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Comparison

keras vs torchtune

Verdict

Pick keras when license: keras is Apache-2.0, torchtune is BSD-3-Clause; pick torchtune when license: torchtune is BSD-3-Clause, keras is Apache-2.0.

Markdown twin · keras alternatives · torchtune alternatives

GraphCanon updated today

keras logo

keras

keras-team/keras

★ 64kpushed Jul 7, 2026
vs
torchtune logo

torchtune

meta-pytorch/torchtune

★ 5.8kpushed Jul 10, 2026

Trust & integrity

Signalkerastorchtune
Maintenance
Very active (4d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No criticals
As of today · osv@v1
No lockfile
As of today · none
keras trust reporttorchtune trust reportTrust methodology

Tagline

keras
Deep Learning for humans
torchtune
PyTorch native post-training library

Stars

keras
64k
torchtune
5.8k

Forks

keras
20k
torchtune
735

Open issues

keras
228
torchtune
445

Language

keras
Python
torchtune
Python

Adopt for

keras
-
torchtune
-

Persona

keras
-
torchtune
-

Runtime

keras
-
torchtune
-

License

keras
Apache-2.0
torchtune
BSD-3-Clause

Last pushed

keras
Jul 7, 2026
torchtune
Jul 10, 2026

Categories

keras
Model Training, Inference & Serving
torchtune
LLM Frameworks, Model Training, Inference & Serving

Trust and health

Days since push

keras
4d
torchtune
0d

Open issues (now)

keras
228
torchtune
445

Security scan

keras
No criticals
torchtune
No lockfile

Full report

keras
Trust report
torchtune
Trust report

Shared compatibility

  • Python · keras: Python runtime · torchtune: Python runtime

Choose keras if…

  • License: keras is Apache-2.0, torchtune is BSD-3-Clause.
  • Tags unique to keras: data-science, neural-networks, deep-learning, machine-learning.
  • More GitHub stars (64k vs 5.8k) - visibility, not fit.

When NOT to use keras

  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
  • Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
GitHubDetails

Choose torchtune if…

  • License: torchtune is BSD-3-Clause, keras is Apache-2.0.
  • Also covers LLM Frameworks.
  • More recently updated (last pushed Jul 10, 2026).

When NOT to use torchtune

  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
  • Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
GitHubDetails

Explore

keras trust report →torchtune trust report →Model Training category →Inference & Serving category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

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

GitHub stars on cards: keras 64k · torchtune 5.8k (synced Jul 11, 2026).

Common questions

What is the difference between keras and torchtune?
keras: Deep Learning for humans. torchtune: PyTorch native post-training library. See the comparison table for live GitHub stats and shared categories.
When should I choose keras over torchtune?
Choose keras over torchtune when License: keras is Apache-2.0, torchtune is BSD-3-Clause; Tags unique to keras: data-science, neural-networks, deep-learning, machine-learning; More GitHub stars (64k vs 5.8k) - visibility, not fit.
When should I choose torchtune over keras?
Choose torchtune over keras when License: torchtune is BSD-3-Clause, keras is Apache-2.0; Also covers LLM Frameworks; More recently updated (last pushed Jul 10, 2026).
When should I avoid keras?
Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
When should I avoid torchtune?
LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Is keras or torchtune more popular on GitHub?
keras has more GitHub stars (64,191 vs 5,782). Stars measure visibility, not whether either tool fits your constraints.
Are keras and torchtune open source?
Yes - both are open-source projects on GitHub (keras: Apache-2.0, torchtune: BSD-3-Clause).
Where can I find alternatives to keras or torchtune?
GraphCanon lists graph-backed alternatives at keras alternatives and torchtune alternatives (keras markdown twin, torchtune 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, keras or torchtune?
keras: Very active. torchtune: 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 keras and torchtune?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: keras trust report; torchtune trust report.