Home/Compare/nas-env vs accelerate

Comparison

nas-env vs accelerate

Verdict

Pick nas-env if nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license; pick accelerate if tool: accelerate.

Markdown twin · nas-env alternatives · accelerate alternatives

GraphCanon updated 2w

nas-env logo

nas-env

gomerudo/nas-env

31pushed May 4, 2020
vs
accelerate logo

accelerate

huggingface/accelerate

9.8kpushed Jul 30, 2026

Trust & integrity

Signalnas-envaccelerate
Maintenance
Dormant (2282d since push)
As of 2w · github_public_v1
Very active (3d 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)
accelerate
A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support.

Stars

nas-env
31
accelerate
9.8k

Forks

nas-env
3
accelerate
1.4k

Open issues

nas-env
0
accelerate
105

Language

nas-env
Python
accelerate
Python

Adopt for

nas-env
nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license.
accelerate
Tool: accelerate

Persona

nas-env
-
accelerate
-

Runtime

nas-env
-
accelerate
-

License

nas-env
MIT
accelerate
Apache-2.0

Last pushed

nas-env
May 4, 2020
accelerate
Jul 30, 2026

Categories

nas-env
Model Training
accelerate
Inference & Serving, Model Training

Trust and health

Maintenance

nas-env
Dormant (18%)
accelerate
Very active (96%)

Days since push

nas-env
2282d
accelerate
3d

Open issues (now)

nas-env
0
accelerate
105

Owner type

nas-env
User
accelerate
Organization

Full report

accelerate
Trust report

Shared compatibility

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

Choose nas-env if…

  • License: nas-env is MIT, accelerate is Apache-2.0.
  • Tags unique to nas-env: neural-architecture-search, 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 accelerate if…

  • License: accelerate is Apache-2.0, nas-env is MIT.
  • Tags unique to accelerate: deepspeed, fsdp, mixed precision, pytorch.
  • Also covers Inference & Serving.
  • Easy mixed-precision support for PyTorch models

When NOT to use accelerate

  • Non-PyTorch projects do not benefit from this tool
  • Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow
  • Limited to Python environments compatible with PyTorch 1.10.0+

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 · accelerate 9.8k (synced Aug 4, 2026).

Common questions

What is the difference between nas-env and accelerate?
nas-env: Simple OpenAI Gym environment for Neural Architecture Search (NAS). accelerate: A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support.. See the comparison table for live GitHub stats and shared categories.
When should I choose nas-env over accelerate?
Choose nas-env over accelerate when License: nas-env is MIT, accelerate is Apache-2.0; Tags unique to nas-env: neural-architecture-search, openai-gym, python, reinforcement-learning; When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym.
When should I choose accelerate over nas-env?
Choose accelerate over nas-env when License: accelerate is Apache-2.0, nas-env is MIT; Tags unique to accelerate: deepspeed, fsdp, mixed precision, pytorch; Also covers Inference & Serving; Easy mixed-precision support for PyTorch 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 accelerate?
Non-PyTorch projects do not benefit from this tool Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow Limited to Python environments compatible with PyTorch 1.10.0+
Is nas-env or accelerate more popular on GitHub?
accelerate has more GitHub stars (9,803 vs 31). Stars measure visibility, not whether either tool fits your constraints.
Are nas-env and accelerate open source?
Yes - both are open-source projects on GitHub (nas-env: MIT, accelerate: Apache-2.0).
Where can I find alternatives to nas-env or accelerate?
GraphCanon lists graph-backed alternatives at nas-env alternatives and accelerate alternatives (nas-env markdown twin, accelerate 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 accelerate?
nas-env: Dormant. accelerate: Very active. 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 accelerate?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: nas-env trust report; accelerate trust report.

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