs 31). Stars measure visibility, not whether either tool fits your constraints.
Are nas-env and autokeras open source?
Yes - both are open-source projects on GitHub (nas-env: MIT, autokeras: Apache-2.0).
Where can I find alternatives to nas-env or autokeras?
GraphCanon lists graph-backed alternatives at nas-env alternatives and autokeras alternatives (nas-env markdown twin, autokeras markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, nas-env or autokeras?
nas-env: Dormant. autokeras: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for nas-env and autokeras?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: nas-env trust report; autokeras trust report.

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